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Archive for the ‘Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough’ Category

From the journal Nature: NFT, Patents, and Intellectual Property: Potential Design

Reporter: Stephen J. Williams, Ph.D.

 

From the journal Nature

Source: https://www.nature.com/articles/s41598-022-05920-6

Patents and intellectual property assets as non-fungible tokens; key technologies and challenges

Scientific Reports volume 12, Article number: 2178 (2022)

Abstract

With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.

Introduction

Distributed ledger technologies (DLTs) such as blockchain are emerging technologies posing a threat to existing business models. Traditionally, most companies used centralized authorities in various aspects of their business, such as financial operations and setting up a trust with their counterparts. By the emergence of blockchain, centralized organizations can be substituted with a decentralized group of resources and actors. The blockchain mechanism was introduced in Bitcoin white paper in 2008, which lets users generate transactions and spend their money without the intervention of banks1. Ethereum, which is a second generation of blockchain, was introduced in 2014, allowing developers to run smart contracts on a distributed ledger. With smart contracts, developers and businesses can create financial applications that use cryptocurrencies and other forms of tokens for applications such as decentralized finance (DeFi), crowdfunding, decentralized exchanges, data records keeping, etc.2. Recent advances in distributed ledger technology have developed concepts that lead to cost reduction and the simplification of value exchange. Nowadays, by leveraging the advantages of blockchain and taking into account the governance issues, digital assets could be represented as tokens that existed in the blockchain network, which facilitates their transmission and traceability, increases their transparency, and improves their security3.

In the landscape of blockchain technology, there could be defined two types of tokens, including fungible tokens, in which all the tokens have equal value and non-fungible tokens (NFTs) that feature unique characteristics and are not interchangeable. Actually, non-fungible tokens are digital assets with a unique identifier that is stored on a blockchain4. NFT was initially suggested in Ethereum Improvement Proposals (EIP)-7215, and it was later expanded in EIP-11556. NFTs became one of the most widespread applications of blockchain technology that reached worldwide attention in early 2021. They can be digital representations of real-world objects. NFTs are tradable rights of digital assets (pictures, music, films, and virtual creations) where ownership is recorded in blockchain smart contracts7.

In particular, fungibility is the ability to exchange one with another of the same kind as an essential currency feature. The non-fungible token is unique and therefore cannot be substituted8. Recently, blockchain enthusiasts have indicated significant interest in various types of NFTs. They enthusiastically participate in NFT-related games or trades. CryptoPunks9, as one of the first NFTs on Ethereum, has developed almost 10,000 collectible punks and helped popularize the ERC-721 Standard. With the gamification of the breeding mechanics, CryptoKitties10 officially placed NFTs at the forefront of the market in 2017. CryptoKitties is an early blockchain game that enables users to buy, sell, collect, and digital breed cats. Another example is NBA Top Shot11, an NFT trading platform for digital short films buying and selling NBA events.

NFTs are developing remarkably and have provided many applications such as artist royalties, in-game assets, educational certificates, etc. However, it is a relatively new concept, and many areas of application need to be explored. Intellectual Property, including patent, trademark, and copyright, is an important area where NFTs can be applied usefully and solve existing problems.

Although NFTs have had many applications so far, it rarely has been used to solve real-world problems. In fact, an NFT is an exciting concept about Intellectual Property (IP). Applying for a patent and trademark is a time-consuming and lengthy process, but it is also costly. That is, registering a copyright or trademark may take months, while securing a patent can take years. On the contrary, with the help of unique features of NFT technology, it is possible to accelerate this process with considerable confidence and assurance about protecting the ownership of an IP. NFTs can offer IP protection while an applicant waits for the government to grant his/her more formal protection. It is cause for excitement that people who believe NFTs and Blockchain would make buying and selling patents easier, offering new opportunities for companies, universities, and inventors to make money off their innovations12. Patent holders will benefit from such innovation. It would give them the ability to ‘tokenize’ their patents. Because every transaction would be logged on a blockchain, it will be much easier to trace patent ownership changes. However, NFT would also facilitate the revenue generation of patents by democratizing patent licensing via NFT. NFTs support the intellectual property market by embedding automatic royalty collecting methods inside inventors’ works, providing them with financial benefits anytime their innovation is licensed. For example, each inventor’s patent would be minted as an NFT, and these NFTs would be joined together to form a commercial IP portfolio and minted as a compounded NFT. Each investor would automatically get their fair share of royalties whenever the licensing revenue is generated without tracking them down.

The authors in13, an overview of NFTs’ applications in different aspects such as gambling, games, and collectibles has been discussed. In addition4, provides a prototype for an event-tracking application based on Ethereum smart contract, and NFT as a solution for art and real estate auction systems is described in14. However, these studies have not discussed existing standards or a generalized architecture, enabling NFTs to be applied in diverse applications. For example, the authors in15 provide two general design patterns for creating and trading NFTs and discuss existing token standards for NFT. However, the proposed designs are limited to Ethereum, and other blockchains are not considered16. Moreover, different technologies for each step of the proposed procedure are not discussed. In8, the authors provide a conceptual framework for token designing and managing and discuss five views: token view, wallet view, transaction view, user interface view, and protocol view. However, no research provides a generalized conceptual framework for generating, recording, and tracing NFT based-IP, in blockchain network.

Even with the clear benefits that NFT-backed patents offer, there are a number of impediments to actually achieving such a system. For example, convincing patent owners to put current ownership records for their patents into NFTs poses an initial obstacle. Because there is no reliable framework for NFT-based patents, this paper provides a conceptual framework for presenting NFT-based patents with a comprehensive discussion on many aspects, ranging from the background, model components, token standards to application domains and research challenges. The main objective of this paper is to provide a layered conceptual NFT-based patent framework that can be used to register patents in a decentralized, tamper-proof, and trustworthy peer-to-peer network to trade and exchange them in the worldwide market. The main contributions of this paper are highlighted as follows:

  • Providing a comprehensive overview on tokenization of IP assets to create unique digital tokens.
  • Discussing the components of a distributed and trustworthy framework for minting NFT-based patents.
  • Highlighting a series of open challenges of NFT-based patents and enlightening the possible future trends.

The rest of the paper is structured as follows: “Background” section describes the Background of NFTs, Non-Fungible Token Standards. The NFT-based patent framework is described in “NFT-based patent framework” section. The Discussion and challenges are presented in “Discussion” section. Lastly, conclusions are given in “Conclusion” section.

Background

Colored Coins could be considered the first steps toward NFTs designed on the top of the Bitcoin network. Bitcoins are fungible, but it is possible to mark them to be distinguishable from the other bitcoins. These marked coins have special properties representing real-world assets like cars and stocks, and owners can prove their ownership of physical assets through the colored coins. By utilizing Colored Coins, users can transfer their marked coins’ ownership like a usual transaction and benefit from Bitcoin’s decentralized network17. Colored Coins had limited functionality due to the Bitcoin script limitations. Pepe is a green frog meme originated by Matt Furie that; users define tokens for Pepes and trade them through the Counterparty platform. Then, the tokens that were created by the picture of Pepes are decided if they are rare enough. Rare Pepe allows users to preserve scarcity, manage the ownership, and transfer their purchased Pepes.

In 2017, Larva Labs developed the first Ethereum-based NFT named CryptoPunks. It contains 10,000 unique human-like characters generated randomly. The official ownership of each character is stored in the Ethereum smart contract, and owners would trade characters. CryptoPunks project inspired CryptoKitties project. CryptoKitties attracts attention to NFT, and it is a pioneer in blockchain games and NFTs that launched in late 2017. CryptoKitties is a blockchain-based virtual game, and users collect and trade characters with unique features that shape kitties. This game was developed in Ethereum smart contract, and it pioneered the ERC-721 token, which was the first standard token in the Ethereum blockchain for NFTs. After the 2017 hype in NFTs, many projects started in this context. Due to increased attention to NFTs’ use-cases and growing market cap, different blockchains like EOS, Algorand, and Tezos started to support NFTs, and various marketplaces like SuperRare and Rarible, and OpenSea are developed to help users to trade NFTs. As mentioned, in general, assets are categorized into two main classes, fungible and non-fungible assets. Fungible assets are the ones that another similar asset can replace. Fungible items could have two main characteristics: replicability and divisibility.

Currency is a fungible item because a ten-dollar bill can be exchanged for another ten-dollar bill or divided into ten one-dollar bills. Despite fungible items, non-fungible items are unique and distinguishable. They cannot be divided or exchanged by another identical item. The first tweet on Twitter is a non-fungible item with mentioned characteristics. Another tweet cannot replace it, and it is unique and not divisible. NFT is a non-fungible cryptographic asset that is declared in a standard token format and has a unique set of attributes. Due to transparency, proof of ownership, and traceable transactions in the blockchain network, NFTs are created using blockchain technology.

Blockchain-based NFTs help enthusiasts create NFTs in the standard token format in blockchain, transfer the ownership of their NFTs to a buyer, assure uniqueness of NFTs, and manage NFTs completely. In addition, there are semi-fungible tokens that have characteristics of both fungible and non-fungible tokens. Semi-fungible tokens are fungible in the same class or specific time and non-fungible in other classes or different times. A plane ticket can be considered a semi-fungible token because a charter ticket can be exchanged by another charter ticket but cannot be exchanged by a first-class ticket. The concept of semi-fungible tokens plays the main role in blockchain-based games and reduces NFTs overhead. In Fig. 1, we illustrate fungible, non-fungible, and semi-fungible tokens. The main properties of NFTs are described as follows15:

figure 1
Figure 1

Ownership: Because of the blockchain layer, the owner of NFT can easily prove the right of possession by his/her keys. Other nodes can verify the user’s ownership publicly.

  • Transferable: Users can freely transfer owned NFTs ownership to others on dedicated markets.
  • Transparency: By using blockchain, all transactions are transparent, and every node in the network can confirm and trace the trades.
  • Fraud Prevention: Fraud is one of the key problems in trading assets; hence, using NFTs ensures buyers buy a non-counterfeit item.
  • Immutability: Metadata, token ID, and history of transactions of NFTs are recorded in a distributed ledger, and it is impossible to change the information of the purchased NFTs.

Non-fungible standards

Ethereum blockchain was pioneered in implementing NFTs. ERC-721 token was the first standard token accepted in the Ethereum network. With the increase in popularity of the NFTs, developers started developing and enhancing NFTs standards in different blockchains like EOS, Algorand, and Tezos. This section provides a review of implemented NFTs standards on the mentioned blockchains.

Ethereum

ERC-721 was the first Standard for NFTs developed in Ethereum, a free and open-source standard. ERC-721 is an interface that a smart contract should implement to have the ability to transfer and manage NFTs. Each ERC-721 token has unique properties and a different Token Id. ERC-721 tokens include the owner’s information, a list of approved addresses, a transfer function that implements transferring tokens from owner to buyer, and other useful functions5.

In ERC-721, smart contracts can group tokens with the same configuration, and each token has different properties, so ERC-721 does not support fungible tokens. However, ERC-1155 is another standard on Ethereum developed by Enjin and has richer functionalities than ERC-721 that supports fungible, non-fungible, and semi-fungible tokens. In ERC-1155, IDs define the class of assets. So different IDs have a different class of assets, and each ID may contain different assets of the same class. Using ERC-1155, a user can transfer different types of tokens in a single transaction and mix multiple fungible and non-fungible types of tokens in a single smart contract6. ERC-721 and ERC-1155 both support operators in which the owner can let the operator originate transferring of the token.

EOSIO

EOSIO is an open-source blockchain platform released in 2018 and claims to eliminate transaction fees and increase transaction throughput. EOSIO differs from Ethereum in the wallet creation algorithm and procedure of handling transactions. dGood is a free standard developed in the EOS blockchain for assets, and it focuses on large-scale use cases. It supports a hierarchical naming structure in smart contracts. Each contract has a unique symbol and a list of categories, and each category contains a list of token names. Therefore, a single contract in dGoods could contain many tokens, which causes efficiency in transferring a group of tokens. Using this hierarchy, dGoods supports fungible, non-fungible, and semi-fungible tokens. It also supports batch transferring, where the owner can transfer many tokens in one operation18.

Algorand

Algorand is a new high-performance public blockchain launched in 2019. It provides scalability while maintaining security and decentralization. It supports smart contracts and tokens for representing assets19. Algorand defines Algorand Standard Assets (ASA) concept to create and manage assets in the Algorand blockchain. Using ASA, users are able to define fungible and non-fungible tokens. In Algorand, users can create NFTs or FTs without writing smart contracts, and they should run just a single transaction in the Algorand blockchain. Each transaction contains some mutable and immutable properties20.

Each account in Algorand can create up to 1000 assets, and for every asset, an account creates or receives, the minimum balance of the account increases by 0.1 Algos. Also, Algorand supports fractional NFTs by splitting an NFT into a group of divided FTs or NFTs, and each part can be exchanged dependently21. Algorand uses a Clawback Address that operates like an operator in ERC-1155, and it is allowed to transfer tokens of an owner who has permitted the operator.

Tezos

Tezos is another decentralized open-source blockchain. Tezos supports the meta-consensus concept. In addition to using a consensus protocol on the ledger’s state like Bitcoin and Ethereum, It also attempts to reach a consensus about how nodes and the protocol should change or upgrade22. FA2 (TZIP-12) is a standard for a unified token contract interface in the Tezos blockchain. FA2 supports different token types like fungible, non-fungible, and fractionalized NFT contracts. In Tezos, tokens are identified with a token contract address and token ID pair. Also, Tezos supports batch token transferring, which reduces the cost of transferring multiple tokens.

Flow

Flow was developed by Dapper Labs to remove the scalability limitation of the Ethereum blockchain. Flow is a fast and decentralized blockchain that focuses on games and digital collectibles. It improves throughput and scalability without sharding due to its architecture. Flow supports smart contracts using Cadence, which is a resource-oriented programming language. NFTs can be described as a resource with a unique id in Cadence. Resources have important rules for ownership management; that is, resources have just one owner and cannot be copied or lost. These features assure the NFT owner. NFTs’ metadata, including images and documents, can be stored off-chain or on-chain in Flow. In addition, Flow defines a Collection concept, in which each collection is an NFT resource that can include a list of resources. It is a dictionary that the key is resource id, and the value is corresponding NFT.

The collection concept provides batch transferring of NFTs. Besides, users can define an NFT for an FT. For instance, in CryptoKitties, a unique cat as an NFT can own a unique hat (another NFT). Flow uses Cadence’s second layer of access control to allow some operators to access some fields of the NFT23. In Table 1, we provide a comparison between explained standards. They are compared in support of fungible-tokens, non-fungible tokens, batch transferring that owner can transform multiple tokens in one operation, operator support in which the owner can approve an operator to originate token transfer, and fractionalized NFTs that an NFT can divide to different tokens and each exchange dependently.Table 1 Comparing NFT standards.

Full size table

NFT-based patent framework

In this section, we propose a framework for presenting NFT-based patents. We describe details of the proposed distributed and trustworthy framework for minting NFT-based patents, as shown in Fig. 2. The proposed framework includes five main layers: Storage Layer, Authentication Layer, Verification Layer, Blockchain Layer, and Application Layer. Details of each layer and the general concepts are presented as follows.

figure 2
Figure 2

Storage layer

The continuous rise of the data in blockchain technology is moving various information systems towards the use of decentralized storage networks. Decentralized storage networks were created to provide more benefits to the technological world24. Some of the benefits of using decentralized storage systems are explained: (1) Cost savings are achieved by making optimal use of current storage. (2) Multiple copies are kept on various nodes, avoiding bottlenecks on central servers and speeding up downloads. This foundation layer implicitly provides the infrastructure required for the storage. The items on NFT platforms have unique characteristics that must be included for identification.

Non-fungible token metadata provides information that describes a particular token ID. NFT metadata is either represented on the On-chain or Off-chain. On-chain means direct incorporation of the metadata into the NFT’s smart contract, which represents the tokens. On the other hand, off-chain storage means hosting the metadata separately25.

Blockchains provide decentralization but are expensive for data storage and never allow data to be removed. For example, because of the Ethereum blockchain’s current storage limits and high maintenance costs, many projects’ metadata is maintained off-chain. Developers utilize the ERC721 Standard, which features a method known as tokenURI. This method is implemented to let applications know the location of the metadata for a specific item. Currently, there are three solutions for off-chain storage, including InterPlanetary File System (IPFS), Pinata, and Filecoin.

IPFS

InterPlanetary File System (IPFS) is a peer-to-peer hypermedia protocol for decentralized media content storage. Because of the high cost of storing media files related to NFTS on Blockchain, IPFS can be the most affordable and efficient solution. IPFS combines multiple technologies inspired by Gita and BitTorrent, such as Block Exchange System, Distributed Hash Tables (DHT), and Version Control System26. On a peer-to-peer network, DHT is used to coordinate and maintain metadata.

In other words, the hash values must be mapped to the objects they represent. An IPFS generates a hash value that starts with the prefix {Q}_{m} and acts as a reference to a specific item when storing an object like a file. Objects larger than 256 KB are divided into smaller blocks up to 256 KB. Then a hash tree is used to interconnect all the blocks that are a part of the same object. IPFS uses Kamdelia DHT. The Block Exchange System, or BitSwap, is a BitTorrent-inspired system that is used to exchange blocks. It is possible to use asymmetric encryption to prevent unauthorized access to stored content on IPFS27.

Pinata

Pinata is a popular platform for managing and uploading files on IPFS. It provides secure and verifiable files for NFTs. Most data is stored off-chain by most NFTs, where a URL of the data is pointed to the NFT on the blockchain. The main problem here is that some information in the URL can change.

This indicates that an NFT supposed to describe a certain patent can be changed without anyone knowing. This defeats the purpose of the NFT in the first place. This is where Pinata comes in handy. Pinata uses the IPFS to create content-addressable hashes of data, also known as Content-Identifiers (CIDs). These CIDs serve as both a way of retrieving data and a means to ensure data validity. Those looking to retrieve data simply ask the IPFS network for the data associated with a certain CID, and if any node on the network contains that data, it will be returned to the requester. The data is automatically rehashed on the requester’s computer when the requester retrieves it to make sure that the data matches back up with the original CID they asked for. This process ensures the data that’s received is exactly what was asked for; if a malicious node attempts to send fake data, the resulting CID on the requester’s end will be different, alerting the requester that they’re receiving incorrect data28.

Filecoin

Another decentralized storage network is Filecoin. It is built on top of IPFS and is designed to store the most important data, such as media files. Truffle Suite has also launched NFT Development Template with Filecoin Box. NFT.Storage (Free Decentralized Storage for NFTs)29 allows users to easily and securely store their NFT content and metadata using IPFS and Filecoin. NFT.Storage is a service backed by Protocol Labs and Pinata specifically for storing NFT data. Through content addressing and decentralized storage, NFT.Storage allows developers to protect their NFT assets and associated metadata, ensuring that all NFTs follow best practices to stay accessible for the long term. NFT.Storage makes it completely frictionless to mint NFTs following best practices through resilient persistence on IPFS and Filecoin. NFT.Storage allows developers to quickly, safely, and for free store NFT data on decentralized networks. Anyone can leverage the power of IPFS and Filecoin to ensure the persistence of their NFTs. The details of this system are stated as follows30:

Content addressing

Once users upload data on NFT.Storage, They receive a CID, which is an IPFS hash of the content. CIDs are the data’s unique fingerprints, universal addresses that can be used to refer to it regardless of how or where it is stored. Using CIDs to reference NFT data avoids problems such as weak links and “rug pulls” since CIDs are generated from the content itself.

Provable storage

NFT.Storage uses Filecoin for long-term decentralized data storage. Filecoin uses cryptographic proofs to assure the NFT data’s durability and persistence over time.

Resilient retrieval

This data stored via IPFS and Filecoin can be fetched directly in the browser via any public IPFS.

Authentication Layer

The second layer is the authentication layer, which we briefly highlight its functions in this section. The Decentralized Identity (DID) approach assists users in collecting credentials from a variety of issuers, such as the government, educational institutions, or employers, and saving them in a digital wallet. The verifier then uses these credentials to verify a person’s validity by using a blockchain-based ledger to follow the “identity and access management (IAM)” process. Therefore, DID allows users to be in control of their identity. A lack of NFT verifiability also causes intellectual property and copyright infringements; of course, the chain of custody may be traced back to the creator’s public address to check whether a similar patent is filed using that address. However, there is no quick and foolproof way to check an NFTs creator’s legitimacy. Without such verification built into the NFT, an NFT proves ownership only over that NFT itself and nothing more.

Self-sovereign identity (SSI)31 is a solution to this problem. SSI is a new series of standards that will guide a new identity architecture for the Internet. With a focus on privacy, security interoperability, SSI applications use public-key cryptography with public blockchains to generate persistent identities for people with private and selective information disclosure. Blockchain technology offers a solution to establish trust and transparency and provide a secure and publicly verifiable KYC (Know Your Customer). The blockchain architecture allows you to collect information from various service providers into a single cryptographically secure and unchanging database that does not need a third party to verify the authenticity of the information.

The proposed platform generates patents-related smart contracts acting as a program that runs on the blockchain to receive and send transactions. They are unalterable privately identifying clients with a thorough KYC process. After KYC approval, then mint an NFT on the blockchain as a certificate of verification32. This article uses a decentralized authentication solution at this layer for authentication. This solution has been used for various applications in the field of the blockchain (exp: smart city, Internet of Things, etc.3334, but we use it here for the proposed framework (patent as NFTs). Details of this solution will be presented in the following.

Decentralized authentication

This section presents the authentication layer similar35 to build validated communication in a secure and decentralized manner via blockchain technology. As shown in Fig. 3, the authentication protocol comprises two processes, including registration and login.

figure 3
Figure 3
Registration

In the registration process of a suggested authentication protocol, we first initialize a user’s public key as their identity key (UserName). Then, we upload this identity key on a blockchain, in which transactions can be verified later by other users. Finally, the user generates an identity transaction.

Login

After registration, a user logs in to the system. The login process is described as follows:

  • 1. The user commits identity information and imports their secret key into the service application to log in.
  • 2. A user who needs to log in sends a login request to the network’s service provider.
  • 3. The service provider analyzes the login request, extracts the hash, queries the blockchain, and obtains identity information from an identity list (identity transactions).
  • 4. The service provider responds with an authentication request when the above process is completed. A timestamp (to avoid a replay attack), the user’s UserName, and a signature are all included in the authentication request.
  • 5. The user creates a signature with five parameters: timestamp, UserName, and PK, as well as the UserName and PK of the service provider. The user authentication credential is used as the signature.
  • 6. The service provider verifies the received information, and if the received information is valid, the authentication succeeds; otherwise, the authentication fails, and the user’s login is denied.

The World Intellectual Property Organization (WIPO) and multiple target patent offices in various nations or regions should assess a patent application, resulting in inefficiency, high costs, and uncertainty. This study presented a conceptual NFT-based patent framework for issuing, validating, and sharing patent certificates. The platform aims to support counterfeit protection as well as secure access and management of certificates according to the needs of learners, companies, education institutions, and certification authorities.

Here, the certification authority (CA) is used to authenticate patent offices. The procedure will first validate a patent if it is provided with a digital certificate that meets the X.509 standard. Certificate authorities are introduced into the system to authenticate both the nodes and clients connected to the blockchain network.

Verification layer

In permissioned blockchains, just identified nodes can read and write in the distributed ledger. Nodes can act in different roles and have various permissions. Therefore, a distributed system can be designed to be the identified nodes for patent granting offices. Here the system is described conceptually at a high level. Figure 4 illustrates the sequence diagram of this layer. This layer includes four levels as below:

figure 4
Figure 4

Digitalization

For a patent to publish as an NFT in the blockchain, it must have a digitalized format. This level is the “filling step” in traditional patent registering. An application could be designed in the application layer to allow users to enter different patent information online.

Recording

Patents provide valuable information and would bring financial benefits for their owner. If they are publicly published in a blockchain network, miners may refuse the patent and take the innovation for themselves. At least it can weaken consensus reliability and encourage miners to misbehave. The inventor should record his innovation privately first using proof of existence to prevent this. The inventor generates the hash of the patent document and records it in the blockchain. As soon as it is recorded in the blockchain, the timestamp and the hash are available for others publicly. Then, the inventor can prove the existence of the patent document whenever it is needed.

Furthermore, using methods like Decision Thinking36, an inventor can record each phase of patent development separately. In each stage, a user generates the hash of the finished part and publishes the hash regarding the last part’s hash. Finally, they have a coupled series of hashes that indicate patent development, and they can prove the existence of each phase using the original related documents. This level should be done to prevent others from abusing the patent and taking it for themselves. The inventor can make sure that their patent document is recorded confidentially and immutably37.

Different hash algorithms exist with different architecture, time complexity, and security considerations. Hash functions should satisfy two main requirements: Pre-Image Resistance: This means that it should be computationally hard to find the input of a hash function while the output and the hash algorithm are known publicly. Collision Resistance: This means that it is computationally hard to find two arbitrary inputs, x, and y, that have the same hash output. These requirements are vital for recording patents. First, the hash function should be Pre-Image Resistance to make it impossible for others to calculate the patent documentation. Otherwise, everybody can read the patent, even before its official publication. Second, the hash function should satisfy Collision Resistance to preclude users from changing their document after recording. Otherwise, users can upload another document, and after a while, they can replace it with another one.

There are various hash algorithms, and MD and SHA families are the most useful algorithms. According to38, Collisions have been found for MD2, MD4, MD5, SHA-0, and SHA-1 hash functions. Hence, they cannot be a good choice for recording patents. SHA2 hash algorithm is secure, and no collision has been found. Although SHA2 is noticeably slower than prior hash algorithms, the recording phase is not highly time-sensitive. So, it is a better choice and provides excellent security for users.

Validating

In this phase, the inventors first create NFT for their patents and publish it to the miners/validators. Miners are some identified nodes that validate NFTs to record in the blockchain. Due to the specialization of the patent validation, miners cannot be inexpert public persons. In addition, patent offices are not too many to make the network fully decentralized. Therefore, the miners can be related specialist persons that are certified by the patent offices. They should receive a digital certificate from patent offices that show their eligibility to referee a patent.

Digital certificate

Digital certificates are digital credentials used to verify networked entities’ online identities. They usually include a public key as well as the owner’s identification. They are issued by Certification Authorities (CAs), who must verify the certificate holder’s identity. Certificates contain cryptographic keys for signing, encryption, and decryption. X.509 is a standard that defines the format of public-key certificates and is signed by a certificate authority. X.509 standard has multiple fields, and its structure is shown in Fig. 5. Version: This field indicated the version of the X.509 standard. X.509 contains multiple versions, and each version has a different structure. According to the CA, validators can choose their desired version. Serial Number: It is used to distinguish a certificate from other certificates. Thus, each certificate has a unique serial number. Signature Algorithm Identifier: This field indicates the cryptographic encryption algorithm used by a certificate authority. Issuer Name: This field indicates the issuer’s name, which is generally certificate authority. Validity Period: Each certificate is valid for a defined period, defined as the Validity Period. This limited period partly protects certificates against exposing CA’s private key. Subject Name: Name of the requester. In our proposed framework, it is the validator’s name. Subject Public Key Info: Shows the CA’s or organization’s public key that issued the certificate. These fields are identical among all versions of the X.509 standard39.

figure 5
Figure 5

Certificate authority

A Certificate Authority (CA) issues digital certificates. CAs encrypt the certificate with their private key, which is not public, and others can decrypt the certificates containing the CA’s public key.

Here, the patent office creates a certificate for requested patent referees. The patent office writes the information of the validator in their certificate and encrypts it with the patent offices’ private key. The validator can use the certificate to assure others about their eligibility. Other nodes can check the requesting node’s information by decrypting the certificate using the public key of the patent office. Therefore, persons can join the network’s miners/validators using their credentials. In this phase, miners perform Formal Examinations, Prior Art Research, and Substantive Examinations and vote to grant or refuse the patent.

Miners perform a consensus about the patent and record the patent in the blockchain. After that, the NFT is recorded in the blockchain with corresponding comments in granting or needing reformations. If the miners detect the NFT as a malicious request, they do not record it in the blockchain.

Blockchain layer

This layer plays as a middleware between the Verification Layer and Application Layer in the patents as NFTs architecture. The main purpose of the blockchain layer in the proposed architecture is to provide IP management. We find that transitioning to a blockchain-based patent as a NFTs records system enables many previously suggested improvements to current patent systems in a flexible, scalable, and transparent manner.

On the other hand, we can use multiple blockchain platforms, including Ethereum, EOS, Flow, and Tezos. Blockchain Systems can be mainly classified into two major types: Permissionless (public) and Permissioned (private) Blockchains based on their consensus mechanism. In a public blockchain, any node can participate in the peer-to-peer network, where the blockchain is fully decentralized. A node can leave the network without any consent from the other nodes in the network.

Bitcoin is one of the most popular examples that fall under the public and permissionless blockchain. Proof of Work (POW), Proof-of-Stake (POS), and directed acyclic graph (DAG) are some examples of consensus algorithms in permissionless blockchains. Bitcoin and Ethereum, two famous and trustable blockchain networks, use the PoW consensus mechanism. Blockchain platforms like Cardano and EOS adopt the PoS consensus40.

Nodes require specific access or permission to get network authentication in a private blockchain. Hyperledger is among the most popular private blockchains, which allow only permissioned members to join the network after authentication. This provides security to a group of entities that do not completely trust one another but wants to achieve a common objective such as exchanging information. All entities of a permissioned blockchain network can use Byzantine-fault-tolerant (BFT) consensus. The Fabric has a membership identity service that manages user IDs and verifies network participants.

Therefore, members are aware of each other’s identity while maintaining privacy and secrecy because they are unaware of each other’s activities41. Due to their more secure nature, private blockchains have sparked a large interest in banking and financial organizations, believing that these platforms can disrupt current centralized systems. Hyperledger, Quorum, Corda, EOS are some examples of permissioned blockchains42.

Reaching consensus in a distributed environment is a challenge. Blockchain is a decentralized network with no central node to observe and check all transactions. Thus, there is a need to design protocols that indicate all transactions are valid. So, the consensus algorithms are considered as the core of each blockchain43. In distributed systems, the consensus has become a problem in which all network members (nodes) agree on accept or reject of a block. When all network members accept the new block, it can append to the previous block.

As mentioned, the main concern in the blockchains is how to reach consensus among network members. A wide range of consensus algorithms has been designed in which each of them has its own pros and cons42. Blockchain consensus algorithms are mainly classified into three groups shown in Table 2. As the first group, proof-based consensus algorithms require the nodes joining the verifying network to demonstrate their qualification to do the appending task. The second group is voting-based consensus that requires validators in the network to share their results of validating a new block or transaction before making the final decision. The third group is DAG-based consensus, a new class of consensus algorithms. These algorithms allow several different blocks to be published and recorded simultaneously on the network.Table 2 Consensus algorithms in blockchain networks.

Full size table

The proposed patent as the NFTs platform that builds blockchain intellectual property empowers the entire patent ecosystem. It is a solution that removes barriers by addressing fundamental issues within the traditional patent ecosystem. Blockchain can efficiently handle patents and trademarks by effectively reducing approval wait time and other required resources. The user entities involved in Intellectual Property management are Creators, Patent Consumers, and Copyright Managing Entities. Users with ownership of the original data are the patent creators, e.g., inventors, writers, and researchers. Patent Consumers are the users who are willing to consume the content and support the creator’s work. On the other hand, Users responsible for protecting the creators’ Intellectual Property are the copyright management entities, e.g., lawyers. The patents as NFTs solution for IP management in blockchain layer works by implementing the following steps62:

Creators sign up to the platform

Creators need to sign up on the blockchain platform to patent their creative work. The identity information will be required while signing up.

Creators upload IP on the blockchain network

Now, add an intellectual property for which the patent application is required. The creator will upload the information related to IP and the data on the blockchain network. Blockchain ensures traceability and auditability to prevent data from duplicity and manipulation. The patent becomes visible to all network members once it is uploaded to the blockchain.

Consumers generate request to use the content

Consumers who want to access the content must first register on the blockchain network. After Signing up, consumers can ask creators to grant access to the patented content. Before the patent owner authorizes the request, a Smart Contract is created to allow customers to access information such as the owner’s data. Furthermore, consumers are required to pay fees in either fiat money or unique tokens in order to use the creator’s original information. When the creator approves the request, an NDA (Non-Disclosure Agreement) is produced and signed by both parties. Blockchain manages the agreement and guarantees that all parties agree to the terms and conditions filed.

Patent management entities leverage blockchain to protect copyrights and solve related disputes

Blockchain assists the patent management entities in resolving a variety of disputes that may include: sharing confidential information, establishing proof of authorship, transferring IP rights, and making defensive publications, etc. Suppose a person used an Invention from a patent for his company without the inventor’s consent. The inventor can report it to the patent office and claim that he is the owner of that invention.

Application layer

The patent Platform Global Marketplace technology would allow many enterprises, governments, universities, and Small and medium-sized enterprises (SMEs) worldwide to tokenize patents as NFTs to create an infrastructure for storing patent records on a blockchain-based network and developing a decentralized marketplace in which patent holders would easily sell or otherwise monetize their patents. The NFTs-based patent can use smart contracts to determine a set price for a license or purchase.

Any buyer satisfied with the conditions can pay and immediately unlock the rights to the patent without either party ever having to interact directly. While patents are currently regulated jurisdictionally around the world, a blockchain-based patent marketplace using NFTs can reduce the geographical barriers between patent systems using as simple a tool as a search query. The ease of access to patents globally can help aspiring inventors accelerate the innovative process by building upon others’ patented inventions through licenses. There are a wide variety of use cases for patent NFTs such as SMEs, Patent Organization, Grant & Funding, and fundraising/transferring information relating to patents. These applications keep growing as time progresses, and we are constantly finding new ways to utilize these tokens. Some of the most commonly used applications can be seen as follows.

SMEs

The aim is to move intellectual property assets onto a digital, centralized, and secure blockchain network, enabling easier commercialization of patents, especially for small or medium enterprises (SMEs). Smart contracts can be attached to NFTs so terms of use and ownership can be outlined and agreed upon without incurring as many legal fees as traditional IP transfers. This is believed to help SMEs secure funding, as they could more easily leverage the previously undisclosed value of their patent portfolios63.

Transfer ownership of patents

NFTs can be used to transfer ownership of patents. The blockchain can be used to keep track of patent owners, and tokens would include self-executing contracts that transfer the legal rights associated with patents when the tokens are transferred. A partnership between IBM and IPwe has spearheaded the use of NFTs to secure patent ownership. These two companies have teamed together to build the infrastructure for an NFT-based patent marketplace.

Discussion

There are exciting proposals in the legal and economic literature that suggest seemingly straightforward solutions to many of the issues plaguing current patent systems. However, most solutions would constitute major administrative disruptions and place significant and continuous financial burdens on patent offices or their users. An NFT-based patents system not only makes many of these ideas administratively feasible but can also be examined in a step-wise, scalable, and very public manner.

Furthermore, NFT-based patents may facilitate reliable information sharing among offices and patentees worldwide, reducing the burden on examiners and perhaps even accelerating harmonization efforts. NFT-based patents also have additional transparency and archival attributes baked in. A patent should be a privilege bestowed on those who take resource-intensive risks to explore the frontier of technological capabilities. As a reward for their achievements, full transparency of these rewards is much public interest. It is a society that pays for administrative and economic inefficiencies that exist in today’s systems. NFT-based patents can enhance this transparency. From an organizational perspective, an NFT-based patent can remove current bottlenecks in patent processes by making these processes more efficient, rapid, and convenient for applicants without compromising the quality of granted patents.

The proposed framework encounters some challenges that should be solved to reach a developed patent verification platform. First, technical problems are discussed. The consensus method that is used in the verification layer is not addressed in detail. Due to the permissioned structure of miners in the NFT-based patents, consensus algorithms like PBFT, Federated Consensus, and Round Robin Consensus are designed for permissioned blockchains can be applied. Also, miners/validators spend some time validating the patents; hence a protocol should be designed to profit them. Some challenges like proving the miners’ time and effort, the price that inventors should pay to miners, and other economic trade-offs should be considered.

Different NFT standards were discussed. If various patent services use NFT standards, there will be some cross-platform problems. For instance, transferring an NFT from Ethereum blockchain (ERC-721 token) to EOS blockchain is not a forward and straight work and needs some considerations. Also, people usually trade NFTs in marketplaces such as Rarible and OpenSea. These marketplaces are centralized and may prompt some challenges because of their centralized nature. Besides, there exist some other types of challenges. For example, the novelty of NFT-based patents and blockchain services.

Blockchain-based patent service has not been tested before. The patent registration procedure and concepts of the Patent as NFT system may be ambiguous for people who still prefer conventional centralized patent systems over decentralized ones. It should be noted that there are some problems in the mining part. Miners should receive certificates from the accepted organizations. Determining these organizations and how they accept referees as validators need more consideration. Some types of inventions in some countries are prohibited, and inventors cannot register them. In NFT-based patents, inventors can register their patents publicly, and maybe some collisions occur between inventors and the government. There exist some misunderstandings about NFT’s ownership rights. It is not clear that when a person buys an NFT, which rights are given to them exactly; for instance, they have property rights or have moral rights, too.

Conclusion

Blockchain technology provides strong timestamping, the potential for smart contracts, proof-of-existence. It enables creating a transparent, distributed, cost-effective, and resilient environment that is open to all and where each transaction is auditable. On the other hand, blockchain is a definite boon to the IP industry, benefitting patent owners. When blockchain technology’s intrinsic characteristics are applied to the IP domain, it helps copyrights. This paper provided a conceptual framework for presenting an NFT-based patent with a comprehensive discussion of many aspects: background, model components, token standards to application areas, and research challenges. The proposed framework includes five main layers: Storage Layer, Authentication Layer, Verification Layer, Blockchain Layer, and Application. The primary purpose of this patent framework was to provide an NFT-based concept that could be used to patent a decentralized, anti-tamper, and reliable network for trade and exchange around the world. Finally, we addressed several open challenges to NFT-based inventions.

References

  1. Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system. Decent. Bus. Rev. 21260, https://bitcoin.org/bitcoin.pdf (2008).
  2. Buterin, V. A next-generation smart contract and decentralized application platform. White Pap. 3 (2014).
  3. Nofer, M., Gomber, P., Hinz, O. & Schiereck, D. Business & infomation system engineering. Blockchain 59, 183–187 (2017).Google Scholar 
  4. Regner, F., Urbach, N. & Schweizer, A. NFTs in practice—non-fungible tokens as core component of a blockchain-based event ticketing application. https://www.researchgate.net/publication/336057493_NFTs_in_Practice_-_Non-Fungible_Tokens_as_Core_Component_of_a_Blockchain-based_Event_Ticketing_Application (2019).
  5. Entriken, W., Shirley, D., Evans, J. & Sachs, N. EIP 721: ERC-721 non-fungible token standard. Ethereum Improv. Propos.https://eips.ethereum.org/EIPS/eip-721 (2018).
  6. Radomski, W. et al. Eip 1155: Erc-1155 multi token standard. In Ethereum, Standard (2018).
  7. Dowling, M. Is non-fungible token pricing driven by cryptocurrencies? Finance Res. Lett. 44, 102097. https://doi.org/10.1016/j.frl.2021.102097 (2021).
  8. Lesavre, L., Varin, P. & Yaga, D. Blockchain Networks: Token Design and Management Overview. (National Institute of Standards and Technology, 2020).
  9. Larva-Labs. About Cryptopunks, Retrieved 13 May, 2021, from https://www.larvalabs.com/cryptopunks (2021).
  10. Cryptokitties. About Cryptokitties, Retrieved 28 May, 2021, from https://www.cryptokitties.co/ (2021).
  11. nbatopshot. About Nba top shot, Retrieved 4 April, 2021, from https://nbatopshot.com/terms (2021).
  12. Fairfield, J. Tokenized: The law of non-fungible tokens and unique digital property. Indiana Law J. forthcoming (2021).
  13. Chevet, S. Blockchain technology and non-fungible tokens: Reshaping value chains in creative industries. Available at SSRN 3212662 (2018).
  14. Bal, M. & Ner, C. NFTracer: a Non-Fungible token tracking proof-of-concept using Hyperledger Fabric. arXiv preprint arXiv:1905.04795 (2019).
  15. Wang, Q., Li, R., Wang, Q. & Chen, S. Non-fungible token (NFT): Overview, evaluation, opportunities and challenges. arXiv preprint arXiv:2105.07447 (2021).
  16. Qu, Q., Nurgaliev, I., Muzammal, M., Jensen, C. S. & Fan, J. On spatio-temporal blockchain query processing. Future Gener. Comput. Syst. 98: 208–218 (2019).Article Google Scholar 
  17. Rosenfeld, M. Overview of colored coins. White paper, bitcoil. co. il 41, 94 (2012).
  18. Obsidian-Labs. dGoods Standard, Retrieved 29 April, 2021, from https://docs.eosstudio.io/contracts/dgoods/standard.html. (2021).
  19. Algorand. Algorand Core Technology Innovation, Retrieved 10 March, 2021, from https://www.algorand.com/technology/core-blockchain-innovation. (2021).
  20. Weathersby, J. Building NFTs on Algorand, Retrieved 15 April, 2021, from https://developer.algorand.org/articles/building-nfts-on-algorand/. (2021).
  21. Algorand. How Algorand Democratizes the Access to the NFT Market with Fractional NFTs, Retrieved 7 April, 2021, from https://www.algorand.com/resources/blog/algorand-nft-market-fractional-nfts. (2021).
  22. Tezos. Welcome to the Tezos Developer Documentation, Retrieved 16 May, 2021, from https://tezos.gitlab.io. (2021).
  23. flowdocs. Non-Fungible Tokens, Retrieved 20 May, 2021, from https://docs.onflow.org/cadence/tutorial/04-non-fungible-tokens/. (2021).
  24. Benisi, N. Z., Aminian, M. & Javadi, B. Blockchain-based decentralized storage networks: A survey. J. Netw. Comput. Appl. 162, 102656 (2020).Article Google Scholar 
  25. NFTReview. On-chain vs. Off-chain Metadata (2021).
  26. Benet, J. Ipfs-content addressed, versioned, p2p file system. arXiv preprint arXiv:1407.3561 (2014).
  27. Nizamuddin, N., Salah, K., Azad, M. A., Arshad, J. & Rehman, M. Decentralized document version control using ethereum blockchain and IPFS. Comput. Electr. Eng. 76, 183–197 (2019).Article Google Scholar 
  28. Tut, K. Who Is Responsible for NFT Data? (2020).
  29. nft.storage. Free Storage for NFTs, Retrieved 16 May, 2021, from https://nft.storage/. (2021).
  30. Psaras, Y. & Dias, D. in 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). 80–80 (IEEE).
  31. Tanner, J. & Roelofs, C. NFTs and the need for Self-Sovereign Identity (2021).
  32. Martens, D., Tuyll van Serooskerken, A. V. & Steenhagen, M. Exploring the potential of blockchain for KYC. J. Digit. Bank. 2, 123–131 (2017).Google Scholar 
  33. Hammi, M. T., Bellot, P. & Serhrouchni, A. In 2018 IEEE Wireless Communications and Networking Conference (WCNC). 1–6 (IEEE).
  34. Khalid, U. et al. A decentralized lightweight blockchain-based authentication mechanism for IoT systems. Cluster Comput. 1–21 (2020).
  35. Zhong, Y. et al. Distributed blockchain-based authentication and authorization protocol for smart grid. Wirel. Commun. Mobile Comput. (2021).
  36. Schönhals, A., Hepp, T. & Gipp, B. In Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems. 105–110.
  37. Verma, S. & Prajapati, G. A Survey of Cryptographic Hash Algorithms and Issues. International Journal of Computer Security & Source Code Analysis (IJCSSCA) 1, 17–20, (2015).
  38. Verma, S. & Prajapati, G. A survey of cryptographic hash algorithms and issues. Int. J. Comput. Secur. Source Code Anal. (IJCSSCA) 1 (2015).
  39. SDK, I. X.509 Certificates (1996).
  40. Helliar, C. V., Crawford, L., Rocca, L., Teodori, C. & Veneziani, M. Permissionless and permissioned blockchain diffusion. Int. J. Inf. Manag. 54, 102136 (2020).Article Google Scholar 
  41. Frizzo-Barker, J. et al. Blockchain as a disruptive technology for business: A systematic review. Int. J. Inf. Manag. 51, 102029 (2020).Article Google Scholar 
  42. Bamakan, S. M. H., Motavali, A. & Bondarti, A. B. A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl. 154, 113385 (2020).Article Google Scholar 
  43. Bamakan, S. M. H., Bondarti, A. B., Bondarti, P. B. & Qu, Q. Blockchain technology forecasting by patent analytics and text mining. Blockchain Res. Appl. 100019 (2021).
  44. Castro, M. & Liskov, B. Practical Byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst. (TOCS) 20, 398–461 (2002).Article Google Scholar 
  45. Muratov, F., Lebedev, A., Iushkevich, N., Nasrulin, B. & Takemiya, M. YAC: BFT consensus algorithm for blockchain. arXiv preprint arXiv:1809.00554 (2018).
  46. Bessani, A., Sousa, J. & Alchieri, E. E. In 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. 355–362 (IEEE).
  47. Todd, P. Ripple protocol consensus algorithm review. May 11th (2015).
  48. Ongaro, D. & Ousterhout, J. In 2014 {USENIX} Annual Technical Conference ({USENIX}{ATC} 14). 305–319.
  49. Larimer, D. Delegated proof-of-stake (dpos). Bitshare whitepaper, Reterived March 31, 2019, from http://docs.bitshares.org/bitshares/dpos.html (2014).
  50. Turner, B. (October, 2007).
  51. De Angelis, S. et al. PBFT vs proof-of-authority: Applying the CAP theorem to permissioned blockchain (2018).
  52. King, S. & Nadal, S. Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. self-published paper, August 19 (2012).
  53. Hyperledger. PoET 1.0 Specification (2017).
  54. Buntinx, J. What Is Proof-of-Weight? Reterived March 31, 2019, from https://nulltx.com/what-is-proof-of-weight/# (2018).
  55. P4Titan. A Peer-to-Peer Crypto-Currency with Proof-of-Burn. Reterived March 10, 2019, from https://github.com/slimcoin-project/slimcoin-project.github.io/raw/master/whitepaperSLM.pdf (2014).
  56. Dziembowski, S., Faust, S., Kolmogorov, V. & Pietrzak, K. In Annual Cryptology Conference. 585–605 (Springer).
  57. Bentov, I., Lee, C., Mizrahi, A. & Rosenfeld, M. Proof of Activity: Extending Bitcoin’s Proof of Work via Proof of Stake. IACR Cryptology ePrint Archive 2014, 452 (2014).Google Scholar 
  58. NEM, T. Nem technical referencehttps://nem.io/wpcontent/themes/nem/files/NEM_techRef.pdf (2018).
  59. Bramas, Q. The Stability and the Security of the Tangle (2018).
  60. Baird, L. The swirlds hashgraph consensus algorithm: Fair, fast, byzantine fault tolerance. In Swirlds Tech Reports SWIRLDS-TR-2016–01, Tech. Rep (2016).
  61. LeMahieu, C. Nano: A feeless distributed cryptocurrency network. Nano [Online resource]. https://nano.org/en/whitepaper (date of access: 24.03. 2018) 16, 17 (2018).
  62. Casino, F., Dasaklis, T. K. & Patsakis, C. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics Inform. 36, 55–81 (2019).Article Google Scholar 
  63. bigredawesomedodo. Helping Small Businesses Survive and Grow With Marketing, Retrieved 3 June, 2021, from https://bigredawesomedodo.com/nft/. (2020).

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Acknowledgements

This work has been partially supported by CAS President’s International Fellowship Initiative, China [grant number 2021VTB0002, 2021] and National Natural Science Foundation of China (No. 61902385).

Author information

Affiliations

  1. Department of Industrial Management, Yazd University, Yazd City, IranSeyed Mojtaba Hosseini Bamakan
  2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan City, IranNasim Nezhadsistani
  3. School of Electrical and Computer Engineering, University of Tehran, Tehran City, IranOmid Bodaghi
  4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, ChinaSeyed Mojtaba Hosseini Bamakan & Qiang Qu
  5. Huawei Blockchain Lab, Huawei Cloud Tech Co., Ltd., Shenzhen, ChinaQiang Qu

Contributions

NFT: Redefined Format of IP Assets

The collaboration between National Center for Advancing Translational Sciences (NCATS) at NIH and BurstIQ

2.0 LPBI is a Very Unique Organization 

 

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#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Curator: Stephen J. Williams, Ph.D.

The advent of direct to consumer (DTC) genetic testing and the resultant rapid increase in its popularity as well as companies offering such services has created some urgent and unique bioethical challenges surrounding this niche in the marketplace. At first, most DTC companies like 23andMe and Ancestry.com offered non-clinical or non-FDA approved genetic testing as a way for consumers to draw casual inferences from their DNA sequence and existence of known genes that are linked to disease risk, or to get a glimpse of their familial background. However, many issues arose, including legal, privacy, medical, and bioethical issues. Below are some articles which will explain and discuss many of these problems associated with the DTC genetic testing market as well as some alternatives which may exist.

‘Direct-to-Consumer (DTC) Genetic Testing Market to hit USD 2.5 Bn by 2024’ by Global Market Insights

This post has the following link to the market analysis of the DTC market (https://www.gminsights.com/pressrelease/direct-to-consumer-dtc-genetic-testing-market). Below is the highlights of the report.

As you can see,this market segment appears to want to expand into the nutritional consulting business as well as targeted biomarkers for specific diseases.

Rising incidence of genetic disorders across the globe will augment the market growth

Increasing prevalence of genetic disorders will propel the demand for direct-to-consumer genetic testing and will augment industry growth over the projected timeline. Increasing cases of genetic diseases such as breast cancer, achondroplasia, colorectal cancer and other diseases have elevated the need for cost-effective and efficient genetic testing avenues in the healthcare market.
 

For instance, according to the World Cancer Research Fund (WCRF), in 2018, over 2 million new cases of cancer were diagnosed across the globe. Also, breast cancer is stated as the second most commonly occurring cancer. Availability of superior quality and advanced direct-to-consumer genetic testing has drastically reduced the mortality rates in people suffering from cancer by providing vigilant surveillance data even before the onset of the disease. Hence, the aforementioned factors will propel the direct-to-consumer genetic testing market overt the forecast timeline.
 

DTC Genetic Testing Market By Technology

Get more details on this report – Request Free Sample PDF
 

Nutrigenomic Testing will provide robust market growth

The nutrigenomic testing segment was valued over USD 220 million market value in 2019 and its market will witness a tremendous growth over 2020-2028. The growth of the market segment is attributed to increasing research activities related to nutritional aspects. Moreover, obesity is another major factor that will boost the demand for direct-to-consumer genetic testing market.
 

Nutrigenomics testing enables professionals to recommend nutritional guidance and personalized diet to obese people and help them to keep their weight under control while maintaining a healthy lifestyle. Hence, above mentioned factors are anticipated to augment the demand and adoption rate of direct-to-consumer genetic testing through 2028.
 

Browse key industry insights spread across 161 pages with 126 market data tables & 10 figures & charts from the report, “Direct-To-Consumer Genetic Testing Market Size By Test Type (Carrier Testing, Predictive Testing, Ancestry & Relationship Testing, Nutrigenomics Testing), By Distribution Channel (Online Platforms, Over-the-Counter), By Technology (Targeted Analysis, Single Nucleotide Polymorphism (SNP) Chips, Whole Genome Sequencing (WGS)), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2028” in detail along with the table of contents:
https://www.gminsights.com/industry-analysis/direct-to-consumer-dtc-genetic-testing-market
 

Targeted analysis techniques will drive the market growth over the foreseeable future

Based on technology, the DTC genetic testing market is segmented into whole genome sequencing (WGS), targeted analysis, and single nucleotide polymorphism (SNP) chips. The targeted analysis market segment is projected to witness around 12% CAGR over the forecast period. The segmental growth is attributed to the recent advancements in genetic testing methods that has revolutionized the detection and characterization of genetic codes.
 

Targeted analysis is mainly utilized to determine any defects in genes that are responsible for a disorder or a disease. Also, growing demand for personalized medicine amongst the population suffering from genetic diseases will boost the demand for targeted analysis technology. As the technology is relatively cheaper, it is highly preferred method used in direct-to-consumer genetic testing procedures. These advantages of targeted analysis are expected to enhance the market growth over the foreseeable future.
 

Over-the-counter segment will experience a notable growth over the forecast period

The over-the-counter distribution channel is projected to witness around 11% CAGR through 2028. The segmental growth is attributed to the ease in purchasing a test kit for the consumers living in rural areas of developing countries. Consumers prefer over-the-counter distribution channel as they are directly examined by regulatory agencies making it safer to use, thereby driving the market growth over the forecast timeline.
 

Favorable regulations provide lucrative growth opportunities for direct-to-consumer genetic testing

Europe direct-to-consumer genetic testing market held around 26% share in 2019 and was valued at around USD 290 million. The regional growth is due to elevated government spending on healthcare to provide easy access to genetic testing avenues. Furthermore, European regulatory bodies are working on improving the regulations set on the direct-to-consumer genetic testing methods. Hence, the above-mentioned factors will play significant role in the market growth.
 

Focus of market players on introducing innovative direct-to-consumer genetic testing devices will offer several growth opportunities

Few of the eminent players operating in direct-to-consumer genetic testing market share include Ancestry, Color Genomics, Living DNA, Mapmygenome, Easy DNA, FamilytreeDNA (Gene By Gene), Full Genome Corporation, Helix OpCo LLC, Identigene, Karmagenes, MyHeritage, Pathway genomics, Genesis Healthcare, and 23andMe. These market players have undertaken various business strategies to enhance their financial stability and help them evolve as leading companies in the direct-to-consumer genetic testing industry.
 

For example, in November 2018, Helix launched a new genetic testing product, DNA discovery kit, that allows customer to delve into their ancestry. This development expanded the firm’s product portfolio, thereby propelling industry growth in the market.

The following posts discuss bioethical issues related to genetic testing and personalized medicine from a clinicians and scientisit’s perspective

Question: Each of these articles discusses certain bioethical issues although focuses on personalized medicine and treatment. Given your understanding of the robust process involved in validating clinical biomarkers and the current state of the DTC market, how could DTC testing results misinform patients and create mistrust in the physician-patient relationship?

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

The following posts discuss the bioethical concerns of genetic testing from a patient’s perspective:

Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org

Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

23andMe Product can be obtained for Free from a new app called Genes for Good: UMich’s Facebook-based Genomics Project

Question: If you are developing a targeted treatment with a companion diagnostic, what bioethical concerns would you address during the drug development process to ensure fair, equitable and ethical treatment of all patients, in trials as well as post market?

Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges

New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field

Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Protecting Your Biotech IP and Market Strategy: Notes from Life Sciences Collaborative 2015 Meeting

Question: What type of regulatory concerns should one have during the drug development process in regards to use of biomarker testing? From the last article on Protecting Your IP how important is it, as a drug developer, to involve all payers during the drug development process?

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MIT Technology Review announced list of “Innovators Under 35, 2020”

Reporter: Aviva Lev-Ari, PhD, RN

 

Innovators Under 35, 2020

In chaotic times it can be reassuring to see so many people working toward a better world. That’s true for medical professionals fighting a pandemic and for ordinary citizens fighting for social justice. And it’s true for those among us striving to employ technology to address those problems and many others.

The 35 young innovators in these pages aren’t all working to fight a pandemic, though some are: see Omar Abudayyeh and Andreas Puschnik. And they’re not all looking to remedy social injustices though some are: see Inioluwa Deborah Raji and Mohamed Dhaouafi. But even those who aren’t tackling those specific problems are seeking ways to use technology to help people. They’re trying to solve our climate crisis, find a cure for Parkinson’s, or make drinking water available to those who are desperate for it.

We’ve been presenting our list of innovators under 35 for the past 20 years. We do it to highlight the things young innovators are working on, to show at least some of the possible directions that technology will take in the coming decade. This contest generates more than 500 nominations each year. The editors then face the task of picking 100 semifinalists to put in front of our 25 judges, who have expertise in artificial intelligence, biotechnology, software, energy, materials, and so on. With the invaluable help of these rankings, the editors pick the final list of 35.

Inventors

Their innovations point toward a future with new types of batteries, solar panels, and microchips.
  • Omar Abudayyeh

    He’s working to use CRISPR as a covid-19 test that you could take at home.

    Omar Abudayyeh
  • Christina Boville

    She modifies enzymes to enable production of new compounds for industry.

    Christina Boville
  • Manuel Le Gallo

    He uses novel computer designs to make AI less power hungry.

    Manuel Le Gallo
  • Nadya Peek

    She builds novel modular machines that can do just about anything you can imagine.

    Nadia Peek
  • Leila Pirhaji

    She developed an AI-based system that can identify more small molecules in a patient’s body, faster than ever before.

    Leila Pirhaji
  • Randall Jeffrey Platt

    His recording tool provides a video of genes turning on or off.

    Randall Jeffrey Platt
  • Rebecca Saive

    She found a way to make solar panels cheaper and more efficient.

    Rebecca Saive
  • Venkat Viswanathan

    His work on a new type of battery could make EVs much cheaper.

    Venkat Viswanathan
  • Anastasia Volkova

    Her platform uses remote sensing and other techniques to monitor crop health—helping farmers focus their efforts where they’re most needed.

    Anastasia Volkova
  • Sihong Wang

    His stretchable microchips promise to make all sorts of new devices possible.

    Sihong Wang

Entrepreneurs

Their technological innovations bust up the status quo and lead to new ways of doing business.
  • Jiwei Li

    In the last few months, Google and Facebook have both released new chatbots. Jiwei Li’s techniques are at the heart of both.

    Jiwei Li
  • Atima Lui

    She’s using technology to correct the cosmetics industry’s bias toward light skin.

    Atima Lui
  • Tony Pan

    His company revamps an old device to allow you to generate electricity in your own home.

    Tony Pan

Visionaries

Their innovations are leading to breakthroughs in AI, quantum computing, and medical implants.
  • Leilani Battle

    Her program sifts through data faster so scientists can focus more on science.

    Leilani Battle
  • Morgan Beller

    She was a key player behind the idea of a Facebook cryptocurrency.

  • Eimear Dolan

    Medical implants are often thwarted as the body grows tissue to defend itself. She may have found a drug-free fix for the problem.

    Eimear Dolan
  • Rose Faghih

    Her sensor-laden wristwatch would monitor your brain states.

    Rose Fagih
  • Bo Li

    By devising new ways to fool AI, she is making it safer.

    Bo Li
  • Zlatko Minev

    His discovery could reduce errors in quantum computing.

    Zlatko Minev
  • Miguel Modestino

    He is reducing the chemical industry’s carbon footprint by using AI to optimize reactions with electricity instead of heat.

    Miguel Modestino
  • Inioluwa Deborah Raji

    Her research on racial bias in data used to train facial recognition systems is forcing companies to change their ways.

    Inioluwa Deborah Raji
  • Adriana Schulz

    Her tools let anyone design products without having to understand materials science or engineering.

    Adriana Schulz
  • Dongjin Seo

    He is designing computer chips to seamlessly connect human brains and machines.

Humanitarians

They’re using technology to cure diseases and make water, housing, and prosthetics available to all.
  • Mohamed Dhaouafi

    His company’s artificial limbs are not only high-functioning but cheap enough for people in low-income countries.

    Mohamed Dhaouafi
  • Alex Le Roux

    A massive 3D-printing project in Mexico could point the way to the future of affordable housing.

    Alex Le Roux
  • Katharina Volz

    A loved one’s diagnosis led her to employ machine learning in the search for a Parkinson’s cure.

    Katharina Volz
  • David Warsinger

    His system could alleviate the drawbacks of existing desalination plants.

    David Warsinger

Pioneers

Their innovations lead the way to biodegradable plastics, textiles that keep you cool, and cars that “see.”
  • Ghena Alhanaee

    Heavy dependence on infrastructure like oil rigs, nuclear reactors, and desalination plants can be catastrophic in a crisis. Her data-driven framework could help nations prepare.

    Ghena Alhanaee
  • Avinash Manjula Basavanna

    His biodegradable plastic protects against extreme chemicals, but heals itself using water.

  • Lili Cai

    She created energy-efficient textiles to break our air-conditioning habit.

    Lili Cai
  • Gregory Ekchian

    He invented a way to make radiation therapy for cancer safer and more effective.

    Gregory Ekchian
  • Jennifer Glick

    If quantum computers work, what can we use them for? She’s working to figure that out.

  • Andrej Karpathy

    He’s employing neural networks to allow automated cars to “see.”

    Karpathy
  • Siddharth Krishnan

    A tiny, powerful sensor for making disease diagnosis cheaper, faster, and easier.

    Siddharth Krishnan
  • Andreas Puschnik

    Seeking a universal treatment for viral diseases, he might leave us much better prepared for the next pandemic.

    Andreas Puschnik

SOURCE

https://www.technologyreview.com/innovators-under-35/2020/?truid=edf020ada5f25f6d6c4b0b32ac4a1ee9&utm_source=weekend_reads&utm_medium=email&utm_campaign=weekend_reads.unpaid.engagement&utm_term=non-subs&utm_content=06-20-2020

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Predicting the Protein Structure of Coronavirus: Inhibition of Nsp15 can slow viral replication and Cryo-EM – Spike protein structure (experimentally verified) vs AI-predicted protein structures (not experimentally verified) of DeepMind (Parent: Google) aka AlphaFold

 

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

 

UPDATED on 8/9/2020

 

UPDATED on 3/11/2020

Coronaviruses

According to the World Health Organization, coronaviruses make up a large family of viruses named for the crown-like spikes found on their surface (Figure 1). They carry their genetic material in single strands of RNA and cause respiratory problems and fever. Like HIV, coronaviruses can be transmitted between animals and humans.  Coronaviruses have been responsible for the Severe Acute Respiratory Syndrome (SARS) pandemic in the early 2000s and the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. While the most recent coronavirus, COVID-19, has caused international concern, accessible and inexpensive sequencing is helping us understand COVID-19 and respond to the outbreak quickly.

Figure 1. Coronaviruses with the characteristic spikes as seen under a microscope.

First studies that explore genetic susceptibility to COVID-19 are now being published. The first results indicate that COVID-19 infects cells using the ACE2 cell-surface receptor. Genetic variants in the ACE2 receptor gene are thus likely to influence how effectively COVID-19 can enter the cells in our bodies. Researchers hope to discover genetic variants that confer resistance to a COVID-19 infection, similar to how some variants in the CCR5 receptor gene make people immune to HIV. At Nebula Genomics, we are monitoring the latest COVID-19 research and will add any relevant discoveries to the Nebula Research Library in a timely manner.

The Role of Genomics in Responding to COVID-19

Scientists in China sequenced COVID-19’s genome just a few weeks after the first case was reported in Wuhan. This stands in contrast to SARS, which was discovered in late 2002 but was not sequenced until April of 2003. It is through inexpensive genome-sequencing that many scientists across the globe are learning and sharing information about COVID-19, allowing us to track the evolution of COVID-19 in real-time. Ultimately, sequencing can help remove the fear of the unknown and allow scientists and health professionals to prepare to combat the spread of COVID-19.

Next-generation DNA sequencing technology has enabled us to understand COVID-19 is ~30,000 bases long. Moreover, researchers in China determined that COVID-19 is also almost identical to a coronavirus found in bats and is very similar to SARS. These insights have been critical in aiding in the development of diagnostics and vaccines. For example, the Centers for Disease Control and Prevention developed a diagnostic test to detect COVID-19 RNA from nose or mouth swabs.

Moreover, a number of different government agencies and pharmaceutical companies are in the process of developing COVID-19 vaccines to stop the COVID-19 from infecting more people. To protect humans from infection inactivated virus particles or parts of the virus (e.g. viral proteins) can be injected into humans. The immune system will recognize the inactivated virus as foreign, priming the body to build immunity against possible future infection. Of note, Moderna Inc., the National Institute of Allergy and Infectious Diseases, and Coalition for Epidemic Preparedness Innovations identified a COVID-19 vaccine candidate in a record 42 days. This vaccine will be tested in human clinical trials starting in April.

For more information about COVID-19, please refer to the World Health Organization website.

SOURCE

https://blog.nebula.org/role-of-genomics-coronavirus-covid-19/?utm_source=Nebula%20Genomics&utm_medium=email&utm_campaign=COVID-19

Aviva Lev-Ari
13.3K Tweets

Aviva Lev-Ari
@AVIVA1950

My BIO lnkd.in/eEyn69r MediaPharma ex-SRI ex-MITRE ex-McGraw-Hill Followed by

Aviva Lev-Ari
@AVIVA1950

Predicting the #ProteinStructure of #Coronavirus: #Inhibition of #Nsp15 #Cryo-EM – #spike #protein structure (#experimentally verified) vs #AI-predicted protein structures (not verified) of

(

) #AlphaFold

Quote Tweet
Eric Topol
@EricTopol
·
The problem w/ visionaries is that we don’t recognize them in a timely manner (too late) Ralph Baric @UNCpublichealth and Vineet Menachery deserve recognition for being 5 yrs ahead of #COVID19 nature.com/articles/nm.39 @NatureMedicine pnas.org/content/113/11 @PNASNews via @hoondy

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Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learningNature 577, 706–710 (2020)https://doi.org/10.1038/s41586-019-1923-7

Abstract

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7. https://doi.org/10.1038/s41586-019-1923-7

[ALA added bold face]

COVID-19 outbreak

The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility.

Knowing a protein’s structure provides an important resource for understanding how it functions, but experiments to determine the structure can take months or longer, and some prove to be intractable. For this reason, researchers have been developing computational methods to predict protein structure from the amino acid sequence.  In cases where the structure of a similar protein has already been experimentally determined, algorithms based on “template modelling” are able to provide accurate predictions of the protein structure. AlphaFold, our recently published deep learning system, focuses on predicting protein structure accurately when no structures of similar proteins are available, called “free modelling”.  We’ve continued to improve these methods since that publication and want to provide the most useful predictions, so we’re sharing predicted structures for some of the proteins in SARS-CoV-2 generated using our newly-developed methods.

It’s important to note that our structure prediction system is still in development and we can’t be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful. We recently shared our results with several colleagues at the Francis Crick Institute in the UK, including structural biologists and virologists, who encouraged us to release our structures to the general scientific community now. Our models include per-residue confidence scores to help indicate which parts of the structure are more likely to be correct. We have only provided predictions for proteins which lack suitable templates or are otherwise difficult for template modeling.  While these understudied proteins are not the main focus of current therapeutic efforts, they may add to researchers’ understanding of SARS-CoV-2.

Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them.

Interested researchers can download the structures here, and can read more technical details about these predictions in a document included with the data. The protein structure predictions we’re releasing are for SARS-CoV-2 membrane protein, protein 3a, Nsp2, Nsp4, Nsp6, and Papain-like proteinase (C terminal domain). To emphasise, these are predicted structures which have not been experimentally verified. Work on the system continues for us, and we hope to share more about it in due course.

Citation:  John Jumper, Kathryn Tunyasuvunakool, Pushmeet Kohli, Demis Hassabis, and the AlphaFold Team, “Computational predictions of protein structures associated with COVID-19”, DeepMind website, 5 March 2020, https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

SARS-COV-2 MEMBRANE PROTEIN: A RENDERING OF ONE OF OUR PROTEIN STRUCTURE PREDICTIONS

SOURCES

Computational predictions of protein structures associated with COVID-19

https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

AlphaFold: Using AI for scientific discovery 

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery

 

DeepMind has shared its results with researchers at the Francis Crick Institute, a biomedical research lab in the UK, as well as offering it for download from its website.

“Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them,” it said. [ALA added bold face]

There are 93,090 cases of COVID-19, and 3,198 deaths, spread across 76 countries, according to the latest report from the World Health Organization at time of writing. ®

SOURCE

https://www.theregister.co.uk/2020/03/06/deepmind_covid19_outbreak/

 

  • MHC content – The spike protein is thought to be the key to binding to cells via the angiotensin II receptor, the major mechanism the immune system uses to distinguish self from non-self

Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies

Syed Faraz Ahmed 1,† , Ahmed A. Quadeer 1, *,† and Matthew R. McKay 1,2, *

1 Department of Electronic and Computer Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China; sfahmed@connect.ust.hk

2 Department of Chemical and Biological Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China

* Correspondence: eeaaquadeer@ust.hk.com (A.A.Q.); m.mckay@ust.hk (M.R.M.)

These authors contributed equally to this work.

Received: 9 February 2020; Accepted: 24 February 2020; Published: 25 February 2020

Abstract:

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

Keywords: Coronavirus; 2019-nCoV; 2019 novel coronavirus; SARS-CoV-2; COVID-19; SARS-CoV; MERS-CoV; T cell epitopes; B cell epitopes; vaccine [ALA added bold face]

SOURCE

https://www.mdpi.com/1999-4915/12/3/254/pdf

 

Selected Online COMMENTS to

https://forums.theregister.co.uk/forum/all/2020/03/06/deepmind_covid19_outbreak/

MuscleguySilver badge

Re: Protein structure prediction has been done for ages…

Not quite, Natural Selection does not measure methods, it measures outputs, usually at the organism level.

Sure correct folding is necessary for much protein function and we have prions and chaperone proteins to get it wrong and right.

The only way NS measures methods and mechanisms is if they are very energetically wasteful. But there are some very wasteful ones out there. Beta-Catenin at the end of point of Wnt signalling comes particularly to mind.

Chemist

Re: Does not matter at all

“Determining the structure of the virus proteins might also help in developing a molecule that disrupts the operation of just those proteins, and not anything else in the human body.”

Well it might, but predicting whether a ‘drug’ will NOT interact with any other of the 20000+ protein in complex organisms is well beyond current science. If we could do that we could predict/avoid toxicity and other non-mechanism related side-effects & mostly we can’t.

rob miller

Title

There are 480 structures on PDBe resulting from a search on ‘coronavirus,’ the top hits from MERS and SARS. PR stunt or not, they did win the most recent CASP ‘competition’, so arguably it’s probably our best shot right now – and I am certainly not satisfied that they have been sufficiently open in explaining their algorithms though I have not checked in the last few months. No one is betting anyone’s health on this, and it is not like making one wrong turn in a series of car directions. Latest prediction algorithms incorporate contact map predictions, so it’s not like a wrong dihedral angle sends the chain off in the wrong direction. A decent model would give something to run docking algorithms against with a series of already approved drugs, then we take that shortlist into the lab. A confirmed hit could be an instantly available treatment, no two year wait as currently estimated. [ALA added bold face]

jelabarre59Silver badge

Re: these structure predictions have not been experimentally verified

Naaaah. Can’t possibly be a stupid marketing stunt.

Well yes, a good possibility. But it can also be trying to build on the open-source model of putting it out there for others to build and improve upon. Essentially opening that “peer review” to a larger audience quicker. [ALA added bold face]

We shall see.

Anonymous Coward

Anonymous CowardWhat bothers me, besides the obvious PR stunt, is that they say this prediction is licensed. How can a prediction from software be protected by, I presume, patents? And if this can be protected without even verifying which predictions actually work, what’s to stop someone spitting out millions of random, untested predictions just in case they can claim ownership later when one of them is proven to work? [ALA added bold face]

 

 

SOURCES

 

  • AI-predicted protein structures could unlock vaccine for Wuhan coronavirus… if correct… after clinical trials It’s not quite DeepMind’s ‘Come with me if you want to live’ moment, but it’s close, maybe

Experimentally derived by a group of scientists at the University of Texas at Austin and the National Institute of Allergy and Infectious Diseases, an agency under the US National Institute of Health. They both feature a “Spike protein structure.”

  • Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

See all authors and affiliations

Science  19 Feb 2020:
eabb2507
DOI: 10.1126/science.abb2507

 

  • Israeli scientists: We have developed a coronavirus vaccine

https://www.fromthegrapevine.com/health/coronavirus-vaccine-israel-migal-research-institute-david-zigdon

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

Reporters: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/06/group-of-researchers-solve-covid-19-structure-and-map-potential-therapeutic/

 

  • Is It Time for the Virtual Scientific Conference?: Coronavirus, Travel Restrictions, Conferences Cancelled Curator:

Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/03/06/is-it-time-for-the-virtual-scientific-conference-coronavirus-travel-restrictions-conferences-cancelled/

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Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Curator: Stephen J. Williams, PhD.

Dr. Cathy N. Davidson from Duke University gives a talk entitled: Now You See It.  Why the Future of Learning Demands a Paradigm Shift

In this talk, shown below, Dr. Davidson shows how our current education system has been designed for educating students for the industrial age type careers and skills needed for success in the Industrial Age and how this educational paradigm is failing to prepare students for the challenges they will face in their future careers.

Or as Dr. Davidson summarizes

Designing education not for your past but for their future

As the video is almost an hour I will summarize some of the main points below

PLEASE WATCH VIDEO

Summary of talk

Dr. Davidson starts the talk with a thesis: that Institutions tend to preserve the problems they were created to solve.

All the current work, teaching paradigms that we use today were created for the last information age (19th century)

Our job to to remake the institutions of education work for the future not the one we inherited

Four information ages or technologies that radically changed communication

  1. advent of writing: B.C. in ancient Mesopotamia allowed us to record and transfer knowledge and ideas
  2. movable type – first seen in 10th century China
  3. steam powered press – allowed books to be mass produced and available to the middle class.  First time middle class was able to have unlimited access to literature
  4. internet- ability to publish and share ideas worldwide

Interestingly, in the early phases of each of these information ages, the same four complaints about the new technology/methodology of disseminating information was heard

  • ruins memory
  • creates a distraction
  • ruins interpersonal dialogue and authority
  • reduces complexity of thought

She gives an example of Socrates who hated writing and frequently stated that writing ruins memory, creates a distraction, and worst commits ideas to what one writes down which could not be changed or altered and so destroys ‘free thinking’.

She discusses how our educational institutions are designed for the industrial age.

The need for collaborative (group) learning AND teaching

Designing education not for your past but for the future

In other words preparing students for THEIR future not your past and the future careers that do not exist today.

In the West we were all taught to answer silently and alone.  However in Japan, education is arranged in the han or group think utilizing the best talents of each member in the group.  In Japan you are arranged in such groups at an early age.  The concept is that each member of the group contributes their unique talent and skill for the betterment of the whole group.  The goal is to demonstrate that the group worked well together.

see https://educationinjapan.wordpress.com/education-system-in-japan-general/the-han-at-work-community-spirit-begins-in-elementary-school/ for a description of “in the han”

In the 19th century in institutions had to solve a problem: how to get people out of the farm and into the factory and/or out of the shop and into the firm

Takes a lot of regulation and institutionalization to convince people that independent thought is not the best way in the corporation

keywords for an industrial age

  • timeliness
  • attention to task
  • standards, standardization
  • hierarchy
  • specialization, expertise
  • metrics (measures, management)
  • two cultures: separating curriculum into STEM versus artistic tracts or dividing the world of science and world of art

This effort led to a concept used in scientific labor management derived from this old paradigm in education, an educational system controlled and success measured using

  • grades (A,B,C,D)
  • multiple choice tests

keywords for our age

  • workflow
  • multitasking attention
  • interactive process (Prototype, Feedback)
  • data mining
  • collaboration by difference

Can using a methodology such as scientific curation affect higher education to achieve this goal of teaching students to collaborate in an interactive process using data mining to create a new workflow for any given problem?  Can a methodology of scientific curation be able to affect such changes needed in academic departments to achieve the above goal?

This will be the subject of future curations tested using real-world in class examples.

However, it is important to first discern that scientific content curation takes material from Peer reviewed sources and other expert-vetted sources.  This is unique from other types of content curation in which take from varied sources, some of which are not expert-reviewed, vetted, or possibly ‘fake news’ or highly edited materials such as altered video and audio.  In this respect, the expert acts not only as curator but as referee.  In addition, collaboration is necessary and even compulsory for the methodology of scientific content curation, portending the curator not as the sole expert but revealing the CONTENT from experts as the main focus for learning and edification.

Other article of note on this subject in this Open Access Online Scientific Journal include:

The above articles will give a good background on this NEW Conceived Methodology of Scientific Curation and its Applicability in various areas such as Medical Publishing, and as discussed below Medical Education.

To understand the new paradigm in medical communication and the impact curative networks have or will play in this arena please read the following:

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

This article discusses a history of medical communication and how science and medical communication initially moved from discussions from select individuals to the current open accessible and cooperative structure using Web 2.0 as a platform.

 

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The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

Curator: Aviva Lev–Ari, PhD, RN

UPDATED on 4/4/2022

Analytics for e-Reputation based on LinkedIn 1st Degree Connections, +7,500 of LPBI Group’s Founder, 2012-2022: An Intangible Asset – Connections’ Position Seniority & Biotech / Pharma Focus

Author: Aviva Lev-Ari, PhD, RN, Founder of 1.0 LPBI, 2012-2020 & 2.0 LPBI, 2021-2025 and Data Scientist, Research Assistant III: Tianzuo George Li

https://pharmaceuticalintelligence.com/2022/04/04/analytics-for-e-reputation-based-on-linkedin-1st-degree-connections-7500-of-lpbi-groups-founder-2012-2022-an-intangible-asset-connections-position-seniority-biotech-pharma-focus/

 

UPDATED on 7/30/2021

Analysis of a corporate Stream of Innovation as reputation builder for venture valuation is presented, below

2.0 LPBI is a Very Unique Organization

Author: Aviva Lev-Ari, PhD, RN, Founder of 1.0 LPBI and 2.0 LPBI, April 2012 to Present

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

Direct reputation, feedback reputation and signaling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. … a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways.

Luıs M B Cabral, New York University and CEPR, 2005

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

  1.  Claiming Distinction
  2.  Recognition Bestowed
  3.  The Technology
  4.  The Sphere of Influence
  5.  The Industrial Benefactors in Potential
  6.  The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE
  7.  1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500
  8.  The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions
  9.  Growth in Twitter Followers and in Global Reach: Who are the NEW Followers? they are OUR COMPETITION   and   other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation
  10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Part 2:   Business Perspectives on Reputation

Part 3:   Economics Perspectives on Reputation

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

This curation attempts to teach-by-example the new reality of the Intangible Asset of Firm’s Reputation when the business is 100% in the cloud, 100% electronic in nature (paperless), the customers are the Global Universe and the organization is 100% Global and 100% virtual.

A Case in Point: Intellectual Property Production Process of Health Care Digital Content using electronic Media Channels

Optimal Testimonial of e-Product Quality and Reputation for an Open Access Online Scientific Journal pharmaceuticalintelligence.com 

 1.   Claiming Distinction

Executive Summary

WHAT ARE LPBI Group’s NEEDS in June 2019: Aviva’s BOLD VISION on June 11, 2019

2.   Recognition Bestowed 

Our Books are here

  • On 8/17/2018, Dr. Lev-Ari, PhD, RN was contacted by the President elect of the Massachusetts Academy of Sciences (MAS), Prof. Katya Ravid of Boston University, School of Medicine, to join MAS in the role of Liaison to the Biotechnology and eScientific Publishing industries for the term of August 2018-July 2021. In the MAS, Dr. Lev-Ari serve as Board member, Fellow, and Advisor to the Governing Board.

http://www.maacadsci.org

MAS FELLOWS 

GOVERNING BOARD

ACTIVITIES

BUNDLED BY AMAZON.COM INTO A SIX-VOLUME SERIES FOR $515

https://lnkd.in/e6WkMgF

Sixteen Volumes ARE ON AMAZON.COM, average book length – 2,400 pages

https://lnkd.in/ekWGNqA

3.   The Technology

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

Detailed Technology Description

LPBI’s Pipeline Map: A Positioning Perspectives – An Outlook to the Future from the Present

4.   The Sphere of Influence 

LPBI Group’s Social Media Presence

JOURNAL Statistics on 2/24/2019

  • LPBI Platform is been used by GLOBAL Communities of Scientists for interactive dialogue of SCIENCE – Four case studies are presented in the link, below

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

Curator and Editor-in-Chief: Journal and BioMed e-Series, Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/04/10/electronic-scientific-agora-comment-exchanges-by-global-scientists-on-articles-published-in-the-open-access-journal-pharmaceuticalintelligence-com-four-case-studies/

5.   The Industrial Benefactors in Potential

Opportunities Map in the Acquisition Arena

Dynamic Contents for LPBI Group’s PowerPoint Presentation

Potential Use of LPBI IP as Value Price Driver by Potential Acquirer: Assumptions per Asset Class 

6.   The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE

Founder’s Role in the Development of Venture’s Factors of Content Production – Biographical Notes by Aviva Lev-Ari, PhD, RN, LPBI Group

Top Authors by Number of eReaders Views

Top Articles by Number of e-Readers for All Days ending 2019-02-17

FIT Members Contribute to Opportunities Map

FINAL IMPROVEMENT TEAM (FIT): Definition of Active, Lapsing of Active Status, COMPs Formulas

FIT members – Who works on WHAT?

Summer 2019 Plan – Research Associates Tasks

7.   1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500

Connections First Level on LinkedIn: 500 CEOs, 200 Big Pharma Professionals, 7,000 in Total: LPBI Group Founder – Aviva Lev-Ari, PhD, RN

8.   The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions

Mostly HONORED to be followed by [from an Excerpt of 117 Followers of the Twitter Account @AVIVA1950 from the List of 359 Followers] by the Number of their Followers on 2/24/2019

LPBI Group is mostly HONORED to be followed by [from an Excerpt of 136 Followers of the Twitter Account @pharma_BI from the List of 505 Followers] by the Number of their Followers on 3/20/2019

Excerpt of 136 Followers of @pharma_BI (from the List of 505 Followers) by the Number of their Followers on 3/20/2019

Excerpt of 117 Followers of @AVIVA1950 (from the List of 359 Followers) by the Number of their Followers

REACH – Two Handles on Twitter.com @AVIVA1950 @pharma_BI

9.   Growth in Twitter Followers and in Global Reach: Who are the NEW Followers: OUR COMPETITION and other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation

@4openjournalFollows you

Follow

4open is a multi- & inter-disciplinary, online, peer-reviewed, open access journal publishing across a broad range of subjects in the STEM domain.

@roll_clausFollows you

Follow

Publishing Editor at 

@EDPSciences

@PubtextoPFollows you

Following

Pubtexto is an International online publishing organization that publishes Scientific literature through its different open access Journals.

@alexanderlabrieFollows you

Following

CEO 

@sphereinc

@BjoernBruecherFollows you

Following

THEODOR-BILLROTH-ACADEMY® 

(link: http://linkedin.com/in/bruecher)

linkedin.com/in/bruecher // 

(link: http://4open-sciences.org)

4open-sciences.org – Editor-in-Chief // Science Profile – 

(link: http://researchgate.net/profile/Bjoern)

researchgate.net/profile/Bjoern

@MPDexpertFollows you

Follow

translate research into life-changing Global manufactured Medical Products – drugs, devices, biotech, combination; anything requiring FDA approval#MedProdDev

@P_A_MORGONFollows you

Following

Life science expert & investor_travel, wine & golf amateur_Proud father of 2 girls_My Tweets are only mine 

@INmuneBioFollows you

Follow

INmune Bio, Inc. is developing therapies that harness patient’s #immunesystem to treat #cancer. Our focus is on #NKcells and #myeloid derived suppressor cells.

@sallyeavesFollows you

Following

Innovating #tech #education #business CEO CTO Advisor & Prof. #blockchain #AI 

@OxfordSBS

@Forbes

 #FinTech #speaker #SDGs #STEM #techforgood #sustainability

@sciencetracker2Follows you

You will hear more recent and cool scientific news here. Besides, some health and tech news. Follow us in

(link: http://facebook.com/sciencetracker2)

facebook.com/sciencetracker2

13.8K Following

24.6K Followers

Followed by Stanford Tweets, Biotech Week Boston, and 23 others you follow

@sgruenwaldFollows you

Following

MD, PhD, scientist, futurist, entrepreneur, managing director of 

(link: http://www.genautica.com)

genautica.com, co-founder 

(link: http://www.diagnomics.com)

diagnomics.com

(link: http://www.scoop.it/t/amazing-science)

scoop.it/t/amazing-scie…user

10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Financial Valuation of Three Health Care Intellectual Property (IP) Content Asset Classes

Global Market Penetration Forecast for each Volume in the 16 Volume BioMed e-Series

2013-2019, On the Medical & Scientific Bookshelf in Kindle Store: eReader Behaviors: Browsing, Page Downloads and Buying e-Books – LPBI Group’s BioMed e-Series, Royalties Payment Analysis 

Part 2: BUSINESS PERSPECTIVES on Reputation

Warren Buffett on reputation: the economic value of values, integrity and corporate culture

Warren Buffett understands that reputation and integrity have economic value. Research that shows that a good reputation is worth real money — in fact, some research indicates that a good reputation might replace a line of credit at the bank. In his book Berkshire Beyond Buffett: The Enduring Value of Values, Lawrence Cunningham argues that one of Berkshire Hathaway’s greatest assets is reputation.

https://www.finn.agency/fr/warren-buffett-reputation-berkshire-hathaway

The Value of Reputation

Thomas Pfeiffer1,2,4,*, Lily Tran5, Coco Krumme5 and David G Rand1,3,* 1 Program for Evolutionary Dynamics, FAS, 2 School of Applied Sciences and Engineering, and 3 Department of Psychology, Harvard University, Cambridge MA 02138, USA 4 New Zealand Institute for Advanced Study, Massey University, Auckland 0745, New Zealand 5 MIT Media Laboratory, Cambridge MA 02139, USA

Reputation plays a central role in human societies.

Empirical and theoretical work indicates that a good reputation is valuable in that it increases one’s expected payoff in the future. Here, we explore a game that couples a repeated Prisoner’s Dilemma (PD), in which participants can earn and can benefit from a good reputation, with a market in which reputation can be bought and sold. This game allows us to investigate how the trading of reputation affects cooperation in the PD, and how participants assess the value of having a good reputation. We find that depending on how the game is set up, trading can have a positive or a negative effect on the overall frequency of cooperation. Moreover, we show that the more valuable a good reputation is in the PD, the higher the price at which it is traded in the market. Our findings have important implications for the use of reputation systems in practice.

Keywords: evolution of cooperation; reciprocal altruism; indirect reciprocity; reputation

http://decisionlab.harvard.edu/_content/research/papers/Krumme_Pfieffer_Tran_and_Rand_Value_of_Reputation.pdf

The Impact of Reputation on Market Value by Simon Cole

One of the most familiar, but least understood, intangible assets is a firm’s reputation.

Simon Cole is the founding partner of the corporate reputation and branding consultancy Reputation Dividend (www. reputationdividend.com).

http://www.reputationdividend.com/files/4713/4822/1479/Reputation_Dividend_WEC_133_Cole.pdf

Part 3:   ECONOMICS PERSPECTIVES on Reputation

The Economics of Trust and Reputation: A Primer

Luıs M B Cabral New York University and CEPR, June 2005, lecture series at the University of Zurich

lcabral@stern.nyu.edu

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

Notes on the literature

Important note: The notes in this section are essentially limited to the ideas discussed in the present version of these lectures notes. They cannot therefore be considered a survey of the literature. There are dozens of articles on the economics of reputation which I do not include here. In a future version of the text, I hope to provide a more complete set of notes on the literature. The notes below follow the order with which topics are presented.

Bootstrap models. The bootstrap mechanism for trust is based on a general result known as the folk theorem (known as such because of its uncertain origins). For a fairly general statement of the theorem (and its proof) see Fudenberg and Makin (1986). One of the main areas of application of the folk theorem has been the problem of (tacit or explicit) collusion in oligopoly. This is a typical problem of trust (or lack thereof): all firms would prefer prices to be high and output to be low; but each firm, individually, has an incentive to drop price and increase output. Friedman (1971) presents one of the earliest formal applications of the folk theorem to oligopoly collusion. He considers the case when firms set prices and history is perfectly observable. Both of the extensions presented in Section 2.2 were first developed with oligopoly collusion applications in mind. The case of trust with noisy signals (2.2.1) was first developed by Green and Porter (1984). A long series of papers have been written on this topic, including the influential work by Abreu, Pearce and Stacchetti (1990). Rotemberg and Saloner (1986) proposed a model of oligopoly collusion with fluctuating market demand. In this case, the intuition presented in Section 2.2.2 implies that firms collude on a lower price during periods of higher demand. This suggests that prices are counter-cyclical in markets where firms collude. Rotemberg and Saloner (1986) present supporting evidence from the cement industry. A number of papers have built on Rotemberg and Saloner’s analysis. Kandori (1992) shows that the i.i.d. assumption simplifies the analysis but is not crucial. Harrington (19??) considers a richer demand model and looks at how prices vary along the business cycle. The basic idea of repetition as a form of ensuring seller trustworthiness is developed in Klein and Leffler (1981). See also Telser (1980) and Shapiro (1983). When considering the problem of free entry, Klein and Leffler (1981) propose advertising as a solution, whereas Shapiro (1983) suggests low intro25 ductory prices. Section ?? is based on my own research notes. The general analysis of selfreinforcing agreements when there is an outside option of the kind considered here may be found in Ray (2002). Watson (1999, 2002) also considers models where the level of trust stars at a low level and gradually increases.

Bayesian models. The seminal contributions to the study of Bayesian models of reputation are Kreps and Wilson (1982) and Milgrom and Roberts (1982). The model in Section 3.2.1 includes elements from these papers as well as from Diamond (1989). H¨olmstrom (1982/1999) makes the point that separation leads to reduced incentives to invest in reputation. The issue of reputation with separation and changing types is treated in detail in the forthcoming book by Mailath and Samuelson (2006). In Section 3.3, I presented a series of models that deal with name as carriers of reputations. The part on changing names (Section 3.3.1) reflects elements from a variety of models, though, to the best of my knowledge, no study exists that models the process of secret, costless name changes in an infinite period adverse selection context. The study of markets for names follows the work by Tadelis (1999) and Mailath and Samuelson (2001). All of these papers are based on the Bayesian updating paradigm. Kreps (1990) presents an argument for trading reputations in a bootstrap type of model. The analysis of brand stretching (Section 3.3.3) is adapted from Cabral (2000). The paper considers a more general framework where the direct reputation, feedback reputation and signalling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. As we saw above, a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways. For other papers on brand stretching and umbrella branding see Choi (1998), Anderson (2002).

Bibliography

Abreu, Dilip, David Pearce and Ennio Stacchetti (1990), “Toward a Theory of Discounted Repeated Games with Imperfect Monitoring,” Econometrica 58, 1041–1064. Andersson, Fredrik (2002), “Pooling reputations,” International Journal of Industrial Organization 20, 715–730. Bernhein, B. Douglas and Michael D. Whinston (1990), “Multimarket Contact and Collusive Behavior,” Rand Journal of Economics 21, 1–26. Cabral, Lu´ıs M B (2000), “Stretching Firm and Brand Reputation,” Rand Journal of Economics 31, 658-673. Choi, J.P. (1998), “Brand Extension and Informational Leverage,” Review of Economic Studies 65, 655–69. Diamond, Douglas W (1989), “Reputation Acquisition in Debt Markets,” Journal of Political Economy 97, 828–862. Ely, Jeffrey C., and Juuso Valim ¨ aki ¨ (2003), “Bad Reputation,” The Quarterly Journal of Economics 118, 785–814. Fishman, A., and R. Rob (2005), “Is Bigger Better? Customer Base Expansion through Word of Mouth Reputation,” forthcoming in Journal of Political Economy. Friedman, James (1971), “A Noncooperative Equilibrium for Supergames,” Review of Economic Studies 28, 1–12. Fudenberg, Drew and Eric Maskin (1986), “The Folk Theorem in Repeated Games with Discounting or with Imperfect Public Information,” Econometrica 54, 533–556. Green, Ed and Robert Porter (1984), “Noncooperative Collusion Under Imperfect Price Information,” Econometrica 52, 87–100. Holmstrom, Bengt ¨ (1999), “Managerial Incentive Problems: A Dynamic Perspective,” Review of Economic Studies 66, 169–182. (Originally (1982) in Essays in Honor of Professor Lars Wahlback.) Kandori, Michihiro (1992), “Repeated Games Played by Overlapping Generations of Players,” Review of Economic Studies 59, 81–92. Klein, B, and K Leffler (1981), “The Role of Market Forces in Assuring Contractual Performance,” Journal of Political Economy 89, 615–641. 27 Kreps, David (1990), “Corporate Culture and Economic Theory,” in J Alt and K Shepsle (Eds), Perspectives on Positive Political Economy, Cambridge: Cambridge University Press, 90–143. Kreps, David M., Paul Milgrom, John Roberts and Robert Wilson (1982), “Rational Cooperation in the Finitely Repeated Prisoners’ Dilemma,” Journal of Economic Theory 27, 245–252. Kreps, David M., and Robert Wilson (1982), “Reputation and Imperfect Information,” Journal of Economic Theory 27, 253–279. Mailath, George J, and Larry Samuelson (2001), “Who Wants a Good Reputation?,” Review of Economic Studies 68, 415–441. Mailath, George J, and Larry Samuelson (1998), “Your Reputation Is Who You’re Not, Not Who You’d Like To Be,” University of Pennsylvania and University of Wisconsin. Mailath, George J, and Larry Samuelson (2006), Repeated Games and Reputations: Long-Run Relationships, Oxford: Oxford University Press. Milgrom, Paul, and John Roberts (1982), “Predation, Reputation, and Entry Deterrence,” Journal of Economic Theory 27, 280–312. Phelan, Christopher (2001), “Public Trust and Government Betrayal,” forthcoming in Journal of Economic Theory. Ray, Debraj (2002), “The Time Structure of Self-Enforcing Agreements,” Econometrica 70, 547–582. Rotemberg, Julio, and Garth Saloner (1986), “A Supergame-Theoretic Model of Price Wars During Booms,” American Economic Review 76, 390–407. Shapiro, Carl (1983), “Premiums for High Quality Products as Rents to Reputation,” Quarterly Journal of Economics 98, 659–680. Tadelis, S. (1999), “What’s in a Name? Reputation as a Tradeable Asset,” American Economic Review 89, 548–563. Tadelis, Steven (2002), “The Market for Reputations as an Incentive Mechanism,” Journal of Political Economy 92, 854–882. Telser, L G (1980), “A Theory of Self-enforcing Agreements,” Journal of Business 53, 27–44. Tirole, Jean (1996), “A Theory of Collective Reputations (with applications to the persistence of corruption and to firm quality),” Review of Economic Studies 63, 1–22. 28 Watson, Joel (1999), “Starting Small and Renegotiation,” Journal of Economic Theory 85, 52–90. Watson, Joel (2002), “Starting Small and Commitment,” Games and Economic Behavior 38, 176–199. Wernerfelt, Birger (1988), “Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond,” Rand Journal of Economics 19, 458–466.

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

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Selection Process for Chief Innovation and Entrepreneurship Officer (CIEO) @Berkeley: Ecosystem Evangelist, Professor Richard Lyons, Berkeley’s ex-Dean of the Haas School of Business

 

Reporter: Aviva Lev-Ari, PhD, RN, Berkeley PhD’83

 

for @Berkeley Alumna Ecosystem Evangelist see

https://pharmaceuticalintelligence.com/2019-vista/executive-summary/

The University of California at Berkeley appointed professor Richard Lyons as the university’s first-ever chief innovation and entrepreneurship officer (CIEO).

The Selection Process

Professor Richard Lyons was selected for the CIEO position through a rigorous recruitment and selection process that attracted several hundred top-notch applications from all over the world. Throughout the process, Lyons stood out as a true visionary, a strategic leader and an ecosystem evangelist who could understand and activate the untapped potential of Berkeley’s innovation and entrepreneurship landscape.

 

“If together we can improve the transformation of Berkeley’s prodigious intellectual product, across the whole campus, into greater societal benefit, then we will have achieved a great deal,” said Lyons, in a statement.

Image Source: Courtesy of University of California, Berkeley, Doe Library Building with the  Campanile Tower in the background

Professor Richard Lyons,  Accomplishments as Berkeley’s ex-Dean of the Haas School of Business

  • He helped launch the Management, Entrepreneurship, & Technology (M.E.T.) dual-degree program in partnership with the College of Engineering.
  • He also initiated the Biology + Business dual degree program with Molecular & Cell Biology and
  • He revitalized the Berkeley-Haas Entrepreneurship Program (BHEP).
  • He helped the campus to launch the Berkeley SkyDeck startup accelerator in 2012 and served on its Governing Board, did that in collaboration with leadership in the Office of Research and College of Engineering.

 

SOURCE

https://venturebeat-com.cdn.ampproject.org/c/s/venturebeat.com/2019/07/10/richard-lyons-will-be-uc-berkeleys-first-ever-chief-innovation-and-entrepreneurship-officer/amp/

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Real Time @BIOConvention #BIO2019:#Bitcoin Your Data! From Trusted Pharma Silos to Trustless Community-Owned Blockchain-Based Precision Medicine Data Trials

Reporter: Stephen J Williams, PhD @StephenJWillia2
Speakers

As care for lifestyle-driven chronic diseases expands in scope, prevention and recovery are becoming the new areas of focus. Building a precision medicine foundation that will promote ownership of individuals’ health data and allow for sharing and trading of this data could prove a great blockchain.

At its core, blockchain may offer the potential of a shared platform that decentralizes healthcare interactions ensuring access control, authenticity and integrity, while presenting the industry with radical possibilities for value-based care and reimbursement models. Panelists will explore these new discoveries as well as look to answer lingering questions, such as: are we off to a “trustless” information model underpinned by Bitcoin cryptocurrency, where no central authority validates the transactions in the ledger, and anyone whose computers can do the required math can join to mine and add blocks to your data? Would smart contracts begin to incentivize “rational” behaviors where consumers respond in a manner that makes their data interesting?

Moderator:  Cybersecurity is extremely important in the minds of healthcare CEOs.  CEO of Kaiser Permenente has listed this as one of main concerns for his company.

Sanjeey of Singularity: There are Very few companies in this space.  Singularity have collected thousands of patient data.  They wanted to do predictive health care, where a patient will know beforehand what health problems and issues to expect.  Created a program called Virtual Assistant. As data is dynamic, the goal was to provide Virtual Assistant to everyone.

Benefits of blockchain: secure, simple to update, decentralized data; patient can control their own data, who sees it and monetize it.

Nebular Genetics: Company was founded by Dr. George Church, who had pioneered the next generation sequencing (NGS) methodology.  The company goal is to make genomics available to all but this currently is not the case as NGS is not being used as frequently.

The problem is a data problem:

  • data not organized
  • data too parsed
  • data not accessible

Blockchain may be able to alleviate the accessibiltiy problem.  Pharma is very interested in the data but expensive to collect.  In addition many companies just do large scale but low depth sequencing.  For example 23andme (which had recently made a big deal with Lilly for data) only sequences about 1% of genome.

There are two types of genome sequencing companies

  1.  large scale and low depth – like 23andme
  2. smaller scale but higher depth – like DECODE and some of the EU EXOME sequencing efforts like the 1000 Project

Simply Vital Health: Harnesses blockchain to combat ineffeciencies in hospital records. They tackle the costs after acute care so increase the value based care.  Most of healthcare is concentrated on the top earners and little is concentrated on the majority less affluent and poor.  On addressing HIPAA compliance issues: they decided to work with HIPAA and comply but will wait for this industry to catch up so the industry as a whole can lobby to affect policy change required for blockchain technology to work efficiently in this arena.  They will only work with known vendors: VERY Important to know where the data is kept and who are controlling the servers you are using.  With other blockchain like Etherium or Bitcoin, the servers are anonymous.

Encrypgen: generates new blockchain for genomic data and NGS companies.

 

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

#blockchain
#bitcoin
#clinicaltrials

 

 

 

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At California Central District Court Juno Therapeutics, Inc. et al v. Kite Pharma, Inc. – Multi-party Patent Infringement

Curator and Reporter: Aviva Lev-Ari, PhD, RN

 

Infringement of Patent: US7446190B2 – which is exclusively licensed to Juno Therapeutics, Inc.

United States

Inventor
Michel Sadelain
Renier Brentjens
John Maher
Current Assignee
Sloan-Kettering Institute for Cancer Research

Worldwide applications
2003  US

Application US10/448,256 events
2002-05-28
Priority to US38387202P
2008-11-04
Application granted
Application status is Active
Adjusted expiration
Show all events

 

SUMMARY OF INVENTION

The present invention provides chimeric TCR’s, nucleic acid polymer encoding the chimeric TCR’s and methods of using the chimeric TCR’s to facilitate T cell response to a specific target. The chimeric TCR’s of the invention combine, in a single chimeric species, the intracellular domain of CD3 ζ-chain (“zeta chain portion”), a signaling region from a costimulatory protein such as CD28 and a binding element that specifically interacts with a selected target. Thus, in accordance with a first aspect of the invention, there is provided a nucleic acid encoding a chimeric T cell receptor, said chimeric T cell receptor comprising a zeta chain, a CD28 signaling region and a binding element that specifically interacts with a selected target. In accordance with a second aspect of the invention, there is provided a chimeric T cell receptor comprising a zeta chain portion, a CD28 signaling region and a binding element.

In accordance with the method of the invention a chimeric TCR is provided which comprises a zeta chain portion, a co-stimulatory signaling element and a binding element which specifically interacts with a cellular marker associated with target cells. T-lymphocytes from the individual to be treated, for example a human individual, are transduced with the chimeric TCR. This transduction may occur ex vivo, after which the transduced cells are reintroduced into the individual. As a result, T cell immune response is stimulated in the individual to the target cells.

SOURCE

https://patents.google.com/patent/US7446190B2/en

  • Prior Art Search results: (cells) (nucleic acid) (acid polymer) (cell) (cd28) before:priority:2002-05-28

Assignees Inventors include:

C12P21
C12P21/00
C12P
C12P21/02
C07K14/52
C07K14/715
C07K14/54
C07K14/521
C07K14/47
C07K14/46
C12N9/6432
C12Y304/21006
C07K14/47
C07K14/46
C07K14/475
C07K14/435
A01K2217
A01K2217/00
A01K
A01K2217/075
C12N2533/00
C12N2533/14
C12N2533/18
C12N2533/30
G01N33/502
G01N33/5041
Y10S435/973
G01N33/5008
B01J2219/00648
B01J2219/00306
B82Y15/00
B01J2219/00646
C07K14/70532
C07K14/70503
C07K16/2827
A61K2039/5158
A61K38/1774
A61K31/33
A61K45
A61K45/06
C07K14/70532
C12N2795
C12N2795/00
C12N2795/00011
C07K14/47
C07K14/46
A61K48/00
C07K14/435
C12N2510/00
C12N2502/99
C12N2501/515
C12N2501/51
C07K14/70503
A61K38/00
A61K
C07K14/705
G01N33/6878
G01N33/68
C07K1/047
C07K1/04
C07K14/70503
A01K2217/05
C07K14/705
A01K2217
Y02A50/38
A61K2039/6068
A61K2039/6025
C07K2319/21
C07K14/47
C07K14/46
A61K48/00
C07K14/435
C07K14/4747
C07K14/70575
A61K45/06
A61K45

SOURCE

https://patents.google.com/?q=cells&q=nucleic+acid&q=acid+polymer&q=cell&q=cd28&before=priority:20020528&scholar

 

IRELL & MANELLA LLP Morgan Chu (SBN 70446) Alan J. Heinrich (SBN 212782) Elizabeth C. Tuan (SBN 295020) 1800 Avenue of the Stars, Suite 900 Los Angeles, California 90067-4276 Telephone: (310) 277-1010 Facsimile: (310) 203-7199 Attorneys for

Plaintiffs JUNO THERAPEUTICS, INC., MEMORIAL SLOAN KETTERING CANCER CENTER, and SLOAN KETTERING INSTITUTE FOR CANCER RESEARCH UNITED STATES DISTRICT COURT CENTRAL DISTRICT OF CALIFORNIA Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, and Sloan Kettering Institute for Cancer Research,,

Plaintiffs, v. Kite Pharma, Inc., Defendant. ) ) ) ) ) ) ) ) ) ) ) )

CASE NO.: 2:17-CV-07639

COMPLAINT FOR PATENT INFRINGEMENT

DEMAND FOR JURY TRIAL

Case 2:17-cv-07639 Document 1 Filed 10/18/17 Page 1 of 14 Page ID #:1

Knowing that it infringes the ’190 Patent, Kite challenged the validity of all claims of the ’190 Patent in an inter partes review (“IPR”) in the United States Patent and Trademark Office (“PTO” or “Office”) before the Patent Trial and Appeal Board (“PTAB” or “Board”). The PTAB instituted the IPR and then upheld all claims of the ’190 Patent in a Final Written Decision issued December 16, 2016. The PTAB concluded that Kite did not even show “by a preponderance of the evidence”—the lower standard applicable to validity challenges in an IPR—that any claim of the ’190 Patent was unpatentable.

Kite recently received marketing approval from the Food and Drug Administration (“FDA”) for its Yescarta™ product (axicabtagene ciloleucel) (“axicel” or “Yescarta,” also known as “KTE-C19”) on October 18, 2017. Plaintiffs accordingly bring suit against Kite for infringement based on Kite’s making, using, offering to sell, and selling of its chimeric antigen receptor products that comprise the claimed nucleic acid polymers of the ’190 Patent. 35 U.S.C. § 271(a). Plaintiffs hereby allege for their Complaint against Defendant Kite, on personal knowledge as to their own actions and on information and belief as to the actions of others,

26. Indeed, the DNA sequence of Kite’s retroviral vector demonstrates that Kite’s anti-CD19 chimeric TCR falls within the scope of the ’190 Patent claims. In a document Kite filed with the Recombinant DNA Advisory Committee (“RAC”), a federal committee that reviews clinical trial protocols that are either directly funded by the National Institutes of Health (“NIH”) or conducted at institutions that receive NIH funding, Kite provided the DNA sequence of KTE-C19’s anti-CD19 chimeric TCR vector. Exhibit 10 (KTE-C19 DNA Sequence). The RAC filing described the retroviral vector used as

encoding a chimeric antigen receptor directed against the B cell antigen, CD19 . . . The retroviral vector utilizes the MSGV1 (murine stem cell virus-based splice-gag vector 1) retroviral vector backbone and consists of 7026 bps including the 5’ long terminal repeat (LTR) from the murine stem cell virus (promoter), packaging signal including the splicing donor (SD) and splicing acceptor sites, FMC63- based (anti-CD19 FMC63-28) CAR protein containing a signal peptide (human GM-CSF receptor), FMC63 light chain variable region (FMC63 VL), linker peptide, FMC63 heavy chain variable region (FMC63 VH), CD28 (hinge, transmembrane and cytoplasmic region), and TCR-zeta (cytoplasmic region), followed by the murine stem cell virus 3’LTR. This particular vector was provided by Dr. Steven A. Rosenberg from the Surgery Branch/NCI and is the same vector used in an ongoing RAC-approved clinical trial of which Dr. Stephen A. Rosenberg is the Principal Investigator (OBA/RAC submission 0809-940). . . . [T]he complete nucleotide sequence as determined by the standard nucleotide sequencing protocol is shown in Appendix 2 of this application.

27. During the IPR Kite initiated against the ’190 Patent, Sloan Kettering’s expert, Prof. Thomas Brocker, the Director of the Institute for Immunology at the Ludwig-Maximilians University in Munich, Germany, compared the chimeric TCR used by Kite’s scientific collaborators to the claims of the ’190 Patent, demonstrating that Kite’s collaborators’ chimeric TCR construct, and thus, Kite’s own KTE-C19 product, falls within the scope of at least claims 1-3 and 5 of the ’190 Patent. Exhibit 12 (Brocker Declaration), ¶ 224. The NCI chimeric TCR analyzed by Prof. Brocker contains the same nucleotide sequence as KTE-C19’s chimeric TCR. See Exhibit 11 (RAC Filing).

28. On October 18, 2017, Kite received approval for the FDA to market and sell Yescarta (axicabtagene ciloleucel) in the United States.

COUNT 1:

INFRINGEMENT OF THE ’190 PATENT UNDER 35 U.S.C. § 271(a)

29. Plaintiffs re-allege and incorporate by reference the allegations contained in paragraphs 1-28 above.

30. to 40. are Plaintiffs’ description of Defendant Infringement on claims of the Patent

MAIN SOURCE for Filings by Plaintiffs

http://litigationtools.maxval-ip.com/UnifiedPatentViewDocument/home/index?caseid=128416

 

 

Plaintiffs:

  • Juno Therapeutics, Inc.,
  • Memorial Sloan Kettering Cancer Center,
  • Sloan Kettering Institute for Cancer Research

Defendant and Counterclaimant

  • Kite Pharma, Inc.

 

Effective April 17, 2018, Magistrate Judge Rozella A. Oliver will be located at the Edward R. Roybal Federal Building and U.S. Courthouse, COURTROOM 590 on the 5th floor, located at 255 East Temple Street, Los Angeles, California 90012. All Court appearances shall be made in Courtroom 590 of the Roybal Federal Building,

100

Oct 9, 2018

MINUTE IN CHAMBERS CLAIM CONSTRUCTION ORDER by Judge S. James Otero: The Court finds that a POSITA encountering the 190 Patent prior to the CoC would have understood SEQ ID NO:6 to begin with nucleotide 336 of the CD28 protein. The Court construes the disputed claim terms as follows: 1. The amino acid sequence encoded by SEQ ID NO:6 before the Certificate of Correction means Amino Acids 113-220 of CD28 (starting with lysine (K)) and after the Certificate of Correction means Amino Acids 114-220 of CD28 (starting with isoleucine (I)). 2. nucleic acid polymer encoding… a binding element that specifically interacts with a selected target is given its plain and ordinary meaning. (shb) (Entered: 10/10/2018)

 

Main Doc

 

Juno Therapeutics, Inc. et al v. Kite Pharma, Inc. (2:17-cv-07639), California Central District Court

California Central District Court
Judge: S James Otero
Referred: Jacqueline Chooljian
Case #: 2:17-cv-07639
Nature of Suit 830 Property Rights – Patent
Cause 35:271 Patent Infringement
Case Filed: Oct 18, 2017
Docket last updated: 03/08/2019 11:59 PM PST 

Thursday, March 07, 2019
150 order For Order Thu 12:50 PM 
ORDER GRANTING DEFENDANT KITE PHARMA, INC.S EX PARTE APPLICATION FOR AN EXTENSION OF TIME FOR THE MAGISTRATE JUDGE TO HEAR MOTIONS TO COMPEL PRODUCTION OF DOCUMENTS AND WITNESSES144 by Judge S. James Otero: 1. Time is extended until April 17, 2019, for the Magistrate Judge to hear (a) any motions to compel Plaintiffs to produce documents that Kite has already identified as deficient in Plaintiffs production and Plaintiffs have not yet produced, and (b) a motion to compel Bristol-Myers Squibb Company to produce documents in response to Kites subpoena; and 2. Time is extended until May 10, 2019, for the Magistrate Judge to hear a motion to compel deposition testimony regarding the documents described in paragraph 1 above. (lc) Modified on 3/7/2019 (lc)
Wednesday, March 06, 2019
149 transcript -Transcript Order Form (G-120) Wed 2:56 PM 
TRANSCRIPT ORDER as to Defendant Kite Pharma, Inc. for Court Smart (CS). Court will contact Adam R. Lawton at adam.lawton@mto.com with further instructions regarding this order. Transcript preparation will not begin until payment has been satisfied with the transcription company. (Lawton, Adam)
Tuesday, March 05, 2019
147 respm Reply (Motion related) Tue 5:31 PM 
REPLY in support of EX PARTE APPLICATION for Order for Extension of Time for the Magistrate Judge to Hear Motions to Compel Production of Documents and Witnesses 144 filed by Defendant Kite Pharma, Inc..(Lawton, Adam)
Att: 1 Reply Declaration of Adam R. Lawton
146 respm Objection/Opposition (Motion related) Tue 12:26 PM 
OPPOSITION Ex Parte Application re: EX PARTE APPLICATION for Order for Extension of Time for the Magistrate Judge to Hear Motions to Compel Production of Documents and Witnesses 144Opposition filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research.(Wells, Crawford)
Att: 1 Declaration,
Att: 2 Exhibit 1
Monday, March 04, 2019
148 minutes Telephone Conference For Order re Discovery Matter Wed 9:27 AM 
MINUTES OF CONTINUED PRE-MOTION TELEPHONIC DISCOVERY CONFERENCE settling139 Motion re: Informal Discovery Dispute held before Magistrate Judge Karen L. Stevenson. Should Judge Otero grant Kite’s Ex Parte Application, Kite may file a motion to compel. In the interim, at the request of counsel for non-party BMS, who does not receive the CM/ECF notifications in this case, the Court ORDERS Defendant Kite, to provide copies to BMS counsel of the following: (1) Minutes of Pre-Motion Telephonic Discovery Conference held on February 26, 2019, (Dkt. No. 138) (see document for further details). Court Recorder: XTR 03-04-19. (hr)
145 respm Declaration (Motion related) Mon 12:52 PM 
DECLARATION of Adam R. Lawton (Corrected) in support of EX PARTE APPLICATION for Order for Extension of Time for the Magistrate Judge to Hear Motions to Compel Production of Documents and Witnesses 144 filed by Defendant Kite Pharma, Inc.. (Lawton, Adam)
144 17 pgs motion Order Mon 11:50 AM 
EX PARTE APPLICATION for Order for Extension of Time for the Magistrate Judge to Hear Motions to Compel Production of Documents and Witnesses filed by Defendant Kite Pharma, Inc.. (Lawton, Adam)
Att: 1 Proposed Order,
Att: 2 Declaration of Adam R. Lawton,
Att: 3 Exhibit 1,
Att: 4 Exhibit 2,
Att: 5 Exhibit 3,
Att: 6 Exhibit 4,
Att: 7 Exhibit 5,
Att: 8 Exhibit 6,
Att: 9 Exhibit 7,
Att: 10 Exhibit 8,
Att: 11 Exhibit 9,
Att: 12 Exhibit 10,
Att: 13 Exhibit 11,
Att: 14 Exhibit 12,
Att: 15 Exhibit 13,
Att: 16 Exhibit 14,
Att: 17 Exhibit 15,
Att: 18 Exhibit 16
Thursday, February 28, 2019
143 order Leave to File Excess Pages Thu 10:50 AM 
ORDER GRANTING-IN-PART DEFENDANT KITE PHARMA, INC.’S APPLICATION FOR LEAVE TO FILE A 10-PAGE REPLY BRIEF IN SUPPORT OF MOTION FOR SUMMARY JUDGMENT OF NONINFRINGEMENT140 by Judge S. James Otero. It is hereby ordered that Defendant Kite Pharma, Inc. may file a reply brief of no more than 10 pages in support of its motion for summary judgment of noninfringement. Plaintiffs are permitted to file a sur-reply, not to exceed 7 pages, addressing the admissibility of the expert declarations submitted in support of its opposition to Defendant’s motion for summary judgment of noninfringement. The sur-reply shall be filed no later than 5 days from Defendant’s reply. IT IS SO ORDERED. (lom)

Juno Therapeutics, Inc. v. Kite Pharma, Inc. (2:17-cv-07639)

District Court, C.D. California

 

 

 

 

 

 

 

Recorded here ONLY if PDF is Downloadable

Oct 18, 2017

COMPLAINT Receipt No: 0973-20685642 – Fee: $400, filed by Plaintiffs Juno Therapeutics, Inc., Sloan Kettering Institute for Cancer Research, Memorial Sloan Kettering Cancer Center. (Attachments: # 1 Exhibit 1, # 2 Exhibit 2, # 3 Exhibit 3, # 4 Exhibit 4, # 5 Exhibit 5, # 6 Exhibit 6, # 7 Exhibit 7, # 8 Exhibit 8, # 9 Exhibit 9, # 10 Exhibit 10, # 11 Exhibit 11, # 12 Exhibit 12, # 13 Exhibit 13, # 14 Exhibit 14) (Attorney Morgan Chu added to party Juno Therapeutics, Inc.(pty:pla), Attorney Morgan Chu added to party Memorial Sloan Kettering Cancer Center(pty:pla), Attorney Morgan Chu added to party Sloan Kettering Institute for Cancer Research(pty:pla))(Chu, Morgan) (Entered: 10/18/2017)

Main Doc

3

Oct 18, 2017

Request for Clerk to Issue Summons on Complaint (Attorney Civil Case Opening),, 1 filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research. (Chu, Morgan) (Entered: 10/18/2017)

SKIPPED

46

Jan 29, 2018

JOINT REPORT Rule 26(f) Discovery Plan ; estimated length of trial 5-12 days, filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research.. (Attachments: # 1 Appendix 2)(Chu, Morgan) (Entered: 01/29/2018)

SKIPPED

66

Mar 29, 2018

AMENDED ANSWER and AMENDED COUNTERCLAIM to Complaint (Attorney Civil Case Opening),, 1 filed by Defendant and Counterclaimant Kite Pharma, Inc.. (Attachments: # 1 Exhibit A, # 2 Exhibit B, # 3 Exhibit C, # 4 Exhibit D, # 5 Exhibit E, # 6 Exhibit F, # 7 Exhibit G, # 8 Exhibit H, # 9 Exhibit I, # 10 Exhibit J, # 11 Exhibit K, # 12 Exhibit L, # 13 Exhibit M, # 14 Appendix (redline version of amended pleading))(Lawton, Adam) (Entered: 03/29/2018)

SKIPPED

74

May 11, 2018

STIPULATION for Protective Order filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research. (Attachments: # 1 Proposed Order)(Tuan, Elizabeth) (Entered: 05/11/2018)

75

May 14, 2018

ORDER GRANTING PROTECTIVE ORDER by Magistrate Judge Rozella A. Oliver re Stipulation for Protective Order 74 (dml) (Entered: 05/14/2018)

Protective Order

SKIPPED

85

Aug 13, 2018

DECLARATION of Alan J. Heinrich re Brief (non-motion non-appeal), 84 ISO Juno’s Claim Construction Brief filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research, Counter Defendants Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research. (Attachments: # 1 Exhibit Exhibit 1, # 2 Exhibit Exhibit 2, # 3 Exhibit Exhibit 3, # 4 Exhibit Exhibit 4, # 5 Exhibit Exhibit 5, # 6 Exhibit Exhibit 6, # 7 Exhibit Exhibit 7, # 8 Exhibit Exhibit 8, # 9 Exhibit Exhibit 9, # 10 Exhibit Exhibit 10, # 11 Exhibit Exhibit 11)(Heinrich, Alan) (Entered: 08/13/2018)

Main Doc

Declaration

115

Dec 3, 2018

SEALED DECLARATION IN SUPPORT OF APPLICATION to file document (Reply in Support of Motion to Dismiss and Exhibits J-M) under seal 114 filed by Defendant Kite Pharma, Inc.. (Attachments: # 1 Unredacted Document Reply in Support of Motion to Dismiss, # 2 Unredacted Document Exhibit J, # 3 Unredacted Document Exhibit K, # 4 Unredacted Document Exhibit L, # 5 Unredacted Document Exhibit M)(Lawton, Adam) (Entered: 12/03/2018)

Main Doc

117

Jan 4, 2019

STIPULATION to AMEND Protective Order 75 filed by Defendant Kite Pharma, Inc.. (Attachments: # 1 Amended Protective Order, # 2 Proposed Order)(Lawton, Adam) (Entered: 01/04/2019)

118

Jan 7, 2019

ORDER GRANTING AMENDED PROTECTIVE ORDER by Magistrate Judge Rozella A. Oliver, re Stipulation to Amend Protective Order 117 (dml) (Entered: 01/07/2019)

119

Jan 7, 2019

AMENDED PROTECTIVE ORDER by Magistrate Judge Rozella A. Oliver, re Order Granting 118 (dml) (Entered: 01/07/2019)

122

Jan 24, 2019

Joint STIPULATION to Extend Discovery Cut-Off Date to March 29, 2019 filed by Plaintiffs Juno Therapeutics, Inc., Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute for Cancer Research. (Attachments: # 1 Proposed Order)(Heinrich, Alan) (Entered: 01/24/2019)

Main Doc

SOURCE

https://www.courtlistener.com/docket/6175992/juno-therapeutics-inc-v-kite-pharma-inc/

Other related sources

35 U.S.C. 271 – Infringement of patent

Other related articles published in this Online Open Access Scientific Journal, include the following:

Economic Potential of a Drug Invention (Prof. Zelig Eshhar, Weitzman Institute, registered the patent) versus a Cancer Drug in Clinical Trials: CAR-T as a Case in Point, developed by Kite Pharma, under Arie Belldegrun, CEO, acquired by Gilead for $11.9 billion, 8/2017.

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/04/economic-potential-of-a-drug-invention-prof-zelig-eshhar-weitzman-institute-registered-the-patent-versus-a-cancer-drug-in-clinical-trials-car-t-as-a-case-in-point-developed-by-kite-pharma-unde/

 

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CRISPR – The Business and Legal Aspects of IP Development, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

Patent on Methods and compositions for RNA-directed target DNA modification and for RNA-directed modulation of transcription was awarded to UC, Berkeley on October 30, 2018

  •  site-specific modification of a target DNA and/or a polypeptide associated with the target DNA, a DNA-targeting RNA
  •  genetically modified cells that produce Cas9; and Cas9 transgenic non-human multicellular organisms.

Reporter: Aviva Lev-Ari, PhD, RN

 

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United States Patent 10,113,167
Doudna ,   et al. October 30, 2018

Methods and compositions for RNA-directed target DNA modification and for RNA-directed modulation of transcription 

AbstractThe present disclosure provides a DNA-targeting RNA that comprises a targeting sequence and, together with a modifying polypeptide, provides for site-specific modification of a target DNA and/or a polypeptide associated with the target DNA. The present disclosure further provides site-specific modifying polypeptides. The present disclosure further provides methods of site-specific modification of a target DNA and/or a polypeptide associated with the target DNA The present disclosure provides methods of modulating transcription of a target nucleic acid in a target cell, generally involving contacting the target nucleic acid with an enzymatically inactive Cas9 polypeptide and a DNA-targeting RNA. Kits and compositions for carrying out the methods are also provided. The present disclosure provides genetically modified cells that produce Cas9; and Cas9 transgenic non-human multicellular organisms.


Inventors: Doudna; Jennifer A. (Berkeley, CA), Jinek; Martin (Berkeley, CA), Chylinski; Krzysztof (Vienna, AT), Charpentier; Emmanuelle (Braunschweig, DE)
Applicant:
Name City State Country Type

The Regents of the University of California
University of Vienna
Charpentier; Emmanuelle
Oakland
Vienna
Braunschweig
CA
N/A
N/A
US
AT
DE
Assignee: The Regents of the University of California (Oakland, CA)
University of Vienna (Vienna, AT)
Charpentier; Emmanuelle (Braunschweig, DE)
Family ID: 1000003617643
Appl. No.: 15/138,604
Filed: April 26, 2016

SOURCE

http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10113167.PN.&OS=PN/10113167&RS=PN/10113167

SAVE

UC Berkeley team awarded second CRISPR-Cas9 patent

 

“Today’s news … represents yet another validation of the historic and field-changing breakthrough invented by scientists Jennifer Doudna, Emmanuelle Charpentier, and their team… The patent announced today specifically highlights the CRISPR-Cas9 invention’s ability to edit DNA in any setting, including within animal and human cells. It also highlights its utility in several formats across both dual-RNA and single-RNA configurations, useful for therapy for genetic diseases and for improving food security.”
— Edward Penhoet, special adviser to the UC Berkeley chancellor, tells Axios

The details: According to the patent, the compositions can be used in animal or human cells, and can work as either 2 separate pieces of RNA or a single piece of RNA.

  • Penhoet says the new patent covers 2 RNA components that together form the “DNA-targeting-RNA,” with one that targets the particular sequence of DNA needed to be edited and the other that binds with the Cas9 protein.
  • This follows another patent given to UC Berkeley in June on methods to use CRISPR-cas9.
  • The patents cover the composites used by CRISPR-Cas9 within human, plant, animal and bacteria cells.
  • Both allow the use of strands of RNA “that can be shorter than naturally-occurring RNA components. This allows them to be more easily used and, therefore, is a form often preferred,” Penhoet says.

Go deeper:

SOURCE

https://www.axios.com/uc-berkeley-awarded-crispr-cas9-gene-edit-patent-5a533f22-929d-4e7d-83fe-0a73ebeb4538.html

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