DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the original application filed on Dec. 15th, 2023.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because it recites, "A non-transitory computer-readable medium storing code for data management, the code comprising instructions executable by one or more processors to:" (emphasis added). The submitted specification recites " Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that can be accessed by a general purpose or special purpose computer.” (emphasis added), (DETAILED DESCRIPTION, [0130], PP. 33), which states this code instructions of this system could be "Signals per se", therefore would not fall under the four categories of patent eligible subject matter per MPEP 2106.06(1): "Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter.". Claims 18-20 are dependent claims of claim 17 and are rejected because of this dependency. Further, the dependent claims 18-20 recite, “The non-transitory computer-readable medium of claim …” (Emphasis added) which also attempts to claim subject matter which is ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-9,12-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tran et al, (Tran et al, “ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems”, 2022, hereinafter “Tran”) in view of Lo et al, (Lo et al, “Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems”, 2023, hereinafter “Lo”).
Regarding claim 1, Trans discloses, “A method for data management, comprising:” Tran (Introduction, pp. 3; “We propose a novel architectural approach called Artefact-as-a-State-Machine (ASM) to utilize blockchain transactions and smart contracts for storing and updating ML provenance information.”)
“receiving, for generating a machine learning model, one or more user inputs associated with the machine learning model;” (User-Driven Provenance Capture, pp. 6; “Figure 2 depicts the process for capturing and submitting ML provenance from a training script. Participants can submit
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to ProML in an ad-hoc manner via a command line interface (CLI) client, programmatically via a software library, or directly by invoking the provenance capturing service of their trusted ProML node.” This model will receive information from the user. This system has an API the client can use to interact with the proposed system.)
“receiving, for generating the machine learning model, an indication of a data source for training the machine learning model;” (User-Driven Provenance Capture, pp. 6-7; “The provenance capturing service uses the provided information (
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) to construct blockchain transactions for deploying smart contracts that represent the given models or datasets. It uses the signer service, which holds participants' private keys, to sign the constructed transactions with participants' credentials.” This model has an API of commands a client can use to input data into a system to train a machine learning model.)
“broadcasting one or more first blockchain messages that are configured to store first information associated with the one or more user inputs and the data source on a blockchain network;” (User-Driven Provenance Capture, pp. 7; “Following the signing, the provenance capturing service invokes the provider service, which wraps around a blockchain client, to publish the transactions. The registration completes when the trans- actions have been added to the ledger, making the smart contracts available on the blockchain.” This model will submit user information and data to a blockchain. The system will submit, or broadcast, the block of information to the blockchain.)
Tran fails to explicitly disclose, “receiving one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” and “broadcasting one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.”.
However, Lo discloses, “receiving one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” (Client, pp. 3278; “The model trainer set up the environment for local model training according to the training job received from the central server. After each local epoch, the local model is transferred to the local model evaluator for performance assessment. The hashed value of the local model versions and their performance are recorded and uploaded to the blockchain for data-model provenance.” The system will receive different training prompts from the central server and the client will train the model. This training includes using, or receiving, training data and recording the performance, or output, of the machine learning model.)
“broadcasting one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.” (Data-Model Registry Smart Contract, pp. 3279; “With the use of blockchain to store the hashed value of data, local, and global model versions, data-model provenance is achievable and users can audit the federated learning model performance. The data-model registry automatically records users’ on-chain addresses for the mapping of model parameters and data versions, while blockchain transactions also include uploaders’ information.” The blockchain will store, or broadcast, information regarding training data and machine learning model training parameters and results. The blockchain in this article “broadcasts” the added block to the other blockchain nodes in the network.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Tran and Lo. Tran teaches a machine learning training system that utilizes a blockchain as a ledger and attempts preserve provenance of the input data and machine learning training actions of a given user. Lo teaches a Blockchain system that is able to ensure user accountability and fairness when training a federated learning system by recording inputs and model updates via a blockchain. One of ordinary skill would have motivation to combine two methods which both ensures provenance of data input and machine learning model training using a blockchain to record updates, training information and user actions in distributed machine learning system to solve the issue of user trustworthiness and model fairness, “To improve the accountability and fairness of federated learning systems, we proposed a blockchain-based trustworthy federated learning architecture. Blockchain and smart contracts are utilized for federated learning to maintain data integrity with its immutability [10]–[12]. The transparency property of blockchain ensures auditability and accountability, and this has been widely studied and evaluated [13]–[15]. Thus, we propose to leverage blockchain and smart contract technology to improve the accountability of federated learning systems. Designing such an integration is feasible as the designs of both federated learning and blockchain systems are decentralized in nature. We chose the COVID-19 detection scenario using X-rays as a use case to demonstrate and validate our approach. The contributions of this article are as follows.” (Lo, Introduction, pp. 3276).
Regarding claim 4, Lo discloses, “wherein the one or more second blockchain messages are configured to associate the second information with the first information on the blockchain network.” (Blockchain, 3278; “Each client and the central server will install at least one blockchain node to form a network. Each node holds a local replica of the complete transaction data in the form of a chain of blocks. The blockchain operations mainly cover the data-model provenance using smart contracts, in which all participants are identified via their blockchain addresses. In every federation epoch, all local and global model parameters are stored in off-chain database of local models and database of global models, respectively. Meanwhile, the hashed local data versions are produced and recorded in the on-chain data-model registry smart contract to achieve the provenance and co-versioning of data and models.” Each of the blocks in the block chain are identifiable via clients blockchain address’. Further parameters and model updates are published to the chain which connect the different blocks in the chain.)
Regarding claim 5, Tran discloses, “wherein the first information associated with the one or more user inputs comprises a respective identifier for one or more users that created the machine learning model, a description of the machine learning model, documentation associated with the machine learning model, preprocessing parameters, training parameters, one or more timestamps associated with creation of the machine learning model, feature descriptions, model specifications, evaluation metrics, tuning parameters, or a combination thereof.” (Artefact-as-a-State-Machine, pp. 8-9; “ML models and datasets map to smart contract instances because they are individually addressable blockchain objects that carry internal variables and functions to act upon those variables. The address of a smart contract becomes an asset's identifier. Internal variables of a smart contract can be used to store an asset's state and metadata. Functions of a smart contract represent workflow activities that update an asset's state. Function parameters can be used to capture the payload of a provenance record corresponding to a work ow activity. Participants record a provenance update
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by submitting a blockchain transaction to the smart contract to invoke a function corresponding to the reported work ow activity. These transactions serve as provenance records. We call this approach of modelling ML assets as state machines to map them to blockchain constructs for management as Artefact-as-a-State-Machine, or ASM. Figure 3 depicts an exemplary implementation of ASM with smart contracts written in Solidity programming language7.” Figure 3 shows an example of the different information that is added to the blockchain. The different workflow activities is information that is stored and submitted to the blockchain along with the participant ID.)
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Regarding claim 6, Tran discloses, “wherein the first information associated with the data source comprises one or more data collection time stamps, data usage agreement information, data source descriptions, or a combination thereof.” (User-Driven Provenance Capture, pp. 6; “Transparency and control: ML development activities can utilize and produce sensitive data, which must conform to predefined Data Use Agreements (DUAs). Therefore, users require visibility and control over the information captured and propagated by the provenance management system so that they can verify and therefore trust the system. The user-driven procedure of ProML meets this ends.” The proposed system will use training data and the clients are required to ensure the data meets the DUAs.)
Regarding claim 7, Lo discloses, “encrypting at least a portion of the first information, the second information, or both to generate encrypted information, wherein the encrypted information is stored on the blockchain network.” (Data-Model Registry Smart Contract, pp. 3279; “To address these two vital issues, we apply both hashing and asymmetric/symmetric encryption techniques. … With the use of blockchain to store the hashed value of data, local, and global model versions, data-model provenance is achievable and users can audit the federated learning model performance.” Lo discloses a process of encrypting information to store the data on in the block chain.)
Regarding claim 8, Tran discloses, “wherein the one or more first blockchain messages or the one or more second blockchain messages are configured to call a self-executing program on the blockchain network to store the first information, the second information, or both.” (User, Driven Provenance Capture, pp. 6; “Participants can submit
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to ProML in an ad-hoc manner via a command line interface (CLI) client, programmatically via a software library, or directly by invoking the provenance capturing service of their trusted ProML node.” The user will submit information to train a ML model using an API. This will then cause the system to execute its own sets of functions to ensure the provenance and actually submit the updates to the model and the block chain.) and (Fig. 2, pp. 6; Figure 2 depicts the different self-executing actions the system will take after a client submits information to the blockchain. Also shown is the API the client can use to submit information to be processed by the system)
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Regarding claim 9, Trans discloses, “An apparatus for data management, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to:” (Experimental Design, pp. 10; “The PoC's smart contracts were deployed on Ropsten, a global Ethereum blockchain network preserved for testing9. We chose this blockchain network due to its scale and similarity in configurations and performance characteristics with the Ethereum main network, which operates most high-pro le and high-value blockchain-based software applications. We used Ethers10 for implementing the blockchain provider and signer services and Node for the provenance capturing and querying services.” This article discloses a process which is executed using known blockchain networks and software. This would require this system to perform actions on generic servers or computing devices which contains processors coupled to memory units which store the computer readable instructions.)
“receive, for generating a machine learning model, one or more user inputs associated with the machine learning model;” (User-Driven Provenance Capture, pp. 6; “Figure 2 depicts the process for capturing and submitting ML provenance from a training script. Participants can submit
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to ProML in an ad-hoc manner via a command line interface (CLI) client, programmatically via a software library, or directly by invoking the provenance capturing service of their trusted ProML node.” This model will receive information from the user. This system has an API the client can use to interact with the proposed system.)
“receive, for generating the machine learning model, an indication of a data source for training the machine learning model;” (User-Driven Provenance Capture, pp. 6-7; “The provenance capturing service uses the provided information (
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) to construct blockchain transactions for deploying smart contracts that represent the given models or datasets. It uses the signer service, which holds participants' private keys, to sign the constructed transactions with participants' credentials.” This model has an API of commands a client can use to input data into a system to train a machine learning model.)
“broadcast one or more first blockchain messages that are configured to store first information associated with the one or more user inputs and the data source on a blockchain network;” (User-Driven Provenance Capture, pp. 7; “Following the signing, the provenance capturing service invokes the provider service, which wraps around a blockchain client, to publish the transactions. The registration completes when the trans- actions have been added to the ledger, making the smart contracts available on the blockchain.” This model will submit user information and data to a blockchain. The system will submit, or broadcast, the block of information to the blockchain.)
Tran fails to explicitly disclose, “receive one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” and “broadcast one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.”.
However, Lo discloses, “receive one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” (Client, pp. 3278; “The model trainer set up the environment for local model training according to the training job received from the central server. After each local epoch, the local model is transferred to the local model evaluator for performance assessment. The hashed value of the local model versions and their performance are recorded and uploaded to the blockchain for data-model provenance.” The system will receive different training prompts from the central server and the client will train the model. This training includes using, or receiving, training data and recording the performance, or output, of the machine learning model.)
“broadcast one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.” (Data-Model Registry Smart Contract, pp. 3279; “With the use of blockchain to store the hashed value of data, local, and global model versions, data-model provenance is achievable and users can audit the federated learning model performance. The data-model registry automatically records users’ on-chain addresses for the mapping of model parameters and data versions, while blockchain transactions also include uploaders’ information.” The blockchain will store, or broadcast, information regarding training data and machine learning model training parameters and results. The blockchain in this article “broadcasts” the added block to the other blockchain nodes in the network.)
Regarding claim 12, Lo discloses, “wherein the one or more second blockchain messages are configured to associate the second information with the first information on the blockchain network.” (Blockchain, 3278; “Each client and the central server will install at least one blockchain node to form a network. Each node holds a local replica of the complete transaction data in the form of a chain of blocks. The blockchain operations mainly cover the data-model provenance using smart contracts, in which all participants are identified via their blockchain addresses. In every federation epoch, all local and global model parameters are stored in off-chain database of local models and database of global models, respectively. Meanwhile, the hashed local data versions are produced and recorded in the on-chain data-model registry smart contract to achieve the provenance and co-versioning of data and models.” Each of the blocks in the block chain are identifiable via clients blockchain address’. Further parameters and model updates are published to the chain which connect the different blocks in the chain.)
Regarding claim 13, Tran discloses, “wherein the first information associated with the one or more user inputs comprises a respective identifier for one or more users that created the machine learning model, a description of the machine learning model, documentation associated with the machine learning model, preprocessing parameters, training parameters, one or more timestamps associated with creation of the machine learning model, feature descriptions, model specifications, evaluation metrics, tuning parameters, or a combination thereof.” (Artefact-as-a-State-Machine, pp. 8-9; “ML models and datasets map to smart contract instances because they are individually addressable blockchain objects that carry internal variables and functions to act upon those variables. The address of a smart contract becomes an asset's identifier. Internal variables of a smart contract can be used to store an asset's state and metadata. Functions of a smart contract represent workflow activities that update an asset's state. Function parameters can be used to capture the payload of a provenance record corresponding to a work ow activity. Participants record a provenance update
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by submitting a blockchain transaction to the smart contract to invoke a function corresponding to the reported work ow activity. These transactions serve as provenance records. We call this approach of modelling ML assets as state machines to map them to blockchain constructs for management as Artefact-as-a-State-Machine, or ASM. Figure 3 depicts an exemplary implementation of ASM with smart contracts written in Solidity programming language7.” Figure 3 shows an example of the different information that is added to the blockchain. The different workflow activities is information that is stored and submitted to the blockchain along with the participant ID.)
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Regarding claim 14, Tran discloses, “wherein the first information associated with the data source comprises one or more data collection time stamps, data usage agreement information, data source descriptions, or a combination thereof.” (User-Driven Provenance Capture, pp. 6; “Transparency and control: ML development activities can utilize and produce sensitive data, which must conform to predefined Data Use Agreements (DUAs). Therefore, users require visibility and control over the information captured and propagated by the provenance management system so that they can verify and therefore trust the system. The user-driven procedure of ProML meets this ends.” The proposed system will use training data and the clients are required to ensure the data meets the DUAs.)
Regarding claim 15, Lo discloses, “wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to: encrypt at least a portion of the first information, the second information, or both to generate encrypted information, wherein the encrypted information is stored on the blockchain network.” (Data-Model Registry Smart Contract, pp. 3279; “To address these two vital issues, we apply both hashing and asymmetric/symmetric encryption techniques. … With the use of blockchain to store the hashed value of data, local, and global model versions, data-model provenance is achievable and users can audit the federated learning model performance.” Lo discloses a process of encrypting information to store the data on in the block chain.)
Regarding claim 16, Tran discloses, “wherein the one or more first blockchain messages or the one or more second blockchain messages are configured to call a self-executing program on the blockchain network to store the first information, the second information, or both.” (User, Driven Provenance Capture, pp. 6; “Participants can submit
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to ProML in an ad-hoc manner via a command line interface (CLI) client, programmatically via a software library, or directly by invoking the provenance capturing service of their trusted ProML node.” The user will submit information to train a ML model using an API. This will then cause the system to execute its own sets of functions to ensure the provenance and actually submit the updates to the model and the block chain.) and (Fig. 2, pp. 6; Figure 2 depicts the different self-executing actions the system will take after a client submits information to the blockchain. Also shown is the API the client can use to submit information to be processed by the system)
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Regarding claim 17, Trans discloses, “A non-transitory computer-readable medium storing code for data management, the code comprising instructions executable by one or more processors to:” (Experimental Design, pp. 10; “The PoC's smart contracts were deployed on Ropsten, a global Ethereum blockchain network preserved for testing9. We chose this blockchain network due to its scale and similarity in configurations and performance characteristics with the Ethereum main network, which operates most high-pro le and high-value blockchain-based software applications. We used Ethers10 for implementing the blockchain provider and signer services and Node for the provenance capturing and querying services.” This article discloses a process which is executed using known blockchain networks and software. This would require this system to perform actions on generic servers or computing devices which contains processors coupled to memory units which store the computer readable instructions.)
“receive, for generating a machine learning model, one or more user inputs associated with the machine learning model;” (User-Driven Provenance Capture, pp. 6; “Figure 2 depicts the process for capturing and submitting ML provenance from a training script. Participants can submit
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to ProML in an ad-hoc manner via a command line interface (CLI) client, programmatically via a software library, or directly by invoking the provenance capturing service of their trusted ProML node.” This model will receive information from the user. This system has an API the client can use to interact with the proposed system.)
“receive, for generating the machine learning model, an indication of a data source for training the machine learning model;” (User-Driven Provenance Capture, pp. 6-7; “The provenance capturing service uses the provided information (
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) to construct blockchain transactions for deploying smart contracts that represent the given models or datasets. It uses the signer service, which holds participants' private keys, to sign the constructed transactions with participants' credentials.” This model has an API of commands a client can use to input data into a system to train a machine learning model.)
“broadcast one or more first blockchain messages that are configured to store first information associated with the one or more user inputs and the data source on a blockchain network;” (User-Driven Provenance Capture, pp. 7; “Following the signing, the provenance capturing service invokes the provider service, which wraps around a blockchain client, to publish the transactions. The registration completes when the trans- actions have been added to the ledger, making the smart contracts available on the blockchain.” This model will submit user information and data to a blockchain. The system will submit, or broadcast, the block of information to the blockchain.)
Tran fails to explicitly disclose, “receive one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” and “broadcast one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.”.
However, Lo discloses, “receive one or more input prompts for the machine learning model and one or more responses generated by the machine learning model; and” (Client, pp. 3278; “The model trainer set up the environment for local model training according to the training job received from the central server. After each local epoch, the local model is transferred to the local model evaluator for performance assessment. The hashed value of the local model versions and their performance are recorded and uploaded to the blockchain for data-model provenance.” The system will receive different training prompts from the central server and the client will train the model. This training includes using, or receiving, training data and recording the performance, or output, of the machine learning model.)
“broadcast one or more second blockchain messages that are configured to store second information associated with the one or more input prompts and the one or more responses on the blockchain network.” (Data-Model Registry Smart Contract, pp. 3279; “With the use of blockchain to store the hashed value of data, local, and global model versions, data-model provenance is achievable and users can audit the federated learning model performance. The data-model registry automatically records users’ on-chain addresses for the mapping of model parameters and data versions, while blockchain transactions also include uploaders’ information.” The blockchain will store, or broadcast, information regarding training data and machine learning model training parameters and results. The blockchain in this article “broadcasts” the added block to the other blockchain nodes in the network.)
Regarding claim 20, Lo discloses, “wherein the one or more second blockchain messages are configured to associate the second information with the first information on the blockchain network.” (Blockchain, 3278; “Each client and the central server will install at least one blockchain node to form a network. Each node holds a local replica of the complete transaction data in the form of a chain of blocks. The blockchain operations mainly cover the data-model provenance using smart contracts, in which all participants are identified via their blockchain addresses. In every federation epoch, all local and global model parameters are stored in off-chain database of local models and database of global models, respectively. Meanwhile, the hashed local data versions are produced and recorded in the on-chain data-model registry smart contract to achieve the provenance and co-versioning of data and models.” Each of the blocks in the block chain are identifiable via clients blockchain address’. Further parameters and model updates are published to the chain which connect the different blocks in the chain.)
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tran and Lo in view of Battah et al, (Battah et al, “Blockchain and NFTs for Trusted Ownership, Trading, and Access of AI Models”, 2022, hereinafter “Battah”).
Regarding claim 2, Battah discloses, “wherein broadcasting the one or more second blockchain messages comprises: broadcasting the one or more second blockchain messages that are configured to mint a non-fungible token using a self-executing program on the blockchain network, wherein the non-fungible token is stored on the blockchain network and references the first information, the second information, or both the first information and the second information.” (System Interactions, pp. 112236; “The Owner/Creator uploads the path to the uploaded data and, if valid, mints it as an NFT on the NFT smart contract. Doing so binds the asset with a token ID and the smart contract used, enabling exclusive access for ownership of the asset on the blockchain.” This system allows for data creators to use and mint NFT’s to show ownership of models and of data published to the blockchain.) and (Algorithm 1 Minting NFT and setting Royalty of Asset, pp. 112240; This algorithm discloses the automated process the system uses to mint a new NFT.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Tran, Lo and Battah. Tran teaches a machine learning training system that utilizes a blockchain as a ledger and attempts preserve provenance of the input data and machine learning training actions of a given user. Lo teaches a Blockchain system that is able to ensure user accountability and fairness when training a federated learning system by recording inputs and model updates via a blockchain. Battah discloses a Blockchain system that is able to use NFTs to uniquely identify owners of models in a blockchain instead of conventual smart contracts. One of ordinary skill would have motivation to combine two methods which both ensures provenance of data input and machine learning model training using a blockchain to record updates, training information and user actions in distributed machine learning system to solve the issue of user trustworthiness and model fairness, with a system that uses NFT’s as unique identifiers of clients and users in a blockchain system “Expanding the scope further, the essence of the approach is applicable to other use cases that require the provenance of digital assets. The blockchain, NFTs, and decentralized storage preserve the history of changes and ownership of the asset. On the other hand, the subsystems of oracles and PRE enable the assets' verification, validation, and assessment based on the set criteria. The assets differ in assessment criteria and metadata information but not in their format and method. As such, it is crucial to dene a standard for the metadata information and assessment of assets to generalize the solution. A simple use case is the exchange of confidential digital documents divided into secure enclaves, each verifying the document. In turn, the document's privacy and the stakeholders' trust in the document are preserved.” (Battah, GENERALIZATION, pp. 112247).
Regarding claim 3, Battah discloses, “broadcasting one or more third blockchain messages that are configured to store third information associated with use of content associated with the non-fungible token.” (System Interactions, pp. 112236; “Furthermore, when minting the asset, the owner/creator can identify any parent assets that contributed to the current asset. They are incentivized to transfer the credibility and exposure of an already existing asset previously verified. Moreover, it is to build a provenance chain that users can utilize to establish trust for the new asset. The owner can also set the royalty they wish to receive for using assets within the set range.” This process as seen in figure 4, the owner will upload the information to the system, an NFT is minted and then the assets are published to the system and other users are allowed to bid on the model. This teaches that the system will upload, or broadcast, the NFT and the associated with model information to the blockchain as on-chain work.)
Regarding claim 10, Battah discloses, “to broadcast the one or more second blockchain messages, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: broadcast the one or more second blockchain messages that are configured to mint a non-fungible token using a self-executing program on the blockchain network, wherein the non-fungible token is stored on the blockchain network and references the first information, the second information, or both the first information and the second information.” (System Interactions, pp. 112236; “The Owner/Creator uploads the path to the uploaded data and, if valid, mints it as an NFT on the NFT smart contract. Doing so binds the asset with a token ID and the smart contract used, enabling exclusive access for ownership of the asset on the blockchain.” This system allows for data creators to use and mint NFT’s to show ownership of models and of data published to the blockchain.) and (Algorithm 1 Minting NFT and setting Royalty of Asset, pp. 112240; This algorithm discloses the automated process the system uses to mint a new NFT.)
Regarding claim 11, Battah discloses, “wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to: broadcast one or more third blockchain messages that are configured to store third information associated with use of content associated with the non-fungible token.” (System Interactions, pp. 112236; “Furthermore, when minting the asset, the owner/creator can identify any parent assets that contributed to the current asset. They are incentivized to transfer the credibility and exposure of an already existing asset previously verified. Moreover, it is to build a provenance chain that users can utilize to establish trust for the new asset. The owner can also set the royalty they wish to receive for using assets within the set range.” This process as seen in figure 4, the owner will upload the information to the system, an NFT is minted and then the assets are published to the system and other users are allowed to bid on the model. This teaches that the system will upload, or broadcast, the NFT and the associated with model information to the blockchain as on-chain work.)
Regarding claim 18, Battah discloses, “wherein the instructions to broadcast the one or more second blockchain messages are executable by the one or more processors to: broadcast the one or more second blockchain messages that are configured to mint a non-fungible token using a self-executing program on the blockchain network, wherein the non-fungible token is stored on the blockchain network and references the first information, the second information, or both the first information and the second information.” (System Interactions, pp. 112236; “The Owner/Creator uploads the path to the uploaded data and, if valid, mints it as an NFT on the NFT smart contract. Doing so binds the asset with a token ID and the smart contract used, enabling exclusive access for ownership of the asset on the blockchain.” This system allows for data creators to use and mint NFT’s to show ownership of models and of data published to the blockchain.) and (Algorithm 1 Minting NFT and setting Royalty of Asset, pp. 112240; This algorithm discloses the automated process the system uses to mint a new NFT.)
Regarding claim 19, Battah discloses, “wherein the instructions are further executable by the one or more processors to: broadcast one or more third blockchain messages that are configured to store third information associated with use of content associated with the non-fungible token.” (System Interactions, pp. 112236; “Furthermore, when minting the asset, the owner/creator can identify any parent assets that contributed to the current asset. They are incentivized to transfer the credibility and exposure of an already existing asset previously verified. Moreover, it is to build a provenance chain that users can utilize to establish trust for the new asset. The owner can also set the royalty they wish to receive for using assets within the set range.” This process as seen in figure 4, the owner will upload the information to the system, an NFT is minted and then the assets are published to the system and other users are allowed to bid on the model. This teaches that the system will upload, or broadcast, the NFT and the associated with model information to the blockchain as on-chain work.)
Conclusion
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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