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 .
Status of Claims
The following is a non-final office action.
Claims [1-20] are currently pending and have been examined based on their merits.
Claims 1-15 are newly amended see REMARKS October 08, 2024.
Claims 16-20 are newly added see REMARKS October 08, 2024.
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.
Step 1: Claim 14 is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims recite “software per se.” The claims merely recite a computer program element executed by a computing unit to carry out the method. As the broadest reasonable interpretation of a computing unit includes software for executing a computer program element, the examiner finds that the claims do not recite any structure and do not have any physical or tangible form. Therefore, the claims are directed to non-statutory subject matter.
Claims 15 is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims recite “signal per se.” The claims merely recite a computer readable medium that generates data. The broadest reasonable interpretation of “computer readable medium” includes non-statutory transitory forms of signal transmissions, such as propagating electrical or electromagnetic signals. The specification merely recites that the computer readable medium “may be” a hard disk, a data disk, or a flash disk. However, the specification does not explicitly recite that the computer readable medium is one of those embodiments or that it cannot be in the form of an electronic signal. Therefore the claims are directed to non-statutory subject matter.
Therefore, claims 14-15 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 10, and 13-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh (US 2018/0122166).
Claims 1, 14, and 15: Anglin discloses (Claim 1) A computer-implemented method for cross- account model deployment, comprising: (Claim 14) a computer program element which when executed by a computing unit is configured to carry out the method according to claim 1: (Claim 15) A computer readable medium that generates data to control a computing unit according to the method according to claim 1: providing at least one artifact identifier of at least one to-be-deployed model artifact (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers);
providing at least one account tuple, the account tuple comprising a source account identifier identifying a source account and a target account identifier identifying a target account (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified);
and moving, the at least one to-be-deployed model artifact from the source account to the target account (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 2: Anglin discloses the method as per claim 1. Anglin further discloses wherein the model is a machine learning model (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data).
Claim 4: Anglin discloses the method as per claim 1. Anglin further discloses wherein the at least one to-be-deployed model artifact is at least one out of model files, sources files, scripts, binary executable files, database tables, development deliverables, word-processing documents and/or mail messages (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data).
Claim 5: Anglin discloses the method as per claim 1. Anglin further discloses further comprising: logging the at least one artifact identifier along with the at least one account tuple (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 6: Anglin discloses the method as per claim 1. Anglin further discloses wherein a list of account tuples is provided, the list comprising at least two account tuples (Paragraph [0005-0007]; [0030-0032]; [0045]; Fig. 4, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. The system may be configured as a tool allowing multiple model owners to create trained machine learning models).
Claim 7: Anglin discloses the method as per claim 1. Anglin further discloses wherein at least one of the source accounts is a development account, at least one of the accounts is a testing account and/or at least one of the target accounts is a production account (Paragraph [0005-0007]; [0030-0032]; [0045-0046]; Fig. 4, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. The system may be configured as a tool allowing multiple model owners to create trained machine learning models from many data sources (e.g. training datasets), to which model owners and/or model consumers (e.g. researchers) would not normally have permission to access. In the example, access controller determines if model owner and/or model consumer has permission to access a device executing as a global model engine).
Claim 8: Anglin discloses the method as per claim 1. Anglin further discloses wherein at least one of the accounts is a cloud-based account (Paragraph [0025] the phrase machine learning broadly describes a function of electronic systems that learn from data. A machine learning system that can be trained such as in an external cloud environment to learn functional relationships between inputs and outputs).
Claim 9: Anglin discloses the method as per claim 1. Anglin further discloses wherein the at least one artifact identifier and/or the at least one account tuple are provided by operator input and/or by a user interface (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0085]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. In some embodiments the computer system may further include a network interface).
Claim 10: Anglin discloses the method as per claim 1. Anglin further discloses wherein the at least one artifact identifier and/or the at least one account tuple are provided automatically by a deployment script and, optionally, the deployment script further performs automated model training, automated model validation and/or quality assurance testing (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers).
Claim 11: Anglin discloses the method as per claim 1. Anglin further discloses wherein the method further comprises initializing the at least one account tuple and the initialization of the account tuple is, optionally, performed upon being provided with the account tuple or right before moving the at least one to-be-deployed model artifact (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 12: Anglin discloses the method as per claim 1. Anglin further discloses wherein initializing the at least one account tuple comprises: obtaining an authorization for the source account to share the at least one to- be-deployed model artifact; and obtaining an authorization for the target account to receive the at least one to-be-deployed model artifact, wherein moving the at least one to-be-deployed model artifact is performed from the source account to an intermediary and from the intermediary to the target account; and, optionally ,wherein access resources required to obtain the authorizations are provided in a set-up step (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 13: Anglin discloses the method as per claim 1. Anglin further discloses wherein initializing the at least one account tuple comprises obtaining an authorization for the source account to access resources of the target account and moving the at least one to-be-deployed model artifact is performed by the source account by pushing the model artifact to the target account; and/or wherein initializing the at least one account tuple comprises obtaining an authorization for the target account to access resources of the source account and moving the at least one to-be-deployed model artifact is performed by the target account by pulling the model artifact from the source account; and, optionally ,wherein access resources required to obtain the authorizations are provided in a set-up step (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 16: Anglin discloses the method as per claim 1. Anglin further discloses wherein the moving comprises copying (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 18: Anglin discloses the method as per claim 6. Anglin further discloses wherein the target account of one account tuple equals the source account of the next account tuple in the list (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 19: Anglin discloses the method as per claim 6. Anglin further discloses wherein the account tuples in the list of account tuples are processed one after the other (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0064-0067]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. When the smart contract is executed and data consumer is authenticated by its public key in the smart contract, the encrypted data can be provided to the data consumer. Whenever the data provider lists a data entity for sale, the smart contract is created that includes, for example, data signature, and access URL, or an API address for retrieval, a list of public keys that grant data access, as well as a selling price for the data access. Smart contracts can include details such as information identifying all owners, information related to data sources, among many others. Once the transaction is confirmed in the ledger, access controller authenticates the data consumer using the consumer’s private key and provides encrypted data of interest to the model engine. The data download begins once the payment is verified).
Claim 20: Anglin discloses the method as per claim 9. Anglin further discloses wherein the user interface is a representational state transfer application programming interface, REST API, and/or a graphical user interface (Paragraph [0005-0007]; [0030-0032]; [0046-0048]; [0085]; Fig. 3, embodiments of the present invention are directed to a distributed machine learning system. The model engine is being operated in accordance with a smart contract to enable two or more entities to collaboratively produce a machine learning model based on the training data using machine learning infrastructure. Contributions of each of the two or more entities are entered into a ledger of the blockchain. The smart contract enables two or more entities to collaboratively produce the machine learning model based on the training data. In various embodiments, one or more distributed model engines may be configured to manage many modeling tasks. Thus, the number of active models could number in the hundreds or even more. Therefore, the inventive subject matter is also considered to include management apparatus or methods of a large number of model objects in the distributed system. For example, each modeling task can be assigned one or more identifiers, or other metadata, for management by the system. Identifies can include a unique model identifier, a model owner identifier, version numbers, or other types of identifiers. A data request/purchase from any data consumer via the model engine triggers a decryption process where the access controller sends the advanced encryption standard (AES) key to a data requesting model engine. Data source selected hosts links to the encrypted testing data entities from data providers. In an embodiment, data access rights to a particular dataset is determined by a predefined agreement specified by the smart contract. In some embodiments the computer system may further include a network interface).
Therefore, claim 1-2, 4-16, and 18-20 are rejected under U.S.C. 102.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anglin (US 2020/0218940) in view of Hu (US 11406053)
Claim 3: Anglin discloses the method as per claim 1. However, Anglin does not disclose wherein the model is an agricultural management model.
In the same field of endeavor of deploying a machine learning model Hu teaches wherein the model is an agricultural management model (Paragraph [Col. 4 ll. 57- Col. 5 ll. 4]; [Col. 19 ll. 18-38]; [Col 27 ll. 29-41]; [Col. 28 ll. 32-35]; Fig. 9A, in one embodiment a computer implemented method includes receiving digital field data from an agricultural field representing one or more parameter of the field; retrieving historical data for the same field; training and/or applying machine learning models to the field data to derive representation of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly; automatically adjusting the treatment or experiment to create a modified treatment. In an embodiment, the agricultural intelligence computer system is programmed to create an agronomic model. A system receives model training data comprising a plurality of datasets. Within the system there may be models that suggest nitrate sampling locations and sample size recommendation for a given field).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of creating and deploying a machine learning model by a plurality of users as disclosed by Anglin with the system of wherein the model is an agricultural management mode as taught by Hu (Hu [Col. 4 ll. 57- Col. 5 ll. 4]). With the motivation of being a simple substitution as Anglin discloses a system for creating and distributing machine learning models to users for various purposes that are trained on different datasets available to the users. While Hu teaches building and training a specific machine learning model for a specific purpose such as creating an agricultural model to help provide recommendations to a user.
Claim 17: Modified Anglin discloses the method as per claim 3. However, Anglin does not disclose wherein the agricultural management model provides agronomic recommendations and/or agronomic control data.
In the same field of endeavor of deploying a machine learning model Hu teaches wherein the agricultural management model provides agronomic recommendations and/or agronomic control data (Paragraph [Col. 4 ll. 57- Col. 5 ll. 4]; [Col. 19 ll. 18-38]; [Col 27 ll. 29-41]; [Col. 28 ll. 32-35]; Fig. 9A, in one embodiment a computer implemented method includes receiving digital field data from an agricultural field representing one or more parameter of the field; retrieving historical data for the same field; training and/or applying machine learning models to the field data to derive representation of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly; automatically adjusting the treatment or experiment to create a modified treatment. In an embodiment, the agricultural intelligence computer system is programmed to create an agronomic model. A system receives model training data comprising a plurality of datasets. Within the system there may be models that suggest nitrate sampling locations and sample size recommendation for a given field).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of creating and deploying a machine learning model by a plurality of users as disclosed by Anglin with the system of wherein the model is an agricultural management mode as taught by Hu (Hu [Col. 4 ll. 57- Col. 5 ll. 4]). With the motivation of being a simple substitution as Anglin discloses a system for creating and distributing machine learning models to users for various purposes that are trained on different datasets available to the users. While Hu teaches building and training a specific machine learning model for a specific purpose such as creating an agricultural model to help provide recommendations to a user.
Therefore, claim 3 and 17 are rejected under U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Khare (US 11605021) Iterative model training and deployment for automated learning systems.
Guan (US 2022/0061236) Accessing agriculture productivity and sustainability.
McMillon (US 2023/0315877) Managing machine learning models via non-fungible tokens on a digital ledger.
Golomb (US 2021/0192620) Machine learning based digital exchange platform.
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/COREY RUSS/Primary Examiner, Art Unit 3629