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 .
The office action is in response to the application filed on May 23, 2023.
Claims 1-24 are pending and have been examined. Claims 1-24 are rejected.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed May 23, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims priority to U.S. Provisional application No. 63/345,232 filed on May 24, 2022.
Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original non-provisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, U.S. Provisional application No. 63/345,232 (hereinafter “the ‘232 provisional application”) fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Dependent claims 13 and 20 recite, using respective similar language, “wherein the one or more selected computing models include a large language model”, Claim 23 recites, using respective similar language, “wherein the first computing model is a first large language model.”, and Claim 24 recites, using respective similar language, “wherein the second computing model is a second large language model.”
The as-filed original specification of the ‘232 provisional application fails to provide adequate support or enablement for at least these elements of claims 13, 20, 23, and 24.
Therefore, the effective filing date for claims 13, 20, 23, and 24 of the instant application is the effective filing date of the instant, non-provisional application, May 23, 2023.
Each claim will receive benefit of the earliest filing date above for which a continuous chain of support can be established for the entirety of the claim.
Claim Objections
Claim 20 is objected to because of the following informalities:
“The method of claim 14” should read “The system of claim 14.”
Appropriate correction is required.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as
mathematical relationships, mathematical formulas or equations, and mathematical
calculations."
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental
processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: "The courts do not distinguish between mental processes that
are performed entirely in the human mind and mental processes that require a human to use a
physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-24 are each directed to non-statutory
subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-13 and 21-24 are directed to a method for requesting and selecting a specified artificial intelligence model from a repository for deployment in a computing infrastructure to allow for models to be inferenced, added, replaced, and or reconfigured without disrupting ongoing operations.
Claims 14-20 are directed to a system comprising of one or more memories and one or more processors configured to carry out a process for requesting and selecting a specified artificial intelligence model from a repository for deployment in a computing infrastructure to allow for models to be inferenced, added, replaced, and or reconfigured without disrupting ongoing operations.
With respect to Claims 1 and 14 which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 14 is directed to a system for providing a computing infrastructure where machine learning models can be selected and deployed for collaborative efforts with an artificial intelligence inference platform, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“selecting one or more computing models based upon the model request;”; Selecting one or more computing models based upon the model request is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“compiling a container request based on the model request and the one or more selected computing models;”; Compiling a container request based on a model request and a selected computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III). Examiner’s Note: Compiling in this case pertains to gathering/listing/mapping necessary parameters and or characteristics as a result of a process (model request), rather than the technical, physical mechanism of how a compiler works within an operating system, thus it is deemed a mental process.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“receiving a model request including one or more request parameters, the one or more request parameters including at least one selected from a group consisting of:” ; Receiving a model request is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“receiving information associated with a plurality of computing models from a model repository;”; Receiving information associated with a plurality of computing models from a model repository is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“transmitting the container request to a container infrastructure;”; Transmitting the container request to a container infrastructure is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“and coupling the one or more selected computing models to an artificial intelligence inference platform (AIP); wherein the method is performed using one or more processors.”; Coupling/deploying the selected computing models to an AIP only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving a model request, receiving information associated with a plurality of computing models from a model repository, and transmitting the container request to a container infrastructure constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Coupling/deploying the selected computing models to an Artificial Intelligence Inference Platform amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of an Artificial Intelligence Inference Platform (AIP) is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claims 1 and 14 are directed to non-statutory subject matter and rejected.
With respect to Claims 2 and 15, which have identical claim limitations and are dependent on Claims 1 and 14 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the container infrastructure is configured to allocate one or more resources associated with at least one of the one or more selected computing models.”; Configuring/allocating resources associated with a selected computing model within the container infrastructure is considered insignificant extra-solution activity (post-solution activity) - see MPEP 2106.05(g).
Step 2B: Configuring/allocating resources associated with a selected computing model after initial container request data has already been transmitted to said container infrastructure constitutes as an insignificant extra-solution activity, specifically a post-solution activity. - see MPEP 2106.05(g).
Therefore, Claims 2 and 15 are directed to non-statutory subject matter and rejected.
With respect to Claim 3, which is dependent on Claim 2 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the one or more resources includes one or more computing resources or one or more storage resources.”; The presence of computing/storage resources is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g).
Step 2B: As per claim 2, allocating computing/storage resources constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner." - See MPEP 2106.05(d)(II)
Therefore, Claim 3 is directed to non-statutory subject matter and rejected.
With respect to Claims 4 and 16, which have identical claim limitations and are dependent on Claims 1 and 14 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“extracting one or more model parameters from the model request;”; Extracting one or more model parameters from the model request is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and generating the container request based upon the one or more model parameters; wherein the container request is generated in a format compliant with an interface of the container infrastructure.”; Generating a container request based upon model parameters is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 4 and 16 are directed to non-statutory subject matter and rejected.
With respect to Claims 5 and 17, which have identical claim limitations and are dependent on Claims 1 and 14 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the container infrastructure is configured to instantiate at least one of the one or more selected computing models.”; Instantiating a computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 5 and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 6 and 18, which have identical claim limitations and are dependent on Claims 5 and 17 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the container infrastructure is configured to instantiate the at least one of the one or more selected computing models based at least in part upon the one or more configurations or the one or more connection requirements.”; Examiner’s Note: These claims are inheriting the interpreted recitation of an abstract idea from claims 5 and 17. Instantiating a computing model based on configurations and or connection requirements is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 6 and 18 are directed to non-statutory subject matter and rejected.
With respect to Claim 7, which is dependent on Claim 5 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the container request includes metadata corresponding to the one or more selected computing models; wherein the container infrastructure is configured to instantiate the at least one of the one or more selected computing models based at least in part upon the metadata.”; Examiner’s Note: The claim is inheriting the interpreted recitation of an abstract idea from claim 5. Instantiating a computing model based on metadata associated with the selected computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 7 is directed to non-statutory subject matter and rejected.
With respect to Claims 8 and 19, which have identical claim limitations and are dependent on Claims 1 and 14 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“wherein the container infrastructure is configured to update at least one of the one or more selected computing models.”; Updating a selected computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claims 8 and 19 are directed to non-statutory subject matter and rejected.
With respect to Claim 9, which is dependent on Claim 1 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“compiling a processing pipeline based at least in part upon the model request.”; Compiling a processing pipeline is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 9 is directed to non-statutory subject matter and rejected.
With respect to Claim 10, which is dependent on Claim 9 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“…operating sequentially, such that an output of the first computing model is an input of the second computing model.”; As a result of operating sequentially, using the first computing model's output as input for the second computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the processing pipeline includes at least a first computing model and a second computing model…”; A first computing model and a second computing model being utilized within a processing pipeline only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a first computing model and a second computing model within a processing pipeline amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)).
Therefore, Claim 10 is directed to non-statutory subject matter and rejected.
With respect to Claim 11, which is dependent on Claim 10 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“…operating in parallel with respect to the first computing model.”; A first computing model and a third computing model operating in parallel to one another is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the processing pipeline includes a third computing model…”; The utilization of a third computing model within the processing pipeline only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a third computing model within a processing pipeline amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)).
Therefore, Claim 11 is directed to non-statutory subject matter and rejected.
With respect to Claim 12, which is dependent on Claim 9 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“…operating in parallel.”; A first computing model and a second computing model operating in parallel to one another is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the processing pipeline includes at least a first computing model and a second computing model…”; The utilization of a first and second computing model(s) within the processing pipeline only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a first computing model and a second computing model within a processing pipeline amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)).
Therefore, Claim 12 is directed to non-statutory subject matter and rejected.
With respect to Claims 13 and 20, which have identical claim limitations and are dependent on Claims 1 and 14 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the one or more selected computing models include a large language model.”; The usage of a computing model, specifically a large language model generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The usage of a computing model, specifically a large language model generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h).
Therefore, Claims 13 and 20 are directed to non-statutory subject matter and rejected.
With respect to Claim 21 which is an independent claim:
Step 1: Claim 21 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“selecting at least a first computing model and a second computing model based upon the first model request;”; Selecting at least a first and second computing model based upon the model request is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“compiling a processing pipeline based at least in part upon the first model request, the processing pipeline including at least the first computing model and the second computing model;”; Compiling a processing pipeline based on a first model request and both a first and second computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Examiner’s Note: Compiling in this case pertains to gathering/listing/mapping necessary parameters and or characteristics as a precursor step to carrying out a process, rather than the technical, physical mechanism of how a compiler works within an operating system, thus it is deemed a mental process.
“compiling a first container request based on the first model request, the first computing model, and the second computing model;”; Compiling a first container request based on a first model request and both a first and second computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Examiner’s Note: Compiling in this case pertains to gathering/listing/mapping necessary parameters and or characteristics as a result of a process (model request), rather than the technical, physical mechanism of how a compiler works within an operating system, thus it is deemed a mental process.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“receiving a first model request including one or more first request parameters, the one or more first request parameters including at least one selected from a group consisting of:” ; Receiving a first model request including first request parameters is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“receiving information associated with a plurality of computing models from a model repository;”; Receiving information associated with a plurality of computing models from a model repository is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“transmitting the first container request to a container infrastructure;”; Transmitting the first container request to a container infrastructure is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“and coupling the first computing model and the second computing model to an artificial intelligence inference platform (AIP); wherein the method is performed using one or more processors.”; Coupling/deploying both the first and second computing models to an AIP only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving a first model request, receiving information associated with a plurality of computing models from a model repository, and transmitting the first container request to a container infrastructure constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Coupling/deploying both the first and second computing models to an Artificial Intelligence Inference Platform amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of an Artificial Intelligence Inference Platform (AIP) is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claim 21 is directed to non-statutory subject matter and rejected.
With respect to Claim 22 which is dependent on claim 21 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“selecting at least a third computing model based upon the second model request;”; Selecting at least a third computing model based upon the second model request is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“compiling a second container request based on the second model request and the third computing model;”; Compiling a second container request based on a second model request and a third computing model is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Examiner’s Note: Compiling in this case pertains to gathering/listing/mapping necessary parameters and or characteristics as a result of a process (model request), rather than the technical, physical mechanism of how a compiler works within an operating system, thus it is deemed a mental process.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“receiving a second model request including one or more second request parameters, the one or more second request parameters including at least one selected from a group consisting of” ; Receiving a second model request including second request parameters is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“transmitting the second container request to the container infrastructure;”; Transmitting the second container request to a container infrastructure is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g)
“decoupling the first computing model or the second computing model from the (AIP);” Decoupling/disconnecting either the first or second computing models from the AIP only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“and coupling the third computing model to the AIP.” Coupling/deploying the third computing models to an AIP only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving a second model request and transmitting the second container request to a container infrastructure constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Coupling/decoupling computing models to/from an Artificial Intelligence Inference Platform amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of an Artificial Intelligence Inference Platform (AIP) is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claim 22 is directed to non-statutory subject matter and rejected.
With respect to Claim 23, which is dependent on Claim 21 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the first computing model is a first large language model.”; The usage of a first computing model, specifically a first large language model generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The usage of a computing model, specifically a large language model generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h).
Therefore, Claim 23 is directed to non-statutory subject matter and rejected.
With respect to Claim 24, which is dependent on Claim 23 respectively:
Step 2A, Prong 1: A judicial exception is not recited in the claims as they do not recite an abstract idea (mathematical concepts, certain methods of organizing human activity, or mental processes), law of nature, or natural phenomenon.
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“wherein the second computing model is a second large language model.”; The usage of a second computing model, specifically a second large language model generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The usage of a computing model, specifically a large language model generally links the use of the abstract idea to a particular technological environment or field of use (AI/ML) - See MPEP § 2106.05(h).
Therefore, Claim 24 is directed to non-statutory subject matter and rejected.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 9, 10, 14-16, 21 and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Khare et. Al, (U.S Patent Application Publication No. US 20220129334 A1, hereinafter "Khare"). Khare was filed on January 10, 2022, and this date is before the effective filing date of this application, i.e., May 23, 2023 as well as the provisional filing date of May 24, 2022 where it applies. Therefore, Khare constitutes prior art under 35 U.S.C. 102(a)(2).
With respect to Claims 1 and 14:
Khare teaches:
"receiving a model request including one or more request parameters, the one or more request parameters including at least one selected from a group consisting of: a type of computing model, a processing characteristic, and a data characteristic;" (Paragraph [0078] discloses that a query request is received, “a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received” Paragraph [0036] further discloses the specific parameters associated with this request, “A query may include one or more of: a category (and subcategory), data information (format of what is available to the requester), resource availability (what resources are available for use such as a processor type), timing information (such as desired latency), an indication of a pipeline that the model/algorithm/data is to be used for), accuracy desired, type of content desired (algorithm, model, and/or data), etc.”) Examiners Note: As per the reference a, “type of content desired” can take the place of “type of computing model”, “resource availability” can take the place of “processing characteristic”, and “data information” can take the place of “data characteristic” in regards to this disclosure.
“receiving information associated with a plurality of computing models from a model repository;” (Paragraph [0023] teaches that a model repository can be accessed to retrieve information associated with various models, “The web services model repository service 121 allows for a producer to share generated content with others. The content that is shared is searchable as listings. In particular, a requester interfaces with a model/algorithm/data application programming interface (API) frontend 109 to find and select shared content through requests serviced by a publishing/listing agent 125.” Paragraph [0019] further teaches that accessibility to information related to multiple variants of computing models can be achieved, “producers who produce data, algorithms, and/or models make them available to third parties via a registry-based system. Requesters search this register to find algorithms, models, and/or data for their own purposes. In some embodiments, one or more schemas are utilized to build machine learning pipelines using one or more of the algorithms, models, and/or data made available via the registry-based system.”)
“selecting one or more computing models based upon the model request;” (Paragraph [0078] discloses determining and selecting a specified computing model based on the model request, “At 801 , in some embodiments, a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received.” Paragraph [0079] describes the selecting of a computing model based on the model request (query), “Using the details of the query, one or more listed repository items that may meet the desires of the query is determined and provided to the requester at 803.”)
“compiling a container request based on the model request and the one or more selected computing models; wherein the method is performed using one or more processors.” (Paragraph [0070] discloses compiling a container request, “A request to list a container is received at 701. For example, a producer requests that a container in the model/algorithm container registry 105 or package published in the source control service 107 is to be listed by the publishing listing agent 125 as available for third party use.” Paragraph [0082] mentions that a container request incorporates the selected computing model that was previously determined by the model request, “In some embodiments, the selected algorithm or model is a part of a container and a copy of the container is allocated to be executed.” Paragraph [0065] discusses the presence of processors/hardware that can perform this container request process, “the publishing/listing agent 125 is provided with hardware configurations to use, or encryption to adhere to, etc.”)
“transmitting the container request to a container infrastructure;” (Paragraph [0068] discloses the initial transmission of the container request after it’s been fulfilled, “When the verification was successful, the package is published in the model/algorithm container registry 105. Published containers are available to the publishing/listing agent 125 to be served as a potential result to a code or data query”. Paragraph [0082] discloses pipeline infrastructure (container infrastructure) that takes in all container request related information in order to perform a particular task, “The selected content may be a part of a pipeline and, in those cases, resources are allocated for the pipeline if it is ready for execution (for example, contains only models). For example, a request to perform one or more of these acts is received by the model/algorithm/data API frontend 109 which calls on the publishing/listing agent 125 to provide necessary information to an execution service 111 (such as a location of the selected algorithm, model, data, pipeline, and/or notebook in algorithm/model/data store 123) which then allocates execution resources 113 including compute resources 117 and storage 115.”)
“and coupling the one or more selected computing models to an artificial intelligence inference platform (AIP);” (Paragraph [0032] discloses an execution service entity that performs inferencing of the selected computing model, “The execution service 111 access the selected algorithm, model, and/or data in data store 123 to use a copy of the selection (an instance) to be used in the execution or training, and starts execution at circle 10. In some embodiments, the requester makes a direct request to the execution service 111 to start execution (e.g. , inference) or training at circle 10.”)
Therefore, Claims 1 and 14 are rejected.
With respect to Claims 2 and 15:
Khare teaches:
“wherein the container infrastructure is configured to allocate one or more resources associated with at least one of the one or more selected computing models.” (Paragraph [0082] teaches allocating resources to the pipeline (container infrastructure) once a computing model has been selected, “Once the requester has the algorithm, model, data, notebook, and/or pipeline it requested, resources are allocated (per a request from the request) to train the selected suggested algorithm(s), execute the selected model(s), or use selected data in training an algorithm at 611. The selected content may be a part of a pipeline and, in those cases, resources are allocated for the pipeline if it is ready for execution (for example, contains only models).”)
Therefore, Claims 2 and 15 are rejected.
With respect to Claim 3 which is dependent on Claim 2:
Khare teaches:
“wherein the one or more resources includes one or more computing resources or one or more storage resources.” (Paragraph [0082] teaches the inclusion of computing and storage resources, “…which then allocates execution resources 113 including compute resources 117 and storage 115.”)
Therefore, Claim 3 is rejected.
With respect to Claims 4 and 16:
Khare teaches:
“wherein the compiling a container request based on the model request and the one or more selected computing models comprises: extracting one or more model parameters from the model request;” (Paragraph [0030] discloses accessing the model request to extract relevant components based on accessibility levels, “In some embodiments, the user account 113 for the requester is accessed to see what the requester is allowed to access in terms of hardware, etc.” Paragraph [0031] further showcases extracting the model parameters from the model request that fit the characteristics of the request, “The requester selects content from the result and provides this selection and/or code using this selection at circle 6. In some embodiments, interaction also includes a request to execute or train. In some embodiments, the publishing/listing agent 125 selects what it feels is best…”)
“and generating the container request based upon the one or more model parameters; wherein the container request is generated in a format compliant with an interface of the container infrastructure.” (Paragraph [0025] discloses the architecture and structure of the container request with respect to the container infrastructure, “In some embodiments, container images include one or more layers, where each layer represents executable instructions. Some or all of the executable instructions together represent an algorithm that defines a machine learning model. The executable instructions (e.g., the algorithm) can be written in any programming language (e.g., Python, Ruby, C++, Java, etc.). Paragraph [0036] further clarifies that formatting is taken into consideration when reviewing a potential model request (query) for a container request, “A request query comes into the publishing/listing agent 125 into a query evaluation service (or engine) 219. A query may include one or more of: a category (and subcategory), data information (format of what is available to the requester),”)
Therefore, Claims 4 and 16 are rejected.
With respect to Claim 9:
Khare teaches:
“compiling a processing pipeline based at least in part upon the model request.” (Paragraph [0023] discloses the presence of a model request, “In particular, a requester interfaces with a model/ algorithm/data application programming interface (API) frontend 109 to find and select shared content through requests serviced by a publishing/listing agent 125.” Paragraph [0025] further discloses compiling a processing pipeline based on that model request, “the publishing listing agent 125 is used by a requester to build a pipeline and/or cause execution or training of a selected model or algorithm”)
Therefore, Claim 9 is rejected.
With respect to Claim 10:
Khare teaches:
“wherein the processing pipeline includes at least a first computing model and a second computing model operating sequentially, such that an output of the first computing model is an input of the second computing model.” (Paragraph [0089] references a processing pipeline that includes a first model and a second model operating sequentially on it to where the output of the first model is used as input for the second model, “The post-training pipeline 921 includes a first model (model 1) 923 that takes in data of format X and outputs data in format Y. The output (Y) of the model 1 903 is an input into model 2 925.”
Therefore, Claim 10 is rejected.
With respect to Claim 21:
Khare teaches:
"receiving a first model request including one or more first request parameters, the one or more first request parameters including at least one selected from a group consisting of: a type of computing model, a processing characteristic, and a data characteristic;" (Paragraph [0078] discloses that a query request for a first model is received, “a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received” Paragraph [0036] further discloses the specific parameters associated with this request, “A query may include one or more of: a category (and subcategory), data information (format of what is available to the requester), resource availability (what resources are available for use such as a processor type), timing information (such as desired latency), an indication of a pipeline that the model/algorithm/data is to be used for), accuracy desired, type of content desired (algorithm, model, and/or data), etc.”) Examiners Note: As per the reference a, “type of content desired” can take the place of “type of computing model”, “resource availability” can take the place of “processing characteristic”, and “data information” can take the place of “data characteristic” in regards to this disclosure.
“receiving information associated with a plurality of computing models from a model repository;” (Paragraph [0023] teaches that a model repository can be accessed to retrieve information associated with various models, “The web services model repository service 121 allows for a producer to share generated content with others. The content that is shared is searchable as listings. In particular, a requester interfaces with a model/algorithm/data application programming interface (API) frontend 109 to find and select shared content through requests serviced by a publishing/listing agent 125.” Paragraph [0019] further teaches that accessibility to information related to multiple variants of computing models can be achieved, “producers who produce data, algorithms, and/or models make them available to third parties via a registry-based system. Requesters search this register to find algorithms, models, and/or data for their own purposes. In some embodiments, one or more schemas are utilized to build machine learning pipelines using one or more of the algorithms, models, and/or data made available via the registry-based system.”)
“selecting at least a first computing model and a second computing model based upon the first model request;” (Paragraph [0078] discloses determining and selecting a specified first computing model based on the model request, “At 801 , in some embodiments, a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received.” Paragraph [0079] describes the selecting of a first computing model based on the model request (query), “Using the details of the query, one or more listed repository items that may meet the desires of the query is determined and provided to the requester at 803.”)
“compiling a processing pipeline based at least in part upon the first model request, the processing pipeline including at least the first computing model and the second computing model;” (Paragraph [0082] discloses compiling a processing pipeline based on a first model request for a first model, “For example, when the publishing/listing agent 125 is helping a user build a pipeline to perform a task (or tasks) this type of request occurs. The publishing/listing agent 125 will evaluate the pipeline as it exists and make the necessary connections within the pipeline at 809.” Paragraph [0089] further teaches that this processing pipeline exists to handle the training of a first and second computing model, “The post-training pipeline 921 includes a first model (model 1) 923 that takes in data of format X and outputs data in format Y. The output (Y) of the model 1 903 is an input into model 2 925.”)
“compiling a first container request based on the first model request, the first computing model, and the second computing model;” (Paragraph [0070] discloses compiling a first container request, “A request to list a container is received at 701. For example, a producer requests that a container in the model/algorithm container registry 105 or package published in the source control service 107 is to be listed by the publishing listing agent 125 as available for third party use.” Paragraph [0082] mentions that a container request incorporates the selected computing model(s) that were previously determined by the model request(s), “In some embodiments, the selected algorithm or model is a part of a container and a copy of the container is allocated to be executed.”)
“transmitting the first container request to a container infrastructure;” (Paragraph [0068] discloses the initial transmission of the first container request after it’s been fulfilled, “When the verification was successful, the package is published in the model/algorithm container registry 105. Published containers are available to the publishing/listing agent 125 to be served as a potential result to a code or data query”. Paragraph [0082] discloses pipeline infrastructure (container infrastructure) that takes in all container request related information in order to perform a particular task, “The selected content may be a part of a pipeline and, in those cases, resources are allocated for the pipeline if it is ready for execution (for example, contains only models). For example, a request to perform one or more of these acts is received by the model/algorithm/data API frontend 109 which calls on the publishing/listing agent 125 to provide necessary information to an execution service 111 (such as a location of the selected algorithm, model, data, pipeline, and/or notebook in algorithm/model/data store 123) which then allocates execution resources 113 including compute resources 117 and storage 115.”)
“and coupling the first computing model and the second computing model to an artificial intelligence inference platform (AIP); wherein the method is performed using one or more processors.” (Paragraph [0032] discloses an execution service entity (artificial intelligence inference platform) that performs inferencing of the selected first and second computing model(s), “The execution service 111 access the selected algorithm, model, and/or data in data store 123 to use a copy of the selection (an instance) to be used in the execution or training, and starts execution at circle 10. In some embodiments, the requester makes a direct request to the execution service 111 to start execution (e.g., inference) or training at circle 10.” Paragraph [0083] further discloses that the pipeline that the first and second computing model operate on can also do this inferencing once coupled with the AIP, “and pipeline being used as needed) is trained or executed as desired at 813 using the allocated resources. For example, execution service 111 causes execution of a pipeline have a selected model, trains a selected algorithm using (selected) training data , etc.” Paragraph [0036] reinforces that this method can be performed via processors as per the initial first model request (query), “A query may include one or more of: a category (and subcategory), data information (format of what is available to the requester), resource availability (what resources are available for use such as a processor type)…”)
Therefore, Claim 21 is rejected.
With respect to Claim 22:
Khare teaches:
"receiving a second model request including one or more second request parameters that are different from the one or more first request parameters, the one or more second request parameters including at least one selected from a group consisting of a type of computing model, a type of computing model, a processing characteristic, and a data characteristic;" (Paragraph [0078] discloses that a query request for a second model is received, “a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received” Paragraph [0036] further discloses the specific parameters associated with this request, “A query may include one or more of: a category (and subcategory), data information (format of what is available to the requester), resource availability (what resources are available for use such as a processor type), timing information (such as desired latency), an indication of a pipeline that the model/algorithm/data is to be used for), accuracy desired, type of content desired (algorithm, model, and/or data), etc.”) Examiners Note: As per, paragraph [0088] and Fig. 9, the second model request parameters are different from the first, in that the outputted data of the first model, (Y) has been manipulated to reflect changes and variance, thus the request, parameters, and requirements to train/invoke
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the second computing model are not the same as the first.
“selecting at least a third computing model based upon the second model request;” (Paragraph [0078] discloses determining and selecting a specified third computing model based on the second model request, “At 801, in some embodiments, a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received.” Paragraph [0079] describes the selecting of a third computing model based on the second model request (query), “Using the details of the query, one or more listed repository items that may meet the desires of the query is determined and provided to the requester at 803.”) Examiner’s Note: As per Fig. 9, the third computing model (model 3 931) [0089] would have already been selected if a data conditioning algorithm/model 929 needed to condition the output parameters/data of model 2 to then be utilized for a third model. A request/selecting of any number of models would have already been predetermined and decided prior to this point in processing. The conditioning of input/output data differentiates the characteristics of the parameters for each individualized computing model.
“compiling a second container request based on the second model request and the third computing model;” (Paragraph [0070] discloses compiling a second container request, “A request to list a container is received at 701. For example, a producer requests that a container in the model/algorithm container registry 105 or package published in the source control service 107 is to be listed by the publishing listing agent 125 as available for third party use.” Paragraph [0082] mentions that a container request incorporates the selected computing model(s) that were previously determined by the model request(s), “In some embodiments, the selected algorithm or model is a part of a container and a copy of the container is allocated to be executed.”)
“transmitting the second container request to a container infrastructure;” (Paragraph [0068] discloses the transmission of the second container request after it’s been fulfilled, “When the verification was successful, the package is published in the model/algorithm container registry 105. Published containers are available to the publishing/listing agent 125 to be served as a potential result to a code or data query”. Paragraph [0082] discloses pipeline infrastructure (container infrastructure) that takes in all container request related information in order to perform a particular task, “The selected content may be a part of a pipeline and, in those cases, resources are allocated for the pipeline if it is ready for execution (for example, contains only models). For example, a request to perform one or more of these acts is received by the model/algorithm/data API frontend 109 which calls on the publishing/listing agent 125 to provide necessary information to an execution service 111 (such as a location of the selected algorithm, model, data, pipeline, and/or notebook in algorithm/model/data store 123) which then allocates execution resources 113 including compute resources 117 and storage 115.”)
“decoupling the first computing model or the second computing model from the AIP;” (Paragraph [0075] discloses that the AIP (execution service 111) can act as an applicable platform that does processing when needed, “and in some embodiments, using execution service 111 as an intermediary.”) Examiner’s Note: The execution service 111 has gap bridging characteristics that allow it to be called upon when needed for whichever model is in operation, thus decoupling occurs and is necessary to allow for the execution service 111’s capabilities to be utilized by another computing model than the one it was previously linked to/assisting.
“and coupling the third computing model to the AIP.” (Paragraph [0032] discloses an execution service entity (artificial intelligence inference platform) that performs inferencing of the third computing model, “The execution service 111 access the selected algorithm, model, and/or data in data store 123 to use a copy of the selection (an instance) to be used in the execution or training, and starts execution at circle 10. In some embodiments, the requester makes a direct request to the execution service 111 to start execution (e.g., inference) or training at circle 10.” Paragraph [0083] teaches that upon selecting a different computing model that is now coupling up with the AIP (execution service 111), said AIP is able to differentiate that the processing requirements for the pipeline are now different and reflect the newly selected model, “For example, execution service 111 causes execution of a pipeline have a selected model, trains a selected algorithm using (selected) training data, etc. In some embodiments, different resources are allocated for different stages of the pipeline.”
Therefore, Claim 22 is rejected.
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 (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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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 non-obviousness.
Claim(s) 5-8, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Khare et. Al, (U.S Patent Application Publication No. US 20220129334 A1, filed on January 10, 2022, hereinafter "Khare"), in view of Hou et. Al, (U.S Patent Application Publication No. US 20210325861 A1, filed on June 25, 2021, hereinafter "Hou").
With respect to Claims 5 and 17:
Khare does not appear to explicitly disclose:
“wherein the container infrastructure is configured to instantiate at least one of the one or more selected computing models.”
However, Hou teaches:
“wherein the container infrastructure is configured to instantiate at least one of the one or more selected computing models.” (Paragraph [0085] discloses a process for instantiating a selected computing model, “FIG. 6 is a flowchart representative of example machine readable instructions and/or example operations 600 that may be executed and/or instantiated by processor circuitry to output candidate model combinations based on a model update process.” Paragraph [0088] further reinforces that an instantiation step can be done on a selected model (first artificial intelligence model), “The machine-readable instructions and/or operations 700 of FIG. 7 begin at block 702, at which the example intelligent trigger circuitry 506 receives at least one of four sources of data . For example, the example intelligent trigger circuitry 506 may receive at least one of four sources of data by accessing either a metric baseline, an output from a first artificial intelligence model operating…”)
Khare and Hou are analogous art and in the same field of invention because both references pertain to the utilization of inference-based potential model selection for the purpose of future deployment in dynamically changing computing environments. While Khare teaches gathering a model request to facilitate a container request for a selected model, Hou teaches instantiating said selected model with respect to the suggested requirements. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Khare (optimizing repositories to refine the selection of necessary machine learning models) with the teachings of Hou (updating/generating feedback-based instantiated artificial intelligence models for autonomous environments) in order to eliminate configuration discrepancies, improve version rollback/control tracking, and standardize overall inference latency. One of ordinary skill in the art would be motivated to do so because by integrating Hou's framework into the methods of Khare one would be able to, "provide the ability to serve and respond to multiple applications of the use cases 205 (e.g., object tracking, video surveillance, connected cars, etc.) in real-time or near real time, and meet ultra-low latency requirements for these multiple applications. These advantages enable a whole new a class of applications (Virtual Network Functions (VNFs), Function as a Service (FaaS), Edge as a Service (EaaS), standard processes, etc.), {[0043] of Hou}."
Therefore Claims 5 and 17 are rejected.
With respect to Claims 6 and 18:
Khare teaches:
“wherein the container request includes one or more configurations or one or more connection requirements of the one or more selected computing models; wherein the container infrastructure is configured to…” (Paragraph [0080] references the selecting of a computing model, “At some point a request for a selected repository item is received at 805…When the requester already knows which algorithm/model/data is desired,” Paragraph [0081] further discloses that container requests to transmit models to the container infrastructure (ML pipeline) can embody connection related protocols, “In some embodiments, a request to add the repository item to a ML pipeline is received at 807. For example, when the publishing/listing agent 125 is helping a user build a pipeline to perform a task (or tasks) this type of request occurs. The publishing/listing agent 125 will evaluate the pipeline as it exists and make the necessary connections within the pipeline at 809.” Paragraph [0024] further teaches that the entity (the publishing/listing agent 125) that receives and responds to the container request also manages certain configuration requirements associated with the selected computing model as per paragraph [0065], “In some embodiments, requirements for using the code or data are received from the producer at 603. For example, the publishing/listing agent 125 is provided with hardware configurations to use, or encryption to adhere to, etc.”)
Khare does not appear to explicitly disclose:
“…instantiate the at least one of the one or more selected computing models based at least in part upon the one or more configurations or the one or more connection requirements.”
However, Hou teaches:
“…instantiate the at least one of the one or more selected computing models based at least in part upon the one or more configurations or the one or more connection requirements.” (Paragraph [0085] references a figure that showcases the process of instantiating a selected computing model, “FIG.6 is a flowchart representative of example machine readable instructions and/or example operations 600 that may be executed and/or instantiated by processor circuitry to output candidate model combinations based on a model update process.” Paragraph [0155] further teaches the instantiation based on configuration and connection requirements, “the FPGA circuitry 1300 of the example of FIG.13 includes interconnections and logic circuitry that may be configured and/or interconnected in different ways after fabrication to instantiate, for example, some or all of the machine-readable instructions represented by the flowchart of FIGS. 6-7.”)
Therefore, Claims 6 and 18 are rejected.
With respect to Claim 7:
Khare does not appear to explicitly disclose:
“wherein the container request includes metadata corresponding to the one or more selected computing models; wherein the container infrastructure is configured to instantiate the at least one of the one or more selected computing models based at least in part upon the metadata.”
However, Hou teaches:
“wherein the container request includes metadata corresponding to the one or more selected computing models; wherein the container infrastructure is configured to instantiate the at least one of the one or more selected computing models based at least in part upon the metadata.” (Paragraph [0056] discloses metadata associated with a particular selected computing model, “The example model update controller circuitry 410 includes access to a database 412 of sensor data or environmental metadata. The other example model update controller circuitry 418 includes access to a database 416 of sensor data or environmental metadata. The other example model update controller circuitry 418 produces an ensemble (e.g., at least one) of artificial intelligence models in the database 420 which is communicated to the example network 406 and distributed to the example model update controller circuitry 410. Paragraph [0088] teaches instantiation of the selected model based on metadata, “FIG . 7 is a flowchart representative of example machine readable instructions and/or example operations 700 that may be executed and/or instantiated by processor circuitry to output candidate model combinations based on a model update process. The machine-readable instructions and/or operations 700 of FIG. 7 begin at block 702, at which the example intelligent trigger circuitry 506 receives at least one of four sources of data. For example, the example intelligent trigger circuitry 506 may receive at least one of four sources of data by accessing either a metric baseline, an output from a first artificial intelligence model operating on the first factory production line, metadata…”)
Therefore, Claims 7 is rejected.
With respect to Claims 8 and 19:
Khare does not appear to explicitly disclose:
“wherein the container infrastructure is configured to update at least one of the one or more selected computing models.”
However, Hou teaches:
“wherein the container infrastructure is configured to update at least one of the one or more selected computing models.” (Paragraph [0032] discloses selecting (deploying) a computing model to be updated, “The model is stored at the model repository as described in FIG. 5. The model may then be executed by the intelligent deployment circuitry as described in FIG. 5. In some examples, multiple models are deployed and evaluated by the intelligent deployment circuitry as described in FIG. 5.” Paragraph [0034] further discloses updating the selected computing model, “If the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, training of an updated model can be triggered using the feedback and an updated training data set, hyperparameters, etc., to generate an updated, deployed model.”)
Therefore, Claims 8 and 19 are rejected.
Claim(s) 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Khare et. Al, (U.S Patent Application Publication No. US 20220129334 A1, filed on January 10, 2022, hereinafter "Khare"), in view of Rogers et. Al, (U.S Patent Application Publication No. US 20210117859 A1, filed on September 9, 2020, hereinafter "Rogers").
With respect to Claim 11:
Khare teaches:
“wherein the processing pipeline includes a third computing model operating in parallel with respect to the first computing model.” (Paragraph [0086] references a processing pipeline that includes a first model “The pre-training pipeline 901 includes a first model a (model 1) 903…” Paragraph [0088] further discloses that a third computing model is present on the same pipeline and relevant to the first computing model which has undergone training by algorithm 2, “The output of the model generated by training algorithm 2 905 is in the format of Z. As shown, the input into selected model 3 911 is Z'. In this example, the pre-training pipeline 901 requires an intermediary, data conditioning algorithm/model 909, between what will be model 2 and model 3 911, which conditions Z to be Z'. Z' is then fed into model 3 911.”)
Khare does not appear to explicitly disclose:
“…computing model operating in parallel...”
However, Rogers teaches:
“…computing model operating in parallel...” (Paragraph [0112] discloses that artificial intelligence related processes/services can run in parallel with each other, “to aid in processing or execution of applications or containers in pipelines, services 1320 may be leveraged. In at least one embodiment, services 1320 may include compute services, artificial intelligence (AI) services…functionality provided by services 1320 may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform 1430 (FIG. 14).” Paragraph [0128] mentions that AI services can encompass multiple models in relation to execution of said parallel services, “, AI services 1418 may leverage AI system 1424 to execute machine learning model (s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline (s) 1410 may use one or more of output models 1316…”)
Khare and Rogers are analogous art and in the same field of invention because both references pertain to the utilization of inference-based potential model selection for the purpose of future deployment in robust operational architectures. While Khare teaches generating a processing pipeline for efficiency in machine learning model task performance, Rogers teaches fostering the means for machine learning models to co-conduct operations in parallel with one another. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Khare (improving machine learning pipeline implementation through the use of model repositories) with the teachings of Rogers (improving machine learning model integrity to run seamlessly) in order to increase prediction accuracy whilst reducing training time and heightening data as well as model parallelism. One of ordinary skill in the art would be motivated to do so because by integrating Rogers' framework into the methods of Khare one would be able to, "provide an ability to update a model with zero downtime, or near-zero downtime with no data loss, and without the need to update or restart the application {[0023] of Rogers}."
Therefore, Claim 11 is rejected.
With respect to Claim 12:
Khare teaches:
“wherein the processing pipeline includes at least a first computing model and a second computing model operating in parallel.” (Paragraph [0086] references a processing pipeline that includes a first model “The pre-training pipeline 901 includes a first model a (model 1) 903…” Paragraph [0089] further discloses that a second computing model is present on the same pipeline and operating parallel to the first computing model “The output (Y) of the model 1 903 is an input into model 2 925.”
Khare does not appear to explicitly disclose:
“…computing model operating in parallel...”
However, Rogers teaches:
“…computing model operating in parallel...” (Paragraph [0112] discloses that artificial intelligence related processes/services can run in parallel with each other, “to aid in processing or execution of applications or containers in pipelines, services 1320 may be leveraged. In at least one embodiment, services 1320 may include compute services, artificial intelligence (AI) services…functionality provided by services 1320 may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform 1430 (FIG. 14).” Paragraph [0128] mentions that AI services can encompass multiple models in relation to execution of said parallel services, “, AI services 1418 may leverage AI system 1424 to execute machine learning model (s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline (s) 1410 may use one or more of output models 1316…”)
Therefore, Claim 12 is rejected.
Claim(s) 13, 20, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Khare et. Al, (U.S Patent Application Publication No. US 20220129334 A1, filed on January 10, 2022, hereinafter "Khare"), in view of Lee et. Al, (U.S Patent Application Publication No. US 20230315856 Al, filed on March 31, 2022, hereinafter "Lee").
With respect to Claims 13 and 20:
Khare teaches:
“wherein the one or more selected computing models…” (Paragraph [0078] discloses determining and selecting a specified computing model based on the model request, “At 801, in some embodiments, a request is received for a listed repository item (algorithm/model/data/pipeline/notebook) suggestion. For example, a query such as that detailed with respect to FIG.2 is received.” Paragraph [0079] describes the selecting of a computing model based on the model request (query), “Using the details of the query, one or more listed repository items that may meet the desires of the query is determined and provided to the requester at 803.”)
Khare does not appear to explicitly disclose:
“…include a large language model.”
However, Lee teaches:
“…include a large language model.” (Paragraph [0066] teaches that the machine learning/computing models can be large language models, “Each of the ML models 212 can be a language model trained to receive natural language data and interpret the natural language data based on user needs and context. The ML models 212 can be implemented using any suitable method, process, or framework.”)
Khare and Lee are analogous art and in the same field of invention because both references pertain to the utilization of inference-based potential model selection for the purpose of future deployment in dynamically changing computing environments. While Khare teaches selecting a particular artificial intelligence model for refinement and implementation based on relative requirement parameters, Lee teaches ensuring the artificial intelligence models embody large language model principles and capabilities. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Khare (employing a repository of diverse and varied machine learning models) with the teachings of Lee (augmenting complex natural language-based training/querying) in order to detect unstructured data nuances, provide flexible architecture versatility, and increase time-efficient automation as well as deep-reasoning abilities. One of ordinary skill in the art would be motivated to do so because by integrating Lee's framework into the methods of Khare one could deliver a beneficial outcome, "resulting in an improved, intuitive, user interface where users can use simple natural language to interact and manipulate complex management systems instead of having to go through rigorous time-consuming training of complicated technical jargon or learning to navigate intricate menu trees with functions listed only to find the correct function to perform each task intended {[0077] of Lee}."
Therefore, Claims 13 and 20 are rejected.
With respect to Claim 23:
Khare does not appear to explicitly disclose:
“wherein the first computing model is a first large language model.”
However, Lee teaches:
“wherein the first computing model is a first large language model.” (Paragraph [0103] teaches that the first computing model can be a large language model, “The first ML model can be a Generative Pre-trained Transformer 3 (GPT-3) model, or a GPT-2 model, OpenAI Davinci Codex, OpenAI Cushman codex, GPT-NE02.7B and finetuned GPT-NE02.7B, or any other suitable language model trained on corpuses of language data.”)
Therefore, Claim 23 is rejected.
With respect to Claim 24:
Khare does not appear to explicitly disclose:
“wherein the second computing model is a second large language model.”
However, Lee teaches:
“wherein the first computing model is a second large language model.” (Paragraph [0105] teaches that the second computing model can be a large language model, “The second ML model can be configured to generate a set of natural language phrases semantically related to the reference input. The second ML model can be a GPT-3 model, GPT-2 model, OpenAI Davinci Codex, OpenAI Cushman codex, GPT-NE02.7B and finetuned GPT-NE02.7B, or any other suitable language model trained on large corpuses of language data.”)
Therefore, Claim 24 is rejected.
Conclusion
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/N.F.C./Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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