CTNF 18/362,726 CTNF 101375 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the application and claims filed 07/31/2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected. Information Disclosure Statement The information disclosure statement (IDS) submitted on 07/31/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 07-29-01 AIA Claim 3 objected to because of the following informalities: “creating, by the number of processor units, create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using the model recipe and the set of artifact models; and” Examiner’s Note: The word “create” after initially stating “creating” is redundant . Appropriate correction is required. 07-29-01 AIA Claim 10 objected to because of the following informalities: “one or more processors units;” should read “processor units” Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 Step 1: The claim recites a method; therefore, it is directed to the statutory category of process. Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas: “selecting, (…) a model recipe for generating the artificial intelligence system for use with assets;” (a person mentally or with a pen and paper selects a model recipe.) “identifying, (…) recipe parameters specified in the model recipe;” (a person mentally or with a pen and paper identifies parameters of the selected recipe.) “creating, (…) a training dataset using the model recipe and input data;” (a person mentally or with a pen and paper creates a training dataset on a paper using input data and the model recipe they selected.) “evaluating, (…) artifact models resulting from training the artificial intelligence models to form an evaluation; and” (a person mentally or with a pen and paper evaluates the artifact models.) “selecting, (…) a set of the artifact models for the artificial intelligence system using the evaluation.” (a person mentally or with a pen and paper selects artifact models based on their evaluation.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “A computer implemented method for generating an artificial intelligence system, the computer implemented method comprising:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites a generic off the shelf computer as a tool to perform the recited abstract ideas .) “by a number of processor units,”, “by a number of processor units,”, “by a number of processor units,”, “by a number of processor units,”, “by a number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) “training, by the number of processor units, artificial intelligence models using the training dataset, the recipe parameters, and the model recipe to create artifact models;” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform generic training using variables .) Claim 2 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 1 above, which claim 2 depends on. Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. “deploying, by the number of processor units, the artificial intelligence system to a set of target platforms.” (Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05(g)) – Examiner’s Note: Deploying the resulting system to target platforms is what happens after the core process is complete. It’s the nominal act of outputting or delivering the end result, therefore this is interpreted as an insignificant post-solution activity .) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. “deploying, by the number of processor units, the artificial intelligence system to a set of target platforms.” (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.) Claim 3 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 2 above, which claim 3 depends on. The claim further recites the following abstract ideas: identifying, (…) a set of target platforms for the artificial intelligence system; (a person mentally or with a pen and paper identifies target platforms.) Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. “by the number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) “creating, by the number of processor units, create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using the model recipe and the set of artifact models; and” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create AI models using abstract ideas .) deploying, by the number of processor units, the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms using the model recipe. (Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05(g)) – Examiner’s Note: Deploying the resulting system to target platforms is what happens after the core process is complete. It’s the nominal act of outputting or delivering the end result, therefore this is interpreted as an insignificant post-solution activity.) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. “by the number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) “creating, by the number of processor units, create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using the model recipe and the set of artifact models; and” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create AI models using abstract ideas .) deploying, by the number of processor units, the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms using the model recipe. (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.) Claim 4 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 2 above, which claim 4 depends on. The claim further recites the following abstract ideas: “monitoring, (…) a number of performance metrics based on evaluation artifacts generated from training the artificial intelligence models; and” (a person mentally or with a pen and paper monitors the performance metrics.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “by the number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) retraining, by the number of processor units, the artificial intelligence system based on the number of performance metrics. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform a generic retraining step .) Claim 5 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 1 above, which claim 5 depends on. The claim further recites the following abstract ideas: identifying, (…) resources for training the artificial intelligence models using the model recipe; (a person mentally or with a pen and paper identifies resources.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “by the number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) creating, by the number of processor units, an execution cluster based on the resources identified; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create execution cluster based on abstract ideas .) training, by the number of processor units, the artificial intelligence models in the execution cluster using the training dataset and the model recipe. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform generic training using variables .) Claim 6 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 1 above, which claim 6 depends on. The claim further recites the following abstract ideas: “creating, (…) a project for creating the artifact models, wherein the project comprises multiple steps; and (a person mentally or with a pen and paper creates a project that has multiple steps.) “running, (…) an experiment for the project that creates artifact models. (a person mentally or with a pen and paper runs or conducts experiments or tests.)” Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “by the number of processor units,” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform the recited abstract ideas .) Claim 7 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 5 above, which claim 7 depends on. The claim further recites the following abstract ideas: “wherein the model recipe is selected from a group comprising a generalized model recipe and customized model recipe.” (a person mentally or with a pen and paper selects the recipe from a group.) Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 8 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 1 above, which claim 8 depends on. The claim further recites the following abstract ideas: “wherein the recipe parameters are selected from at least one of an application specific parameter, a learning parameter, and training level.” (a person mentally or with a pen and paper selects the recipe parameters from a group.) Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 9 Step 1: A method, as above. Step 2A prong 1: See the rejection of claim 1 above, which claim 9 depends on. Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “wherein the artificial intelligence system comprises at least one of a single artificial intelligence model intelligence model for a single asset in the assets, multiple artificial intelligence models for each asset in the assets, a single artificial intelligence model for a group of assets in the assets, or multiple artificial intelligence models for multiple assets in the assets.” (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).) Claim 10 Step 1: The claim recites a system; therefore, it is directed to the statutory category of machine. Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas: “select a model recipe for generating the artificial intelligence system for use with assets;” (a person mentally or with a pen and paper selects a model recipe.) “identify recipe parameters specified in the model recipe;” (a person mentally or with a pen and paper identifies parameters of the selected recipe.) “create a training dataset using the model recipe and input data;” (a person mentally or with a pen and paper creates a training dataset on a paper using input data and the model recipe they selected.) “evaluate artifact models resulting from training the artificial intelligence models to form an evaluation; and” (a person mentally or with a pen and paper evaluates the artifact models.) “select a set of the artifact models for the artificial intelligence system using the evaluation.” (a person mentally or with a pen and paper selects artifact models based on their evaluation.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “A computer system for generating an artificial intelligence system, comprising: one or more processors units; one or more computer readable storage devices; and computer program instructions, the computer program instructions being stored on the one or more computer readable storage devices for execution by the one or more processor units to perform one or more operations to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf computer and processor units as tools to perform the recited abstract ideas .) “train artificial intelligence models using the training dataset, the recipe parameters, and the model recipe to create artifact models;” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Claim 11 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 11 depends on. Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. “the one or more processor units further executes the computer program instructions to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) “deploy the artificial intelligence system to a set of target platforms. ” (Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05(g)) – Examiner’s Note: Deploying the resulting system to target platforms is what happens after the core process is complete. It’s the nominal act of outputting or delivering the end result, therefore this is interpreted as an insignificant post-solution activity .) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. “the one or more processor units further executes the computer program instructions to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) “deploy the artificial intelligence system to a set of target platforms. ” (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.) Claim 12 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 12 depends on. The claim further recites the following abstract ideas: identify a set of target platforms for the artificial intelligence system; (a person mentally or with a pen and paper identifies target platforms.) Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. wherein as part of deploying the artificial intelligence system, the one or more processor units further executes the computer program instructions to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) “create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using the model recipe and the set of artifact models; and” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create AI models using abstract ideas .) “deploy the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms using the model recipe.” (Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05(g)) – Examiner’s Note: Deploying the resulting system to target platforms is what happens after the core process is complete. It’s the nominal act of outputting or delivering the end result, therefore this is interpreted as an insignificant post-solution activity.) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. wherein as part of deploying the artificial intelligence system, the one or more processor units further executes the computer program instructions to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) “create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using the model recipe and the set of artifact models; and” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create AI models using abstract ideas .) “deploy the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms using the model recipe.” (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.) Claim 13 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 11 above, which claim 13 depends on. The claim further recites the following abstract ideas: “monitor a number of performance metrics based on evaluation artifacts generated from training the artificial intelligence models; and” (a person mentally or with a pen and paper monitors the performance metrics.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “wherein the one or more processor units further executes the computer program instructions to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) “retrain the artificial intelligence system based on the number of performance metrics.” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform a generic retraining step .) Claim 14 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 14 depends on. The claim further recites the following abstract ideas: identify resources for training the artificial intelligence models using the model recipe; (a person mentally or with a pen and paper identifies resources.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. wherein as part of training the artificial intelligence models, the one or more processor units further executes the computer program instructions to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) create an execution cluster based on the resources identified; and (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to create execution cluster based on abstract ideas .) train the artificial intelligence models in the execution cluster using the training dataset and the model recipe. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units as tools to perform generic training using variables .) Claim 15 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 15 depends on. The claim further recites the following abstract ideas: “create a project for creating the artifact models, wherein the project comprises multiple steps; and ” (a person mentally or with a pen and paper creates a project that has multiple steps.) “run an experiment for the project that creates artifact models.” (a person mentally or with a pen and paper runs or conducts experiments or tests.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. wherein as part of training the artificial intelligence models, the one or more processor units further executes the computer program instructions to: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf processor units that executes computer program instructions as tools to perform the recited abstract ideas .) Claim 16 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 14 above, which claim 16 depends on. The claim further recites the following abstract ideas: “wherein the model recipe is selected from a group comprising a generalized model recipe and customized model recipe.” (a person mentally or with a pen and paper selects the recipe from a group.) Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 17 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 17 depends on. The claim further recites the following abstract ideas: “wherein the recipe parameters are selected from at least one of an application specific parameter, a learning parameter, and training level.” (a person mentally or with a pen and paper selects the recipe parameters a group.) Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 18 Step 1: A system, as above. Step 2A prong 1: See the rejection of claim 10 above, which claim 18 depends on. Step 2A prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “wherein the artificial intelligence system comprises at least one of a single artificial intelligence model intelligence model for a single asset in the assets, multiple artificial intelligence models for each asset in the assets, a single artificial intelligence model for a group of assets in the assets, or multiple artificial intelligence models for multiple assets in the assets.” (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).) Claim 19 Step 1: Claim 19 recites a computer program product, therefore claim 19 is directed to the statutory category of manufacture. Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. The claim recites the following abstract ideas: “select a model recipe for generating the artificial intelligence system for use with assets;” (a person mentally or with a pen and paper selects a model recipe.) “identify recipe parameters specified in the model recipe;” (a person mentally or with a pen and paper identifies parameters of the selected recipe.) “create a training dataset using the model recipe and input data;” (a person mentally or with a pen and paper creates a training dataset on a paper using input data and the model recipe they selected.) “evaluate artifact models resulting from training the artificial intelligence models to form an evaluation; and” (a person mentally or with a pen and paper evaluates the artifact models.) “select a set of the artifact models for the artificial intelligence system using the evaluation.” (a person mentally or with a pen and paper selects artifact models based on their evaluation.) Step 2A prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “A computer program product for generating an artificial intelligence system, the computer program product comprising a computer readable storage device having computer program instructions embodied therewith, the computer program instructions executable by a computer system to cause the computer system to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf computer and storage device as tools to perform the recited abstract ideas .) “train artificial intelligence models using the training dataset, the recipe parameters, and the model recipe to create artifact models;” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic training using abstract variables. ) Claim 20 Step 1: A manufacture, as above. Step 2A prong 1: See the rejection of claim 19 above, which claim 20 depends on. Step 2A prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. “wherein the computer program instructions executable by the computer system further cause the computer system to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf computer program and computer system instructions as tools to perform the recited abstract ideas.) “deploy the artificial intelligence system to a set of target platforms. ” (Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05(g)) – Examiner’s Note: Deploying the resulting system to target platforms is what happens after the core process is complete. It’s the nominal act of outputting or delivering the end result, therefore this is interpreted as an insignificant post-solution activity .) Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. “wherein the computer program instructions executable by the computer system further cause the computer system to:” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note: claim recites generic off the shelf computer program and computer system instructions as tools to perform the recited abstract ideas.) “deploy the artificial intelligence system to a set of target platforms.” (MPEP 2106.05(d)(II) indicates that receiving or transmitting data over a network, is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.) Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. Examiner’s Note: Some rejections will include an Examiner’s Note (labeled ‘EN’) to provide additional context or rationale explaining the basis for the rejection. 07-15 AIA Claim s 1, 2, 8, 9 and 10, 11, 17, 18 and 19, 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by US Patent, Song et al. (US 11443237 B1) hereafter (Song) . Claim 1 Song teaches: A computer implemented method for generating an artificial intelligence system, the computer implemented method comprising: (Abstract, “Systems and techniques are disclosed for a centralized platform for enhanced automated machine learning using disparate datasets. An example method includes receiving user specification of one or more data sources to be integrated with the system, the data sources storing datasets to be utilized to train one or more machine learning models by the system” Col 19, lines 4-10 “At block 506 , the system trains a machine learning model according to a selected recipe. The system may train the model based on the training dataset. Optionally, the system may train multitudes of models, for example with varying hyperparameters. The trained machine learning model may be utilized to determine forecasts, for example as illustrated in FIG. 6A.”) selecting, by a number of processor units, a model recipe for generating the artificial intelligence system for use with assets; (Col 18, lines 52-61 “At block 502 , the system receives selection of a particular machine learning model recipe. As described in FIG. 4, a user (e.g., associated with an entity) may select from amongst presented machine learning model recipes. Optionally, the system may receive an API call associated with initiating training. The machine learning model recipe may inform a particular type of machine learning model to be trained. Thus, for certain users with knowledge of such models, the user may prefer that a particular model be trained.” Col 22, lines 40-43 “For example, the query may relate to a forecast utilized by a server hosting, or cloud-based processing, entity. Examples of forecasts may include forecasts regarding capacity planning, storage demand, and so on.”) identifying, by the number of processor units, recipe parameters specified in the model recipe; (Col 5 line 63 to Col 6 line 2 “A recipe, however, may additionally be fine-grained. For example, the recipe described above may refer to a recurrent neural network with gated recurrent units or long short-term memory cells. As another example, a recipe may further indicate that certain hyperparameters are to be utilized or that respective ranges of hyperparameters are to be utilized.”) creating, by the number of processor units, a training dataset using the model recipe and input data; (Col 18, line 62 to Col 19 line 3 “At block 504 , the system accesses stored datasets. As described in FIGS. 2A-3B, the system may store, or have access to, datasets associated with the entity. The system may obtain these datasets and generate a training dataset, a validation dataset, and optionally a test dataset. For example, the system may assign a threshold quantity of data (e.g., 85%, 90%, and so on) to be a training dataset. The system may then assign the remaining data to be the validation dataset.” Also see FIG. 5A-5B. Col 20, line 9-15, “Optionally, each machine learning model which is being trained may have a different subset of the datasets utilized as training data and validation data. For example, the system may randomly select 90% of the data as being training data, with the random selection occurring for each machine learning model.” Col 17 lines 27-33, “These templates may inform the machine learning model recipes which are utilized by the system to train machine learning models. For example, the system may utilize the template to indicate that a first type of machine learning model (e.g., a neural network) will likely produce less error than a second type of machine learning model (e.g., a clustering model).” – EN: this denotes that once a recipe is selected (Step 502), the system accesses datasets and generates a training dataset (Step 504). The recipe determines which type of model is trained, and different model types receives different data subsets. Templates that inform recipe selection also determine what category of data is relevant, example, a supply chain recipe calls for supply chain data, not staffing data. Therefore, under BRI, the recipe functionally shapes what data is assembled into the training dataset, satisfying the “using the model recipe” requirement.) training, by the number of processor units, artificial intelligence models using the training dataset, the recipe parameters, and the model recipe to create artifact models; (Col 19, lines 4-10 “At block 506 , the system trains a machine learning model according to a selected recipe. The system may train the model based on the training dataset. Optionally, the system may train multitudes of models, for example with varying hyperparameters. The trained machine learning model may be utilized to determine forecasts, for example as illustrated in FIG. 6A.” Also see FIG. 5A-5B. – EN: In the instant application, paragraph 77 states “artifact models 238 are the artificial intelligence models resulting from training artificial intelligence models 232.” Therefore, it is reasonable to interpret “artifact models” as the trained model that result from the training process in Song .) evaluating, by the number of processor units, artifact models resulting from training the artificial intelligence models to form an evaluation; and (Col 19, lines 19-25 “At block 508 , the system generates error metrics associated with the trained machine learning model. For example, the system may generate multitudes of models with varying hyperparameters. The system may thus compute error metrics, for example based on the validation dataset described above. Without being constrained by theory, it should be appreciated that there may be multitudes of error metrics.” Also see FIG. 5A-5B) selecting, by the number of processor units, a set of the artifact models for the artificial intelligence system using the evaluation . (Col 20, lines 20-25 “At block 520 the system selects one or more machine learning models. The system may select a machine learning model which provides superior performance to that of other machine learning models. Optionally, the system may determine that two or more machine learning models are to be implemented.” Also see FIG. 5A-5B) Claim 2 Song teaches: The computer implemented method of claim 1 further comprising: deploying, by the number of processor units, the artificial intelligence system to a set of target platforms. (Col 8, lines 22-33 “To access the trained machine learning model, API 26 may be utilized. This example API 26 may enable an entity to provide queries to the automated machine learning system 100 for processing. Results associated with the queries may optionally be provided to the entity as a visual response (e.g., presented via a user interface). Results may also be provided as a CSV file, for example as a time-series forecast. Optionally, the results may be provided in a particular file format (e.g., JavaScript Object Notation). Using the API 26 , the results may be automatically ingested via an enterprise system, a cloud-storage system, a database, and so on. In this way, the entity may easily obtain the results .”) Claim 8 Song teaches: The computer implemented method of claim 1, wherein the recipe parameters are selected from at least one of an application specific parameter, a learning parameter, and training level. (Col 5, line 66 to Col 6 line 7 “As another example, a recipe may further indicate that certain hyperparameters are to be utilized or that respective ranges of hyperparameters are to be utilized… Examples of hyperparameters may include a number of layers, a number of hidden layers, a number of neurons, activation functions, and so on.” “As an example, an entity may prefer that the automated machine learning system 100 generate a forecast. The example API 26 may be called (e.g., via REST) as: forecast (itemId, time interval, [optional parameters])”) Claim 9 Song teaches: The computer implemented method of claim 1, wherein the artificial intelligence system comprises at least one of a single artificial intelligence model intelligence model for a single asset in the assets, multiple artificial intelligence models for each asset in the assets, a single artificial intelligence model for a group of assets in the assets, or multiple artificial intelligence models for multiple assets in the assets. (Col 6, lines 18-24 “the system may determine that a first machine learning model is to be utilized for a first category of items. Thus, for a second category of items the system may use a second, different, machine learning model. Optionally, the system may combine outputs associated with different machine learning models when responding to a query.”) Claim 10 Song teaches: A computer system for generating an artificial intelligence system, comprising: ( Col 19, lines 4-10 “At block 506 , the system trains a machine learning model according to a selected recipe. The system may train the model based on the training dataset. Optionally, the system may train multitudes of models, for example with varying hyperparameters. The trained machine learning model may be utilized to determine forecasts, for example as illustrated in FIG. 6A.”) one or more processors units; (Col 24, lines 6-10 “Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware.”) one or more computer readable storage devices; and (Col 24, lines 10-14 “The code modules (or “engines”) may be stored on any type of, one or more, non-transitory computer-readable media (e.g., a computer storage product) or computer storage devices, such as hard drives, solid state memory, optical disc, and/or the like.”) computer program instructions, the computer program instructions being stored on the one or more computer readable storage devices for execution by the one or more processor units to perform one or more operations to: (Col 23 lines 39-49 “The memory 710 may include computer program instructions that the processing unit 704 executes in order to implement one or more embodiments. The memory 710 generally includes RAM, ROM, and/or other persistent or non-transitory memory. The memory 710 may store an operating system 714 that provides computer program instructions for use by the processing unit 704 in the general administration and operation of the server 700 . The memory 710 may further include computer program instructions and other information for implementing aspects of the present disclosure.”) The remaining limitations of claim 10 are substantially the same as claim 1, therefore claim 10 is rejected under the same rationale as claim 1. Claim 11 is system claim that recite substantially the same limitations as claim 2, therefore claim 11 is rejected under the same rationale as claim 2. Claim 17 is system claim that recite substantially the same limitations as claim 8, therefore claim 17 is rejected under the same rationale as claim 8. Claim 18 is system claim that recite substantially the same limitations as claim 9, therefore claim 18 is rejected under the same rationale as claim 9. Claim 19 Song teaches: A computer program product for generating an artificial intelligence system, the computer program product comprising a computer readable storage device having computer program instructions embodied therewith, the computer program instructions executable by a computer system to cause the computer system to: (Col 24, lines 6-19 “Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The code modules (or “engines”) may be stored on any type of, one or more, non-transitory computer-readable media (e.g., a computer storage product) or computer storage devices, such as hard drives, solid state memory, optical disc, and/or the like. The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, for example, volatile or non-volatile storage.” Col 19, lines 4-10 “At block 506 , the system trains a machine learning model according to a selected recipe. The system may train the model based on the training dataset. Optionally, the system may train multitudes of models, for example with varying hyperparameters. The trained machine learning model may be utilized to determine forecasts, for example as illustrated in FIG. 6A.”) The remaining limitations of claim 19 are substantially the same as claim 1, therefore claim 19 is rejected under the same rationale as claim 1. Claim 20 is product claim that recite substantially the same limitations as claim 2, therefore claim 20 is rejected under the same rationale as claim 2 . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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 nonobviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 3, 4 and 12, 13 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent, Song et al. (US 11443237 B1) hereafter “Song” in view of US patent application, Montanari et al. (US 20210350280 A1), hereinafter “Montanari” . Claim 3 song teaches: “the model recipe” and “using the model recipe” (“The user interface 400 further indicates machine learning model recipes 26 . As described above, a machine learning model recipe may indicate a type of machine learning model optionally along with certain hyperparameters or ranges of hyperparameters. The user may optionally select a particular machine learning model recipe to utilize. For example, the user may select from among the presented recipes 30 , such as neural network type A, neural network type B, factorization machine, and so on. An example neural network may be a sequence model with or without attention, DeepAR, and so on. The presented recipes 30 may optionally be selected according to the template specified by selection option 24 .”) Song does not explicitly disclose: identifying, by the number of processor units, a set of target platforms for the artificial intelligence system; creating, by the number of processor units, create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using (…) and the set of artifact models; and deploying, by the number of processor units, the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms (…). However, Montanari teaches: identifying, by the number of processor units, a set of target platforms for the artificial intelligence system; (Para 78, “ The resource discoverer module 72 may read low-level hardware description data of the first device.” Para 82, “The algorithm 80 starts at operation 81 , where the requirements of the first device are received, wherein the requirements are based at least in part on hardware of the first device.” Para 47, “ FIG. 1 is a block diagram of an example system, indicated generally by the reference numeral 10 . The example system 10 shows a pre-trained model 11 being deployed at a plurality of edge devices 14 a , 14 b , and 14 c .” – Examiner’s note: this denotes the system identifying the platform by having a resource discoverer read its low-level hardware description .) creating, by the number of processor units, create a set of production artificial intelligence models to form artificial intelligence system to run on the target platforms using (…) and the set of artifact models; and (Para 47, “ In order to be deployed at each of the edge devices 14 , the pre-trained model 11 may need to undergo manual modification 13 in order to be optimized according, for example, to the hardware capabilities of a specific edge device. For example, the pre-trained model 11 may be modified differently at modification steps 12 a , 12 b , and 12 c , in order to be compatible with the hardware requirements of the edge devices 14 a , 14 b , and 14 c respectively.” Para 64, “As such, when running a plurality of models, the plurality of models may be compiled in a way that maximizes the use of the on-board memory, thus obtaining the lowest compilation latency.”) deploying, by the number of processor units, the artificial intelligence system comprising the set of production artificial intelligence models to the set of target platforms (…). (Para 47, “ FIG. 1 is a block diagram of an example system, indicated generally by the reference numeral 10 . The example system 10 shows a pre-trained model 11 being deployed at a plurality of edge devices 14 a , 14 b , and 14 c .”) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the automated machine learning architecture of Song that includes model recipes with the dynamic modification of the models to accommodate for target platforms of Montanari in order to create and deploy models that are the right fit for the specific platform. The benefit of doing so would be to allow the models to be properly set up for deployment at various target platforms. See paragraph 48 of Montanari, “Machine learning models (e.g. deep learning models, neural networks etc.) may be designed for a variety of sensing tasks, including speech, vision and motion sensing. These models may be trained on GPU servers to benefit from the computational capabilities and parallelization power of such servers. However, once developed, it may be a lengthy and manual process to deploy these models on edge devices and accelerators as different edge devices (e.g. smartphones, smartwatches, laptops etc.) and accelerators may have different hardware architecture and resource constraints than the servers on which the model was trained.” Claim 4 Montanari teaches: monitoring, by the number of processor units, a number of performance metrics based on evaluation artifacts generated from training the artificial intelligence models; and (Para 72, “At operation 60 , the modified first model is profiled, for example, for determining the performance of the modified first model. Similar to the operation 52 , the profiling in the operation 60 may comprise running a fixed number of inferences and collecting average values about accuracy, latency, memory and/or energy consumption.”) retraining, by the number of processor units, the artificial intelligence system based on the number of performance metrics. (Para 53, “The profiling results may be provided to the tuner module 31 , such that the tuner module may determine whether the user requirements 36 are satisfied, and may further modify the model if required.” Para 71, “At operation 58 , it is determined whether the modified first model needs to be retrained. If yes, the modified first model is retrained at operation 59 , for example, using the sample data 57.”) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the automated machine learning architecture of Song with the monitoring and retraining based on performance metrics of Montanari. The motivation for doing so would be improve the accuracy of the models. See Paragraph 2 and 71of Montanari, “Manual tuning for models, such as machine learning models, is known. There remains a need for further improvements related to deployment of such models at different devices. (Paragraph 2)” “The modified first model may might need to be re-trained in order to recover the accuracy that might be lost during the modification. (Paragraph 71)” Claim 12 is product claim that recite substantially the same limitations as claim 3, therefore claim 12 is rejected under the same rationale as claim 3. Claim 13 is product claim that recite substantially the same limitations as claim 4, therefore claim 13 is rejected under the same rationale as claim 4 . 07-21-aia AIA Claim s 5-7 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent, Song et al. (US 11443237 B1) hereafter “Song” in view of US patent application, Capelo et al. (US 20220107744 A1), hereinafter “Capelo” . Claim 5 Song teaches: using the model recipe; (“The user interface 400 further indicates machine learning model recipes 26 . As described above, a machine learning model recipe may indicate a type of machine learning model optionally along with certain hyperparameters or ranges of hyperparameters. The user may optionally select a particular machine learning model recipe to utilize. For example, the user may select from among the presented recipes 30 , such as neural network type A, neural network type B, factorization machine, and so on. An example neural network may be a sequence model with or without attention, DeepAR, and so on. The presented recipes 30 may optionally be selected according to the template specified by selection option 24 .”) using the training dataset and the model recipe. (“At block 506 , the system trains a machine learning model according to a selected recipe. The system may train the model based on the training dataset.”) Song does not explicitly disclose the dynamic steps of: identifying, by the number of processor units, resources for training the artificial intelligence models (…) creating, by the number of processor units, an execution cluster based on the resources identified; and training, by the number of processor units, the artificial intelligence models in the execution cluster (…) However, Capelo teaches: identifying, by the number of processor units, resources for training the artificial intelligence models (…) (Para 131, “Determining the computing resources for the run functions to determine the machine parameters required to execute one or more experiments (e.g., for the run, for an experiment, etc.). Examples of machine parameters can include: the number of machines, the type of machines, and/or any other suitable set of machine parameters.”) creating, by the number of processor units, an execution cluster based on the resources identified; and (Para 140 and 141, “ Provisioning a machine set S 500 functions to prepare the computing environment within the cloud computing system for experiment execution. S 500 is preferably performed after S 200 , more preferably after the user approval of the run metrics but alternatively at any other time. The machine set (e.g., cluster) preferably includes the types of machines associated with the run (e.g., specified by the run specification, specified by the experiment specification, etc.), but can alternatively include a subset of the machine types. The machine set preferably includes the number of machines associated with the run (e.g., total number of machines for concurrent execution, number of machines per experiment epoch, etc.), but can alternatively include a subset of the number of machines. However, the machine set can be otherwise constructed. [0141] S 500 can include: optionally accessing the user's cloud account using the user's access credentials; optionally initializing a cluster orchestrator instance (e.g., to initialize a cluster); provisioning the machine set (e.g., by loading and running a set of container images on each machine); provisioning a dataset storage volume; provisioning an object storage volume; loading agents onto the cluster (e.g., wherein the agents can be specific to the cluster orchestrator and/or machine provider; wherein the agents can be included within the container image, etc.); loading the model onto each machine; and/or otherwise provisioning the machine set. The provisioned environment preferably includes a single dataset volume, but can alternatively include multiple (e.g., one for each cluster, one for each machine, one for training data and one for test data, etc.”) training, by the number of processor units, the artificial intelligence models in the execution cluster (…) (Para 152, “Running the experiments S 700 functions to execute the experiments determined in S 200… S 700 preferably includes training each experiment's instance of the model using the dataset”) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the automated machine learning platform of Song that includes model recipes and training datasets with the dynamic cluster provisioning of Capelo in order to identify resources, create clusters and train in the cluster using the recipe and dataset. The motivation to combine would be optimization and reducing overhead. See Para 32 of Capelo, “In addition to automatically provisioning and controlling the requested machines, the technology can further reduce user overhead by automatically configuring, scheduling, and deploying the experiments. This frees users from manually testing different experiments to determine the best or optimal hyperparameter value permutation, from manually debugging experiments, and from waiting around for each experiment to complete before executing the next one (or from having to manage multiple experiments in parallel).” Claim 6 Capelo teaches: creating, by the number of processor units, a project for creating the artifact models, wherein the project comprises multiple steps; and (Para 86, “The system can function to run a set of experiments (e.g., a “run”), wherein each experiment trains (or attempts to train) one or more models… Additionally or alternatively, a run can be a process that generates one or more experiments for execution, and/or execute the experiments.”) running, by the number of processor units, an experiment for the project that creates artifact models. (Para 152, “Running the experiments S 700 functions to execute the experiments determined in S 200… and storing the experiment outputs (e.g., model parameters, model artifacts, logs, etc.) in the object storage volume.” Para 88, “ Each experiment can generate one or more experiment outputs, which can be stored in the object store (e.g., for the cluster, for the experiment set, etc.), provided to the agent, provided to the platform, provided to a user, and/or otherwise managed. Examples of experiment outputs can include: artifacts, logs, model metrics, experiment metrics, and/or any other suitable output. Artifacts can include: the trained model (e.g., weights, equations, etc.); model checkpoints (e.g., for resuming training later); files created during the training process (e.g., image generated by the models; Tensorflow records; etc.); intermediary results; model outputs; and/or other artifacts.” ) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the automated machine learning platform of Song that includes model recipes and artifact models with the structured tracking (projects/runs and experiments) of Capelo in order to create artifact models by utilizing the projects/runs and experiments. The motivation to combine would be to provide an organized, automated framework that reduces user overhead. See para 23 and 32 of Capelo, “The method functions to automatically orchestrate large scale model training across different training parameter combinations and multiple machines (e.g., concurrently or asynchronously), which enables a user to train the same model at both a small scale and a large scale without manual model modifications, result monitoring, or active cloud computing management. (Para 23)”,“ In addition to automatically provisioning and controlling the requested machines, the technology can further reduce user overhead by automatically configuring, scheduling, and deploying the experiments. This frees users from manually testing different experiments to determine the best or optimal hyperparameter value permutation, from manually debugging experiments, and from waiting around for each experiment to complete before executing the next one (or from having to manage multiple experiments in parallel). (Para 32)” Claim 7 Song further teaches: wherein the model recipe is selected from a group comprising a generalized model recipe and customized model recipe. (“The presented recipes 30 may optionally be selected according to the template specified by selection option 24 .” ) PNG media_image1.png 617 696 media_image1.png Greyscale Claim 14 is a system claim that recite substantially the same limitations as claim 5, therefore claim 14 is rejected under the same rationale as claim 5. Claim 15 is a system claim that recite substantially the same limitations as claim 6, therefore claim 15 is rejected under the same rationale as claim 6. Claim 16 is a system claim that recite substantially the same limitations as claim 7, therefore claim 16 is rejected under the same rationale as claim 7. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAYMUR RAHMAN ALI whose telephone number is (571)272-0007. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NAYMUR RAHMAN ALI/Examiner, Art Unit 2123 /ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123 Application/Control Number: 18/362,726 Page 2 Art Unit: 2123 Application/Control Number: 18/362,726 Page 3 Art Unit: 2123 Application/Control Number: 18/362,726 Page 4 Art Unit: 2123 Application/Control Number: 18/362,726 Page 5 Art Unit: 2123 Application/Control Number: 18/362,726 Page 6 Art Unit: 2123 Application/Control Number: 18/362,726 Page 7 Art Unit: 2123 Application/Control Number: 18/362,726 Page 8 Art Unit: 2123 Application/Control Number: 18/362,726 Page 9 Art Unit: 2123 Application/Control Number: 18/362,726 Page 10 Art Unit: 2123 Application/Control Number: 18/362,726 Page 11 Art Unit: 2123 Application/Control Number: 18/362,726 Page 12 Art Unit: 2123 Application/Control Number: 18/362,726 Page 13 Art Unit: 2123 Application/Control Number: 18/362,726 Page 14 Art Unit: 2123 Application/Control Number: 18/362,726 Page 15 Art Unit: 2123 Application/Control Number: 18/362,726 Page 16 Art Unit: 2123 Application/Control Number: 18/362,726 Page 17 Art Unit: 2123 Application/Control Number: 18/362,726 Page 18 Art Unit: 2123 Application/Control Number: 18/362,726 Page 19 Art Unit: 2123 Application/Control Number: 18/362,726 Page 20 Art Unit: 2123 Application/Control Number: 18/362,726 Page 21 Art Unit: 2123 Application/Control Number: 18/362,726 Page 22 Art Unit: 2123 Application/Control Number: 18/362,726 Page 23 Art Unit: 2123 Application/Control Number: 18/362,726 Page 24 Art Unit: 2123 Application/Control Number: 18/362,726 Page 25 Art Unit: 2123 Application/Control Number: 18/362,726 Page 26 Art Unit: 2123 Application/Control Number: 18/362,726 Page 27 Art Unit: 2123 Application/Control Number: 18/362,726 Page 28 Art Unit: 2123 Application/Control Number: 18/362,726 Page 29 Art Unit: 2123 Application/Control Number: 18/362,726 Page 30 Art Unit: 2123 Application/Control Number: 18/362,726 Page 31 Art Unit: 2123 Application/Control Number: 18/362,726 Page 32 Art Unit: 2123 Application/Control Number: 18/362,726 Page 33 Art Unit: 2123 Application/Control Number: 18/362,726 Page 34 Art Unit: 2123 Application/Control Number: 18/362,726 Page 35 Art Unit: 2123 Application/Control Number: 18/362,726 Page 36 Art Unit: 2123 Application/Control Number: 18/362,726 Page 37 Art Unit: 2123
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