SYSTEM AND METHOD FOR SECURE MANAGEMENT, LINKING, OPERATIONS TO GENERATE INSIGHTS AND ACCELERATE ANALYTICS AND AI MODELING

§101 §103

DETAILED ACTION Response to Amendment The amendment filed on 6 July 2026 has been entered. Claims 1-20 are pending. Claims 1, 6, 16-17, 19-20 are amended. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 6 April 2026. Applicant’s amendments to the Claims have overcome the provisional statutory double patenting rejection previously set forth in the Non-Final Office Action mailed 6 April 2026. Response to Arguments Applicant’s remarks, regarding the rejections of claims under 35 USC 101, have been fully considered. Examiner notes Applicant’s arguments, regarding the rejections of claims under 35 USC 101, are directed to newly amended claim limitations for which Examiner has not yet made a prima facie case for, rendering Applicant’s arguments moot. Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered. Applicant submits Sanchez and Johnson do not disclose or suggest, "applying the trained machine learning model to generate a predictive output relating to a new set of linkable synthetic trends data based on data from the first segregated data environment and the second segregated data environment, wherein the linkable synthetic trends data is adapted to be a privacy-preserving interface to the first source dataset and the second source dataset," as recited in amended Claims 1, 19, and 20. Applicant’s arguments have been considered, but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 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 . Allowable Subject Matter Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if rewritten or amended to overcome the objections and rejections under 35 USC 101, set forth in this Office Action. The following is a statement of reasons for the indication of allowable subject matter. The prior art does not anticipate, or render obvious as a whole, the claim limitations of Claim 15 as disclosed in Applicant’s claims. Presented with the additional limitations of Claim 15, the prior art fails to make a prima facie case of obviousness for limitations, “further comprising transmitting, from the model inference AI agent to a delivery AI agent operating within the model serving data environment, results of the inference process, wherein the delivery AI agent is configured to package the results of the inference process for consumption by a second entity operating within the model serving data environment”, because the method for generating a trained machine learning model shown to be found in the prior arts, made of record (see current Office Action) cannot be reconciled with the above limitations and an obviousness determination would require impermissible hindsight. In summary, the references made of record, fail to disclose the required claimed technical features recited by the Claim 15 limitations, as a whole. Claim Objections Claim 1 and analogous claims 19, 20 are objected to because of the following informalities: “a first segregated environment” in line 3 should be “a first segregated data environment”. Appropriate correction is required. Claim 1 and analogous claims 19, 20 are objected to because of the following informalities: “a second segregated environment” in line 6 should be “a second segregated data environment”. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more. Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory category. MPEP 2106.03: According to the first part of the Alice analysis, in the instant case, the claims were determined to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1-18), a machine/system/product (Claims 19-20), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a judicial exception. Regarding independent claims 1, 19, 20, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below: Claims 1, 19, 20: “by transforming a first source dataset, the transforming of the first source dataset comprising generating an embedded representation of the first source dataset and adding privacy parameters to the first source dataset” (abstract manipulation of basic mathematical constructs) “by transforming a second source dataset, the transforming of the second source dataset comprising generating an embedded representation of the second source dataset and adding privacy parameters to the second source dataset” (abstract manipulation of basic mathematical constructs) “generating a combined dataset comprising the first dataset and a ground truth dataset from the first segregated data environment combined with the second dataset from the second segregated data environment” (abstract manipulation of basic mathematical constructs) If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One. Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below: “generating, in a first segregated environment, a first dataset” “generating, in a second segregated environment, a second dataset” “to generate a predictive output relating to a new set of linkable synthetic trends data based on data from the first segregated data environment and the second segregated data environment” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). “training a machine learning model with training data, the training data comprising a subset of the combined dataset” “applying the trained machine learning model” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). “wherein the first dataset and the ground truth dataset are stored in a first bridge database, and the second dataset is stored in a second bridge database” “wherein model parameters of the trained machine learning model are stored in a storage device” “wherein the linkable synthetic trends data is adapted to be a privacy-preserving interface to the first source dataset and the second source dataset” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole. Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. First, the additional elements considered as part of the preamble and the additional elements directed to the use of computer technology are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h). Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f). Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g). Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to qualify as "significantly more", see MPEP 2106.05(g). Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering): The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d). The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 19, 20 do not recite what the courts have identified as "significantly more". Furthermore, regarding dependent claims 2-18, which depend from claim 1, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B: Claim 2: Incorporates the rejection of claim 1. “wherein the first source dataset corresponds to health data” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 3: Incorporates the rejection of claim 1. “wherein the privacy parameters comprise injected noise” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 4: Incorporates the rejection of claim 1. “wherein the second source dataset corresponds to consumer data” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 5: Incorporates the rejection of claim 1. “wherein the transformation of the first source dataset is performed by a first artificial intelligence (AI) agent operating within the first segregated data environment” “wherein the transformation of the second source dataset is performed by a second AI agent operating within the second segregated data environment” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 6: Incorporates the rejection of claim 5. “wherein the transformation of the first source dataset and the transformation of the second source dataset are performed by processing the respective dataset with an embedding neural network model” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 7: Incorporates the rejection of claim 5. “wherein the second AI agent is configured to receive the transformation of the first source dataset from the first AI agent using a model context protocol (MCP) framework of communication between AI agents” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 8: Incorporates the rejection of claim 1. “wherein training the machine learning model is performed by a model training artificial intelligence (AI) agent operating within a model training environment, wherein the model training environment is different from the first segregated data environment and different from the second segregated data environment” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 9: Incorporates the rejection of claim 1. “validating the trained machine learning model by a model validation artificial intelligence (AI) agent, wherein the validation comprises evaluating the trained machine learning model based on a subset of the training data” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). “wherein the model validation AI agent is configured to receive model parameters of the trained machine learning model” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 10: Incorporates the rejection of claim 9. “wherein the validation further comprises verifying calibration of predicted probabilities” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 11: Incorporates the rejection of claim 9. “further comprising transmitting, from the model validation AI agent to an entity operating within a model serving data environment, results of the validation” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 12: Incorporates the rejection of claim 9. “further comprising storing the model parameters of the trained machine learning model in a storage device within a model serving data environment” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 13: Incorporates the rejection of claim 11. “further comprising receiving, at a model inference AI agent operating within the model serving data environment, a task signal from the entity operating within the model serving data environment, wherein the task signal initiates a model inference process performed by the model inference AI agent” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 14: Incorporates the rejection of claim 13. “further comprising loading, by the model inference AI agent, the model parameters of the trained machine learning model from the storage device within the model serving environment to perform the model inference process” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 15: Incorporates the rejection of claim 13. “further comprising transmitting, from the model inference AI agent to a delivery AI agent operating within the model serving data environment, results of the inference process, wherein the delivery AI agent is configured to package the results of the inference process for consumption by a second entity operating within the model serving data environment” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 16: Incorporates the rejection of claim 1. “wherein the first dataset and the second dataset include one or more data elements associated with a shared individual, wherein a data element of the one or more data elements comprises a linking key that links a data element of the first dataset with a data element of the second dataset” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h). Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 17: Incorporates the rejection of claim 16. “further comprising generating a linking database comprising the first dataset, the second dataset, and corresponding linking keys, wherein a linking key of the corresponding linking keys is associated with a particular individual” These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g). Limitations directed to mere instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Claim 18: Incorporates the rejection of claim 1. “further comprising selecting a model training strategy from a strategy library database and training the machine learning model according to the selected model training strategy, wherein the strategy library database comprises a plurality of model training strategies” The recitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f). Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 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. 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. Claims 1-6, 8, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez et al. (U.S. Pre-Grant Publication No. 20220300853, hereinafter ‘Sanchez'), in view of Johnson et al. (U.S. Pre-Grant Publication No. 20250358251, hereinafter 'Johnson'), and further in view of Levitan et al. (U.S. Pre-Grant Publication No. 20240135015, hereinafter 'Levitan'). Regarding claim 1 and analogous claims 19, 20, Sanchez teaches A method for generating a trained machine learning model trained on a plurality of segregated data sources, the method comprising ([0055] A processing system 500 may include one or multiple processors 504 (only one shown), such as a central processing unit (CPU) of a computing device or a distributed processor system. The processor(s) 504 executes processor-executable instructions for performing the functions and methods described above. In embodiments, the processor executable instructions are locally stored or remotely stored and accessed from a non-transitory computer readable medium, such as storage 510, which may be a hard drive, cloud storage, flash drive, etc. Read Only Memory (ROM) 506 includes processor-executable instructions for initializing the processor(s) 504, while the random-access memory (RAM) 508 is the main memory for loading and processing instructions executed by the processor(s) 504.): generating, in a first segregated environment, a first dataset by transforming a first source dataset, the transforming of the first source dataset comprising generating an embedded representation of the first source dataset and adding privacy parameters to the first source dataset ([0034] In an embodiment, at step 2, the involved entities (including the leader) use the PPF to generating, in a first segregated environment, a first dataset by transforming a first source dataset generate a privacy preserving version of their datasets (e.g., “Dataset A,” “Dataset B,” “Dataset ZZ” for entities A, B ZZ and “Leader Dataset” for the leader entity). For example, each involved entity may apply the PPF to the dataset to transform the dataset into a privacy preserving version of the dataset (“protected dataset”).; [0037] The PPF 210, which is an important component for improving the data privacy according to embodiments of the present invention, may include an algorithm that, given a dataset composed by a set of elements (e.g., tuples, images, sound, nodes of a graph, etc.), the transforming of the first source dataset comprising generating an embedded representation of the first source dataset transforms the dataset into a vector representation that and adding privacy parameters to the first source dataset does not include private data. The PPF 210 performs the transformation in a way that creates extreme technical difficulties to recover the original data from the data representation. The PPF 210 may be configured to either transform all the raw data or transform only the parts of the data that include private/confidential information. Furthermore, the PPF 210 is configured in a way in which the transformed/protected data output can be used to train the downstream ML/AI model. For example, the PPF 210 advantageously removes the human readability of the data to increase data security and privacy, while at the same time maintaining the utility of the data for the ML/AI training and associated tasks.); generating, in a second segregated environment, a second dataset by transforming a second source dataset, the transforming of the second source dataset comprising generating an embedded representation of the second source dataset and adding privacy parameters to the second source dataset ([0034] In an embodiment, at step 2, the involved entities (including the leader) use the PPF to generating, in a second segregated environment, a second dataset by transforming a second source dataset, the transforming of the second source dataset comprising generating an embedded representation of the second source dataset and adding privacy parameters to the second source dataset generate a privacy preserving version of their datasets (e.g., “Dataset A,” “Dataset B,” “Dataset ZZ” for entities A, B ZZ and “Leader Dataset” for the leader entity). For example, each involved entity may apply the PPF to the dataset to transform the dataset into a privacy preserving version of the dataset (“protected dataset”). At step 3, in an embodiment, one or more of these privacy preserving versions of the datasets (“protected datasets”) may be used as input to train an ML/AI model.); Sanchez fails to teach generating a combined dataset comprising the first dataset and a ground truth dataset from the first segregated data environment combined with the second dataset from the second segregated data environment, wherein the first dataset and the ground truth dataset are stored in a first bridge database, and the second dataset is stored in a second bridge database; training a machine learning model with training data, the training data comprising a subset of the combined dataset, wherein model parameters of the trained machine learning model are stored in a storage device; and applying the trained machine learning model to generate a predictive output relating to a new set of linkable synthetic trends data based on data from the first segregated data environment and the second segregated data environment, wherein the linkable synthetic trends data is adapted to be a privacy-preserving interface to the first source dataset and the second source dataset. Johnson teaches generating a combined dataset comprising the first dataset and a ground truth dataset from the first segregated data environment combined with the second dataset from the second segregated data environment ([0041] In another example, AI agent platform 205 may comprise a generating a combined dataset comprising the first dataset and a ground truth dataset from the first segregated data environment combined with the second dataset from the second segregated data environment feature store in which network-based service providers may contribute respective data sets that are securely stored in a segregated manner, e.g., in separate sandboxes 226-228 with different encryption keys, different access codes/password protection, etc., and where user data of a particular user may be requested from the respective sandboxes 226-228 for limited use in connection with AI agent training and/or operations as described herein. For instance, sandbox 229 may be instantiated and assigned to a user and may be populated with various user data, e.g., from one or more sandboxes 226-228 that may be associated with different service providers (e.g., a social media service provider, an OTT messaging application service provider, etc.).), wherein the first dataset and the ground truth dataset are stored in a first bridge database, and the second dataset is stored in a second bridge database ([0039] In one example, any one or more of the sandboxes 226-228 may be hosted on shared infrastructure within the network-based processing system 210 (e.g., where a trusted entity controls and operates the data sharing platform, but where the data owners (e.g., respective users) have independent control and access of the respective sandboxes 226-228 and the data sets 281-283 stored therein). For instance, a provider of an AI/ML-based agent development service via the AI agent platform 205 may also wherein the first dataset and the ground truth dataset are stored in a first bridge database, and the second dataset is stored in a second bridge database securely and separately store data sets 281-283 in sandboxes 226-228 on behalf of the respective users. Thus, AI agent platform 205 may provide services to a number of different users, and may interact with a number of user devices, such as data owner devices 231-233. Each of the user devices may comprise a desktop computer, a cellular smart phone, a laptop, a tablet computer, a cloud based processing system providing a user environment, and so forth. In particular, AI agent platform 205 may be operated by a trusted party to store data sets on behalf of data owners in a secure and restricted manner, to provide for the use of restricted data features, e.g., for training of AI/ML-based agent via the AI agent platform 205 and for providing additional contextual data to AI/ML-based agents in operation in accordance with authorizations from users, and so on.); and training a machine learning model with training data, the training data comprising a subset of the combined dataset, wherein model parameters of the trained machine learning model are stored in a storage device ([0043] In one example, sandboxes 226-229 may also store AI agents and/or one or more personas, e.g., local language models that may be used by an AI agent as described herein. For instance, a sandbox 229 may be instantiated for a user and may be populated with various user data. In addition, an AI agent may be created from a template (e.g., a machine learning algorithm with various pre-set or user-selected hyperparameters) that is available via an MLM catalog/repository of the AI agent platform 205. In one example, development of the AI agent may be the training data comprising a subset of the combined dataset provided via sandbox 229, such as wherein model parameters of the trained machine learning model are stored in a storage device hyperparameter selection, training of the AI agent via the data owner's own data features, etc.; [0044] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model), may broadly include models for prediction, classification, forecasting, and/or detection, and may comprise a training a machine learning model with training data machine learning algorithm (MLA) that has been “trained” or configured in accordance with input data (e.g., training data) to perform a particular service, e.g., to respond to prompts/queries and/or to otherwise generate text outputs in response to prompts, to forecast weather, to classify images, to predict user arrival times, and so forth.); and Sanchez and Johnson are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Johnson to Sanchez before the effective filing date of the claimed invention in order to store data sets on behalf of data owners in a secure and restricted manner, to provide for the use of restricted data features, e.g., for training of AI/ML-based agent via the AI agent platform 205 and for providing additional contextual data to AI/ML-based agents in operation in accordance with authorizations from users (cf. Johnson, [0039] In one example, any one or more of the sandboxes 226-228 may be hosted on shared infrastructure within the network-based processing system 210 (e.g., where a trusted entity controls and operates the data sharing platform, but where the data owners (e.g., respective users) have independent control and access of the respective sandboxes 226-228 and the data sets 281-283 stored therein). For instance, a provider of an AI/ML-based agent development service via the AI agent platform 205 may also securely and separately store data sets 281-283 in sandboxes 226-228 on behalf of the respective users. Thus, AI agent platform 205 may provide services to a number of different users, and may interact with a number of user devices, such as data owner devices 231-233. Each of the user devices may comprise a desktop computer, a cellular smart phone, a laptop, a tablet computer, a cloud based processing system providing a user environment, and so forth. In particular, AI agent platform 205 may be operated by a trusted party to store data sets on behalf of data owners in a secure and restricted manner, to provide for the use of restricted data features, e.g., for training of AI/ML-based agent via the AI agent platform 205 and for providing additional contextual data to AI/ML-based agents in operation in accordance with authorizations from users, and so on.). Levitan teaches applying the trained machine learning model to generate a predictive output relating to a new set of linkable synthetic trends data based on data from the first segregated data environment and the second segregated data environment, wherein the linkable synthetic trends data is adapted to be a privacy-preserving interface to the first source dataset and the second source dataset ([0027] The eyes-off environment 108 is an environment that receives, stores, and analyzes data that is confidential, private, or proprietary. In one example, the data comprises customer data or customer feedback. However, any type of confidential data can be stored and analyzed within the eyes-off environment 108.; [0038] The training incorporates an anonymity technique. In one embodiment, the anonymity technique is differential privacy. The synthetic data generation model is trained to replicate the confidential data but in a way that incorporates privacy into the model training process. wherein the linkable synthetic trends data is adapted to be a privacy-preserving interface to the first source dataset and the second source dataset Although the synthetic data generation model learns a rough structure or general concept of what is in the confidential data, the synthetic data generation model does not know the specifics. Instead, the synthetic data generation model is trained to encode patterns such as, for example, common phrasing, sentiment, and/or structure of the data (e.g., how users provide feedback).; [0040] In operation 306, synthetic data is generated in the eyes-on environment 110. In example embodiments, the evaluation component 216 runs the synthetic data generation model to obtain the relating to a new set of linkable synthetic trends data based on data from the first segregated data environment and the second segregated data environment synthetic data that is the equivalent of the underlying confidential data.; [0044] In operation 314, applying the trained machine learning model analyzed data is obtained as the output of the target model. In some embodiments, the target model categorizes (e.g., annotates or labels) the confidential data. The analyzed data can then be stored back to the data storage 204 or used to generate analytic results or dashboards. The owner of the confidential data can access and view the analyzed data, analytic results, or dashboards. In the customer feedback scenario, the output of the target model can be, for example, to generate a predictive output an indication of whether the customer feedback is positive, negative, or neutral.). Sanchez, Johnson, and Levitan are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez and Johnson, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Levitan to Sanchez before the effective filing date of the claimed invention in order to generate models using a data set that is representative of eyes-off confidential data (e.g., also referred to as “eyes-off data”) without violating trust and privacy (cf. Levitan, [0016] Example embodiments address the technical problem of how to generate models using a data set that is representative of eyes-off confidential data (e.g., also referred to as “eyes-off data”) without violating trust and privacy. To address the technical problem, example embodiments train a synthetic data generation model (e.g., a generative model) in an eyes-off environment having the confidential data wherein the training data is not labeled (e.g., unsupervised machine learning). The synthetic data generation model is then used to create synthetic data that closely represent the confidential data. The synthetic data is then annotated (e.g., labeled) and used to train a target model in an eyes-on environment. The target model can then be deployed back in the eyes-off environment. As a result, example embodiments provide a technical solution for generating, based on eyes-off data, accurate machine learning models for use in the eyes-off environment.). Regarding claim 2, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Sanchez teaches wherein the first source dataset corresponds to health data ([0060] Digital health: Different hospitals may want to share data of patients to improve the ML models they create. Again, this will bring privacy implications. Moreover, other health-related companies such as those developing health monitors (e.g., FITBIT) could also share the data with third parties if they could efficiently anonymize it.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 3, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Sanchez teaches wherein the privacy parameters comprise injected noise ([0042] FIG. 3 graphically shows results from an initial benchmark study on the effectiveness of the PPF using different state of the art algorithms. In particular, PCA, an autoencoder and a wherein the privacy parameters comprise injected noise random noise adder were used as the PPFs. The reconstructed data, which is an obfuscated version of the original data, is then used to train an ML model on a simple classification task.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 4, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Sanchez teaches wherein the second source dataset corresponds to consumer data ([0040] Generating private datasets: All the participants in the process (i.e., involved entities and leader entity(ies)) use the PPF to create private representations of their own raw data. This data could be wherein the second source dataset corresponds to consumer data personal data, sensor data, medical data or any other kind of private, confidential or sensitive data. In any case, after the PPF is applied, a numeric vector representation will be created and will not include any of the original values.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 5, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Johnson teaches wherein the transformation of the first source dataset is performed by a first artificial intelligence (AI) agent operating within the first segregated data environment, and wherein the transformation of the second source dataset is performed by a second AI agent operating within the second segregated data environment ([0039] For instance, a provider of an AI/ML-based agent development service via the AI agent platform 205 may also securely and separately store data sets 281-283 in sandboxes 226-228 on behalf of the respective users. Thus, AI agent platform 205 may provide services to a number of different users, and may interact with a number of user devices, such as data owner devices 231-233. Each of the user devices may comprise a desktop computer, a cellular smart phone, a laptop, a tablet computer, a cloud based processing system providing a user environment, and so forth. In particular, wherein the transformation of the first source dataset is performed by a first artificial intelligence (AI) agent operating within the first segregated data environment, wherein the transformation of the second source dataset is performed by a second AI agent operating within the second segregated data environment AI agent platform 205 may be operated by a trusted party to store data sets on behalf of data owners in a secure and restricted manner, to provide for the use of restricted data features, e.g., for training of AI/ML-based agent via the AI agent platform 205 and for providing additional contextual data to AI/ML-based agents in operation in accordance with authorizations from users, and so on.; [0041] For instance, in another example, data set 283 in sandbox 228 may comprise user data associated with a social media service, such as user contact data, transcripts/records of text-based communications via the social media service, etc. As in the preceding example(s), the data set 282 may include raw data and/or may include data that have been normalized, transformed, tagged, and/or anonymized, etc. before uploading to the AI agent platform 205.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 6, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 5. Sanchez teaches wherein the transformation of the first source dataset and the transformation of the second source dataset are performed by processing the respective dataset with an embedding neural network model ([0038] wherein the transformation of the first source dataset and the transformation of the second source dataset are performed by processing the respective dataset with an embedding neural network model Examples of a PPF (110, 210) include algorithms commonly used to reduce the dimensionality of the data such as principal component analysis (PCA) or autoencoders, privacy preserving transformation such as noise addition (see Zhang, Tianwei, et al., “Privacy-preserving Machine Learning through Data Obfuscation,” arXiv:1807.01860 (Jul. 13, 2018), which is hereby incorporated by reference herein), complex representation learning algorithms such as embedding propagation (EP) (see Garcia-Duran, Alberto, et al., “Learning Graph Representations with Embedding Propagation,” Advances in Neural Information Processing Systems, Vol. 30, pp. 5119-5130 (Oct. 9, 2017), which is hereby incorporated by reference herein), or algorithms specially tailored for the task (including neural networks). Embodiments of the present invention are not limited to these specific algorithms, and include the use of adapted or modified versions of these algorithms, as well as other data processing algorithms.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 8, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Johnson teaches wherein training the machine learning model is performed by a model training artificial intelligence (AI) agent operating within a model training environment, wherein the model training environment is different from the first segregated data environment and different from the second segregated data environment ([0039] In particular, AI agent platform 205 may be operated by a trusted party to store data sets on behalf of data owners in a secure and restricted manner, to provide for the use of restricted data features, e.g., for training of AI/ML-based agent via the AI agent platform 205 and for providing additional contextual data to AI/ML-based agents in operation in accordance with authorizations from users, and so on.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Regarding claim 18, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Johnson teaches further comprising selecting a model training strategy from a strategy library database and training the machine learning model according to the selected model training strategy, wherein the strategy library database comprises a plurality of model training strategies ([0043] In one example, sandboxes 226-229 may also store AI agents and/or one or more personas, e.g., local language models that may be used by an AI agent as described herein. For instance, a sandbox 229 may be instantiated for a user and may be populated with various user data. In addition, an AI agent may be created from a further comprising selecting a model training strategy template (e.g., a machine learning algorithm with various wherein the strategy library database comprises a plurality of model training strategies pre-set or user-selected hyperparameters) that is available via an from a strategy library database MLM catalog/repository of the AI agent platform 205. In one example, development of the AI agent may be provided via sandbox 229, such as hyperparameter selection, training the machine learning model according to the selected model training strategy training of the AI agent via the data owner's own data features, etc.). Sanchez, Johnson, and Levitan are combinable for the same rationale as set forth above with respect to claim 1. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sanchez, in view of Johnson, Levitan, and further in view of Triplet et al. (U.S. Pre-Grant Publication No. 20200287788, hereinafter 'Triplet'). Regarding claim 7, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 5. Sanchez teaches wherein the second AI agent is configured to receive the transformation of the first source dataset from the first AI agent using a model context protocol (MCP) framework of communication between AI agents ([0035] In a subsequent phase, new data of the leader or the other involved entities can be used to query the trained ML/AI model. Preferably, the data passes through the PPF 110 before it is used to query the model to ensure privacy, but it need not if a particular involved entity does not require privacy. One or more or all of the protected datasets, e.g., protected Dataset A . . . Dataset ZZ, may be sent wherein the second AI agent is configured to receive the transformation of the first source dataset from the first AI agent between AI agents back to the leader so that the leader may perform training and/or querying using the received protected dataset(s). Additionally, or alternately, one or more of the involved entities may perform training and/or querying.). Sanchez, as modified by Johnson and Levitan, fails to teach wherein the second AI agent is configured to receive the transformation of the first source dataset from the first AI agent using a model context protocol (MCP) framework of communication between AI agents. Triplet teaches wherein the second AI agent is configured to receive the transformation of the first source dataset from the first AI agent using a model context protocol (MCP) framework of communication between AI agents ([0018] As opposed to a hierarchical system, agents may be related to each other in a peer-to-peer structure, where every agent has equal authority to make updates as the agent sees fit. In such collaborative systems, several agents (also referred to as entities, parties, operators, actors, etc.) are able to make modifications to the network to update or optimize the network. The between AI agents agents, as suggested above, may be human agents or AI agents. However, without a supervisor, unwanted changes or error may result.; [0027] The configuration of the network 2 is typically handled by the NMS 22 and is registered in the database 24. The database 24 may either be centralized (e.g., MySQL), or distributed (e.g., using a model context protocol (MCP) framework of communication Cassandra/Kafka for MCP, Neo4J), and may rely on logs or a particular database EER model to provide historical data.). Sanchez, Johnson, Levitan, and Triplet are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, Johnson, and Levitan, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Triplet to Sanchez before the effective filing date of the claimed invention in order to provide an improvement in the trustworthiness and accountability of AI-based smart policy engines and to provide visibility to carriers over all network configuration operations (whether automated or manual), while enabling collaboration between AI and human operators (cf. Triplet, [0024] The present disclosure solves these issues by recording configuration updates in blockchain format, where each successive block includes all the content of the previous blocks in an encrypted form. Thus, the implementations described in the present disclosure seek to provide an improvement in the trustworthiness and accountability of AI-based smart policy engines and to provide visibility to carriers over all network configuration operations (whether automated or manual), while enabling collaboration between AI and human operators. The implementations described in the present disclosure can be adopted into adaptive network technologies in production networks and can provide a secure and reliable distributed transactional system to manage and distribute IoT devices or network elements configurations using permissioned blockchains, that can clearly and efficiently establish the liability of each party for service assurance and smart contract purposes.). Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez, in view of Johnson, Levitan, and further in view of Wang et al. (U.S. Pre-Grant Publication No. 20210133376, hereinafter 'Wang'). Regarding claim 9, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Sanchez, as modified by Johnson and Levitan, fails to teach further comprising validating the trained machine learning model by a model validation artificial intelligence (AI) agent, wherein the validation comprises evaluating the trained machine learning model based on a subset of the training data, and wherein the model validation AI agent is configured to receive model parameters of the trained machine learning model. Wang teaches further comprising validating the trained machine learning model by a model validation artificial intelligence (AI) agent, wherein the validation comprises evaluating the trained machine learning model based on a subset of the training data, and wherein the model validation AI agent is configured to receive model parameters of the trained machine learning model ([0035] The present disclosure presents a novel DRL-based parameter calibration platform for stability models, which employs multi-layer DRL agents with adaptive action step sizes to automate the parameter calibration process for multiple events. Through massive interactions with the simulation environment (commercial transient stability simulators without the need of modifying existing models), reinforcement learning (RL) agents can learn to find the best parameters that minimize the overall fitting errors between the measured response and simulated response of multiple events and continue to adaptively update their policies for better parameters until convergence.; [0040] In the further comprising validating the trained machine learning model by a model validation artificial intelligence (AI) agent model validation module 110, the wherein the validation comprises evaluating the trained machine learning model based on a subset of the training data input information contains power flow files, dynamic model files and PMU measurements for multiple recorded events. Recorded measurements with events are first played into the dynamic simulation environment to launch the model validation process. If there is no obvious mismatch between the simulated and the measured responses, the existing model is considered as valid and no calibration is necessary. Otherwise, wherein the model validation AI agent is configured to receive model parameters of the trained machine learning model parameters that need to be updated are selected by the bad parameter identification module.; [0044] The parameter calibration module 130 is DRL-based according to embodiments of the present disclosure, and adopts a multi-layer structure to enable coarse-fine search of parameter sets with adaptive step sizes. In some embodiments, a DRL agent is trained for a coarse level (L1) with large action step sizes, and another DRL agent is trained for a fine level (L2) with small action step sizes. Agent L1 is activated to search for the best initial conditions to improve efficiency in training agent L2, which then continues to search for the best fit with a smaller step size. In some embodiments, more levels can be added if necessary, without loss of generality. The calibrated parameters are sent back to the model validation module 110 for further verification considering multiple events.). Sanchez, Johnson, Levitan, and Wang are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, Johnson, and Levitan, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Wang to Sanchez before the effective filing date of the claimed invention in order to automate the parameter calibration process for multiple events (cf. Wang, [0035] The present disclosure presents a novel DRL-based parameter calibration platform for stability models, which employs multi-layer DRL agents with adaptive action step sizes to automate the parameter calibration process for multiple events. Through massive interactions with the simulation environment (commercial transient stability simulators without the need of modifying existing models), reinforcement learning (RL) agents can learn to find the best parameters that minimize the overall fitting errors between the measured response and simulated response of multiple events and continue to adaptively update their policies for better parameters until convergence. In an embodiment, the convergence is defined as the loss of the policy being less than a predetermined threshold which is usually set at 10E-4. The proposed DRL-based process can serve multiple objectives, simultaneously consider multiple events and derive optimal parameter sets from random initial conditions.). Regarding claim 10, Sanchez, as modified by Johnson, Levitan, and Wang, teaches The method of claim 9. Wang teaches wherein the validation further comprises verifying calibration of predicted probabilities ([0044] The parameter calibration module 130 is DRL-based according to embodiments of the present disclosure, and adopts a multi-layer structure to enable coarse-fine search of parameter sets with adaptive step sizes. In some embodiments, a DRL agent is trained for a coarse level (L1) with large action step sizes, and another DRL agent is trained for a fine level (L2) with small action step sizes. Agent L1 is activated to search for the best initial conditions to improve efficiency in training agent L2, which then continues to search for the best fit with a smaller step size. In some embodiments, more levels can be added if necessary, without loss of generality. The wherein the validation further comprises verifying calibration of predicted probabilities calibrated parameters are sent back to the model validation module 110 for further verification considering multiple events.). Sanchez, Johnson, Levitan, and Wang are combinable for the same rationale as set forth above with respect to claim 9. Regarding claim 11, Sanchez, as modified by Johnson, Levitan, and Wang, teaches The method of claim 9. Wang teaches further comprising transmitting, from the model validation AI agent to an entity operating within a model serving data environment, results of the validation ([0063] FIG. 3 shows a flowchart illustrating an overall model validation and parameter calibration process according to embodiments of the present disclosure. In step 375, if a predetermined convergence level is not achieved, the evaluation process will adjust training parameters in step 380 and activate agent L2 to perform search again in step 370. If a satisfactory convergence is achieved in step 375, the model further comprising transmitting, from the model validation AI agent to an entity operating within a model serving data environment, results of the validation validation and parameter calibration process obtains updated dynamic model data in step 390 and accomplishes its mission.). Sanchez, Johnson, Levitan, and Wang are combinable for the same rationale as set forth above with respect to claim 9. Regarding claim 12, Sanchez, as modified by Johnson, Levitan, and Wang, teaches The method of claim 9. Wang teaches further comprising storing the model parameters of the trained machine learning model in a storage device within a model serving data environment ([0062] FIG. 2 shows s a flowchart illustrating a model validation process in accordance with embodiments of the present disclosure. In a loop, the TD simulation engine 160 receives both dynamic model data files and PMU signals for multiple recorded events to run time domain simulations. The simulation results will be evaluated by the model validation module 110. If a simulated response deviates from a measured response, then a corresponding dynamic model needs to be calibrated by the exemplary DRL agent 130. The DRL agent 130 chooses actions to modify parameters for the dynamic models. The chosen actions are stored in an action pool 215, and further comprising storing the model parameters of the trained machine learning model in a storage device within a model serving data environment certain selected actions will be carried out to update the dynamic model parameters. With the updated parameters, the dynamic models will be simulated again by the TD simulation engine 160, and re-evaluated by the model validation module 110. Such looping process continues until the simulated response converges with the measured response.). Sanchez, Johnson, Levitan, and Wang are combinable for the same rationale as set forth above with respect to claim 9. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sanchez, in view of Johnson, Levitan, Wang, and further in view of Yang et al. (U.S. Pre-Grant Publication No. 20250284561, hereinafter 'Yang'). Regarding claim 13, Sanchez, as modified by Johnson, Levitan, and Wang, teaches The method of claim 11. Sanchez, as modified by Johnson, Levitan, and Wang, fails to teach further comprising receiving, at a model inference AI agent operating within the model serving data environment, a task signal from the entity operating within the model serving data environment, wherein the task signal initiates a model inference process performed by the model inference AI agent. Yang teaches further comprising receiving, at a model inference AI agent operating within the model serving data environment, a task signal from the entity operating within the model serving data environment, wherein the task signal initiates a model inference process performed by the model inference AI agent ([0026] In some embodiments, the second agent may generate a large language model prompt for configuring a task corresponding to a text of a further comprising receiving, at a model inference AI agent operating within the model serving data environment, a task signal from the entity operating within the model serving data environment request target step included in the first task processing request signal in a step-by-step manner, wherein the task signal initiates a model inference process performed by the model inference AI agent receive a task definition result of a step-by-step manner from a large language model that has received the large language model prompt, and transmit the received task definition result to the first agent in response to the first task processing request signal. In addition, the second agent may generate the large language model prompt specifying an output format to include confidence level information of the task definition result and allows the confidence level information to be further included in the task definition result transmitted to the first agent, when a value of an immediate execution flag included in the first task processing request signal is a value indicating that the second agent should not immediately generate a first external processing result according to the first task processing request signal. In addition, the first agent may finally determine whether to request the second agent to execute the request target step by using the confidence level information included in the task definition result received from the second agent.). Sanchez, Johnson, Levitan, Wang, and Yang are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, Johnson, Levitan, and Wang, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Yang to Sanchez before the effective filing date of the claimed invention in order to generate a processing result for a user request through collaboration with another agent when one of multiple agents receives the user request requiring a task processing capability beyond his or her processing capability range (cf. Yang, [0013] An object of the present disclosure is to provide a system and method for generating a processing result for a user request through collaboration with another agent when one of multiple agents receives the user request requiring a task processing capability beyond his or her processing capability range.; [0014] Another object of the present disclosure is to provide a system and method in which each agent self-determines a multi-agent collaboration-based processing method for a user request requiring a task processing capability beyond a processing capability range even without prior determination of a method of collaboration or a process of collaboration.). Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Sanchez, in view of Johnson, Levitan, Wang, Yang, and further in view of Amthor et al. (U.S. Pre-Grant Publication No. 20220067444, hereinafter 'Amthor'). Regarding claim 14, Sanchez, as modified by Johnson, Levitan, Wang, and Yang, teaches The method of claim 13. Sanchez, as modified by Johnson, Levitan, Wang, and Yang, fails to teach further comprising loading, by the model inference AI agent, the model parameters of the trained machine learning model from the storage device within the model serving environment to perform the model inference process. Amthor teaches further comprising loading, by the model inference AI agent, the model parameters of the trained machine learning model from the storage device within the model serving environment to perform the model inference process ([0037] The verification machine learning model can also comprise a neural network trained by a generally known reinforcement learning method. In this case, a so-called reinforcement learning agent, i.e. a decision function, learns in a defined training environment/simulation environment, for an input comprising a training learning progression, a validation learning progression and associated model parameter values, how to modify the model parameter values in order to optimize a quality assessment. In the learning phase, the agent further comprising loading, by the model inference AI agent, the model parameters of the trained machine learning model from the storage device within the model serving environment to perform the model inference process tries out different parameters and receives the training/validation learning progressions as well as an assessment of the finally determined model parameters as input. The relationship between training/validation learning progressions and improved model parameter values can be learned in this manner.). Sanchez, Johnson, Levitan, Wang, Yang, and Amthor are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, Johnson, Levitan, Wang, and Yang, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Amthor to Sanchez before the effective filing date of the claimed invention in order to assess the training/validation learning progressions before the completion of a training process, accelerating the entire learning process (cf. Amthor, [0020] The invention thus does not use the quality measure at the completion of the training alone for the assessment of the learning process, but rather the progressions, in particular qualitative or quantitative changes in quality over the epochs of the training. The verification model is thereby able to make more precise statements regarding the learning process than a simple statement regarding precision based on test data. In particular, it is possible to assess the training/validation learning progressions before the completion of a training process, whereby a training process that has been assessed as poor does not have to be completed but rather can be modified while still in progress or re-initiated with modified settings. The invention thus enables an acceleration of the entire learning process.). Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez, in view of Johnson, Levitan, and further in view of Eberlein et al. (U.S. Pre-Grant Publication No. 20210390090, hereinafter 'Eberlein'). Regarding claim 16, Sanchez, as modified by Johnson and Levitan, teaches The method of claim 1. Sanchez, as modified by Johnson and Levitan, fails to teach wherein the first dataset and the second dataset include one or more data elements associated with a shared individual, wherein a data element of the one or more data elements comprises a linking key that links a data element of the first dataset with a data element of the second dataset. Eberlein teaches wherein the first dataset and the second dataset include one or more data elements associated with a shared individual, wherein a data element of the one or more data elements comprises a linking key that links a data element of the first dataset with a data element of the second dataset ([0020] A creation process can be automatically performed by a framework, for creating GDO and GDR instances from enterprise data objects (DO) that are stored, for example, in relational database(s). For instance, wherein the first dataset and the second dataset include one or more data elements associated with a shared individual graph vertices can be derived from objects and entities in the relational database(s) and created as GDO instances in the data graph. Relationships can be determined from object associations and relational database foreign key relationships and can be stored as GDR instances in the data graph. Changes to DO instances can be automatically replicated by the framework to corresponding GDOs to keep DOs and GDOs in sync. Additionally, GDO and GDR methods can be used for enforcement of consistency constraints for individual GDO types.; [0027] A note 110 indicates that data objects may provide other methods, such as methods to return or traverse associations to other data objects. For instance, the first data object DO1 102 can provide a method to return a reference to the second data object DO2 104, based on an association 112 between the two objects. In some implementations, the second data object DO2 104 can also provide a method, for returning a reference to the associated first data object DO1 102. The association 112 can be manifested as a wherein a data element of the one or more data elements comprises a linking key that links a data element of the first dataset with a data element of the second dataset foreign key relation in a relational database. Data object associations can be used to create a relation in a data graph between graph objects that correspond to data objects.). Sanchez, Johnson, Levitan, and Eberlein are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Sanchez, Johnson, and Levitan, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Eberlein to Sanchez before the effective filing date of the claimed invention in order to automatically provide the benefits of a EKG (which can be referred to as a data graph) to enterprise applications (cf. Eberlein, [0019] Alternatively, a systematic approach described herein can be used to automatically provide the benefits of a EKG (which can be referred to as a data graph) to enterprise applications. A data graph can include two types of modeled objects: a Graph Data Object (GDO) and a Graph Data Relation (GDR). GDO and GDR instances can be persisted using a graph database.). Regarding claim 17, Sanchez, as modified by Johnson, Levitan, and Eberlein, teaches The method of claim 16. Eberlein teaches further comprising generating a linking database comprising the first dataset, the second dataset, and corresponding linking keys, wherein a linking key of the corresponding linking keys is associated with a particular individual ([0020] A creation process can be automatically performed by a framework, for creating GDO and GDR instances from enterprise data objects (DO) that are stored, for example, in relational database(s). For instance, graph vertices can be derived from objects and entities in the relational database(s) and created as GDO instances in the data graph. Relationships can be determined from object associations and relational database foreign key relationships and can be stored as GDR instances in the data graph. Changes to DO instances can be automatically replicated by the framework to corresponding GDOs to keep DOs and GDOs in sync. Additionally, GDO and GDR methods can be used for enforcement of consistency constraints for individual GDO types.; [0027] A note 110 indicates that comprising the first dataset, the second dataset data objects may provide other methods, such as methods to return or traverse associations to other data objects. For instance, the first data object DO1 102 can provide a method to return a reference to the second data object DO2 104, based on an association 112 between the two objects. In some implementations, the second data object DO2 104 can also provide a method, for returning a reference to the associated first data object DO1 102. The association 112 can be manifested as a corresponding linking keys foreign key relation further comprising generating a linking database in a relational database. Data object associations can be used to create a wherein a linking key of the corresponding linking keys is associated with a particular individual relation in a data graph between graph objects that correspond to data objects.). Sanchez, Johnson, Levitan, and Eberlein are combinable for the same rationale as set forth above with respect to claim 16. Conclusion Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAGGIE MAIDO whose telephone number is (703) 756-1953. The examiner can normally be reached M-Th: 6am - 4pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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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. /MM/Examiner, Art Unit 2129 /MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129