DETAILED ACTION
Status of Claims
This communication is the final action on the merits in response to the amendments and arguments filed on May 7, 2026. Claims 1-2, 4, 7-10, 12, 15-18, 20, 23-24, and 31-36 were amended. Claim 22 was canceled. Claims 1-2, 4-5, 7-10, 12-13, 15-18, 20-21, 23-24, and 31-36 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 32 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 32 recites the limitation "the marketplace platform." There is insufficient antecedent basis for this limitation in the claim.
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 1-2, 4-5, 7-10, 12-13, 15-18, 20-21, 23-24, and 31-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2, 4-5, 7-8, 17-18, 20-21, 23-24, and 31-36 are directed to a machine. Claims 9-10, 12-13, and 15-16 are directed to a process. As such, each claim is directed to a statutory category of invention.
Step 2A Prong 1
The examiner has identified independent Claim 17 as the claim that represents the claimed invention for analysis and is similar to independent Claims 1 and 9.
Independent Claim 17 recites the following abstract ideas: “dynamically integrating proprietary datasets from data providers into a model optimization process: receive proprietary datasets comprising machine learning and artificial intelligence assets from data providers, wherein the assets comprise models, datasets, embeddings, Retrieval Augmented Generations (RAG) models, knowledge corpora, simulations, expert judgment models, surveys, and wherein each asset is associated with one or more defined characteristics selected from: quality, relevance, suitability, credibility, reliability, efficiency, scalability, interoperability, and delivery status; analyze a user query to determine a context and domain for the user query; define a decomposable workflow comprising a hierarchy of reasoning tasks based on the determined context and domain for the user query; for each reasoning task, select one or more of the machine learning artificial intelligence assets based on one or more of the defined characteristics of said asset for use in said reasoning task; assess the interoperability and combinability of the selected machine learning and artificial intelligence assets for use in the decomposable workflow, wherein the interoperability and combinability is assessed using neuro-symbolic representations and rules specific to the determined context and domain that are applied to each reasoning task; executing the decomposable workflow ; suggest improvements based on performance results of execution of the decomposable workflow; and track provenance of each selected machine learning and artificial intelligence asset and of decisions made by said assets to enable policy enforcement mechanisms to support transparency, accountability, and compliance of use of the proprietary datasets in handling the user query.”
The limitations, as drafted, are a process that, under its broadest reasonable interpretation, relates to commercial interactions including marketing or sales activities or behaviors / business relations (i.e., dynamically integrating proprietary datasets from data providers into a model optimization process: receive proprietary datasets comprising machine learning and artificial intelligence assets from data providers, wherein the assets comprise models, datasets, embeddings, Retrieval Augmented Generations (RAG) models, knowledge corpora, simulations, expert judgment models, surveys, and wherein each asset is associated with one or more defined characteristics selected from: quality, relevance, suitability, credibility, reliability, efficiency, scalability, interoperability, and delivery status; analyze a user query to determine a context and domain for the user query; define a decomposable workflow comprising a hierarchy of reasoning tasks based on the determined context and domain for the user query; for each reasoning task, select one or more of the machine learning artificial intelligence assets based on one or more of the defined characteristics of said asset for use in said reasoning task; assess the interoperability and combinability of the selected machine learning and artificial intelligence assets for use in the decomposable workflow, wherein the interoperability and combinability is assessed using neuro-symbolic representations and rules specific to the determined context and domain that are applied to each reasoning task; executing the decomposable workflow; suggest improvements based on performance results of execution of the decomposable workflow; and track provenance of each selected machine learning and artificial intelligence asset and of decisions made by said assets to enable policy enforcement mechanisms to support transparency, accountability, and compliance of use of the proprietary datasets in handling the user query), but for the recitation of generic computer components (i.e., a system comprising one or more computers with executable instructions, a distributed computational graph, and combining the selected machine learning and artificial intelligence assets into an integrated asset for execution orchestrated by the distributed computational graph). If a claim limitation, under its broadest reasonable interpretation, relates to commercial interactions including marketing or sales activities or behaviors / business relations, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). In particular, the claim recites the additional elements of a system comprising one or more computers with executable instructions, a distributed computational graph, and combining the selected machine learning and artificial intelligence assets into an integrated asset for execution orchestrated by the distributed computational graph (in addition to the computing system and one or more hardware processors of Claim 1). The computer hardware is recited at a high level of generality (i.e., generic computers receiving, processing, and determining information, and generic recitation of combining, using, and tracking ML and AI models) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application, since they do not involve improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)), they do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and they do not apply or use the abstract idea in some other meaningful way beyond generally linking its use to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim is directed to an abstract idea without a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of using computer hardware (a system comprising one or more computers with executable instructions, a distributed computational graph, and combining the selected machine learning and artificial intelligence assets into an integrated asset for execution orchestrated by the distributed computational graph (in addition to the computing system and one or more hardware processors of Claim 1)) amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent-eligible.
Dependent claims 7, 15, and 23 recite selecting, creating, and incorporating “trained models,” which is described in paragraph [0068] of the specification. Dependent claims 8, 16, and 24 recite optimizing hyperparameters of and updating the “machine learning and artificial intelligence assets,” which is described in paragraphs [0232] – [0235] of the specification. Dependent claim 31 recites implementing “federated learning across a plurality of edge devices,” and Dependent claim 33 recites a “distributed computational graph” and “distributed computing resources.” The additional elements are generic technology / models used to implement the abstract idea, and they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Dependent claims 2, 4-5, 10, 12-13, 18, 20-21, 32, and 34-36 do not include any additional elements beyond those identified above. They further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. As such, they do not integrate the abstract idea into a practical application, nor are they sufficient to amount to significantly more than the abstract idea when considered both individually and as an ordered combination.
Therefore, dependent claims 2, 4-5, 7-8, 10, 12-13, 15-16, 18, 20-21, 23-24, and 31-36 are directed to an abstract idea, and do not include additional elements that integrate the abstract idea into a practical application, or that are sufficient to amount to significantly more than the abstract idea. Thus, the aforementioned claims are not patent-eligible.
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.
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 1-2, 5, 7, 9-10, 13, 15, 17-18, 21, 23, and 32-36 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksel et al. (US-20220318619) in view of Crabtree et al. (US-20240179185) and Chandra et al. (US-20240281742).
Claim 1 (and Similarly Claims 9, and 17)
Yuksel teaches the following limitations:
A computing system for dynamically integrating proprietary datasets from data providers into a model optimization process, the computing system comprising: one or more hardware processors configured for: receiving proprietary datasets comprising machine learning and artificial intelligence assets from data providers, wherein the assets comprise models, datasets, embeddings, Retrieval Augmented Generations (RAG) models, knowledge corpora, simulations, expert judgment models, surveys, and wherein each asset is associated with one or more defined characteristics selected from: quality, relevance, suitability, credibility, reliability, efficiency, scalability, interoperability, and delivery status ([0053] a machine learning model generation platform, of which the Mentalist 302 may be a component. In a variety of embodiments, Mentalist 302 data may rely on thousands of model and AI/ML publications from internet sources, commercial AI/ML, and data suppliers in the platform's community… It uses efficient, plug-and-play functional building blocks to synthesize solution architectures. It also generates a list of data sources required to train ML models when the customer's own data is not sufficient for the solution requested. Using measurements and knowledge from previously designed architectures, it provides the specified solution, the recommended vendors, time estimates and indications of off-the-shelf vs the need of custom built technology using available AI/ML resources; [0065] Matchmaker 404 may determine a source for each of the models. Sources may include both data stores within the platform and data stores external to the platform, and may correspond to any number of suitable vendors. In one embodiment, matchmaker 404 may also determine when a new model needs to be created to satisfy a particular requirement of the architecture 404, such as when a suitable existing model does not exist or is not accessible. In such a case, Matchmaker 402 may send a request for the generating of the new model to users of the platform or third-party developers; [0096] FIG. 7 illustrates a method to use a secure environment in a machine learning model generation platform in accordance with some embodiments; [0099] after a model has been validated, Data Owner 700 may make the model available on a platform for consumption by other users… Specialist Dashboard 756 may also be used to view anonymous datasets that have been made available on Data & RFP Marketplace 758. Data & RFP Marketplace, in some embodiments, includes a list of available datasets (anonymous or otherwise), that a user of an online marketplace (i.e. AI Specialist 716) may use to view and purchase/license datasets);
analyzing a user query to determine a context and domain for the user query ([0049] FIG. 3 is a block diagram 300 illustrating a first component of a machine learning model generation platform… the operations and components described with respect to FIG. 3 (e.g., “the Mentalist” 302) may be the first AI-powered virtual architect that empowers product owners to architect AI solutions without the need to have a deep understanding of artificial intelligence or machine learning; [0053] Mentalist 302 can be also deployed in third party applications, such as Slack, to engage product team members in a guided conversation that assists them to describe their project needs, objectives and key results systematically; [0054] Referring to FIG. 3, Mentalist 302 may receive a description 304 of a desired operator (e.g., a desired result, solution, etc.) as an input);
defining a decomposable workflow comprising a hierarchy of reasoning tasks based on the determined context and domain for the user query ([0053] It uses efficient, plug-and-play functional building blocks to synthesize solution architectures; [0054] Referring to FIG. 3, Mentalist 302 may receive a description 304 of a desired operator (e.g., a desired result, solution, etc.) as an input and generate a logical architecture (e.g., framework) 306 as an output; [0055] the mentalist 304 may determine a logical architecture that describes a technical solution 306 to the desired operator. For example, the mentalist 302 may determine one or more categories of machine learning models that may be combined to generate the desired operator. In the example illustrated in FIG. 3, the Mentalist 302 may analyze the natural language input 304, translate the natural language input 304 into a machine readable format, and determine that the desired operator is the dubbing of videos from English to Italian; [0056] After determining the desired operator, the mentalist 302 may determine (e.g., using ML itself) that the operators of source separation, speaker diarization, speech recognition, machine text translation, speech synthesis, and channel merging ML models may be combined to generate the desired overall operator, which may be provided as an architecture output 306),
for each reasoning task, selecting one or more of the machine learning and artificial intelligence assets based on one or more of the defined characteristics of said asset for use in said reasoning task ([0053] It uses efficient, plug-and-play functional building blocks to synthesize solution architectures. It also generates a list of data sources required to train ML models when the customer's own data is not sufficient for the solution requested. Using measurements and knowledge from previously designed architectures, it provides the specified solution, the recommended vendors, time estimates and indications of off-the-shelf vs the need of custom built technology using available AI/ML resources; [0055] the mentalist 302 may determine one or more categories of machine learning models that may be combined to generate the desired operator. In the example illustrated in FIG. 3, the Mentalist 302 may analyze the natural language input 304, translate the natural language input 304 into a machine readable format, and determine that the desired operator is the dubbing of videos from English to Italian; [0056] After determining the desired operator, the mentalist 302 may determine (e.g., using ML itself) that the operators of source separation, speaker diarization, speech recognition, machine text translation, speech synthesis, and channel merging ML models may be combined to generate the desired overall operator, which may be provided as an architecture output 306; [0057] FIG. 4 is a block diagram 400 illustrating a second component of a machine learning model generation platform… the operations and components described with respect to FIG. 4 (e.g., “the Matchmaker” 402) may be a virtual AI implementer; [0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements. It includes an easy to understand description for each AI asset need with examples and a fair and single-number benchmark with explanation; [0064] Referring to FIG. 4, Matchmaker receives an architecture 404 representing an AI-based solution to a desired operator, such as architecture 306 of FIG. 3. Based on the architecture 404, Matchmaker 402 generates one or more recommended AI-based solutions to the desired operator, using one or more machine learning models and other components to satisfy requirements of the architecture 404; [0065] In one embodiment, Matchmaker 404 may determine a source for each of the models. Sources may include both data stores within the platform and data stores external to the platform, and may correspond to any number of suitable vendors. In one embodiment, matchmaker 404 may also determine when a new model needs to be created to satisfy a particular requirement of the architecture 404, such as when a suitable existing model does not exist or is not accessible. In such a case, Matchmaker 402 may send a request for the generating of the new model to users of the platform or third-party developers; [0066] In one embodiment, Matchmaker 402 may provide estimates of benchmark data, costs, and time to build. Such data may be generated based on past knowledge of relative values for each model provided in the solution, or estimated using any number of statistical methods);
assessing the interoperability and combinability of the selected machine learning and artificial intelligence assets for use in the decomposable workflow, wherein the interoperability and combinability is assessed using… rules specific to the determined context and domain that are applied to each reasoning task ([0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements. It includes an easy to understand description for each AI asset need with examples and a fair and single-number benchmark with explanation; [0060] Matchmaker 402 provides a variety of benefits over existing technologies, including: [0061] 1. Generating the best implementation that fits the budget, quality and product success metric; [0064] Referring to FIG. 4, Matchmaker receives an architecture 404 representing an AI-based solution to a desired operator, such as architecture 306 of FIG. 3. Based on the architecture 404, Matchmaker 402 generates one or more recommended AI-based solutions to the desired operator, using one or more machine learning models and other components to satisfy requirements of the architecture 404; [0065] matchmaker 404 may also determine when a new model needs to be created to satisfy a particular requirement of the architecture 404, such as when a suitable existing model does not exist or is not accessible; [0066] In one embodiment, Matchmaker 402 may provide estimates of benchmark data, costs, and time to build. Such data may be generated based on past knowledge of relative values for each model provided in the solution, or estimated using any number of statistical methods; [0070] In one embodiment, to generate the AI-based solution, processing logic may perform a variety of operations. For example, processing logic may identify a first machine learning model in a first database within the marketplace platform, wherein the first machine learning model is a first portion of the AI-based solution and identify a second machine learning model in a second database external to the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution. Processing logic may further generate the AI-based solution by combining the first machine learning model and the second machine learning model; [0072] In one embodiment, processing logic may optionally generate a benchmark for the AI-based solution (e.g., by combining known benchmarks for models included in the solution or generating benchmark estimates based on similar models) and display the benchmark with the option to access the AI-based solution in the marketplace platform);
combining the selected machine learning and artificial intelligence assets into an integrated asset for executing the decomposable workflow ([0064] Based on the architecture 404, Matchmaker 402 generates one or more recommended AI-based solutions to the desired operator, using one or more machine learning models and other components to satisfy requirements of the architecture 404; [0070] In one embodiment, to generate the AI-based solution, processing logic may perform a variety of operations. For example, processing logic may identify a first machine learning model in a first database within the marketplace platform, wherein the first machine learning model is a first portion of the AI-based solution and identify a second machine learning model in a second database external to the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution. Processing logic may further generate the AI-based solution by combining the first machine learning model and the second machine learning model)
tracking provenance of each selected machine learning and artificial intelligence asset and of decisions made by said assets for enabling policy enforcement mechanisms to support transparency, accountability, and compliance of use of the proprietary datasets in handling the user query ([0059] Matchmaker may also handle licensing aggregation and contract setup; [0095] in order to generate models, a dataset must be used. However, the entity generating a model may not have rights to the dataset that is needed to generate the model. Further, there may be use restrictions on the dataset. For example, the dataset or a subset thereof may include sensitive information and be subject to compliance laws, such as privacy regulations regarding children's personal information. Traditionally, if the dataset owner licensed or otherwise gave rights to the entity to access the dataset, the dataset owner would have a limited ability to monitor or track the entity's usage. In some embodiments of the present invention, the entity may still access and use the dataset in a secure environment, such that the dataset owner is satisfied with its ability to verify the entity's usage and access privileges to the dataset).
However, Yuksel does not explicitly teach the following limitations:
wherein the decomposable workflow is orchestrated by a distributed computational graph;
assessed using neuro-symbolic representations
the decomposable workflow orchestrated by the distributed computational graph;
suggesting improvements to the selected machine learning and artificial intelligence assets based on performance results of execution of the decomposable workflow; and
Crabtree, in the same field of endeavor, teaches the following limitations:
wherein the decomposable workflow is orchestrated by a distributed computational graph ([0049] instantiating a distributed directed computational graph comprising nodes representing data transformations and edges representing messages between the nodes, wherein: the directed computational graph comprises a data processing workflow for analyzing a large-scale operational plan and associated risk factors based on market, operations and financial data);
the decomposable workflow orchestrated by the distributed computational graph ([0049] instantiating a distributed directed computational graph comprising nodes representing data transformations and edges representing messages between the nodes, wherein: the directed computational graph comprises a data processing workflow for analyzing a large-scale operational plan and associated risk factors based on market, operations and financial data);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the business solutions platform of Yuksel with the limitations taught by Crabtree. One of ordinary skill in the art would have been motivated to make this modification for the benefit of the ability to accomplish processor-intensive tasks involving large amounts of data by distribution of computer resources to a plurality of systems (Crabtree – [0075]).
However, Yuksel, in combination with Crabtree, does not explicitly teach the following limitations:
assessed using neuro-symbolic representations
suggesting improvements to the selected machine learning and artificial intelligence assets based on performance results of execution of the decomposable workflow; and
Chandra, in the same field of endeavor, teaches the following limitations:
assessed using neuro-symbolic representations ([0072] The machine learning engine 412 may, in some approaches, perform predetermined observing and/or assessing operations defined within an AI reasoning model. In some preferred approaches, the AI reasoning model is a neuro-symbolic AI model)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the business solutions platform of Yuksel, in combination with Crabtree, with the limitations taught by Chandra. One of ordinary skill in the art would have been motivated to make this modification for the benefit of improving performance of computer devices as neuro-symbolic AI may not need a subject matter expert and/or iteratively applied training with reward feedback in order to accurately make assessments (Chandra – [0072]).
Chandra further teaches the following limitations:
suggesting improvements to the selected machine learning and artificial intelligence assets based on performance results of execution of the decomposable workflow ([0057] data corresponding to acceptance of the requirements by a human and corresponding weightage scores is fed into the machine learning engine to improve the decision making of the machine learning engine. For example, some or all of the requirements, their scores, and/or the underlying information relating thereto may be fed into the machine learning engine with indications of the human's acceptance, rejection, and/or adjustment of a requirement and/or its score); and
This known technique is applicable to the system of Yuksel, in combination with Crabtree, as they both share characteristics and capabilities, namely, they are directed to using machine learning to implement business improvements and solutions. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Chandra would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chandra to the teachings of Yuksel, in combination with Crabtree, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., suggesting improvements to AI / ML based on performance results) into similar systems.
Claim 2 (and Similarly Claims 10, and 18)
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for: collecting evaluations and ratings of the proprietary datasets from human experts or artificial intelligence expert models while browsing external data sources ([0053] Mentalist 302 data may rely on thousands of model and AI/ML publications from internet sources, commercial AI/ML, and data suppliers in the platform's community);
quantifying the one or more defined characteristics based on the collected evaluations and ratings ([0050] Mentalist 302 may provide an AI solution architecture through understanding the product needs, the relevant product success metrics, and any data resource constraints; [0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements. It includes an easy to understand description for each AI asset need with examples and a fair and single-number benchmark with explanation; [0060] Matchmaker 402 provides a variety of benefits over existing technologies, including: [0061] 1. Generating the best implementation that fits the budget, quality and product success metrics); and
assessing the credibility and reliability of experts providing evaluations based on their historical evaluations and community feedback ([0053] Using measurements and knowledge from previously designed architectures, it provides the specified solution; [0066] Matchmaker 402 may provide estimates of benchmark data, costs, and time to build. Such data may be generated based on past knowledge of relative values for each model provided in the solution, or estimated using any number of statistical methods).
Claim 5 (and Similarly Claims 13, and 21)
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for proactively suggesting relevant goods, experts, or collaborators based on user preferences, transaction history, and platform interactions ([0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements; [0095] In some embodiments, in order to generate models, a dataset must be used. However, the entity generating a model may not have rights to the dataset that is needed to generate the model. Further, there may be use restrictions on the dataset. For example, the dataset or a subset thereof may include sensitive information and be subject to compliance laws, such as privacy regulations regarding children's personal information. Traditionally, if the dataset owner licensed or otherwise gave rights to the entity to access the dataset, the dataset owner would have a limited ability to monitor or track the entity's usage. In some embodiments of the present invention, the entity may still access and use the dataset in a secure environment, such that the dataset owner is satisfied with its ability to verify the entity's usage and access privileges to the dataset).
Claim 7 (and Similarly Claims 15 and 23)
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for selecting, creating, and incorporating trained models based on the expert judgment models ([0066] Matchmaker 402 may provide estimates of benchmark data, costs, and time to build. Such data may be generated based on past knowledge of relative values for each model provided in the solution, or estimated using any number of statistical methods. Matchmaker 402 may provide any relevant information for display and selection (e.g., purchase) on the platform).
Claim 32
Yuksel further teaches the following limitations:
wherein the expert judgment models comprise both human expert responses and artificial intelligence system responses from entities that are registered as experts on the marketplace platform ([0049] the operations and components described with respect to FIG. 3 (e.g., “the Mentalist” 302) may be the first AI-powered virtual architect that empowers product owners to architect AI solutions; [0053] Mentalist 302 data may rely on thousands of model and AI/ML publications from internet sources, commercial AI/ML, and data suppliers in the platform's community… Using measurements and knowledge from previously designed architectures, it provides the specified solution, the recommended vendors, time estimates and indications of off-the-shelf vs the need of custom built technology using available AI/ML resources).
Claim 33
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for incorporating the selected machine learning and artificial intelligence assets into the decomposable workflow… that dynamically orchestrates the selection and execution of the machine learning and artificial intelligence assets ([0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements. It includes an easy to understand description for each AI asset need with examples and a fair and single-number benchmark with explanation; [0064] Based on the architecture 404, Matchmaker 402 generates one or more recommended AI-based solutions to the desired operator, using one or more machine learning models and other components to satisfy requirements of the architecture 404; [0065] matchmaker 404 may also determine when a new model needs to be created to satisfy a particular requirement of the architecture 404, such as when a suitable existing model does not exist or is not accessible; [0066] In one embodiment, Matchmaker 402 may provide estimates of benchmark data, costs, and time to build. Such data may be generated based on past knowledge of relative values for each model provided in the solution, or estimated using any number of statistical methods)
Crabtree further teaches the following limitations:
incorporating… assets into the decomposable workflow using a distributed computational graph that dynamically orchestrates… execution… across distributed computing resources ([0049] instantiating a distributed directed computational graph comprising nodes representing data transformations and edges representing messages between the nodes, wherein: the directed computational graph comprises a data processing workflow for analyzing a large-scale operational plan and associated risk factors based on market, operations and financial data; [0075] In cases where there are both large amounts of data to be cleansed and formalized and then intricate transformations such as those that may be associated with deep machine learning, predictive analytics and predictive simulations, distribution of computer resources to a plurality of systems may be routinely required to accomplish these tasks due to the volume of data being handled and acted upon. The business operating system employs a distributed architecture that is highly extensible to meet these needs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the business solutions platform of Yuksel with the limitations taught by Crabtree. One of ordinary skill in the art would have been motivated to make this modification for the benefit of the ability to accomplish processor-intensive tasks involving large amounts of data by distribution of computer resources to a plurality of systems (Crabtree – [0075]).
Claim 34
Yuksel further teaches the following limitations:
wherein the expert judgment models are trained to specialize in specific contexts or domains within a larger field ([0020] a variety of machine learning models that each perform an individual task may be combined together as subcomponents in a larger system).
Claim 35
Chandra further teaches the following limitations:
wherein the one or more hardware processors are further configured for incorporating user feedback and preferences into the model optimization process for continuous improvement and alignment with user expectations ([0057] data corresponding to acceptance of the requirements by a human and corresponding weightage scores is fed into the machine learning engine to improve the decision making of the machine learning engine. For example, some or all of the requirements, their scores, and/or the underlying information relating thereto may be fed into the machine learning engine with indications of the human's acceptance, rejection, and/or adjustment of a requirement and/or its score); and
This known technique is applicable to the system of Yuksel, in combination with Crabtree, as they both share characteristics and capabilities, namely, they are directed to using machine learning to implement business improvements and solutions. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Chandra would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chandra to the teachings of Yuksel, in combination with Crabtree, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., incorporating user feedback into a model) into similar systems.
Claim 36
Yuksel further teaches the following limitations:
wherein the machine learning and artificial intelligence assets include… packages combining machine learning models ([0070] In one embodiment, to generate the AI-based solution, processing logic may perform a variety of operations. For example, processing logic may identify a first machine learning model in a first database within the marketplace platform, wherein the first machine learning model is a first portion of the AI-based solution and identify a second machine learning model in a second database external to the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution. Processing logic may further generate the AI-based solution by combining the first machine learning model and the second machine learning model)
Chandra further teaches the following limitations:
neurosymbolic packages combining machine learning models with symbolic reasoning rules and workflows ([0072] The machine learning engine 412 may, in some approaches, perform predetermined observing and/or assessing operations defined within an AI reasoning model. In some preferred approaches, the AI reasoning model is a neuro-symbolic AI model).
Chandra shows that neurosymbolic models were known in the prior art before the effective filing date of the claimed invention. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function, but in the very combination itself; that is, in the substitution of the neurosymbolic models of Chandra for the general machine learning models of Yuksel. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksel et al. (US-20220318619) in view of Crabtree et al. (US-20240179185) and Chandra et al. (US-20240281742), and further in view of Luk et al. (US-20240119486).
Claim 4 (and Similarly Claims 12, and 20)
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for: sharing and co-developing machine learning projects ([0053] Mentalist 302 data may rely on thousands of model and AI/ML publications from internet sources, commercial AI/ML, and data suppliers in the platform's community. Mentalist 302 can be also deployed in third party applications, such as Slack, to engage product team members in a guided conversation that assists them to describe their project needs, objectives and key results systematically. It uses efficient, plug-and-play functional building blocks to synthesize solution architectures. It also generates a list of data sources required to train ML models when the customer's own data is not sufficient for the solution requested. Using measurements and knowledge from previously designed architectures, it provides the specified solution, the recommended vendors, time estimates and indications of off-the-shelf vs the need of custom built technology using available AI/ML resources); and
the proprietary datasets ([0096] FIG. 7 illustrates a method to use a secure environment in a machine learning model generation platform in accordance with some embodiments; [0099] after a model has been validated, Data Owner 700 may make the model available on a platform for consumption by other users… Specialist Dashboard 756 may also be used to view anonymous datasets that have been made available on Data & RFP Marketplace 758. Data & RFP Marketplace, in some embodiments, includes a list of available datasets (anonymous or otherwise), that a user of an online marketplace (i.e. AI Specialist 716) may use to view and purchase/license datasets).
However, Yuksel, in combination with Crabtree and Chandra, does not explicitly teach the following limitations:
documenting best practices, tutorials, and case studies related to [products].
Luk, in the same field of endeavor, teaches the following limitations:
documenting best practices, tutorials, and case studies related to [products] ([0018] Companies and individuals produce short-form videos using multiple formats and approaches, demonstrating the use of products and related services, comparing their products to competitors, etc. In some cases, endorsements by celebrities and known experts are included, as are instructional videos detailing the uses of products in particular applications… consumers can comment on their experiences with products and companies and share them with others; [0019] Techniques for dynamic population of contextually relevant videos in an ecommerce environment are disclosed. First, a repository of short-form videos is assembled. The short-form video collection may be composed of professionally produced videos on behalf of a vendor or group of vendors, short-form video demonstrations or commentaries by users of products, celebrity endorsements, or a combination of these and other related videos. As the collection is brought together, metadata related to the short-form videos is captured. The metadata may include hashtags, user history, ranking, view history, and so on. As the videos are added to the repository, associations are made with one or more products or services for sale. The associations indicate what products or services are highlighted by the short-form videos. The associations can be generated using short-form video metadata or machine learning techniques. In some cases, multiple products or related services may be highlighted by a single video, leading to multiple associations within the repository. Machine learning can also aid in analyzing and categorizing the videos, using additional user data or conversion rate information).
This known technique is applicable to the system of Yuksel, in combination with Crabtree and Chandra, as they both share characteristics and capabilities, namely, they are directed to an online marketplace. One of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that applying the known technique of Luk would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Luk to the teachings of Yuksel, in combination with Crabtree and Chandra, would have yielded predictable results because the level of one of ordinary skill in the art would have known to incorporate such features (i.e., documenting best practices and tutorials for products in an online marketplace) into similar systems.
Claims 8, 16, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Yuksel et al. (US-20220318619) in view of Crabtree et al. (US-20240179185) and Chandra et al. (US-20240281742), and further in view of Hightower (US-20240403984; supported by US Provisional Application 63/505,524 filed 6/1/2023).
Claim 8 (and Similarly Claims 16 and 24)
Yuksel further teaches the following limitations:
wherein the one or more hardware processors are further configured for: continuously adjusting and optimizing hyperparameters of the machine learning and artificial intelligence assets based on performance metrics and user feedback ([0022] The variety of embodiments described herein provide the infrastructure for building and deploying portable and scalable end-to-end artificial intelligence (AI) solution workflows. The embodiments allow for end-to-end orchestration, enabling & simplifying the orchestration of full AI workflows (e.g., pipelines) during both training and inference (deployment). The embodiments further allow for easy experimentation—making it easy to try numerous ideas and techniques and manage various trials/experiments for hyper-parameter tuning and benchmarking. The embodiments also allow for easy re-use—enabling the re-use of AI components and pipelines to quickly cobble together end-to-end solutions, without rebuilding each time; [0098] Additional tuning of the model, in Model Hypertuning 742, may be done after the model has been validated);
dynamically updating and fine-tuning the machine learning and artificial intelligence assets based on newly available data and evolving user requirements ([0047] a ML inference component may use one or more trained machine learning models to make a recommendation and/or prediction. Such machine learning models may be provided directly to the ML inference component or be received as output from a machine learning training component; [0048] ML training component may optionally receive the output of the ML inference component and, along with other information (e.g., data, references, metrics, etc.) generate new ML models or fine-tune existing ML models; [0059] Matchmaker 402 presents a recommended, well catalogued, bill of material that implements the blueprint (e.g., architecture 306) of the solution and matches the budget and metrics to any specified requirements. It includes an easy to understand description for each AI asset need with examples and a fair and single-number benchmark with explanation. Whenever needed, Matchmaker 402 may auto-procure non-existing assets (inference nodes, models, datasets) and connects the product owner with two or more recommended suppliers. In one embodiment, each asset may have least one swappable replacement option, if desired);
However, Yuksel, in combination with Crabtree and Chandra, does not explicitly teach the following limitations:
optimizing retrieval and generation components of the RAG models, including fine-tuning retrieval algorithms, updating knowledge bases, and enhancing generation quality; and
incorporating user feedback and preferences into the model optimization process, ensuring continuous improvement and alignment with user expectations.
Hightower, in the same field of endeavor, teaches the following limitations:
optimizing retrieval and generation components of the RAG models, including fine-tuning retrieval algorithms, updating knowledge bases, and enhancing generation quality ([0025] the resulting answer from block 206 may be parsed for possible hallucinations. In certain implementations, the data that exists in the proprietary system may be used to validate the answer and/or to determine if the resulting answer includes any hallucinations. One approach that may be used to reduce hallucinations is the Retrieval-Augmented Generation (RAG) model in conjunction with vector databases. This approach may enable efficient leveraging of large language models (LLM) with proprietary data. In accordance with certain exemplary implementations of the disclosed technology, a trusted knowledge source (i.e., data from the proprietary system) may be searched for relevant data. The model may use those results to generate a user-friendly response and consolidate the pertinent details into a single concise answer. In certain implementations, vector databases may be used to improve the performance of the RAG model. In certain implementations, vector databases may store text as embeddings, or numerical vectors that capture its meaning. Questions may also be converted into a numerical vector. Relevant documents or passages can then be found in the vector database, even when they don't share the same words); and
incorporating user feedback and preferences into the model optimization process, ensuring continuous improvement and alignment with user expectations ([0035] Certain implementation of the disclosed technology may provide improvements in flexibility. For example, the code generation step can be in the SQL language to run in proprietary databases, or code to execute within systems. Certain implementations may create surveys or other tools to collect information from customers in proprietary systems without sharing the data. In certain implementations, the full process may be automated to run in seconds or over a predetermined time period (such as months, for example) with collection of external user input as part of the steps).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the marketplace platform of Yuksel, in combination with Crabtree and Chandra, with the limitations taught by Hightower. One of ordinary skill in the art would have been motivated to make this modification for the benefit of mitigating the risk of hallucinations that can occur when relying on an AI/ML/LLM model to generate answers (Hightower – [0025]).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Yuksel et al. (US-20220318619) in view of Crabtree et al. (US-20240179185) and Chandra et al. (US-20240281742), and further in view of Ambite Molina et al. (US-20230394516).
Claim 31
Yuksel, in combination with Crabtree and Chandra, does not explicitly teach the following limitations:
wherein the one or more hardware processors are further configured for implementing federated learning across a plurality of edge devices while maintaining data privacy by keeping data on local nodes.
Ambite Molina, in the same field of endeavor, teaches the following limitations:
wherein the one or more hardware processors are further configured for implementing federated learning across a plurality of edge devices while maintaining data privacy by keeping data on local nodes ([0006] The federated learning marketplace includes a central coordinator that is responsible to orchestrate the execution of the federated learning environment, and a plurality of clients that jointly train machine learning and deep learning models (i.e., federated models) on client computing devices without sharing their local private datasets. The clients only share their locally trained model parameters with the central coordinator. The central coordinator aggregates local models and computes a new global model. This process repeats for a number of synchronization periods until specific convergence criteria are met. The federated learning marketplace also includes a plurality of model consumers that are provided licenses to use trained machine learning and deep learning models).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the AI marketplace platform of Yuksel, in combination with Crabtree and Chandra, with the limitations taught by Ambite Molina. One of ordinary skill in the art would have been motivated to make this modification for the benefit of incentivizing data providers to join federations, and to facilitate the use of machine-learning models to organizations outside of the federation (Ambite Molina – Abstract).
Response to Arguments
Applicant’s Argument Regarding 35 USC 112(b) Rejection of Claims 8, 16, and 24: Claims 8, 16, and 24 have been amended.
Examiner’s Response: Applicant’s amendments have been fully considered and they resolve the identified issue. However, claim 32 is now rejected for lack of antecedent basis. Refer to the 112 rejection above.
Applicant’s Argument Regarding 35 USC 101 Rejection of Claims 1-2, 4-5, 7-10, 12-13, 15-18, 20-24, and 31-36:
Independent claims 1, 9, and 17 have been amended in several ways to capture the underlying technical architecture upon which the marketplace is built. In doing so, Applicant respectfully submits that the pending claims, as amended herein, now define architectural features that clearly ground the pending claims as a technological solution to a technological problem, and which therefore integrate any alleged judicial exception into a practical application. The claims recited are not directed merely to organizing marketplace activity, but rather recite a specific computer-implemented architecture for the underlying AI workflow execution that enables the marketplace by combining proprietary datasets to accomplish various tasks in a decomposable workflow that is orchestrated by a distributed computational graph. For example, the claims have been amended to recite "analyzing a user query to determine a context and domain for the user query," "defining a decomposable workflow comprising a hierarchy of reasoning tasks based on the determined context and domain for the user query, wherein the decomposable workflow is orchestrated by a distributed computational graph," "assessing the interoperability and combinability of the selected machine learning and artificial intelligence assets for use in the decomposable workflow, wherein the interoperability and combinability is assessed using neuro-symbolic representations and rules specific to the determined context and domain that are applied to each reasoning task," and "combining the selected machine learning and artificial intelligence assets into an integrated asset for executing the decomposable workflow orchestrated by the distributed computational graph."
First, receiving a user query and developing a hierarchy of reasoning tasks to perform based on the context and domain of the user query is a step that only makes sense in the technological field of artificial intelligence, as there would be no reason for a decomposable workflow to need to be constructed to respond to a query except in the context of retrieval of digital, and in particular AI/ML-related, resources used to address or complete the workflow.
The assessment of machine learning and artificial intelligence assets with respect to this workflow is a further demonstration of the integration of the claimed invention into the particular technological context of AI/ML. Even further, the pending claims, as amended herein, now even recites specific steps particular to the technological field for evaluating such interoperability and combinability, particularly the use of neuro-symbolic representations and rules in order to perform the assessments. Thus, not only is the subject of the steps technologically based, but the claims recite a specific technological step unique to the field on that technology-based subject.
Finally, the step of combining selected AI/ML assets into an integrated asset for the purpose of executing the decomposable workflow creates something new that (1) could not be treated in the human mind or via pencil-and-paper, and (2) is itself an asset particular to the technological field of AI/ML for a particular purpose of satisfying the decomposable workflow.
The claims therefore are ground in a technological solution to a technological problem that exists specifically in the real-world application of AI workloads.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive.
The steps of analyzing a user query to determine a context and domain for the user query, defining a decomposable workflow comprising a hierarchy of reasoning tasks based on the determined context and domain for the user query, assessing the interoperability and combinability of the selected machine learning and artificial intelligence assets for use in the decomposable workflow, wherein the interoperability and combinability is assessed using neuro-symbolic representations and rules specific to the determined context and domain that are applied to each reasoning task, and executing the decomposable workflow are all steps that are part of the abstract idea. The presently amended claims are directed to Certain Methods of Organizing Human Activity, falling under commercial interactions including business relations, as they are directed to receiving and analyzing a user query to provide the user with a product to satisfy the user query, and tracking provenance of the product for enabling policy enforcement mechanisms to support transparency, accountability, and compliance of use of proprietary data in handling the user query. The additional elements are recited at a high level of generality, such as using a distributed computational graph to orchestrate a decomposable workflow. Further, the combining of the assets into an integrated asset is recited at a high level of generality, such as for example simply combining two separate ML / AI products together as part of a package.
Further, the additional elements do not integrate the abstract idea into a practical application as the claimed invention does not pertain to an improvement in the functioning of the computer itself or any other technology or technical field.
Applicant’s Argument Regarding 35 USC 103 Rejections of Claims 1-2, 4-5, 7-10, 12-13, 15-18, 20-24, and 31-36:
Claim 1 has been amended, and none of the cited prior art teaches or suggests the amended limitations of the claim.
Examiner’s Response: Applicant’s arguments have been considered but are moot in light of the new ground of rejection above.
Conclusion
The prior art made of record and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art, is listed on the enclosed PTO-892.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARMA EL-CHANTI whose telephone number is (571)272-3404. The examiner can normally be reached T-Sa 10am-6pm ET.
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/KARMA A EL-CHANTI/Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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