DETAILED ACTION
This is a Non-Final Office Action in response to Claims on 04/02/2025. Claims 1-20 are pending. The effective filing date is 09/17/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/24/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 abstract idea without significantly more.
Step 1- Claims 1-15 are directed to a method, a statutory category. Claims 16-19 are directed to a non-transitory computer readable medium, an article of manufacture, a statutory category. Claim 20 is directed to a system, a statutory category. Claims 1-20 pass step 1.
Step 2A, Prong 1-The independent claim 1, and similarly claim 16 and 20, recite:
A method, performed at a computer system comprising a processor and a computer-readable medium (additional elements to be analyzed in Step 2A, prong 2), comprising:
initializing a user artificial intelligence (AI) agent on a device of an entity, the user Al agent comprising a machine-learned language model and a reinforcement learning model (initializing a model is the using those models, models are a way to make a determination based on a relationship between variable, and is being considered as a mathematical relationship, see MPEP 2106.04(a)(2)(I)(A). The application of the models as an AI agent on a device will be analyzed in Step 2A, Prong 2);
monitoring, by the Al agent, events from one or more event sources (monitoring events is a way to perform analysis, which is a mental process under MPEP 2106.04(a)(2)(III)(A), and can be shown specifically under examples of mental processes under MPEP 2106.04(a)(2)(III)(D) A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1);
detecting, from the events, an occurrence of an event from a set of predetermined events that are associated with a task that is assigned to the entity (detecting an event occurring is a mental process, and even when performed in a computer environment under MPEP 2106.04(a)(2)(III)(C) The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296);
prompting the machine-learned language model of the Al agent to generate a set of candidate actions based in part on the detected event, the prompt including source data about a source associated with the task and entity data about the entity (generating an action of a candidate is an instruction to a candidate, and therefore is grouped as a the management of personal behavior, under the larger grouping of certain methods of organizing human activity under MPEP 2106.04(a)(2)(II)(C));
scoring, by the reinforcement learning model of the Al agent, each candidate action of the set of candidate actions to form a scored set of candidate actions (giving score is performing an analysis, and outputting the decisions, and therefore applies a relationship between actions as a model under MPEP 2106.04(a)(2)(I) as well as analyzing data as a mental process under MPEP 2106.04(a)(2)(III));
prompting the machine-learned language model of the Al agent with the scored set of candidate actions to select a scored candidate action from the scored set of candidate actions as a recommended response to the event (generating an action of a candidate is an instruction to a candidate, and therefore is grouped as a the management of personal behavior, under the larger grouping of certain methods of organizing human activity under MPEP 2106.04(a)(2)(II)(C));
generating, by the Al agent, a recommendation for the entity based in part on the recommended response (generating an action of a candidate is an instruction to a candidate, and therefore is grouped as a the management of personal behavior, under the larger grouping of certain methods of organizing human activity under MPEP 2106.04(a)(2)(II)(C) as well as analyzing data as a mental process under MPEP 2106.04(a)(2)(III)); and
communicating the recommendation to the computer system that causes the entity to perform the selected candidate action in accordance with the recommendation (communicating, or displaying the results of an analysis is a mental process under MPEP 2106.04(a)(2)(III)).
Therefore, the independent claims recite an abstract idea and are ineligible under Step 2A, Prong 1.
Step 2A, Prong 2- The additional elements of independent claim 1, and similarly claim 16 and 20, includes a computer system, a processor, a computer readable medium, AI agent, machine-learned language model and reinforcement learning model. This judicial exception is not integrated into a practical application because the computer system include as a processor and memory, which in their ordinary capacity perform instructions, such as determinations and analyzing, and are used as a tool to perform the abstract idea under MPEP 2106.05(f)(2) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone). An AI agent, machine-learned language model and reinforcement learning model is more descriptive as the type of processing being performed, however, this AI agent is described by how the result is accomplished, application of the models. The claim does not provide any description of the mechanism for accomplishing the result of “scoring” or “prompting”, it merely recites the application of the idea of making a decision on the additional element of the model, making this the use of a tool to perform the abstract idea under MPEP 2106.05(f)(1) The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). The independent claims therefore are ineligible under Step 2A, Prong 2.
Step 2B-The independent claim 1, and similarly claim 16 and 20, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements are used to perform in their ordinary capacity, and the Ai models are being used as the solution to a problem, without providing any details of the mechanism of that solution, and therefore under MPEP 2106.05(f), the combination of the elements do not provide more than the abstract idea. The independent claims there are ineligible under Step 2B.
Dependent Claims
Claims 2-10, 12-15 and 17-19 adds different steps that include determining steps, and displaying the results of those determinations, which continues to be a mental process, of making a determination using data, under MPEP 2106.04(a)(2)(III)and the use of the AI agent to perform the determination is suing that agent as a tool to perform the abstract idea under MPEP 2106.05(f) and fails t integrate the abstract idea into a practical application or provide significantly more.
Claim 11 adds the step of training the AI agent. Training AI agents, has frequently been described as mathematical calculations under MPEP 2106.04(a)(2)(I). The plain meaning of the terms are to optimize algorithms, using mathematical calculations. This is being performed by the computer, which is recited at a high level of generality, and does not integrate the abstract idea into a practical application or showcase significantly more than the abstract idea under MPEP 2106.05(f).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2019/0347118 A1 Mukherjee et al. (hereinafter Mukherjee) teaches a digital assistant to automatically perform digital commands (Abstract); but fails to explicitly disclose the generation of candidate action that would prompt an entity to perform those actions.
US 2023/0316104 A1 Ott teaches an AI agent to analyze data (Abstract); but fails to explicitly disclose the generation of candidate action that would prompt an entity to perform those actions.
US 2022/0239711 A1 Kulkarni et al. (hereinafter Kulkarni) teaches interactions between devices (Abstract); but fails to explicitly disclose the generation of candidate action that would prompt an entity to perform those actions.
US 2024/0241897 A1 Wang et al. (hereinafter Wang) teaches models to predict items (Abstract); but fails to explicitly disclose the generation of candidate action that would prompt an entity to perform those actions.
“A Three-Stage model for Clustering, Storage, and joint online order batching and picker routing Problems: Heuristic algorithms” Tabrizi et al. (hereinafter Tabrizi) teaches batching and creating picker routes; but fails to explicitly disclose the AI agent with a machine-learned language model and reinforcement model and communicating the recommendation to the computer system…to perform the selected candidate action.
US 2023/0360058 A1 Lundell et al. (hereinafter Lundell) teaches am AI agent to respond to customer incidents, and prompt a specific response (Abstract); but fails to explicitly disclose the Ai agent being used to determine actions to suggest a candidate to perform.
Claim Limitations Not Found in Prior Art
Claims 1-20 remain ineligible under 101.
The following is a statement of reasons for the indication of allowable subject matter:
The specific claim limitations when, when viewed together, that are not found in the prior are as follows:
detecting, from the events, an occurrence of an event from a set of predetermined events that are associated with a task that is assigned to the entity. (Examiner notes that detection of a specific event occurring, such entering a store, is common, but that combination of the detection of that event, associating the event with a specific task, and that task is assigned to a specific entity is not found).
prompting the machine-learned language model of the Al agent to generate a set of candidate actions based in part on the detected event, the prompt including source data about a source associated with the task and entity data about the entity. (Examiner notes that generating a set of actions is found in prior art, but that action specifically being a candidate action, which is described as movement within the store by a customer, such as picking a self-checkout register, or specific lane based on all data points collected from the store applied to a machine learned language model, is not found within the prior art).
Conclusion
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/JESSICA E SULLIVAN/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627
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