DETAILED ACTION
This Office Action is responsive to amendments and arguments filed on January 13th, 2026.
Claims 1, 7 and 15 are amended. Claims 1-20 are pending and have been examined.
Any previous objections/rejections not mentioned in this Office Action have been withdrawn by the Examiner.
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 October 23 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments and Arguments
Regarding rejections made under 35 U.S.C. 103, Applicant argues, "Nowhere in the prior art is there any mention of at least the features "adjusting altering a capability of the trained model to limit the trained model to a specific category of users to perform a task in the first task category." Support for these features may be found at least in paragraphs [0031], [0076], and [0079] of the specification," (emphasis original, page 9 of Remarks).
Examiner respectfully disagrees. Kamkar teaches at paragraph [0064], "S210 can include: selecting a model type of the alternative model (F), wherein training the alternative model (F) includes training the alternative model (F) as a model of the selected model type… In some implementations, the model type is automatically selected based on a list of candidate model types and a set of optimization and selection criteria, in some variations, provided by an operator. In some variations, the optimization criteria includes a fairness metric and an accuracy metric (or a fairness and an economic metric), and selection criteria include thresholds set based on the optimization criteria applied to the original model, or any computable function on optimization criteria, such as the ratio of the fairness metric improvement and the decrease in profitability, economic criteria, demographic criteria, etc… In some variations, demographic criteria includes one or more of: race, ethnicity, gender, age, military status, disabled status, marriage status, sexual orientation, geographic criteria, membership in a group, religion, political party, and any other suitable criteria. In some variations, the model type is selected based on received user input (e.g., via the operator device 120, the user interface 115, etc.)."
Kamkar contemplates user groupings in the teachings for retraining a model. Further, the claims do not disclose a mechanism by which the user’s category may be determined by the model. As such, the limit on user category would be functionally identical to a limit on the model’s task category; an adjustment of the model’s parameters to permit or prevent certain behaviors at training time, rather than adaptively at inference time.
Applicant’s argument is not persuasive. Accordingly, the rejections under 35 U.S.C. 103 are maintained. Further detail is provided below.
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.
Claims 1, 5, 7-8, 13, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2025/0077795 to Krabach et al. (hereinafter, "Krabach") in view of Internation Publication WO 2020/191057 to Kamkar et al. (hereinafter, "Kamkar").
Regarding claims 1, 7 and 15, Krabach teaches a computer-implemented method, a computer program product, and a computer system comprising: training, using a database of tasks, a classifier model to classify an input task into a task category, the training resulting in a trained classifier model (paragraph [0024], "Referring now to FIG. 2, a conceptual illustration of the training process for the rubric classifier 34 is depicted. Rubric classifier training module 32 is configured to train the rubric classifier 34 using a rubric database 30, which in the illustrated example comprises outputs 31a-d of a plurality of generative language models with one or more output characteristics evaluated by human or machine evaluators observing the outputs 31a-d or responses of each model to a variety of prompts, and then scoring or rating the performance of each generative language model for each output characteristic that is evaluated.")generating a plurality of prompts (paragraph [0015], "At a high level, the generative model program 22 implements an interaction interface 26 by which a text input 42 is received, and passes the text input 42 to a prompt generator 38, which generates a prompt 40 based on the text input 42. The prompt 40 is input to a generative model 46, which in turn generates output 48 which can be passed to interaction interface 26. In a typical turn based chat bot implementation, this process can happen multiple times in a session, and the record of multiple text inputs 42 and outputs 48 forms an interaction history 27, the full or abbreviated content of which can be provided in the context 44 of each prompt 40 sent to the generative model 46 so that subsequent responses can take into account the context 44 of the interaction history 27.")applying a first prompt in the plurality of prompts to a trained model, the trained model comprising a model purpose, the trained model producing a first model output in response to the first prompt (paragraph [0017], "The processing circuitry 14 executing the generative model program 22 is configured to interface with the trained generative language model 46 that receives input of a prompt 40 including natural language text input 42 and, in response, generate an output 48 that includes natural language text output.")classifying, using the trained classifier model, the first model output into a first task category (paragraph [0051], "At step 204, the method 200 includes monitoring compliance of the generative language model with the rubric, by feeding the output of the generative language model to a rubric classifier configured to generate a predicted classification for an output characteristic in the rubric. The predicted classification may be a numerical classification or a qualitative classification.").
Krabach does not explicitly teach “determining that the first task category is a type of output that is inconsistent with the model purpose of the trained model and thereby an undesired task category,” or “adjusting, responsive to determining the first task category is the undesired task category, the trained model, the adjusting altering a capability of the trained model to limit the trained model to a specific category of users to perform a task in the first task category,” and thus, Kamkar is introduced.
Kamkar teaches determining that the first task category is a type of output that is inconsistent with the model purpose of the trained model and thereby an undesired task category (paragraph [0073], "In some variations, S250 functions to evaluate the initial model by using the adversarial classifier trained at S240. In some variations, S250 includes determining whether the initial model satisfies one or more constraints. In some variations, the constraints include fairness constraints. In some implementations, fairness constraints include prediction accuracy thresholds for one or more sensitive attributes whose values are predicted by the adversarial classifier based on outputs from the initial model, and the initial model satisfies the fairness constraints if prediction accuracy of the adversarial classifier 112 for the initial model are below one or more of the thresholds. However, the initial model can otherwise be evaluated by using the adversarial classifier 112.")adjusting, responsive to determining the first task category is the undesired task category, the trained model, the adjusting altering a capability of the trained model to limit the trained model to a specific category of users to perform a task in the first task category (paragraph [0075], "In some variations, responsive to a determination at S250 that the initial model does not satisfy one or more constraints, a new model is generated (e.g., at S260)," and paragraph [0077], "In a first variation, at S260, the model training system 110 generates the new model by re-training the initial model (e.g., 111a) by performing an adversarial training process (e.g., at S262). In some implementations, re-training the initial model includes selecting a new set of model parameters for the new model.").
Krabach and Kamkar are considered analogous because they are each concerned with training machine 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 Krabach with the teachings of Kamkar for the purpose of effectively guiding model training. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 5, 13 and 19 Krabach further teaches generating the plurality of prompts comprises adjusting an initial prompt producing an initial model output, the adjusting generating an adjusted prompt producing an adjusted model output, the initial model output having an initial correctness lower than a correctness threshold, the adjusted model output having an adjusted correctness higher than the initial correctness (paragraph [0047], "As shown in FIG. 3, the output 48 of the generative language model 46 includes exchanges in which John Smith anxiously mentioned his history of ACL tear as he asked if running was a safe workout activity for him. This output 48 is inputted into a rubric classifier 34, which outputs a predicted classification 36 for the output 48 indicating a friendliness score of 4 out of 5. The prompt generator 38 receives the predicted classification 36 as input, determines that the friendliness score of 4 is less than the target friendliness score of 5, and generates a prompt context 44 which suggests, “use more personalized and warm responses, use the user's name in the conversation, express more empathy, add a bit of positive emotional tone” so that the friendliness score of subsequent responses can be raised to a 5.").
Regarding claim 8, Krabach further teaches the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (paragraph [0045], "The client computing device 62 may be responsible for communicating over a computer network between the user operating the client computing device 62 and the server computing device 60 which executes the generative model program 22 and contains the rubric classifier 34 and the generative language model 46, via an application programming interface (API) 66 of the generative model program 22. The client computing device 62 may take the form of a personal computer, laptop, tablet, smartphone, smart speaker, etc. The same processes described above with reference to FIG. 1A may be performed, except in this case the natural language text input 42 and output 48 may be communicated between the server computing device 60 and the client computing device via a computer network such as the Internet.").
Claims 2, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Krabach and Kamkar as applied to claims 1, 7 and 15 above, and further in view of U.S. Patent Application Publication 2024/0354503 to Baruch et al. (hereinafter, "Baruch").
Regarding claims 2, 10 and 16, the combination of Krabach and Kamkar does not teach “generating the plurality of prompts comprises training a reinforcement learning agent to reward a prompt invoking generation of a novel task higher than a prompt invoking generation of a non-novel task, the training resulting in a trained reinforcement learning agent,” and thus, Baruch is introduced.
Baruch teaches generating the plurality of prompts comprises training a reinforcement learning agent to reward a prompt invoking generation of a novel task higher than a prompt invoking generation of a non-novel task, the training resulting in a trained reinforcement learning agent (paragraph [0292], "In some implementations, feedback processor 1210 includes a reinforcement learning component such as a reinforcement learning model that machine-learns a reward function based on feedback associated with prompt-output pairs. For example, given a prompt-output pair 1208, feedback processor 1210 receives or identifies feedback that pertains to the prompt-output pair 1208. The feedback can include pre-distribution feedback and/or post-distribution feedback received from one or more other components of the thought starter generation system. The feedback processor 1210 applies the reward function to the received or identified feedback to generate a reward score for the corresponding prompt-output pair based on the feedback associated with the prompt-output pair. The reward scores are incorporated into the prompt-feedback pairs 1212 and/or output-feedback pairs 1214, which are then used to train or fine tune the generative model 1206 using, for example, supervised or semi-supervised machine learning.").
Krabach, Kamkar and Baruch are considered analogous because they are each concerned with training machine 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 Krabach and Kamkar with the teachings of Baruch for the purpose of effectively guiding model training. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Claims 3-4, 6, 11-12, 14, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Krabach and Kamkar as applied to claims 1, 5, 7, 13, 15 and 19 above, and further in view of U.S. Patent Application Publication 2025/0111147 to Pryzant et al. (hereinafter, "Pryzant").
Regarding claims 3, 11 and 17, the combination of Krabach and Kamkar does not teach “generating the plurality of prompts comprises using the trained reinforcement learning agent to reward a derived prompt, the derived prompt derived from an existing prompt,” and thus, Pryzant is introduced.
Pryzant teaches generating the plurality of prompts comprises using the trained reinforcement learning agent to reward a derived prompt, the derived prompt derived from an existing prompt (paragraph [0036], "Second, the textual gradients g.sub.1-g.sub.x 230a-230m are provided to another LLM prompt, in this case, editing prompt δ 235, which instructs the LLM to edit the current prompt P 205 in order to fix the problems described by the textual gradients g1-gx 230a-230m. In this way, the LLMs are engaged in a recursive feedback loop.").
Krabach, Kamkar and Pryzant are considered analogous because they are each concerned with training machine 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 Krabach and Kamkar with the teachings of Pryzant for the purpose of improving training results. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 4, 12 and 18, the combination of Krabach and Kamkar do not teach “generating the plurality of prompts comprises prompting the trained model to generate a generated prompt, the generated prompt resulting in a desired output of the trained model,” however, Pryzant teaches generating the plurality of prompts comprises prompting the trained model to generate a generated prompt, the generated prompt resulting in a desired output of the trained model (paragraph [0036], "Third, additional candidate prompts are generated by running the existing candidate prompts or optimized prompts P′11-P′mq 240 through a paraphrasing prompt mc 245 or an LLM referred to as LLMmc, to explore the local Monte Carlo search space around the new prompt candidates. This prompt 245 asks the LLM to generate new candidate prompts or paraphrased prompts P″111-P″mqs 250, which are worded differently but semantically similar to their inputs (i.e., optimized prompts P′11-P′mq 240).").
Krabach, Kamkar and Pryzant are considered analogous because they are each concerned with training machine 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 Krabach and Kamkar with the teachings of Pryzant for the purpose of improving training results. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 6, 14 and 20, the combination of Krabach and Kamkar does not teach “the adjusted prompt has a semantic meaning above a semantic meaning threshold,” however, Pryzant teaches the adjusted prompt has a semantic meaning above a semantic meaning threshold (paragraph [0073], "An example paraphrasing prompt, which was used for each of the examples shown in FIGS. 4A-4D, may include prompt language such as: “Generate a variation of the following instruction while keeping the semantic meaning. Input: {prompt_instruction}. Output: ______.”").
Krabach, Kamkar and Pryzant are considered analogous because they are each concerned with training machine 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 Krabach and Kamkar with the teachings of Pryzant for the purpose of improving training results. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Krabach and Kamkar as applied to claim 7 above, and further in view of U.S. Patent 11,775,867 to Jamei (hereinafter, "Jamei").
Regarding claim 9, Krabach further teaches the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system (paragraph [0045], "The client computing device 62 may be responsible for communicating over a computer network between the user operating the client computing device 62 and the server computing device 60 which executes the generative model program 22 and contains the rubric classifier 34 and the generative language model 46, via an application programming interface (API) 66 of the generative model program 22. The client computing device 62 may take the form of a personal computer, laptop, tablet, smartphone, smart speaker, etc. The same processes described above with reference to FIG. 1A may be performed, except in this case the natural language text input 42 and output 48 may be communicated between the server computing device 60 and the client computing device via a computer network such as the Internet.").
The combination of Krabach and Kamkar does not teach “program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use,” and thus, Jamei is introduced.
Jamei teaches program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use (column 7, lines 12-19, "Each testing container (or “worker”) logs the resources that it uses to evaluate a submitted model. This includes required RAM, CPU memory, processing time, data storage, network traffic to transfer the data, etc. The log data is aggregated by user ID and used for billing and enforcement of limits or quotas set on resource use. This allows for a system in which each user pays for the amount of services that they use as part of the model evaluation processes.").
Krabach, Kamkar and Jamei are considered analogous because they are each concerned with assessing machine 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 Krabach and Kamkar with the teachings of Jamei for the purpose of expanding monetization opportunities for model training. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent 8,762,299 to Breckenridge et al.
U.S. Patent 11,494,689 to Efstathiou et al.
U.S. Patent 11,983,238 to Nagalapatti et al.
U.S. Patent 12,361,215 to Wei et al.
U.S. Patent Application Publication 2024/0428937 to Natarajan et al.
U.S. Patent Application Publication 2024/0135113 to Zorn et al.
U.S. Patent Application Publication 2023/0267307 to Wang et al.
U.S. Patent Application Publication 2024/0119361 to Yin et al.
U.S. Patent Application Publication 2025/0124300 to Maia et al.
Internation Publication WO 2018/153806 to Gendron-Bellemare et al.
U.S. Patent Application Publication 2019/0095557 to Sehgal et al.
"Coauthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities” by Lee et al.
“FairRover: Explorative Model Building for Fair and Responsible Machine Learning” by Zhang et al.
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/SEAN THOMAS SMITH/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659