Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This communication is responsive to the applicant’s response filed on 03/02/2026.
3. Claims 1-10, 16-18 and 52-53 are currently pending in this Office action.
Election/Restrictions
4. Applicant’s election without traverse of Species I [claims 1-10, 16-18 and 52-53] in the reply filed on 03/02/2026 is acknowledged. Claims 11-15 and 19-51 are cancelled.
Information Disclosure Statement
5. The information disclosure statement (IDS) filed on 03/04/2026 was considered by the examiner.
Claim Rejections - 35 USC § 112
6. 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claim 9-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 9-10, claim 9 recites the limitation of “for display by a user device” [emphasis added] in line 3. However, it is unclear how this limitation is related to the limitation of “a user device” [i.e., line 9 of claim 1]. Are they referring to the same device? Clarification is required.
Claim 10 is also rejected by virtue of its dependency on a rejected claim.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 52-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Regarding claim 52, this claim is not statutory because this claim lacks the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 U.S.C. 101. These claims are, at best, functional descriptive material (i.e., software) per se.
Regarding claim 53, the preamble of this claim recites the phrase of “A computer-readable medium storing…” (in line 1). Claim 53 is not statutory because the specification does not clearly define and limit definition of the recited “computer-readable medium” to only a non-transitory medium. Thus, claim 53 is not statutory under the 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
10. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
11. 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.
12. Claims 1-4, 16-18 and 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0173363 (hereinafter Madisetti) in view of U.S. 2021/0390455 (hereinafter Schierz).
Regarding claims 1 and 52-53, Madisetti discloses a generative AI system development method, the method comprising:
constructing, by one or more processors, a plurality of generative AI systems, wherein constructing the plurality of generative AI systems includes executing at least one modeling blueprint ([0022]; “…A family of generative models, LLM1, trained with a data set T1, can be represented as h-LLM1, while a family of models, LLM2, trained with data set T2, can be represented as h-LLM12. Further, a family of models, LLM1, trained with a data set T3, can be represented as h-LLM35. The combination of models and their training sets (T1 could be a subset of T3, for example, or they can be different) may be used in our proposed invention and they are referred to as h-LLMs, throughout…”);
providing, by the one or more processors, a plurality of queries to each generative AI system in the plurality of generative AI systems, the plurality of queries being part of an evaluation dataset ([0030 and 0143-0144]; “…The relevant context allows the LLM to respond to the user query (including through use of the derived queries) even though the LLM may not be trained with the data from the knowledge base. The retrievers 2702 are used to retrieve the relevant context from the knowledge base 2700. The retrievers 2702 may use different search and similarity matching strategies to search and retrieve the documents…”);
during processing of the plurality of queries by each generative AI system, monitoring values of one or more quantitative metrics ([0023 and 0171-0172]; “…An evaluation and fine-tuning module 3824 includes a performance analyzer which is operable to monitor system-wide metrics and evaluate the response quality, and a model fine-tuner which is operable to adjust the system parameters for optimization”);
providing data indicating the values [i.e., the SCORE-RAG] of the one or more quantitative metrics for each generative AI system ([0023 and 0176]; “…This multi-faceted approach allows CORE-RAG to process a wide range of input types and also generate appropriate and context-aware multi-modal outputs. A User 4026 sends a query 4028 (comprising multi-modal inputs) to the SCORE-RAG system 4002 and received a multi-modal response 4030. The multimodal capability of the SCORE-RAG system 4002 enhances the system's utility across diverse fields such as education, research, software development, multimedia content creation, and data analysis, for instance”).
While Madisetti discloses the features of utilizing the generative AI system as explained above, the reference does not explicitly disclose the features of providing, by the one or more processors for display by a user device, data indicating the values of the one or more quantitative metrics for each generative AI system; and providing, by the one or more processors for display by the user device, a recommendation regarding use or non-use of at least one generative AI system included in the plurality of generative AI systems. However, Schierz discloses that “…For example, the adaptive drift learner 126 can be trained using the test output to predict a suitable (e.g., optimal) binning strategy and/or drift metric. Once trained, the adaptive drift learner 126 can receive as input one or more characteristics or features for a set of data (e.g., length, distribution, minimum, maximum, mean, skewness, number of unique values, or any combination thereof) and provide as output a recommended binning strategy and/or drift metric…” ([0028, 0115 and 0120]). Schierz further discloses that “…Further, new segmentation strategies can be tried, models can be developed for the new segmentation strategies, and the models can be evaluated for performance (e.g., accuracy and/or efficiency). In some
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examples, recommendations for new segmentation strategies can be sent to users for feedback or approval. Additionally or alternatively, the systems and methods may evaluate alternative means
of assigning entities in the time series to segments or clusters, based on signals of drift and performance measured after deployment…” ([0136]) and “… If the classifier (or other AI
model) can successfully tell the two datasets apart, then this can imply that the drift has had a system-wide effect. Once the impact of the drift has been assessed at both an individual and systemic level, a user of the system 100 can be alerted with a recommended course of action, or other corrective action can be taken or facilitated, as described herein” ([0117, 0136 and 0146]; figs. 4A and 4B as shown above) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Schierz in the system of Madisetti in view of the desire to enhance the generative artificial intelligence process by utilizing the monitoring and managing machine learning model schemes resulting in improving the efficiency of generating the outputs. Madisetti additionally discloses a computer-readable storage medium, and a computer-readable medium executed by one or more processors ([0198]).
Regarding claim 2, Madisetti in view of Schierz discloses the method wherein each generative AI system in the plurality of generative AI systems is configured to operate as a chat bot, a natural language interface to a knowledge base, or content generation engine (Madisetti: [0139]; the supervised or labeled learning data).
Regarding claim 3, Madisetti in view of Schierz discloses the method wherein each generative AI system in the plurality of generative AI systems is a retrieval-augmented generation (RAG)-based generative AI system (Madisetti: [0020 and 0176]).
Regarding claim 4, Madisetti in view of Schierz discloses the method wherein each generative AI system in the plurality of generative AI systems includes a knowledge base, a prompt construction facility, and a generative model (Madisetti: [0123-0124]).
Regarding claim 16, Madisetti in view of Schierz discloses the method wherein the evaluation dataset is a synthetic evaluation dataset (Madisetti: [0139]) and (Schierz: [0136]). Therefore, the limitations of claim 16 are rejected also rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 17, Madisetti in view of Schierz discloses the method further comprising constructing the synthetic evaluation dataset (Madisetti: [0117]) and (Schierz: [0136]). Therefore, the limitations of claim 17 are rejected also rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 18, Madisetti in view of Schierz discloses the method wherein, for each generative AI system in the plurality of generative AI systems, the one or more quantitative metrics include:
a factual accuracy metric indicating an extent to which completions generated by the respective generative AI system in response to the plurality of queries are factual;
a faithfulness metric indicating an extent to which the completions generated by the respective generative AI system include hallucinated information;
a grounded-ness metric indicating an extent to which the completions generated by the respective generative AI system are based on context data extracted from the knowledge base of the respective generative AI system;
a toxicity metric indicating an extent to which the completions generated by the respective generative AI system include toxic content;
a latency metric indicating a latency associated with the processing of the queries and/or generation of the completions by the respective generative AI system;
a token count metric derived from a number of tokens included in the completions generated by the respective generative AI system; and/or
a cost metric indicative a cost incurred by using the generative model of the respective generative AI system to generate the completions (Madisetti: [0022, 0146 and 0262]; the token counts and the factual accuracy) and (Schierz: [0136]). Therefore, the limitations of claim 18 are rejected also rejected in the analysis of claim 1, and the claim is rejected on that basis.
13. Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Madisetti in view of Schierz, and further in view of U.S. 2024/0370764 (hereinafter Goyal).
Regarding claim 5, Madisetti in view of Schierz discloses the method wherein the respective set of hyperparameter values corresponding to each generative AI system determines one or more attributes of the knowledge base, the prompt construction facility, or the generative model included in the generative AI system (Madisetti: [0124 and 0126]).
The references do not explicitly disclose the feature of wherein the constructing of each generative AI system in the plurality of generative AI systems is performed based on a set of values of a set of hyperparameters. However, Goya discloses that “…For example, different AI services can be configured with different hyperparameters including different trained LLMs and generative AI prompts to support context-specific solutions such as AI summarization and AI search…” ([0015]) and “…The configuration module allows an administrator to target an integrated AI service and provide corresponding configuration parameters when the selected AI service is requested. For example, hyperparameters including a specific model, temperature, and token parameters can be configured. Similarly, parameters for an epoch number, a batch size, and/or a learning rate multiplier can be configured, if appropriate” ([0026]) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Goya in the modified system of Madisetti in view of the desire to enhance the generative artificial intelligence process by utilizing the generative AI configuration scheme resulting in improving the efficiency of generating the outputs.
Regarding claim 6, Madisetti in view of Schierz and Goyal disclose the method wherein the one or more attributes of the knowledge base include a type of encoder used to create a plurality of embeddings of the knowledge base, the plurality of embeddings representing a plurality of portions of source data (Madisetti: [0006-0010]).
Regarding claim 7, Madisetti in view of Schierz and Goyal disclose the method wherein the one or more attributes of the prompt construction facility include (i) a process by which the prompt construction facility identifies one or more embeddings in the knowledge base matching an embedding representing a query, (ii) a process by which source data corresponding to the identified one or more embeddings is added to a constructed prompt, and/or (iii) a configuration of a prompt template used to construct the constructed prompt (Goyal: [0015 and 0046]; “At 505, a prompt is configured. For example, a prompt for interfacing with a selected AI service, such as with a generative AI service utilizing the LLM selected at 501 is configured. In some embodiments, the prompt is configured as a prompt template. Using the configured prompt template, a prompt can be generated using predefined text portions of the prompt and dynamic portions of the prompt where the dynamic portions can change based on the specific AI request and/or context of the AI request…”). Therefore, the limitations of claim 7 are rejected also rejected in the analysis of claim 5, and the claim is rejected on that basis.
Regarding claim 8, Madisetti in view of Schierz and Goyal disclose the method wherein the one or more attributes of the generative model include a type of the generative model (Madisetti: [0010-0012]).
14. Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Madisetti in view of Schierz, and further in view of U.S. 2021/0224588 (hereinafter Sikand).
Regarding claim 9, Madisetti in view of Schierz discloses the method wherein the plurality of generative AI systems include a first generative AI system, and wherein the method further comprises: providing, by the one or more processors for display by a user device, a visual representation of the first generative AI system (Madisetti: [0022]).
The references do not explicitly disclose the features of wherein a visual representation of an embedding space of the knowledge base. However, such features are well known in the art as disclosed by Sikand ([0072 and 0075]; figs. 4a-4b) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Sikand in the modified system of Madisetti in view of the desire to enhance the generative artificial intelligence process by utilizing the specific data type of visualization resulting in improving the efficiency of generating the outputs.
Regarding claim 10, Madisetti in view of Schierz and Sikand discloses the method wherein the visual representation of the embedding space includes a plurality of topic labels indicating the topics of a respective plurality of clusters of embeddings (Madisetti: [0163 and 0172]) and (Sikand: figs. 4a-4b). Therefore, the limitations of claim 10 are rejected also rejected in the analysis of claim 9, and the claim is rejected on that basis.
Conclusion
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm.
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/MONICA M PYO/ Primary Examiner, Art Unit 2161