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 .
Detailed Action
Objections
Claims 1 and 11 are objected to because of the following informality: the eighth limitation in each claim recites “based on the evaluation,” but there is an evaluation recited in both the third and seventh limitations to the antecedent basis of the evaluation in the eighth limitation is unclear.
Rejections under 35 U.S.C. 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 mental processes without significantly more. Independent claims 1 and 11 each recites generating, via one or more processors, a prompt for input to the generative Al model based on prompt criteria defining an inquiry associated with a corpus of documents; generating, via the one or more processors, a classification of an initial set of documents from the corpus of documents by inputting the initial set of documents and the prompt to the generative Al model; evaluating, via the one or more processors, classification performance of the prompt based on ground truth data associated with the initial set of documents; based on the evaluation, generating, via the one or more processors, one or more modified prompt criteria; generating, via the one or more processors, one or more modified prompts respectively associated with the one or more modified prompt criteria; generating, via the one or more processors, one or more respective classifications of the initial set of documents associated with each of the one or more modified prompts by inputting the initial set of documents and each of the one or more modified prompts to the generative Al model; evaluating, via the one or more processors, classification performance of the one or more modified prompts based on the ground truth data; and based on the evaluation, selecting, via the one or more processors, a preferred prompt from among the prompt and the one or more modified prompts. Generating a prompt and a modified prompt are recited broadly and are mental processes accomplishable in the human mind or on paper. Generating a classification of a set of documents by inputting the documents and a prompt or a modified prompt into an AI model is merely applying the AI model and is not more significant than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Evaluating the classification performance of a prompt or a modified prompt and selecting a preferred prompt are each evaluating and are mental processes. Each claim recites an additional element of providing, via the one or more processors, an indication of preferred prompt criteria associated with the preferred prompt, which is an output step and insignificant extra-solution activity. Claim 11 recites one or more processors and one or more non-transitory memories, which are each generic components of a computer. Examiner notes specification paragraph 0003 describes how attorneys deploy machine learning models in an eDiscovery process to identify documents responsive to an inquiry. Paragraphs 0004 and 0032 describe said deploying of machine learning models can be cumbersome and inefficient due to different attorneys deploying models in different ways creating conflicts in the models and training a classifier on thousands of documents as cumbersome and inefficient. Paragraph 0033-0034 begin discussing techniques to address the shortcomings mentioned in paragraphs 0004 and 0032. The claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the output step is recited broadly and amounts to sending data across a network per specification paragraphs 0045-0046 and 0054 and figures 1 and 10, and sending data is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. The one or more processors and one or more non-transitory memories are each still generic components of a computer. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2 and 12 each recites evaluating, via the one or more processors, classification performance of one or more respective component fields of each of the one or more modified prompt criteria based on the ground truth data, and evaluating classification performance is recited broadly and is a mental process. Claims 3 and 13 each recites evaluating, via the one or more processors, classification performance of the one or more component fields of the prompt criteria based on the ground truth data, and evaluating classification performance is recited broadly and is a mental process. Claims 4 and 14 each recites based on the evaluation, selecting, via the one or more processors, one or more first preferred component fields from among the one or more component fields of the prompt criteria and one or more second preferred component fields from among the one or more respective component fields of each of the one or more modified prompt criteria, and selecting fields is evaluating and a mental process. Claims 5 and 15 each recites generating, via the one or more processors, one or more modified component fields each corresponding to a component field of the one or more component fields by inputting the prompt and the classification performance of the one or more modified component fields to the generative AI model, and generating modified components by inputting a prompt and a classification performance into an AI model is applying the AI model and is not more significant than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628); and generating, via the one or more processors, the modified prompt criteria based on the one or more modified component fields, and generating modified prompt criteria is recited broadly and a mental process accomplishable in the human mind or on paper.
Claims 6 and 16 each recites wherein the classification performance of the prompt includes one or more of: one or more respective indications of one or more misclassifications of documents from the initial set of documents, or one or more respective indications of one or more low-confidence classifications of documents from the initial set of documents, and the recited indications are each data and are mental processes accomplishable in the human mind or on paper. Claims 7 and 17 each recites determining, via the one or more processors, one or more component fields of the prompt criteria associated with at least one of the one or more misclassifications of documents, and determining is recited broadly and is a mental process; and modifying, via the one or more processors and by the generative AI model, the one or more component fields to generate the one or more modified component fields, and applying the AI model and is not more significant than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claims 8 and 18 each recites determining, via the one or more processors, one or more component fields of the prompt criteria associated with at least one of the one or more low-confidence classifications of documents, and determining associated prompt criteria is evaluating and a mental process; and modifying, via the one or more processors and by the generative AI model, the one or more component fields to generate the one or more modified component fields, and applying the AI model and is not more significant than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628).
Claims 9 and 19 each recites generating, via the one or more processors, the prompt criteria by inputting one or more of: a review protocol, a complaint, a request for production, one or more of key documents, one or more background documents, to a generative AI model, and inputting criteria into an AI model is a mental process accomplishable in the human mind or on paper. Claims 10 and 20 each recites obtaining, via the one or more processors, a preliminary set of documents associated with the inquiry and the corpus of documents, wherein the preliminary set of documents include at least one of: (i) one or more key documents or (ii) one or more background documents, which is a data gathering step and amounts to receiving data across a network per specification paragraphs 0045-0046 and 0054 and figures 1 and 10, and is routine and conventional per the list of such activities in MPEP 2106.05(d) part II; and generating, via the one or more processors, the initial prompt criteria by inputting the preliminary set of documents to the generative AI model, and inputting criteria into an AI model is a mental process accomplishable in the human mind or on paper.
Rejections under 35 U.S.C. 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.
Claim(s) 1-2, 9, 11-12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al (US 20240273291), hereafter Smith, in view of Rankin (US 20250156484).
With respect to claims 1 and 11, Smith teaches:
generating, via one or more processors, a prompt for input to the generative Al model based on prompt criteria defining an inquiry (paragraphs 0038-0039 publishing of documents as an inquiry, paragraph 0029 generate the prompt from the task description (criteria, paragraph 0031) associated with document);
generating, via the one or more processors, a classification of an initial set of documents from the corpus of documents by inputting the initial set of documents and the prompt to the generative Al model (paragraph 0145 generate classification of document with prompt, also rating applied to prompt by classification model, also paragraph 0170);
evaluating, via the one or more processors, classification performance of the prompt based on ground truth data associated with the initial set of documents (paragraph 0170 training a model with ground truth data includes classifications, also for tuning and re-engineering the model so evaluated with feedback), paragraph 0040 measure quality of prompt with feedback, also paragraph 0041 post-publication feedback adding contributions;
based on the evaluation, generating, via the one or more processors, one or more modified prompt criteria (paragraph 0292 feedback for modifying parameters for model, paragraph 0033 modify input parameters for task descriptions in prompts for models);
generating, via the one or more processors, one or more modified prompts respectively associated with the one or more modified prompt criteria (repetition of step in paragraph 0029 first limitation above, paragraph 0033 modifying task descriptions based on evaluating model outputs known in the art, paragraph 0060 refine/modify prompt from parameters based on feedback);
generating, via the one or more processors, one or more respective classifications of the initial set of documents associated with each of the one or more modified prompts by inputting the initial set of documents and each of the one or more modified prompts to the generative Al model (repetition of step in paragraph 0145 in second limitation above, paragraph 0124 using pre-publication and post-publication feedback to iteratively refine prompts, refine the classification model);
evaluating, via the one or more processors, classification performance of the one or more modified prompts based on the ground truth data (repetition of step in third limitation above, paragraph 0054 use ground-truth for example to refine/train model with prompts);
based on the evaluation, selecting, via the one or more processors, a preferred prompt from among the prompt and the one or more modified prompts (paragraph 0140 prompts selected after evaluating so preferred); and
providing, via the one or more processors, an indication of preferred prompt criteria associated with the preferred prompt (paragraph 0170 prompts, parameters provided to models, paragraph 0030 generate task description-output pair).
Smith does not teach generating, via one or more processors, a prompt for input to the generative Al model based on prompt criteria defining an inquiry associated with a corpus of documents. Ranking teaches this with comparison, analysis, and modification of contract documents (paragraph 0005) wherein the documents are in a corpus reviewed by legal practices in inquiries such as contracts (paragraphs 0003, 0015). It would have been obvious to have combined the techniques for inputting prompts to a generative AI model in Smith with the use of such techniques for criteria associated wit a corpus of documents in Rankin as both Smith and Rankin are in the same field of endeavor and the combination would handle the analysis of such a corpus more efficiently than using manual techniques.
With respect to claims 2 and 12, all the limitations in claims 1 and 11 are addressed by Smith and Rankin above. Smith also teaches evaluating, via the one or more processors, classification performance of one or more respective component fields of each of the one or more modified prompt criteria based on the ground truth data (paragraph 0170 evaluating model with prompts, parameters (components of input to model) with ground truth).
With respect to claims 9 and 19, all the limitations in claims 1 and 11 are addressed by Smith and Rankin above. Smith also teaches generating, via the one or more processors, the prompt criteria by inputting one or more of: a review protocol, a complaint, a request for production, one or more of key documents, one or more background documents, to a generative AI model (paragraph 0029 task description as digital content (key/background document)).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 1/10/26