DETAILED ACTION
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 .
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea in regards to claims 1-20 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagree with Applicant’s arguments because amended claims 1 and 10 remain directed to the abstract idea of evaluating employee performance by collecting information, analyzing that information, and generating suggested answers for a performance evaluation. The additional limitations directed to a transcript data processor, an evaluation form data processor, processed transcript data, question prompts lists, and prompt generation merely describe how information is organized and prepared before being submitted to a generative AI service. These steps are part of the abstract process of collecting, organizing and analyzing information to assist in completing an evaluation form. While the claim recites the use of an LLM and various software components, these components simply automate what is fundamentally a mental process and a method of organizing human activity. The claims therefore remain directed to an abstract idea under Step 2A, prong one.
Step 2A, prong two, Applicant’s arguments that the amended claims integrate the abstract idea into a practical application are not persuasive. Although the claims recite a modular prompt-generation architecture using a transcript data processor, an evaluation form data processor, and prompt generation based on processed transcript data, question prompt lists, and system configuration data, these features merely prepare and format information for submission to an existing generative AI service. The claims do not improve the operation of the LLM, the CRM system, transcript processing technology or any other computer technology. Instead, the claims use conventional computer processing to organize data and generate prompts so that a generic LLM can provide suggested answers. Any improvement identified by Applicant relates to improving the quality or efficiency of the business process of preparing performance evaluations, rather than improving the functioning of a computer or another technological field. Accordingly, the additional limitations do not integrate the judicial exception into a practical application.
Step 2B, Applicant’s reliance on the alleged novelty of the prompt-generation architecture, the Examiner’s prior art findings, and Example 47 is also not persuasive. Whether the claimed architecture is novel or non-obvious under 102 and 103 does not determine eligibility under 101. Likewise, the fact that the prior art may not disclose the particular arrangement of processors or prompt-generation pipeline does not establish the presence of an inventive concept. Unlike Example 47, which involved a specific improvement to computer network security, the present claims merely use conventional processors, data processing operations, prompt engineering techniques, and an existing LLM to automate the abstract task of generating suggested answers for performance evaluations. Considered individually and as an ordered combination, the claimed elements performs their expected functions of collecting, processing, formatting, transmitting, receiving, and displaying information using generic computer technology. Therefore, the claims do not recite significantly more than the abstract idea itself and remain ineligible under 101 Abstract Idea.
Applicant's arguments with respect to 35 U.S.C. 103 rejection of claims 1 and 10 have been considered and found persuasive, and the rejection has been withdrawn. See detailed reason for allowance below.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-10 and 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 10 and 19 are ineligible because it is directed to an abstract idea—namely, automating human performance evaluation and form filling (a method of organizing human activity and a mental process) using generic computer components. At its core, the claim just has a system that selects an agent–customer interaction in a CRM, retrieves an evaluation form, obtains a transcript, sends that data to a generative AI/LLM to get suggested answers, writes those answers back into the form, and displays the updated evaluation. The “processor,” “non transitory computer readable medium,” “CRM interface,” “generative AI service,” and “request object” are all conventional computing elements performing routine data gathering, formatting, sending to a black box service, receiving a response, and displaying results; they do not improve the functioning of the computer or the LLM itself, nor do they recite any unconventional technical implementation. As a result, the additional elements do not amount to “significantly more” than the abstract idea, and the claims are rejected under 101 Abstract Idea as being directed to an abstract idea without an inventive concept.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 3-9 and 12-18 are further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in question and answer management system.
Allowable Subject Matter
Claims 1, 3-10 and 12-19 are allowed if the Applicant can overcome the 101 Abstract Idea rejection set forth.
The following is a statement of reasons for the indication of allowable subject matter: Tapuhi et al. (US Claim 10,902,737) in view of Tai et al. (“An Exanimation…of LLM to Aid Analysis of Textual Data; Jan 2024; pgs. 1-14).
Claims 1, 10 and 19,
Tapuhi teaches a performance evaluation system configured to intelligently suggest answers to questions on performance evaluations using a generative artificial intelligence (AI) service, the performance evaluation system comprising: a processor and a non-transitory computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform intelligent suggestion operations ([Fig. 12B] [ processor; non-transitory CRM; receiving an evaluation form, an interaction, and compute the overall evaluation score) which comprise:
receiving a selection of an interaction between a user and an agent of a customer relationship management (CRM) system, wherein the selection designates a performance evaluation that evaluates the agent for the interaction ([Fig. 2] step 202; an interaction for evaluation is identified along with an evaluation form to use to evaluate the interaction);
fetching evaluation form data for the performance evaluation, wherein the evaluation form data includes one or more questions with one or more answer options to each of the one or more questions ([Fig. 3] [col. 8 line 8 to col 9 line 5] step 310; an evaluation form includes one or more questions that relate to an agent’s performance; the form developer also set the data type of the answers whether the answers are yes or no type);
determining a transcript of the interaction between the user and the agent, wherein the transcript comprises text data available or converted from the interaction ([col. 5 lines1-16] [col 7 lines 16-31] recorded calls may be processed by speech recognition module 44 to generate recognized text; topics are detected within a speech to text transcript of a voice interaction or the transcript of a text based chat session);
responsive to receiving the one or more suggested answers, updating the performance evaluation to include the one or more suggested answer to the one or more questions ([Fig. 10] automatic evaluation: a quality monitoring system is capable of automatically filling in answers to at least some portions of the evaluation form based on an automatic analysis of the interaction; the scores and answer are stored for later output after answering each question); and
outputting the updated performance evaluation in an interface of the CRM system for an evaluation process that utilizes the performance evaluation ([Figs. 5, 9A, 10] evaluation scores are stored for outputs and used for training/coaching; the customized training session is presented to the agent via the agent device).
Tapuhi teaches building inputs for an automatic filling engine, not an LLM. The difference between the prior art and the claimed invention is that Tapuhi does not explicitly teach generating, for the generative AI service comprising at least one large language model (LLM), a request object for one or more suggested answers to the one or more questions based on the one or more answer options and the transcript, wherein the request object prompts the at least one LLM of the generative AI service to respond to the one or more questions with the one or more suggested answers based at least on the one or more answer options and the transcript.
Tai teaches generating, for the generative AI service comprising at least one large language model (LLM), a request object for one or more suggested answers to the one or more questions based on the one or more answer options and the transcript, wherein the request object prompts the at least one LLM of the generative AI service to respond to the one or more questions with the one or more suggested answers based at least on the one or more answer options and the transcript ([Literature Review] [ LLM and Data Processing] [LLMs and Empirical Research] [Results] LLM; a codebook and inputted the sample text and codebook into LLM; LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding; code (questions) and sample text (transcript) transmitted as a prompt to the LLM which returns answer per code);
Tai further teaches requesting the one or more suggested answers from the generative AI service based on the request object ([Literature Review] [ LLM and Data Processing] [LLMs and Empirical Research] [Results] inputted the sample text and codebook into an LLM; determining by the LLM code if the codes were present in each prompt submission as a request for answer on the coders);
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Tapuhi with teachings of Tai by modifying the system and method for automatic quality evaluation of interactions as taught by Tapuhi to include generating, for the generative AI service comprising at least one large language model (LLM), a request object for one or more suggested answers to the one or more questions based on the one or more answer options and the transcript, wherein the request object prompts the at least one LLM of the generative AI service to respond to the one or more questions with the one or more suggested answers based at least on the one or more answer options and the transcript; requesting the one or more suggested answers from the generative AI service based on the request object as taught by Tai for the benefit of providing a systematic and reliable platform for code identification and offering a means of avoiding analysis misalignment (Thai [Abstract]).
The difference between the prior art and the claimed invention is that Tapuhi nor Thai explicitly teach processing, using a transcript data processor, the transcript associated with at least one of the interaction and additional relevant data associated with the evaluation form data to create processed transcript data with instructions to set the context of the transcript; processing, using an evaluation form data processor, the one or more questions, the one or more answer options, and user defined question prompt data to create a question prompt list; and generating at least one prompt to the at least one LLM based on the processed transcript data, the question prompt list, and a system configuration for communicating with the generative AI service.
Therefore, it would not have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Tapuhi and Thai to include processing, using a transcript data processor, the transcript associated with at least one of the interaction and additional relevant data associated with the evaluation form data to create processed transcript data with instructions to set the context of the transcript; processing, using an evaluation form data processor, the one or more questions, the one or more answer options, and user defined question prompt data to create a question prompt list; and generating at least one prompt to the at least one LLM based on the processed transcript data, the question prompt list, and a system configuration for communicating with the generative AI service. Therefore, allowable over the prior art.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/Examiner, Art Unit 2659
Prosecution Insights