DETAILED ACTION
Status of the Claims
This office action is submitted in response to the application filed on 12/20/24.
Examiner notes that this application claims foreign priority to 113100337.
Examiner further notes Applicant’s foreign priority date of 1/4/24.
Claims 1-10 are currently pending, and have been examined.
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 .
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 1-10 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.
Independent claims 1 and 6, in part, describe an invention comprising: collecting customer service Q&A text as test material; receiving a Q&A report based on an analysis of the test material; receiving an evaluation score based on the report; and determining whether or not to use the Q&A report to serve as training material based on the evaluation score. As such, the invention is directed to the abstract idea of testing and developing an automated customer Q&A feature, which, pursuant to MPEP 2106.04(a), is aptly categorized as a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional elements including: “one or more processors” for executing the method; and a “memory” for storing executable instructions. These limitations are recited at a high level of generality, and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
The aforementioned claims also disclose the training and use of an AI model (i.e., a software algorithm) to generate reports and scores corresponding to the analysis and evaluation of Q&A test material. These limitations generally recite the use of machine learning to perform these functions. It also amounts to mere instructions to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment (e.g. a generic computer). In furtherance of the above referenced AI model, Examiner notes that merely using an AI or ML model to perform an otherwise abstract analysis does not integrate the exception into a practical application.
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions are merely facilitated by generic computer implementation.
Claims 2-5 and 7-10 are dependent on the aforementioned independent claims, and include all the limitations contained therein. These claims do not recite any additional technical elements, and simply disclose additional limitations that further limit the abstract idea with details regarding the inclusion of an “item list,” the content of the item list, the basis for the evaluation score, and what is included in the “evaluation condition.” Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1-10 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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 1–10 are rejected under 35 U.S.C. 103 as being unpatentable over Dempsey (US 12,113,934 B1) in view of Khetan (US 2023/0252287 A1).
Claims 1 and 6: Dempsey discloses a system and method comprising:
Step (A): inputting a customer service question and answer text as test material to test a question-and-answer artificial intelligence (AI) model (col. 2:60–col. 3:18; col. 5:39–col. 6:15; FIG. 2, 202–210. Dempsey teaches receiving a call audio, converting to a text-based transcript, and supplying the transcript and predefined questions to a trained LLM to obtain scores.);
Step (B): by the question-and-answer AI model, outputting a question-and-answer report based on the customer service question and answer text (col. 2:49–59; col. 7:1–20; FIG. 2, 210–214; FIGS. 3–5. Dempsey presents a call summary and quantitative outputs (per-question scores and combined score) in a GUI as the report generated from the transcript by the LLM process.); and
Step (C): by an expert AI model, outputting an evaluation score based on the question-and-answer report (col. 5:39–col. 6:15; col. 6:60–col. 7:10; FIG. 2, 210–212. Dempsey’s AI score module inputs predefined questions and the transcript to a trained LLM to produce per-question scores and a combined score that quantitatively evaluates the call.).
Dempsey does not appear to explicitly describe a method further comprising: Step (D): determining whether to input the question-and-answer report into the question-and-answer AI model to serve as training material based on the evaluation score.
Khetan, however, describes a method further comprising: Step (D): determining whether to input the question-and-answer report into the question-and-answer AI model to serve as training material based on the evaluation score (Abstract; Paragraphs 0008–0010 and 0013–0016; FIG. 3. Khetan discloses computing a performance score and using a threshold to decide whether to re-train (feedback as training material) or deploy, i.e., a score-gated retraining decision.).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing date of the invention to combine these features of Khetan with those of Dempsey. One would have been motivated to do this in order to add a score-threshold feedback loop that gates retraining using the generated report, improve reliability, and avoid ambiguous retraining in Dempsey’s evaluation pipeline.
Claims 2 and 7: Dempsey further discloses a system and method where in Step (B), the question-and-answer AI model outputs the question-and-answer report based on the customer service question and answer text and an item list (col. 2:49–59; col. 7:1–20; FIG. 2, 210–214; FIGS. 3–5. Dempsey’s report organizes outputs against selected topic criteria and predefined questions (an itemized list) and includes the call summary and combined score presented in the GUI.).
Claims 3 and 8: Dempsey further discloses a system and method wherein the item list includes a summary of customers’ questions, manners to resolve questions, extracted keywords, ways to respond to customers in a comfortable manner, customers’ emotional states and intensities, noting items of reminding customers’ service staffs, a primary intent of a customer's questions, or a combination of these (col. 7:1–20; FIG. 3 call summary; FIG. 5 strengths/opportunities/verbiage; FIG. 6 individual stats and trends; FIG. 2, 210–216. Dempsey’s summarized call content, coaching outputs (strengths, opportunities, verbiage), topic/intent identification, and quality metrics correspond to the enumerated items.).
Claims 4 and 9: Dempsey further discloses a system and method wherein in Step (C), the expert AI model outputs the evaluation score based on the question-and-answer report and an evaluation condition combination (col. 5:39–col. 6:15; col. 6:60–col. 7:10; FIG. 2, 206–212. Dempsey computes scores using predefined questions under criteria selected for the identified topic (the evaluation condition combination), then calculates a combined score.).
Claims 5 and 10: Dempsey further discloses a system and method wherein the evaluation condition combination includes response speed, resolution efficiency, communication skills, customer satisfaction, compliance with procedures and policies, or a combination of these. (col. 7:1–20; FIG. 5 Score Results/Coaching Summary; FIG. 6 agent stats. Dempsey’s criteria and coaching categories reflect standard contact-center dimensions including speed, effectiveness, communication, satisfaction, and compliance.).
Other Relevant Prior Art
Though not recited in the above rejections, the following references are nevertheless deemed to be relevant to Applicant’s disclosures:
Chen et al. (CN 120316496), directed to a method of AI customer service question and answer system for collecting data and amplifying training data.
Shao et al. (CN 111666385), directed to a customer service question answering system based on deep learning.
Liu et al. (CN 118861223), directed to an intelligent customer service question answering system and method.
Wang et al. (WO 2018166115), directed to a method for processing customer service question-answer data.
Chen et al. (CN 119809649), directed to an amplified construction method and system of customer service question answering system based on AI.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BUSCH whose telephone number is (571)270-7953. The examiner can normally be reached M-F 10-7.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621