DETAILED ACTION
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by U.S Pub. No. 2023/0297778 A1 to CAN et al. (hereinafter “CAN”).
Regarding claim 1, CAN teaches a method for improving agent-user interactions for a phone call, the method comprising:
recording a phone call between a client and an agent; generating real-time transcript chunks from the recording of the phone call; determining an input query phrase from one or more of the real-time transcript chunks (paragraphs [0016], [0020] and [0082]; Call center system 100 may capture high effort level statements made during interactive communications and may include, but is not limited to, a real-time voice-to-text transcription (transcribe the call), an effort level detector (e.g., a multi-class classification machine learning model) and an interactive communication summarizer); applying the determined input query phrase to a pre-determined embedding model to generate an input feature embedding for the input query phrase; retrieving a number, n, of links associated document links, each associated with respective link feature embeddings that are the closest n link feature embeddings to the input feature embedding (paragraphs [0082]-[0085]; FIG. 5, FIGS. 6A and 6B; illustrate multiple examples of real-time summaries for agents, as per some embodiments. The tables show sample utterances from transcripts that had high attention scores from the customer effort classifier 204 and the rightmost column provides sample summaries. As can be seen in the examples, utterances with high attention scores may generate high quality call summaries that may be quickly read to understand what the call was about and the effort level of the call made in an attempt to resolve an issue), each of the link feature embeddings generated by applying the pre-determined embedding model to a document text indicated by the respective link; displaying to the agent one or more of the retrieved n links; receiving from the agent a selection of one of the one or more displayed links; and retrieving and displaying the document text indicated by the link to the agent (paragraphs [0082]-[0085]; FIG. 5, FIGS. 6A and 6B ;call center system 100, based on at least the selected utterances, generates a summary of the interactive communication with at least the one or more selected utterances with supportive text or phrasing).
Regarding claim 2, CAN teaches the method of claim 2, wherein recording the phone call comprises: recording an agent portion of the phone call from a microphone source; and recording a client portion of the phone call from a speaker source (paragraphs [0020] and [0048]; virtual assistant is essentially a prerecorded voice menu that can be navigated verbally or manually and collects information about the customer inquiry before automatically transferring the call to the most appropriate queue).
Regarding claim 3, CAN teaches the method of claim 1, further comprising cleaning the real-time transcript chunks to remove extraneous words or text (Fig. 6A elements 602 and 610; converting sample utterances to possible summaries, some words have been removed).
Regarding claim 4, CAN teaches the method of claim 1, wherein the input query phrase is generated from a plurality of real-time transcript chunks combined together (paragraphs [0039] and [0040]; interactive communication summarizer 114 subsequently receives the ranked, scored utterances and selects highly ranked utterances for combination with other contextually supportive words to create summary wording/phrasing).
Regarding claim 5, CAN teaches the method of claim 4, further comprising removing duplicate start and/or end words or phrases from transcript chunks that are combined together (paragraphs [0039] and [0040]; interactive communication summarizer 114 subsequently receives the ranked, scored utterances and selects highly ranked utterances for combination with other contextually supportive words to create summary wording/phrasing).
Regarding claim 6, CAN teaches the method of claim 1, wherein the input query phrase has a maximum length (Fig. 6A, element 606).
Regarding claim 7, CAN teaches the method of claim 1, further comprising: generating a transcript log from the transcript chunks (Fig. 6A; please see “possible summaries”).
Regarding claim 8, CAN teaches the method of claim 7 further comprising: upon termination of the phone call, automatically generating a summary of the phone call using at least the transcript log; and displaying the generated summary to the agent (paragraphs [0015] and [0039]; generating an intelligent summary of the transcription text representing the caller/agent interaction).
Regarding claim 9, CAN teaches the method of claim 8, further comprising receiving from the agent a modification to the generated summary (Fig.6A and paragraph [0039]; a call agent may receive possible summations of a current or previous call displayed on their computer screen).
Regarding claim 10, CAN teaches the method of claim 9, further comprising displaying the transcript log with the summary of the phone call (Abstract, paragraphs [0016] and [0020]; system receives a transcript as the input and generates a textual summary as the output. In order to improve a call summary and customize a summarization task to a call center domain, the technology disclosed herein may employ a classifier that predicts an effort level and attention score for individual utterances within a call transcript, ranks the attention scores and uses selected ones of the ranked utterances in the summary).
Regarding claim 11, CAN teaches the method of claim 1, further comprising passing the one or more transcript chunks to a fraud detection machine learning model and displaying an indication of the results of the fraud detection machine learning model to the agent (paragraphs [0082]-[0085]; FIG. 5, FIGS. 6A and 6B ;call center system 100, based on at least the selected utterances, generates a summary of the interactive communication with at least the one or more selected utterances with supportive text or phrasing).
Regarding claim 12, CAN teaches the method of claim 1, further comprising passing the one or more transcript chunks to a social engineering detection machine learning model and displaying an indication of the results of the social engineering detection machine learning model to the agent (paragraphs [0039] and [0040]; interactive communication summarizer 114 subsequently receives the ranked, scored utterances and selects highly ranked utterances for combination with other contextually supportive words to create summary wording/phrasing).
Regarding claim 13, CAN teaches a system for improving agent-user interactions for a phone call, the system comprising: at least one processor; and at least one memory storing instructions which when executed by the at least one processor configure the system to provide a method according to claim 1 (please see claim 1 rejections above).
Regarding claim 14, CAN teaches a non-transitory computer readable memory storing instructions which when executed by at least one processor provide a method according to claim 1 (please see claim 1 rejections above).
Regarding claim 15, CAN teaches a method for automatically generating a call summary, the method comprising: receiving a call transcript of a call audio to be summarized; determining at least one scenario of a plurality of predefined scenarios that apply to the call transcript (paragraphs [0016], [0020] and [0082]; Call center system 100 may capture high effort level statements made during interactive communications and may include, but is not limited to, a real-time voice-to-text transcription (transcribe the call), an effort level detector (e.g., a multi-class classification machine learning model) and an interactive communication summarizer); retrieving one or more pre-defined prompts based on the determined at least one scenario; combining the retrieved one or more pre-defined prompts with the call transcript to generate respective summary prompts (paragraphs [0082]-[0085]; FIG. 5, FIGS. 6A and 6B; illustrate multiple examples of real-time summaries for agents, as per some embodiments. The tables show sample utterances from transcripts that had high attention scores from the customer effort classifier 204 and the rightmost column provides sample summaries. As can be seen in the examples, utterances with high attention scores may generate high quality call summaries that may be quickly read to understand what the call was about and the effort level of the call made in an attempt to resolve an issue); applying the respective summary prompts to a large language model to generate respective call summaries; and storing the respective call summaries (paragraphs [0082]-[0085]; FIG. 5, FIGS. 6A and 6B ;call center system 100, based on at least the selected utterances, generates a summary of the interactive communication with at least the one or more selected utterances with supportive text or phrasing).
Regarding claim 16, CAN teaches the method of claim 15, further comprising: redacting personally identifiable information from the call transcript prior to applying to the large language model (paragraphs [0012], [0020] and [0048]; virtual assistant is essentially a prerecorded voice menu that can be navigated verbally or manually and collects information about the customer inquiry before automatically transferring the call to the most appropriate queue).
Regarding claim 17, CAN teaches the method of claim 15, wherein the call summary matches a predefined format (paragraphs [0036]- [0037]; FIG. 2, effort level detector 110 subsequently analyzes the utterances generated by the caller. For example, the effort level detector 110 may identify, in conjunction with automatic speech recognizer 106, utterances made by the caller based on a voice matching algorithm).
Regarding claim 18, CAN teaches the method of claim 15, further comprising, prior to storing the respective call summaries, presenting the respective call summaries to an agent for review for approval (paragraphs [0076] and [0083]; the call agent may then approve or disapprove the summary).
Regarding claim 19, CAN teaches a system for improving agent-user interactions for a phone call, the system comprising: at least one processor; and at least one memory storing instructions which when executed by the at least one processor configure the system to provide a method according to claim 15 (please see claim 15 rejections above).
Regarding claim 20, CAN teaches a non-transitory computer readable memory storing instructions which when executed by at least one processor provide a method according to claim 15 (please see claim 15 rejections above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S Pub. No. 2021/0158234 A1 to Sivasubramanian et al. discloses systems and methods to implement customer contact service with real-time agent assistance. A service of a computing resource service provider may establish a connection between an agent to obtain audio data of the agent and a customer, transcribe the audio data to generate at least a portion of a transcript, execute one or more natural language processing techniques to generate metadata associated with the transcript, determine, based at least in part on the metadata, whether one or more categories match the transcript, generate information by processing the transcript, the metadata, and the one or more categories, and provide, to the agent, a notification that encodes the information (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKELAW A TESHALE whose telephone number is (571)270-5302. The examiner can normally be reached 9 am -6pm.
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AKELAW TESHALE
Primary Examiner
Art Unit 2694
/AKELAW TESHALE/Primary Examiner, Art Unit 2694
Prosecution Insights