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 .
Continuation
This application is a continuation (“CON”) application of U.S. application no. 17/943,083, filed 09/12/2022 (“Parent Applications’). See MPEP §201.08. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Applications. Also, in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered “of record’ in the Parent Applications are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Applications are relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Status of Claims
The following is a Non-Final Office Action.
Claims 1-20 are considered in this Office Action. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/11/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 1-14 is 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.
Claims 1, 2, and 9 recite the limitation "the processor". There is insufficient antecedent basis for this limitation in the claim.
Claims 2-8 and 10-14 depend from one of claims 1/9 and fail to cure the deficiency noted above, and are therefore rejected based on dependency.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility Guidance” (as explained in MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-8), the system (claims 9-14), and the non-transitory computer readable medium (claims 15-20) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “mental process” group within the enumerated groupings of abstract ideas set forth in the 2019 PEG, wherein the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. (See MPEP 2106.04(a)(2)). The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are: A method for assessing an interaction, comprising: obtaining a digital recording of an interaction between a customer service representative and a customer, wherein the digital recording comprises at least an audio component; processing, by the processor, the digital recording to generate feature-annotated discourse transcript information comprising inferred features of the interaction between the customer service representative and the customer; processing, by the processor, the feature-annotated discourse transcript information to generate scored compendium information comprising evaluations of the interaction between the customer service representative and the customer with respect to a plurality of attributes of a compendiums determining, by the processor, one or more defects within the digital recording based on the generated scored compendium information; and automatically generating a graphical user interface comprising a visual indication for each of the one or more defects based on the determination.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to system, a network interface that is representative of a first application program executable by the system, a transcript generation engine that is representative of a second application program executable by the system, a transcript generation engine, a transcript evaluation engine, a transcript evaluation engine that is representative of a third application program executable by the system, obtain digital recording(amounts to data gathering means), processor, automatically generating a graphical user interface comprising a visual indication for each of the one or more defects based on the determination(amounts to displaying data), and non-transitory computer readable medium comprising instructions that, when executed by one or more processors (recited at high level) to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s Specification figure [0057] describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: system, a network interface that is representative of a first application program executable by the system, a transcript generation engine that is representative of a second application program executable by the system, a transcript generation engine, a transcript evaluation engine, a transcript evaluation engine that is representative of a third application program executable by the system, obtain digital recording(amounts to data gathering means), processor, automatically generating a graphical user interface comprising a visual indication for each of the one or more defects based on the determination(amounts to displaying data), and non-transitory computer readable medium comprising instructions that, when executed by one or more processors (recited at high level) to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification ([0057]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well (i.e., claims 2, 10-12, and 16 recite representative of a first application program executable by the processor, representative of a second application program executable by the processor, representative of a fourth application program executable by the system, and representative of a fifth application program executable by the system, claim 7 recites the digital recording comprises a digital media file, wherein the digital media file comprises content capturing the interaction between the representative and the customer, however the claim amounts to high-level data gathering and claims 8/14/20 recite an artificial neural network (recited at high level). The additional elements are directed to the use of generic computing elements (Applicant’s Specification figure 1 describe high level general purpose computer) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification ([0057]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter ), however, similar to the finding for claims above, these claims are similarly directed to the abstract idea of certain method of organizing human activity, mathematical concept, and a mental process, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
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 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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Swaminathan Sivasubramanian (US 2021/0158805 A1, hereinafter “Sivasubramanian”) in view of Stephen Gates (US 2010/0332287 A1, hereinafter “Gates”).
Claim 1/9/15
Sivasubramanian teaches:
A method for assessing an interaction (step functions workflow 114 illustrated in Fig. 1. Computing environment 100, in which a contacts analytics service can be implemented para. [(0061], [0181] describes non-transitory computer-readable medium comprising instruction executed by a processor,), comprising:
obtaining a digital recording of an interaction between a customer service representative and a customer ([0063] network interface; [0072] copy input data from data store 116. a role associated with client 104 is assumed and, upon assumption of the client role, a request is made to client data store 106 for contacts data. Contacts data may include audio recordings of calls between agents and customers, chat logs of online conversations between agents and customers, video interactions between agents and customers, and more), wherein the digital recording comprises at least an audio component ([0072] Audio recordings may be stored as audio files, such as MP3 files);
processing, by the processor, the digital recording to generate feature-annotated discourse transcript information comprising inferred features of the interaction between the customer service representative and the customer ([0184] one or more processor; [0073] once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130. Speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker);
determining, by the processor, one or more defects within the digital recording based on the generated scored compendium information ([0042] customer calls are automatically transcribed and indexed, and can be accessed within a customer contact service UI. Call audio and transcripts may be provided together with additional metadata associated with the call, such as sentiment scoring for different segments of a call. In some cases, calls are analyzed to extract different call characteristics which may include one or more of the following non-limiting examples: talk speed, interruptions, silence (e.g., gaps in speech), speaker energy, pitch, tone, and other voice characteristics. A rich set of filtering parameters can be leveraged by users based on criteria such as silence duration and number of interruptions to identify potential areas for improvement. [0049] contacts analytics service provides a dashboard that allows supervisors to track live calls being handled by agents and displays call scores, customer sentiment scores, categorizations, and other information that can be used by supervisors to prioritize calls that need their attention);
and automatically generating a graphical user interface comprising a visual indication for each of the one or more defects based on the determination ([0048] Contacts analytics service provides real-time analytics capabilities which can be used analyze call and chat data in real-time and provide assistance to supervisors and/or agents. Contacts analytics service exposes a graphical dashboard to supervisors that shows real-time analytics of all live calls of a customer contact center. Real-time analytics dashboards may present sentiment scores for calls as interactions evolve, allowing supervisors to look across live calls and see where they may be needed to engage and/or de-escalate and/or help an agent. Figs. 12-16 describe a generated GUI for each of the one or more defects based on the determination).
While Sivasubramanian teaches [0072] copy input data from data store 116. a role associated with client 104 is assumed and, upon assumption of the client role, a request is made to client data store 106 for contacts data. Contacts data may include audio recordings of calls between agents and customers, chat logs of online conversations between agents and customers, video interactions between agents and customers, and more and [0073] once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130. Speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker, Sivasubramanian does not explicitly teach the following, however, analogues reference, in the field of analyzing customer and agent interaction, Gates teaches
processing, by the processor, the feature-annotated discourse transcript information to generate scored compendium information comprising evaluations of the interaction between the customer service representative and the customer with respect to a plurality of attributes of a compendium ([0032] as a first step, shown in FIG. 2 at reference numeral 202, the conversation or interaction between a customer and an agent is captured. As noted above, the interaction may include entry of content from multiple media, including audio input communicated. Once the interaction is captured, an interaction text is generated at step 204. For solely audio input, the captured conversation will be provided to an automatic speech recognition engine for generation of the call transcript. The interaction text is provided to the C-SAT Prediction component for analysis. [0033] Analysis of the interaction text includes a step, at 206, of using Natural Language Processing (NLP) for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features are combined with structured features which are obtained at 208 from other contact center data stored in one or more contact center databases. At step 210, a customer satisfaction prediction score is generated from the combination of identified unstructured features and structured features on the basis of a previously-created C-SAT Model (a plurality of attributes of a compendium). The predicted customer satisfaction score is presented at step 212).
It would have been obvious for a person having ordinary skill in the art at the time of invention to modify the teachings of Sivasubramanian to include those of Gates such as processing the feature-annotated discourse transcript information to generate scored compendium information comprising evaluations of the interaction between the customer service representative and the customer with respect to a plurality of attributes of a compendium, because doing so would enable identifying unstructured features in the interaction text previously identified as being related to customer satisfaction(Gates [0033]).
Claim 2/10/16
Sivasubramanian teaches:
The method of claim 1, wherein the inferred features of the generated feature- annotated discourse transcript information comprises: an initial discourse transcript component that is representative of a first application program executable by the processor containing text data corresponding to a written representation of the speech between the customer service representative and the customer([0184] some or all of the process 2100 (or any other processes described herein, or variations and/or combinations thereof) is performed under the control of one or more computer systems configured with computer-executable instructions and may be implemented as code (e.g., computer-executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, software, or combinations thereof. [0073] Once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130., where the speech-to-text service 130 receives audio data (e.g., in the form of an audio or video file) and generates a text-based transcript of the audio data);
and an annotation component that is representative of a second application program executable by the processor containing information about a plurality of features of sections of the text data contained in the initial discourse transcript component ([0184] some or all of the process 2100 (or any other processes described herein, or variations and/or combinations thereof) is performed under the control of one or more computer systems configured with computer-executable instructions and may be implemented as code (e.g., computer-executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, software, or combinations thereof. [0073] Speech-to-text service 130 may generate metadata for audio which can include periods of silence, cross-talk (e.g., where multiple speakers talk over each other), and more. Metadata may be included as part of a transcript output, while [0075] sentiment analysis 120A may refer to analyzing text (e.g., a turn, being a portion of a text-based transcript of an audio recording) and determining one or more characteristics of the call).
Claim 3/11/17
Sivasubramanian teaches:
The method of claim 2, wherein processing the digital recording to generate the feature-annotated discourse transcript information comprises: generating the text data contained in the initial discourse transcript component by evaluating the audio component of the digital recording ([0073] Once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130., where the speech-to-text service 130 receives audio data (e.g., in the form of an audio or video file) and generates a text-based transcript of the audio data);
generating feature data representing inferred information about a plurality of features of sections of the text data contained in the initial discourse transcript component ([0073] speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker. Transcripts may be partitioned by speaker, by sentence, by time (e.g., a fixed duration wherein each turn lasts 15 seconds or a fixed number wherein an entire call is partitioned into N segments of equal length). For example, if an agent speaks for the first 10 seconds of a call and a customer speaks for the next 15 seconds, text for the first turn may include the agent's speech from the first 10 seconds and text for the second turn may include the customer's speech from the next 15 seconds. [0075] Sentiment analysis 120A may refer to analyzing text (e.g., a turn, being a portion of a text-based transcript of an audio recording) and determining one or more characteristics of the call);
and annotating the generated initial discourse transcript component with the generated feature data([0073] Speech-to-text service 130 may generate metadata for audio which can include periods of silence, cross-talk (e.g., where multiple speakers talk over each other), and more. Metadata may be included as part of a transcript output, while [0075] sentiment analysis 120A may refer to analyzing text (e.g., a turn, being a portion of a text-based transcript of an audio recording) and determining one or more characteristics of the call).
Claim 4/18
While Sivasubramanian teaches [0072] copy input data from data store 116. a role associated with client 104 is assumed and, upon assumption of the client role, a request is made to client data store 106 for contacts data. Contacts data may include audio recordings of calls between agents and customers, chat logs of online conversations between agents and customers, video interactions between agents and customers, and more and [0073] once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130. Speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker, Sivasubramanian does not explicitly teach the following, however, analogues reference, in the field of analyzing customer and agent interaction, Gates teaches
The method of claim 1, wherein processing the feature-annotated discourse transcript information to generate the scored compendium information comprises: extracting behavioral feature data using the feature-annotated discourse transcript information and already extracted behavioral feature data ([0033] analysis of the interaction text includes a step, at 206, of using Natural Language Processing [NLP] for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features [mapped as compendium information] are combined with structured features [mapped as compendium information] which are obtained at 208 from other contact center data stored in one or more contact center databases);
determining one or more applicable attributes of the compendium using the feature- annotated discourse transcript information, the extracted behavioral feature data, and already determined applicable attributes ([0032] For solely audio input, the captured conversation will be provided to an automatic speech recognition engine for generation of the call transcript. The interaction text is provided to the C-SAT Prediction component for analysis. [0033] Analysis of the interaction text includes a step, at 206, of using Natural Language Processing (NLP) for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features are combined with structured features which are obtained at 208 from other contact center data stored in one or more contact center databases);
and generating an assessment for each of the determined one or more applicable attributes using a corresponding attribute assessment model to process the feature-annotated discourse transcript information, the extracted behavioral feature data, and already generated applicable attribute assessments([0033] At step 210, a customer satisfaction prediction score is generated from the combination of identified unstructured features and structured features on the basis of a previously-created C-SAT Model (described below). The predicted customer satisfaction score is presented at step 212).
It would have been obvious for a person having ordinary skill in the art at the time of invention to modify the teachings of Sivasubramanian to include those of Gates such as processing the feature-annotated discourse transcript information to generate the scored compendium information comprises: extracting behavioral feature data using the feature-annotated discourse transcript information and already extracted behavioral feature data, determining one or more applicable attributes of the compendium using the feature- annotated discourse transcript information, the extracted behavioral feature data, and already determined applicable attributes, and generating an assessment for each of the determined one or more applicable attributes using a corresponding attribute assessment model to process the feature-annotated discourse transcript information, the extracted behavioral feature data, and already generated applicable attribute assessments, because doing so would enable identifying unstructured features in the interaction text previously identified as being related to customer satisfaction(Gates [0033]).
Claim 5/13/19
Sivasubramanian teaches:
The method of claim 2, wherein the generated feature-annotated discourse transcript information further comprises timestamps indicating for the text data of the initial discourse transcript component when the corresponding speech between the customer service representative and the customer occurred ([0073] speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker. Transcripts may be partitioned by speaker, by sentence, by time (e.g., a fixed duration wherein each turn lasts 15 seconds or a fixed number wherein an entire call is partitioned into N segments of equal length). For example, if an agent speaks for the first 10 seconds of a call and a customer speaks for the next 15 seconds, text for the first turn may include the agent's speech from the first 10 seconds and text for the second turn may include the customer's speech from the next 15 seconds. [0148] Contact trace record page 1200 may be a visualization of some or all data of an output file generated by a contacts analytics service. Contact trace record page 1200 may include a contact summary section that includes some or all of the following information: contact id, start and end times (e.g., based on an initiation timestamp and a disconnect timestamp), contact duration, customer number, agent, queue, and actions triggered (e.g., categories)).
Claim 6
Sivasubramanian teaches:
The method of claim 3, wherein evaluating the audio component of the digital recording to generate initial discourse transcript data corresponding to the written representation of the speech between the customer service representative and the customer comprises using automatic speech recognition to process the audio component of the digital recording([0073] speech-to-text service 130 receives audio data (e.g., in the form of an audio or video file) and generates a text-based transcript of the audio data).
Claim 7
Sivasubramanian teaches:
The method of claim 1, wherein the digital recording comprises a digital media file, wherein the digital media file comprises content capturing the interaction between the representative and the customer ([0072] a role associated with client 104 is assumed and, upon assumption of the client role, a request is made to client data store 106 for contacts data. Contacts data may include audio recordings of calls between agents and customers, chat logs of online conversations between agents and customers, video interactions between agents and customers, and more. Audio recordings may be stored as audio files, such as MP3 files.).
Claim 8/14/20
Sivasubramanian teaches:
The method of claim 4, wherein at least one of the corresponding attribute assessment models comprises an artificial neural network ([0074] text-based contacts data (e.g., transcripts generated by speech-to-text service 130 or text-based contacts data obtained from client data store 106) are analyzed using a natural language processing (NLP) service. NLP service 132 uses artificial intelligence and/or machine learning techniques to perform sentiment analysis 120A, entity detection 120B, key phrase detection 120C, and various combinations thereof).
Claim 12
While Sivasubramanian teaches [0072] copy input data from data store 116. a role associated with client 104 is assumed and, upon assumption of the client role, a request is made to client data store 106 for contacts data. Contacts data may include audio recordings of calls between agents and customers, chat logs of online conversations between agents and customers, video interactions between agents and customers, and more and [0073] once contacts data has been copied, a step of step functions workflow is to transcribe calls 118 included in the input data. Audio recordings of customer calls may be transcribed using a speech-to-text service 130. Speech-to-text service 130 organizes the transcript by turns, breaking down the audio into different turns based on the speaker, Sivasubramanian does not explicitly teach the following, however, analogues reference, in the field of analyzing customer and agent interaction, Gates teaches:
The system of claim 9, wherein the third application program representing the transcript evaluation engine comprises: a behavioral feature extraction engine configured to extract behavioral feature data using the feature-annotated discourse transcript information and already extracted behavioral feature data ([0033] analysis of the interaction text includes a step, at 206, of using Natural Language Processing [NLP] for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features [mapped as compendium information] are combined with structured features [mapped as compendium information] which are obtained at 208 from other contact center data stored in one or more contact center databases);
an attribute applicability engine configured to determine one or more applicable attributes of the compendium using the feature-annotated discourse transcript information, the extracted behavioral feature data, and already determined applicable attributes ([0032] For solely audio input, the captured conversation will be provided to an automatic speech recognition engine for generation of the call transcript. The interaction text is provided to the C-SAT Prediction component for analysis. [0033] Analysis of the interaction text includes a step, at 206, of using Natural Language Processing (NLP) for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features are combined with structured features which are obtained at 208 from other contact center data stored in one or more contact center databases);
and one or more attribute assessment models, wherein: each of the one or more attribute assessment models corresponds to one of the one or more determined applicable attributes; and the one or more attribute assessment models are configured to generate an assessment for their corresponding attribute by processing the feature-annotated discourse transcript information, the extracted behavioral feature data, and already generated applicable attribute assessments ([[0033] Analysis of the interaction text includes a step, at 206, of using Natural Language Processing (NLP) for identifying a plurality of unstructured features in the interaction text that have been previously identified as being related to customer satisfaction. Those unstructured features are combined with structured features which are obtained at 208 from other contact center data stored in one or more contact center databases. At step 210, a customer satisfaction prediction score is generated from the combination of identified unstructured features and structured features on the basis of a previously-created C-SAT Model (described below). The predicted customer satisfaction score is presented at step 212).
It would have been obvious for a person having ordinary skill in the art at the time of invention to modify the teachings of Sivasubramanian to include those of Gates such as a behavioral feature extraction engine configured to extract behavioral feature data using the feature-annotated discourse transcript information and already extracted behavioral feature data; an attribute applicability engine configured to determine one or more applicable attributes of the compendium using the feature-annotated discourse transcript information, the extracted behavioral feature data, and already determined applicable attributes; and one or more attribute assessment models, wherein: each of the one or more attribute assessment models corresponds to one of the one or more determined applicable attributes; and the one or more attribute assessment models are configured to generate an assessment for their corresponding attribute by processing the feature-annotated discourse transcript information, the extracted behavioral feature data, and already generated applicable attribute assessments, because doing so would enable identifying unstructured features in the interaction text previously identified as being related to customer satisfaction(Gates [0033]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Henry Truong (US 20200111377 A1): system and method of use to train customer service agents. The training system employs intelligent systems to facilitate or enable the training of customer service agents. The training system provides training to customer service agents and tracks the progress of the customer service trainees. In one aspect, the training system emulates a customer engaging with the customer service trainee, by emulating one or both of the persona of the customer and the scenario of the customer/trainee interaction.
Derek M. Miller (US 20180091654 A1): method for automatically managing a recorded interaction between a customer and an agent of a contact center includes: extracting, by a processor, features from the recorded interaction; computing, by the processor, a score of the recorded interaction by supplying the features to a prediction model; detecting, by the processor, a condition based on the score; matching, by the processor, the condition with an action; and controlling, by the processor, a workforce management server to assign a training session to the agent of the contact center.
John Ripa (US 20140140496 A1): Systems and methods are provided for analyzing conversations between customers and call center agents in real-time. An agent may be located at an agent station having a display screen. A continuous audio feed of the conversation between a customer and an agent may be received. For every second that the customer is speaking, a customer emotion score may be calculated in real-time. A frequency at which calculated customer emotion scores equal or exceed an emotion score threshold during a specified time interval may be calculated in real-time during the conversation. The calculated frequency for the customer may be compared, in real-time, to a plurality of specified frequency thresholds. A visual representation corresponding to a highest of the plurality of specified frequency thresholds that is equaled or exceeded by the calculated frequency for the customer may be displayed in real-time on the display screen of the agent station.
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/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625
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