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
1. This is in response to application filed 06/20/2024.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted 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 § 103
3. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCord (Pub.No.: 2019/0050875 A1) in view of Sivasubramanian et al. (Pub.No.: 2021/0158813 A1).
Regarding claims 1, 15 and 16, McCord teaches a visualized information generation apparatus, method and non-transitory computer readable medium (reads on analyzing customer interactions and producing analytics output from conversation data, see [0065] and abstract) comprising:
a memory (reads on computing systems storing interaction transcript and analysis data, see [0065]); and
at least one processor connected to the memory (reads on processor-based conversational analytics system processing interaction transcripts, see [0065]), wherein the processor is configured to
generate visualized information (reads on generating analytics and monitoring results based on transcripts similarity evaluation and scripts adherence analysis, see [0065]. Note that McCord teaches generating alerts and monitoring output based on compliance evaluation, see [0078]) in response to an input of information indicating compliance, non- compliance, or both (reads on determining conversational similarity and script adherence, including identifying divergence from expected script behavior, see [0069] and [0078]) between an utterance content expressed by an utterance text and an utterance content expressed by a predetermined script (reads on conversational similarity scoring and monitoring script adherence and deviation, see [0069] and [0078]).
Note for the claimed limitations of “the visualized information being for visualizing a range estimated to be in compliance in a manner that is different from a manner in which a range estimated to be in non-compliance is visualized”, “the range estimated to be in compliance being a range of the utterance content expressed by one of the utterance text or the script in which the utterance content expressed by one of the utterance text or the script is estimated to comply with the utterance content expressed by another of the utterance text or the script” , and “. the range estimated to be in non-compliance being a range of the utterance content expressed by one of the utterance text or the script in which the utterance content expressed by one of the utterance text or the script is estimated not to comply with the utterance content expressed by another of the utterance text or the script”.
McCord teaches detecting divergence (see [0069] and [0078]) but does not display compliant and non-compliant portions in different visual formats, McCord also analyzes conversational passages (see [0069]) but does not specifically disclose visual transcript segment presentation and last McCord teaches alerts and monitoring (see [0078]) but it does not teach dashboard visualization interfaces, In other words, McCord teaches the compliance analysis engine but does not teach visual presentation of compliant and non-compliant transcript ranges.
However, Sivasubramanian teaches contact analytics system and method that analyze customer interactions (see [0031]) and generate generated visualized analytics information presented through graphical user interfaces, dashboards and visualization tools displaying portions of conversations and flagged interaction issues, see [0036], [0038], [0043] and [0074]. Sivasubramanian also teaches analyzing calls against defined interaction rules and protocols and flagging interactions that fail to meet compliance requirements, thereby identifying non-compliant portions of interactions and presenting these portions visually to supervisors and agents, see [0043]. Last, Sivasubramanian teaches presenting specific portions of conversations and organizing transcripts into conversations segments such as speaking turns, thereby identifying ranges of utterance content corresponding to interaction evaluation results, see [0036] and [0074].
Thus, it would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to modify Mehta to modify McCord to include the visual analytics presentation techniques taught by Sivasubramanian so that compliance and non-compliance determinations generated by McCord could be visually displayed through graphical dashboards and transcript segment visualization interfaces as taught by Sivasubramanian. Such modification would improve stability, interpretability and monitoring efficiency of conversational compliance evaluation systems by providing visual feedback to supervisors and agents regarding compliant and non-compliant conversation portions.
Regarding claim 2, the combination of McCord and Sivasubramanian teaches wherein the utterance text is previously divided into one or more divided utterance texts (McCord analyzes passages of conversation, see [0069] and Sivasubramanian explicitly teaches transcripts divided by speaking turns, see [0074] and [0085]), and the script is previously divided into one or more divided scripts (see McCord [0069] and Sivasubramanian [0043]), and
the information indicating the compliance, the non-compliance, or both is information indicating compliance, non-compliance, or both between the divided utterance texts and the divided scripts (the combined references teach segment-level compliance monitoring, see McCord [0069] and [0078] and Sivasubramanian [0043]).
Claim 3 recites “wherein the processor is configured calculate a degree of compliance between the utterance content expressed by the divided utterance texts and the utterance content expressed by the divided scripts, as a score, based on the information indicating the compliance, the non-compliance, or both, and generate visualized information for visualizing the score for each of the divided scripts of a same script that is the script”. McCord teaches generating similarity scores and conversational distance metric (see [0074]) and Sivasubramanian teaches scoring calls and evaluating segments using scoring metric (see [0075])
Regarding claim 4, the combination of McCord and Sivasubramanian teaches wherein the score includes
a precision rate that expresses a degree in which the divided utterance texts estimated to be in compliance are included in all of the divided utterance texts (McCord teaches quantitative similarity metrics, see [0075] and Sivasubramanian teaches scoring metrics for call evaluation [0075], which would reasonably include performance metrics such as precision type evaluation), or
a recall rate that expresses a degree in which the divided scripts estimated to be in compliance are included in all of the divided scripts.
Regarding claim 5, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to generate visualized information for items of a same script that is the script or for same divided scripts that are the divided scripts, the visualized information being for visualizing, as a list, the items (reads on identifies matching conversational passages, see McCord [0069] and visual listing of conversation portions and issues, see Sivasubramanian [0036]), and
the divided utterance texts of utterance contents estimated to comply with utterance contents expressing the divided scripts corresponding to the items (the combined references teach listing compliant transcript portions, see McCord [0069] and Sivasubramanian [0043]).
Regarding claim 6, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to generate visualized information for visualizing, as a list, utterance contents of the divided utterance texts estimated not to comply with utterance contents expressed by the divided scripts (McCord identifies script divergence, see [0078] and Sivasubramanian teaches flagged non-compliant conversation portions displayed in dashboards, see [0043] and [0036]).
Regarding claim 7, the combination of McCord and Sivasubramanian teaches wherein
the processor is configured to generate the visualized information for visualizing, as the list,
the utterance contents of the divided utterance texts estimated not to comply with the utterance contents expressed by the divided scripts, by a unit of an utterance subject that utters the utterance text (McCord processes dialogue transcripts involving multiple participants, see [0065] and Sivasubramanian teaches organizing transcripts by speaking turns and participants, see [0085]).
Regarding claim 8, the combination of McCord and Sivasubramanian teaches wherein
the processor is configured to generate visualized information for visualizing items before and after the item corresponding to the divided script or divided utterance texts before and after the divided utterance text, upon generating the visualized information for visualizing, as the list, the utterance contents of the divided utterance texts estimated not to comply with the utterance contents expressed by the divided scripts (McCord analyzes conversational passages in context, see [0069] and Sivasubramanian teaches visual display of conversation portions including related segments, see [0036]).
Regarding claim 9, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to obtain rating information on the utterance text from exterior (McCord evaluates interaction performance, see [0065] and Sivasubramanian teaches agent performance feedback and analytics scoring retrieved from external evaluation sources, see [0036] and [0052]), and generate visualized information for visualizing, as a list, the obtained rating information in association with the divided utterance texts, and in association with the information indicating the compliance, the non-compliance, or both between the divided utterance texts and the divided scripts (the combined references teach associating ratings with transcript analytics dashboard, see McCord [0065] and Sivasubramanian [0036] and [0052]).
Regarding claim 10, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to obtain relevant information (McCord analyzes contextual customer interaction data, see [0065] also [0038] and [0053] in Sivasubramanian) to the utterance text from exterior, and
generate visualized information for visualizing, as a list, the obtained relevant information in association with the divided utterance texts (visualization dashboard display contextual analytics with transcript segments, see McCord [0065] and Sivasubramanian [0038]), and
the relevant information includes
a search keyword of FAQ used upon being uttered by an utterance subject that utters the utterance text,
a browsing history of FAQ used upon being uttered by the utterance subject that utters the utterance text (see McCord [0065] and Sivasubramanian [0038] and [0053]),
history information indicating an inquiry to others upon being uttered by the utterance subject that utters the utterance text, or
any combination of the search keyword, the browsing history, and the history information.
Regarding claim 11, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to identify a modification proposal for the utterance text or the script, based on rating information on the utterance text obtained from exterior, and based on the information indicating the compliance, the non-compliance, or both (reads on coaching to improve script adherence, see McCord [0078] and agent coaching suggestions and recommended corrective action in Sivasubramanian [0052] and [0053]).
Regarding claim 12, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to identify a modification proposal for the utterance text or the script, based on the information indicating the compliance, the non-compliance, or both and based on the score (reads on similarity scoring see McCord [0074] combined with Sivasubramanian performance scoring [0075] and [0052] that teaches score-based modification).
Regarding claim 13, the combination of McCord and Sivasubramanian teaches wherein the processor is configured to of the utterance content expressed by the utterance text in which the rating information is equal to or higher than a predetermined rating, identify an utterance content of the range estimated not to comply with the utterance content expressed by the script, as the modification proposal expressing an utterance content to be added to the script (McCord teaches detecting divergence from script, see [0078] and Sivasubramanian teaches providing suggested conversational responses and script guidance, see [0053]), of the utterance content expressed by the utterance text in which the rating information is lower than the predetermined rating, identify an utterance content of the range estimated to comply with the utterance content expressed by the script, as the modification proposal expressing an utterance content to be deleted from the script (the combination of the applied arts teaching for corrective coaching , see McCord [0078] and Sivasubramanian [0053]), and of the utterance content expressed by the utterance text in which the rating information is lower than the predetermined rating, identify an utterance content of the range estimated not to comply with the utterance content expressed by the script, as the modification proposal expressing an utterance content that is unnecessary in the utterance text (the combined references teach identifying improper conversational behavior and recommending corrections, see McCord [0078] and Sivasubramanian [0053]).
Regarding claim 14, the combination of McCord and Sivasubramanian teaches wherein processor is configured to generate visualized information for visualizing the modification proposal. (McCord generates adherence alerts, see [0078] and Sivasubramanian teaches dashboards displaying coaching feedback and improvement suggestions, see [0038] and [0052]).
Conclusion
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rasha S. AL-Aubaidi whose telephone number is (571) 272-7481. The examiner can normally be reached on Monday-Friday from 8:30 am to 5:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ahmad Matar, can be reached on (571) 272-7488.
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/RASHA S AL AUBAIDI/Primary Examiner, Art Unit 2693