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 .
1. This Office Action is made non-final. The previous Office Action (i.e., advisory action) mailed 07/02/2026 was sent in error and has been reclassified as a Miscellaneous Communication to Applicant. Applicant’s amendment and remarks filed June 16, 2026, are treated as a response to a response to the non-final office action mailed April30, 2026 and have been fully considered in this action.
Claim Rejections - 35 USC § 103
2. 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) 1-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahmoud et al. (Pub.No.: 2022/0383153 A1) and further in view of Erhart et al. (Pub. No.: 2021/0029249 A1).
Regarding claims 1, 8 and 15, Mahmoud teaches a method (see [0020-0022]), server (see [0037-0038]) and non-transitory computer-readable medium (see [0022] and [0039]) comprising:
determining, by a contact center server, for one or more customer messages received as part of one or more contact center interactions (reads on contact center infrastructure and agent-assist system processing communication sessions and user queries, see [0020], [0031-0036] and [0040-0042]), one or more changes between (reads on comparing agent input 424 with recommended answers, matches the agent’s reply with recent recommendations, performs sentence/token-level textual comparison using cosine similarity and intersection-over union, see [0079-0084]);
one or more automated response recommendations provided to one or more human agent devices (reads on presenting recommended answers through the agent-assistant interface on agent device 114, see [0020-0021] and [0068-0070]; also Fig. 4A); and
the corresponding one or more human agent responses provided to one or more customer devices (reads on receiving the agent’s answer corresponding to the query and comparing the agent’s answer/input with the recommendation in the same interaction, see [0020-0021] and [0079-0082]).
Mahmoud does not specifically teach “automatically modifying, by the contact center server, execution logic of one or more use cases of the one or more customer messages based on the determining”. In other words, Mahmoud does not characterize the automatically updated recommendation behavior as modification of execution logic of one or more use cases.
However, Erhart teaches a contact center chatbot and dialog engine implemented by a contact management server. The chatbot engine may comprise processor executable instructions and may update conversation models as it learns from ongoing conversations (see [0101-0102]). Erhart teaches that the chatbot engine operates using guidelines, static instructions, or machine learning, learns from feedback provided by a human agent confirming or denying whether a prepared response was appropriate, building and updates conversation models based on the feedback, update, revises, edits, or deletes responses within a bot-response database used by a recommendation engine to generate responses for subsequent customer messages (see [0103-0105] and Fig. 2). Erhart also teaches changing conversation state, initiating a dialog corresponding to a new case or topic, and transitioning between states and different dialogs (see [0116], [0134-0137] and Fig. 5).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the applicant’s claimed invention to incorporate Erhart’s feedback-based updating of conversation models’ response data, and dialog-state logic into Mahmoud’s feedback system so that differences between automated recommendations and corresponding responses actually provided by human agents automatically update the execution logic governing the applicable customer-message use case. One of an ordinary skill would have combined Mahmoud’s response-comparison feedback with Erhart’s dialog-flow modification to improve execution logic for future contact-center interactions, with predictable results.
Regarding claims 2, 9 and 16, the combination of Mahmoud in view of Erhart teaches providing, by the contact center server, a notification of the modification of the execution logic to one or more enterprise user devices (Erhart provides proposed agent response 316 and indication 404 to human agent 172 on human agent communication device 168, see [0131]; see also [0094]); and
receiving, by the contact center server, an approval of the modification of the execution logic (human agent 172 is required to approve or edit the message prior to being transmitted/committed, see [0094]. Erhart further teaches that feedback may be human-provided by a human agent confirming or denying whether a chatbot-prepared response is appropriate, see [0103], that the conversation models are built and updated based on the feedback and stored responses may be updated, revised, edited or deleted, see [0104] and that human-approved suggested responses are recorded and used to further tune or train the chatbot engine for future message analysis, see [0114]. Accordingly, it would have been obvious to configure Mahmoud’s automatic modification to notify an enterprise user of the proposed modification and obtain approval before implementation, because Erhart teaches obtaining human validation of chatbot-prepared responses and using the approved responses to update, tune, or train the chatbot engine, thereby preventing inappropriate responses from being incorporated into future operation).
Regarding claims 3, 10 and 17, the combination of Mahmoud in view of Erhart teaches wherein the one or more automated response recommendations comprise: one or more message prompts configured in the execution logic of the one or more-use cases ((see [0116] and [0117] of Erhart), or one or more content snippets retrieved by executing one or more web requests configured in the execution logic of the one or more-use cases (see Erhart [0091]).
Regarding claims 4, 11 and 18, the combination of Mahmoud in view of Erhart teaches wherein the one or more automated response recommendations are generated by executing the execution logic of the one or more-use cases (see Erhart [0116] and [0117]. In addition, to example (s) and Figs. 3A and 4A of Erhart).
Regarding claims 5, 12 and 19, the combination of Mahmoud in Erhart teaches wherein the one or more changes comprise addition, deletion, or the modification of one or more-use cases one or more entities (Erhart teaches entity values, different entity values, see [0118-0119], causing a different dialog, and updating/editing/deleting learned responses, see [0104]. Also, Mahmoud supplies the comparison between the recommendation and corresponding agent answer- including sentence/token matching and nonmatching text. See [0094-0098]), or one or more entity values.
Regarding claims 6, 13 and 20, the combination of Mahmoud in view of Erhart teaches wherein the one or more changes are determined using a classification model or by a textual comparison of each of the one or more automated response recommendations with the corresponding one of the one or more human agent responses (Mahmoud teaches comparing agent input 424 with the recommended answers, see [0079], matching the agent’s reply with recent recommendations using exact and fuzzy matching, see [0080], expressly measuring text similarity between each recommendation and each agent turn, by breaking both into sentences, and calculates similarity using cosine similarity or interaction-over union, see [008], covers semantic and exact lexical matching [0083] and records the resulting pairs [0084]).
Regarding claims 7, 14 and 21, the combination of Mahmoud in view of Erhart teaches wherein the modification of the execution logic comprises modifying a functional flow of the execution logic (see Fig.5 and [0136]-[0137] of Erhart).
Regarding claim 22, Mahmoud teaches a method (see [0020]) comprising:
automatically modifying, by a contact center server (the contact center infrastructure and agent assistant system include servers and processors executing stored instructions (see [0037]). The feedback pipeline automatically modifies recommendation rankings, confidence scores, and query/response mappings used in subsequent conversations (see [0094]-[0098] and Fig. 5)).
Mahmoud further teaches determining from one or more human agent messages of the one or more contact center interactions (reads on the agent-assistant system compares agent input 424 and the agent’s reply with recent recommendations, determines matches or differences, records those results, and uses validated customer query and agent answer pairs for subsequent learning and modification, see [0079]-[0084] and [0094]-[0098]).
Mahmoud does not specifically teach “execution logic of one or more use cases of one or more contact center interactions based on one or more human agent initiated use cases”.
However, Erhart teaches that contract center conversations may involve human agents and that outbound contact center messages may initiate a change of topic (see [0008]- [0010]). Erhart further teaches using human agent/customer conversations to train topic-specific chatbot skills and automatically modifying conversation models, response data, conversation states, and dialog flow (see [0101]- [0105], [0116], [0134], [0137] and Figs. 2 and 5]).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the applicant’s claimed invention to combine Mahmoud’s agent-message-based learning with Erhart’s topic-based dialog modification to improve subsequent contact-center interactions, with predictable results.
Conclusion
3. 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
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