DETAILED ACTION
Introduction
This office action is in response to applicant’s amendment filed 7/15/2026. Claims 1, 3, 5-7, 9-23 are currently pending and have been examined. There is no claim to foreign priority.
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 .
Response to Arguments
Applicant’s arguments, see remarks, filed 7/15/2026, with respect to the 35 USC 102 and 35 USC 103 rejections of the pending claims have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the previously cited prior art, and further in view of at least Wheeler et al. (US 2024/0356881), Henryson et al. (Henryson, US 2023/0376970), Epstein Koch et al. (Epstein Koch, US 2024/0127800) and Matsuoka et al. (Matsuoka, US 2024/0127800).
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 (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 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.
Claim(s) 1, 3, 5-7, 9, 10, 13-21 are rejected under 35 U.S.C. 103 as being unpatentable over Can (US 2024/0073321) in view of Wheeler et al. (Wheeler, US 2024/0356881) and further in view of Henryson et al. (Henryson, US 2023/0376970).
As per claim 1, Can teaches a computer-implemented method for dynamically generating, via a generative artificial intelligence system, natural language output that provides assistance to a representative associated with a contact center, the method comprising:
providing, by a computing system comprising one or more processors, a contact center assistant comprising a representative coach that:
is based upon a [generative large language] model [(LLM)] that is trained on, a training dataset (paragraph [0021, 0030-0031, 0021-0032]-his call center, call data to train a machine learning model, his virtual assistant as the representative coach, and agent as the representative); and
is configured to assist the representative associated with the contact center [during contacts handled by the representative] (paragraph [0028]-see his processor, non-transitory computer readable medium and system discussion as used hereinafter for implementation of the method, his abstract-method discussion, paragraph [0021, 0030-0031, 0021-0032]-his call center, call data to train a machine learning model, his virtual assistant as the representative coach, and agent as the representative);
monitoring, by the computing system, and via the contact center assistant, a contact between a caller and the representative associated with the contact center (ibid-his calls analyzed as the monitored contact, current dialogue and suggestions);
identifying, by the computing system, and based at least in part upon monitoring the contact, a task associated with the caller;
dynamically generating, by the computing system, and via the generative LLM of the representative coach, natural language output that prompts the representative to address the task during the contact (ibid-his input to the assistant, dialogue as the natural language generated, paragraph [0051-0055]-his natural language text generation, as the output and response from the system based on the input to the representative coach); and
presenting, by the computing system, the natural language output to the representative via a user interface of the contact center assistant (ibid-his text response, pop-up chat session, and NLP interface GUI, for receiving the response, dialog boxes, etc.).
Can lacks explicitly teaching that which Wheeler teaches, providing, by a computing system comprising one or more processors, a contact center assistant comprising a representative coach that:
is based upon a generative large language model (LLM) that is trained on, a training dataset (paragraph [0069-0073]-his LLM as trained, including his GPT model); and is configured to assist the representative associated with the contact center during contacts handled by the representative (ibid, see also paragraphs [0028, 0030]-during call handled by the representative, the representative is assisted by the contact center assistant, or bot, in real time or dynamically).
identifying, by the computing system, and based at least in part upon monitoring the contact, a task associated with the caller (ibid-paragraph [0192]-his activity component, identifying a task associated with the caller, based on monitoring the contact);
dynamically generating, by the computing system, and via the generative LLM of the representative coach, natural language output [that prompts the representative to address the task] during the contact (ibid, paragraph [0028, 0098, 0112]-his natural language response, and communication to human agent/dispatch, to address task required information).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Wheeler to combine the prior art element of an representative assistant for handling user input and tasks using a neural network and text generative language model as taught by Can with using a generative large language model (LLM), to generate a natural language output, as noted hereinafter with respect to generating the generative output, as taught by Wheeler as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be using a LLM, such as ChatGPT to assist a human agent with an activity request and provided a generative response (ibid-Wheeler, see also abstract).
Can with Wheeler lacks explicitly teaching that which Henryson teaches, dynamically generating, by the computing system, and via the generative LLM of the representative coach, natural language output that prompts the representative to address the task during the contact (paragraphs [0045, 0061-0063, 0093]-his natural language output to the service representative as a prompt to address the task, his resolution task for live chat session input by a user, tasks identified from the call, to be resolved via prompt to the service representative to address said task).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Wheeler and Henryson to combine the prior art element of an representative assistant for handling user input and tasks using a neural network and text generative language model as taught by Can with using a generative large language model (LLM) to generate a natural language output, as taught by Wheeler with a prompt to a representative to address a task during contact as taught by Henryson as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be identifying/predicting tasks, and generating corresponding natural language output to an agent to address or handle, thereby assisting the agent with respect to a monitored communication (ibid-Henryson, paragraphs [0012, 0015, 0057-0061], see also abstract).
As per claim 3, Can with Wheeler with Henryson make obvious the computer-implemented method of claim 1, wherein the training dataset comprises at least one of:
contact data that is associated with a set of historical contacts and comprises feedback data distinguishing desirable historical contacts from undesirable historical contacts, or caller data comprising profiles of callers (Can, ibid-see paragraph [0031]-his historical contacts as his historical customers and contact data, and his caller profile data, ibid, paragraph [0022]-his call center calls, feedback, and positive and negative interactions as part of the historical contacts, paragraphs [0047-0049]-see additional feedback for call center and contact data for customers).
As per claim 5, Can with Wheeler with Henryson make obvious the computer-implemented method of claim 1, wherein dynamically generating the natural language output further comprises generating, via the generative LLM of the representative coach at least one of:
a recommended answer to a question posed by the caller during the contact, or a suggested question to pose to the caller during the contact (ibid-Can, see previous dialogue discussion, see also paragraph [0025, 0043]-his assistant generated dialog suggestions, as combined with Wheeler, with respect to the LLM as established, for generating the natural language output, as seen in claim 1).
As per claim 6, Can teaches the computer-implemented method of claim 1, further comprising: identifying, by the computing system, a caller profile of the caller that indicates language preferences of the caller, wherein the generative LLM of the representative coach generates the natural language output in accordance with the language preferences of the caller (ibid-see claim 1, caller profile discussion, paragraph [0031, 0043]-his customer profile as derived, his indication of what language, and corresponding output language preferences in dialog, claim 1, LLM discussion).
As per claim 7, Can teaches the computer-implemented method of claim 1, further comprising:
identifying, by the computing system, a caller profile of the caller that indicates one or more products or services associated with the caller, wherein the natural language output generated by the generative LLM of the representative coach expresses at least one of a question or a statement that corresponds with the one or more products or services associated with the caller (ibid-see above dialog discussion-paragraph [0031, 0154-0156]-his customer profile, indicating previous products/services, and corresponding dialog and suggestions, and generative LLM natural language output discussion).
As per claim 9, Can teaches the computer-implemented method of claim 1, wherein:
the contact is initiated to address a first task associated with the caller, (ibid-paragraph [0031]-his call reason, as addressed), and the task, identified based at least in part upon monitoring the contact, comprises a second task different from the first task (ibid-Can, paragraph [0031]-his “reason” for caller and routing based thereon, and aggregating profile data, as a second task associated with the caller, based on his call reasons and Fig.12 identifying a plurality of different call reasons as applied to the call reasons from a particular caller while generating that caller profile data, i.e. sentiment data, complaint data, time data, suggestion data, paragraphs [0031-0038, 0045- 0049, 0074, 0075]).
As per claim 10, Can teaches the computer-implemented method of claim 9, further comprising: identifying, by the computing system, and via the contact center assistant during the contact, a third task associated with the caller; and
automatically performing, by the computing system, and via the contact center assistant, the third task during the contact without user input from the representative (ibid-paragraph [0031]-his “reason” for caller and routing based thereon, and aggregating profile data, as a third task associated with the caller, i.e. sentiment data, complaint data, time data, suggestion data, paragraphs [0031-0038, 0045- 0049, 0074, 0075]-the third task associated with the caller is performed independent of the representative, stolen card, access issue, and account information, each call reason associated with a caller, and task is performed independent of the representative as discussed above. The Examiner notes, any of the call reasons, including a fourth, fifth, or tenth, from a particular caller, may be performed as described above, and this claim and concept is not novel, based on the contact center assistant, user input and resolving a number of different call reasons associated with the caller).
As per claim 13, claim 13 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the system is deemed to embody the method, such that Can with Wheeler with Henryson makes obvious a computing system configured to dynamically generate, via a generative artificial intelligence system, natural language output that provides real-time assistance to a representative associated with a contact center during contacts with callers, the computing system comprising: one or more processors, and memory storing computer-executable instructions associated with a contact center assistant that, when executed by the one or more processors, cause the one or more processors to (paragraph [0028]-see his processor, memory and instructions, non-transitory computer readable medium and system discussion): monitor, via the contact center assistant, a contact between a caller and the representative associated with the contact center (ibid-see claim 1, corresponding and similar limitation); identify, based at least in part upon monitoring the contact, a task associated with the caller (ibid); dynamically generate, by a representative coach of the contact center assistant using a generative large language model (LLM), natural language output associated with the contact, wherein: the generative LLM is trained on a training dataset (ibid), and the natural language output prompts the representative to address the task during the contact (ibid); and present the natural language output to the representative via a user interface of the contact center assistant (ibid).
As per claim 17, claim 17 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the non-transitory computer-readable media is deemed to embody the method, such that Can with Wheeler with Henryson make obvious one or more non-transitory computer-readable media storing computer-executable instructions, associated with a contact center assistant configured to dynamically generate, via a generative artificial intelligence system, natural language output that provides real-time
assistance to a representative associated with a contact center during contacts with callers, that, when executed by one or more processors of a computing system, cause the one or more processors to (paragraph [0028]-see his processor, memory and instructions, non-transitory computer readable medium and system discussion): monitor, via the contact center assistant, a contact between a caller and the representative associated with the contact center (ibid-see claim 1, corresponding and similar limitation); identify, based at least in part upon monitoring the contact, a task associated with the caller (ibid);
dynamically generate, by a representative coach of the contact center assistant using a generative large language model (LLM), natural language output associated with the contact, wherein: the generative LLM is trained on a training dataset (ibid), and the natural language output prompts the representative to address the task during the contact (ibid); and present the natural language output to the representative via a user interface of the contact center assistant (ibid).
As per claim 14, Can further makes obvious the computing system of claim 13, wherein the training dataset comprises contact data, associated with historical contacts, comprising (ibid-see claim 3, historical contacts discussion): transcripts of the historical contacts, and feedback data distinguishing desirable instances of the historical contacts from undesirable instances of the historical contacts (ibid-see claims 3 and 4, corresponding and similar limitations, paragraphs [0031-0034]-his transcripts of the historical contacts data).
As per claims 15 and 18, Can with Wheeler with Henryson makes obvious the computing system of claim 13, wherein the computer-executable instructions further cause the one or more processors to dynamically generate, via the generative LLM of the representative coach, at least one of: a recommended answer to a question posed by the caller during the contact, a suggested question to pose to the caller during the contact, or a suggested transfer to a different representative. (ibid-see claim 5, corresponding and similar limitation, answer/suggested question discussion).
As per claims 16 and 19, Can teaches the computing system of claim 13, wherein the computer-executable instructions further cause the generative LLM of the representative coach to generate the natural language output based upon at least one of: language preferences of the caller, or sentiment analysis of the contact (ibid-see claim 6, corresponding and similar limitation, his language preferences of the caller, see also paragraphs [0022, 0045, 0045, 0151-0156]-his output based on sentiment analysis of the contact).
As per claim 20, claim 20 sets forth limitations similar to claim 10 and is rejected under similar reasons and rationale, wherein Can teaches the one or more non-transitory computer-readable media of claim 17, wherein: the contact is initiated to address a first task associated with the caller,
the task identified based at least in part upon monitoring the contact comprises a second task that is different from the first task (ibid-see claim 9, corresponding and similar limitation); and the computer-executable instructions further cause the one or more processors to: identify, during the contact, a third task associated with the caller; and automatically perform the third task during the contact without user input from the representative (ibid-see claim 10, corresponding and similar limitation).
As per claim 21, Can with Wheeler with Henryson make obvious the computer-implemented method of claim 1, wherein the generative LLM of the representative coach:
accesses data associated with the caller from one or more data sources during the contact (ibid-his caller profile data, and history data source), and
dynamically generates the natural language output to convey information, retrieved from at least one of the one or more data sources, in response to a question or statement made by the caller during the contact (ibid-see claim 1, 3 and 5, natural language output, and question/answer discussion, Can, paragraphs [0135, 0136]-see claim 1, LLM discussion, Wheeler, paragraphs [0030, 0078, 0078-0098]-which draws from one or more data sources and generates a natural language response, during contact, see claim 1, similar motivation and combination discussion).
Claim(s) 11, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Can in view of Wheeler in view of Henryson as applied to claim 9 above, and further in view of Tremblay et al. (Tremblay, US 2022/0343250).
As per claim 11, Can makes obvious the computer-implemented method of claim 9, wherein the natural language output expresses at least one of a question or a statement [associated with the second task] (ibid-paragraph [0031]-his “reason” for caller and routing based thereon, and aggregating profile data, as a second task associated with the caller, i.e. sentiment data, complaint data, time data, suggestion data, paragraphs [0031-0038, 0045- 0049, 0074, 0075, 0153]-his voice prompt as a question, about a second task).
Can lacks explicitly teaching that which Tremblay teaches, wherein the natural language output expresses at least one of a question or a statement associated with the second task (paragraphs [0130, 0254]-his user entered topic, and subtopics, and corresponding feedback prompt task, associated as a feedback secondary and associated task).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Tremblay to combine the prior art element of an representative assistant for handling user input and tasks as taught by Can with providing a natural language output associated with a second task as taught by Tremblay as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be allowing the virtual assistant to perform an action associated with a secondary task based on contact with a user (ibid, Tremblay, paragraph [0254]).
As per claim 12, Can makes obvious the computer-implemented method of claim 9, but lacks that which Tremblay teaches, wherein the natural language output is associated with a recommended transfer of the contact to a different representative associated with the second task (paragraphs [0276, 0250]-his “transfer contact to a specialist”, based on a natural language output, sent to the contact).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Tremblay to combine the prior art element of an representative assistant for handling user input and tasks as taught by Can with providing a natural language output and associated therewith a transfer to a specialist as taught by Tremblay as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be allowing the virtual assistant to perform an action associated with a task based on contact with a user, such as transferring a contact to a specialist along with an indication thereof (ibid, Tremblay, paragraph [0276]).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Can in view of Wheeler in view of Henryson as applied to claim 1 above, and further in view of Epstein Koch et al. (Epstein Koch, US 2024/0127800).
As per claim 22, Can with Wheeler with Henryson make obvious the computer-implemented method of claim 1, but lack further teaching that which Epstein Koch teaches, wherein:
the task is associated with an inconsistency between (paragraph [0025, 0030, 0037]-his error, as the inconsistency):
first information, associated with the caller, stored in a first database, and
second information, associated with the caller, stored in a second database (ibid-his validation event, of stored information across multiple sources, as different databases, recorded data and stored, ibid, paragraphs [0005, 0006, 0018]-see his multiple information types, and stored information validation), and
the natural language output prompts the representative to resolve the inconsistency based on communications with the caller during the contact (ibid-paragraph [0057]-as his prompt to the representative to correct the inconsistency found during the communication with the caller).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Epstein Koch to combine the prior art element of an representative assistant for handling user input and tasks as taught by Can with providing a natural language output prompt to a representative for conflict, inconsistency resolution as taught by Epstein Koch as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be assisting a representative in resolving conflict in between different sources of information, during an interaction (ibid, Epstein Koch, see above cited sections, and abstract).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Can in view of Wheeler in view of Henryson as applied to claim 1 above, and further in view of Matsuoka et al. (Matsuoka, US 2024/0127800).
As per claim 23, Can with Wheeler with Henryson make obvious the computer-implemented method of claim 1, but lack explicitly teaching that which Matsuoka teaches, wherein:
the task is associated with a pending process, associated with the caller, that has stalled, and the natural language output prompts the representative to obtain, from the caller during the contact, information to permit the pending process to resume (paragraph [0121]-his task or project, as pending completion, based on missing data, and the prompt to the representative for the missing information, to resume or complete the project or task, based on the customer service, needs/requirements, Figs. 1-11, Fig. 1 including caller, and representative and task facilitation service).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Can and Matsuoka to combine the prior art element of an representative assistant for handling user input and tasks as taught by Can with providing a natural language output prompt to a representative for information needed to complete a task, that has been stalled or incomplete due to missing information, as taught by Matsuoka, as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be assisting a representative acquiring required information needed to complete a project or task (ibid, Matsuoka, see above cited sections, and abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAMONT M SPOONER whose telephone number is (571)272-7613. The examiner can normally be reached 8:00 AM -5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at (571)272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAMONT M SPOONER/Primary Examiner, Art Unit 2657
8/20/26
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