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 .
Response to Amendment
The amendments filed on July 15, 2026 have been entered. Claims 1-20 were previously cancelled. Applicant have amended claims 21, 27, 29, 35, 37, and 39. Claims 21-40 remain pending in the application.
Response to Arguments
Applicant’s arguments filed on July 15, 2026 in response to the Non-Final Office Action dated May 13, 2026 have been fully considered but they are not persuasive.
Applicant argues, in page 10 of the Remarks, “To the extent the Office Action relies on Rath's retraining of its machine-learning models as disclosing any reconfiguration of the system, such retraining is automatic and feedback-driven. Rath describes that "[f]eedback collected from the support agents on whether or not they are satisfied with the assignments ... can then be used to retrain the machine-learning models." Rath, paragraph [0058]. Retraining a model based on collected agent feedback is not reconfiguring the external communications support routing system "in response to a user interaction with the interactive interface," as recited in the amended claims. Rath is silent regarding rendering insight data to an interactive interface whose user interaction drives reconfiguration of an external communications support routing system.”.
In response, Examiner respectfully disagrees with the interpretations. As shown in Fig. 3 of Rath, the support tickets assignment system comprises machine models that extract topic/complexity of support ticket to assign the support ticket to appropriate support agent. Therefore, re-training the machine learning model is reconfiguring the support tickets assignment system. Paragraph 0080 discloses various features extracted by the machine learning model are rendered in the user interface as stated “The information captured is in essence the support organization management team's follow-up behavior on the estimated complexity and/or identified topic as recorded via the user interface.”. It also discloses the ticket routing is modified based on the complexity/topic and modification by human.
Examiner’s Note about the Format of 35 U.S.C. 102/103 Rejections
Generally, limitations of a claim are reproduced identically and followed by examiner’s explanation with citation from prior art in Italic enclosed by a parenthesis, (), for each limitation. In examiner’s explanation, the mapping of the key elements of a limitation to the disclosed elements of prior art is shown by stating the disclosed element immediately followed by the claimed element inside a parenthesis. Specific quotation from prior art is delineated with quotation mark, ““. If primary art fails to teach a limitation or part of the limitation, the limitation or the part of the limitation is placed inside double square brackets, [[ ]], for better understandability, and appropriate secondary art(s) is/are applied later addressing the deficiency of the primary art.
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 21, 25-29, and 33-40 are rejected under 35 U.S.C. 103 as being unpatentable over Rath et al. (US PGPUB No US 20210014136 A1), hereinafter, Rath, in view of Jayapalan et al. (US Patent No. US 10715668 B1), hereinafter, Jayapalan.
Regarding claim 21:
Rath teaches:
A computer-implemented method comprising:
extracting request record data from support request record files associated with prior client support sessions, wherein the support request record files comprise chat transcription data, [[short message service (SMS) transcription data, and email transcription data]] (Fig. 4, steps 402, shows receiving all closed support tickets as explained paragraph 0086. Fig. 1 shows support ticket data include chat transcript data as explained in paragraph 0012);
training a predictive machine learning model on a training data set, wherein the training data set comprises the request record data (Fig. 4, steps 404-412, show training a machined learning model with closed tickets data as training data. Also see paragraph 0086 stating “For example, and without limitation, this can include the training support tickets assignment system 318 receiving a training set of support ticket communications, which includes the support ticket 100 that contains all subsequent communications 102 and 104 and the support ticket's metadata 106, as depicted by FIG. 1.”);
deploying the predictive machine learning model after the training to generate insight data related to an external communications support request routing system, wherein the insight data comprises usage statistics and recommendations to optimize operational efficiency, costs, and scalability of the external communications support request routing system (paragraph 0081-0083 disclose deploying the machine leaning model. Fig. 4, steps 414-418, show the machine learning model is applied to routing new support tickets by generating various features (insight data). Fig. 4, steps 420-426, discloses optimize the process to improve efficiency by considering workload, complexity, skill etc. Paragraph 0084 discloses optimizing cost and efficiency);
rendering the insight data to an interactive interface (paragraph 0080 discloses various features extracted by the machine learning model is rendered in the user interface as stated “The information captured is in essence the support organization management team's follow-up behavior on the estimated complexity and/or identified topic as recorded via the user interface.”); and
reconfiguring the external communications support request routing system based at least in part on the insight data in response to a user interaction with the interactive interface (paragraph 0080 discloses the ticket routing is modified based on the complexity/topic and modification by human as stated “For example, the management team may reject an estimated complexity and/or an identified topic for a variety of reasons, and in some cases manually modify the support ticket's complexity and/or topic. Sweeping in these follow-up actions back into the machine-learning models 320-330 and/or 334-344 can enable the machine-learning models 320-330 and/or 334-344 to be re-trained in a manner that closely follows the human-decision making component, which the training support tickets assignment system 318 and/or the production support tickets assignment system 332 attempts to model.” ).
Rath does not teach short message service (SMS) transcription data, and email transcription data.
Jayapalan teaches short message service (SMS) transcription data, and email transcription data (Col. 14, lines 12-24, discloses service request data include SMS, email communication).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rath to incorporate the teaching of Jayapalan about service request data include SMS, email communication. One would be motived to do that to gather sufficient information about previous service requests for better training of the machine learning model (Col. 1, lines 47-67, of Jayapalan).
As to claim 25, the rejection of claim 21 is incorporated. Rath in view of Jayapalan teaches all the limitations of claim 21 as shown above.
Rath further teaches wherein the support request record files further comprise [[audio data and video data]], and where the request record data comprises audio data, video data, chat transcription data, SMS transcription data, email transcription data, or timestamp data (Fig. 1 shows support ticket data include chat transcript data as explained in paragraph 0012).
Rath does not teach audio data and video data.
Jayapalan discloses audio data and video data (Col. 13, line 67, and Col. 14, lines 1-12, discloses service request data includes audio and video data as stated “For example, an individual 102 may use a voice telephony network or data network to make a telephone and/or VOIP call to a call center. In such examples, the request interface 612 may enable the individual 102 may navigate through a sequence of audio menus and/or communicate with an interactive voice response (IVR) system to generate a service request 602 based on the individual's speech inputs and/or inputs through a telephone numeric keypad. As another example, an individual 102 may access a request interface 612 through an online application and/or web application to submit a service request 602 for a real time text chat session, video chat session, and/or audio chat session.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rath to incorporate the teaching of Jayapalan about service request data includes audio and video data. One would be motived to do that to gather sufficient information about previous service requests for better training of the machine learning model (Col. 1, lines 47-67, of Jayapalan).
As to claim 26, the rejection of claim 21 is incorporated. Rath in view of Jayapalan teaches all the limitations of claim 21 as shown above.
Rath further teaches wherein the training data set is assembled from a configuration data store, an on-call schedule data store, or a communications data store (paragraph 0053 and 0054 discloses taking agent schedule in account for routing support request ticket).
As to claim 27, the rejection of claim 21 is incorporated. Rath in view of Jayapalan teaches all the limitations of claim 21 as shown above.
Rath further teaches wherein deploying the predictive machine learning model to generate insight data further comprises: causing an update to one or more components of the external communications support request routing system (paragraph 0058 discloses retraining the machine learning model for the next iteration as stated “Feedback collected from the support agents on whether or not they are satisfied with the assignments based on the breakdown of factors can then be used to retrain the machine-learning models that can capture additional features that were missed or under-valued in the previous iteration of the system.” . Also see paragraph 0081 stating “When required, the machine-learning models 320-330 and/or 334-344 may be retrained to remain up to date and capture all the variations in incoming data.”).
As to claim 28, the rejection of claim 21 is incorporated. Rath in view of Jayapalan teaches all the limitations of claim 21 as shown above.
Rath further teaches further comprising: extracting additional request record data from additional support request record files associated with new client support sessions; aggregating the additional request record data into the training data set to form an updated training data set; and iteratively retraining the predictive machine learning model on the updated training data set (paragraph 0058 discloses retraining the machine learning model for the next iteration. Also see paragraph 0081 stating “When required, the machine-learning models 320-330 and/or 334-344 may be retrained to remain up to date and capture all the variations in incoming data.” ).
Regarding claim 29:
Claim 29 is directed towards a system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform the method of claim 17 (see Fig. 5 for system detail). Accordingly, it is rejected under similar rationale.
Claim 33 is directed towards a system performing the method of claim 21. Accordingly, it is rejected under similar rationale.
Claim 34 is directed towards a system performing the method of claim 22. Accordingly, it is rejected under similar rationale.
Claim 35 is directed towards a system performing the method of claim 23. Accordingly, it is rejected under similar rationale.
Claim 36 is directed towards a system performing the method of claim 24. Accordingly, it is rejected under similar rationale.
Regarding claim 37:
Claim 37 is directed towards a computer program product, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to perform the method of claim 17 (see Fig. 5 for system detail). Accordingly, it is rejected under similar rationale.
Claim 38 is directed towards a computer program product to perform the method of claim 25. Accordingly, it is rejected under similar rationale.
Claim 39 is directed towards a computer program product to perform the method of claim 27. Accordingly, it is rejected under similar rationale.
Claim 40 is directed towards a computer program product to perform the method of claim 28. Accordingly, it is rejected under similar rationale.
Allowable Subject Matter
Claims 22-24 and 30-32 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. 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 KAMAL M HOSSAIN whose telephone number is (571)270-3070. The examiner can normally be reached 9:30-5:30 M-F.
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July 21, 2026
/KAMAL M HOSSAIN/Primary Examiner, Art Unit 2444
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