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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 8, 2026 has been entered.
Status of the Claims
Claims 1-20 are all the claims pending in the application.
Claims 1, 8, and 15 are amended.
Claims 1-20 are rejected.
The following is a Non-Final Office Action in response to amendments and remarks filed June 8, 2026.
Response to Arguments
Regarding the 103 rejections, the rejections are withdrawn because the previously cited limitations do not teach the newly added limitations. Accordingly the 103 rejections are withdrawn, please see below for the complete rejections of the claims as amended.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior 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.
Monegan et al, US Pub. No. 2014/0044243, herein referred to as “Monegan”, in view of Appel et al, US Pub. No. 2017/0351962, herein referred to as “Appel”, further in view of Ghose et al., US Pub. No. 2016/0239897, herein referred to as “Ghose”.
Regarding claim 1, Monegan teaches:
accessing, by the at least one computing device, real-time transaction account activity data associated with a user (uses multiple data sources including recent transactions from the same number and email communications from the same customer, ¶¶[0026]- [0030]; see also e.g., ¶¶[0054]-[0057] and Fig. 4 discussing computer system);
generating, by the at least one computing device using the predictive computer model, a ranking of a plurality of intent insights associated with a communication according to one or more of a plurality of intent prediction rules, wherein the ranking of intent insights is based at least in part on account activity data of the user reaching the specified threshold (where multiple intents are possible, the algorithm orders intents based on likelihood, ¶[0036], and determines an intent prediction based the date being within a certain number of days from an aspect of the bookings, ¶¶[0039], [0041]; see also ¶[0007] noting customer intent is predicted),
transmitting, by the at least one computing device to a client device of the user, the plurality of intent insights in the generated ranking and a user feedback inquiry comprising an accuracy inquiry to confirm whether individual ones of the plurality of intent insights are accurate (caller is asked a proactive question to validate predicted intent, e.g., “Are you calling to about your upcoming trip to Boston?” ¶[0035]; see also ¶[0053] noting customer communication occurs using cellular telephone).
However Monegan does not teach but Appel does teach:
training a predictive computer model of at least one computing device to determine a ranking of intent insights (trains supervised learning algorithm, ¶¶[0027], [0054]-[0055]; see also e.g., ¶[0032] and Abstract discussing predicting user questions; and ¶¶[0059]-[0060] discussing computer system);
and retraining the predictive computer model of the at least one computing device based on the user feedback (collects feedback on predictions to retrain machine learning algorithm, ¶[0030]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customer intent prediction of Monegan with using machine learning to predict customer intent as taught by Appel because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the intent prediction in Monegan would likely be improved by using machine learning (e.g., in situations where there is sufficient training data to benefit from using machine learning) and accordingly would have modified Monegan to use machine learning, e.g., as taught by Appel.
However the combination of Monegan and Appel does not teach but Ghose does teach:
determining, by the at least one computing device from the real-time transaction account activity data, that an accumulation of account activity data of the user has reached a specified threshold of accumulated account activity (determines threshold related to one or more past activities of the online visitor on the website that are relevant to a current activity of the online visitor on the website, ¶¶[0041]-[0042]; see also ¶[0075] discussing predicting visitors intent)
wherein the ranking of the plurality of intent insights is based at least in part on the accumulation of account activity data of the user reaching the specified threshold of accumulated account activity (makes and scores predictions based use activity, ¶¶[0067], [0073]; see also Fig. 8 summarizing process).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customer intent prediction using machine learning of Monegan and Appel with the predictions based on user activity of Ghose because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the intent prediction in Monegan would likely be improved by analyzing past user behavior (e.g., to identify recent issues the user experienced) and accordingly would have modified Monegan to analyze user activity, e.g., as taught by Ghose.
Regarding claim 2, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and Monegan further teaches:
receiving an accuracy response from the user via a wireless communication channel, the accuracy response being entered via an electronic input (caller provides validation of the proactive intent, ¶[0035]).
Regarding claim 3, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and Monegan further teaches:
routing a communication from the user to a service system based on the intent prediction alert (provides accelerated service if the intent prediction is validated or personalized service if the intent prediction is not validated, ¶[0035]).
Regarding claim 4, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and Monegan further teaches:
determining a priority insight of the plurality of intent insights (positive and negative points are awarded based on recentness of events, ¶[0038]);
and adjusting the ranking of the plurality of intent insights based at least in part on the priority insight such that the priority insight is a highest ranking intent insight of the plurality of intent insights (prediction of intent is determined to be the most recent event, ¶[0038]).
Regarding claim 5, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and Monegan further teaches:
wherein generating the ranking of the plurality of intent insights is based at least in part on a chronological order (positive and negative points are awarded based on recentness of events and prediction of intent is determined based on recentness of events, ¶[0038]).
Regarding claims 8 and 15, claims 8 and 15 recite similar limitations as claim 1 and further recite “a processor; a memory; and instructions stored in the memory and executable by the processor” and a “non-transitory computer-readable medium storing instructions executable in a processor”, respectively. These concepts are taught by Monegan in ¶¶[0054]-[0055]. Accordingly, claims 8 and 15 are rejected for similar reasons as claim 1.
Regarding claims 9-12, 16, 17, and 20, claims 9-12, 16, 17, and 20 recite similar limitations as claims 2-5 and accordingly are rejected for similar reasons as claims 2-5.
Claim(s) 6, 7, 13, 14, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Monegan, Appel and Ghose further in view of Spencer et al, US Pub. No. 2010/0076895, herein referred to as “Spencer”.
Regarding claim 6, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and does not teach but Spencer does teach:
wherein the ranking of the plurality of intent insights is further based at least in part on a fraud alert communicated to the user regarding the transaction account of the user (fraud alert, pg. 14, Rule i.d. 20, 21).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customer intent prediction using machine learning of Monegan, Appel and Ghose with the fraud alerts of Spencer because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized noting fraud alerts would likely be useful when predicting a caller’s intent.
Regarding claim 7, the combination of Monegan, Appel and Ghose teaches all the limitations of claim 1 and does not teach but Spencer does teach:
wherein the accumulation of activity data comprises an accumulation of late fees for the user (accounts delinquency, pgs. 11-12, Rule i.d. 5, 6) .
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the customer intent prediction using machine learning of Monegan, Appel and Ghose with the account delinquency of Spencer because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized noting account delinquency would likely be useful when predicting a caller’s intent.
Regarding claims 13, 14, 18 and 19, claims 13, 14, 18 and 19 recite similar limitations as claims 6 and 7 and accordingly are rejected for similar reasons as claim 6 and 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Srivastava et al, US Pub. No. 2014/0079195 teaches customer intent predictions.
Kannan et al, US Pub. No. 2010/0191658 teaches predicting customer issues.
Vijayaraghavan et al, US Pub. No. 2014/0207622 teaches recommending products and features based on intent.
Watkins et al, US Pub. No. 2006/0018440 teaches predictive interactive voice recognition.
Cooper et al, US Pat. No. 9,049,295 teaches an intelligent interactive voice response system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
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