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
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/02/2026 has been entered.
Response to Amendment
2. This in response to an RCE amendment filed 06/02/2026. No claims have been added. No claims have been canceled. Claims 1, 11 and 16 have been amended. Claim 1-20 are still pending in this application.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnapuram et al. (Pub.No.: 2012/0039460 A1) in view of Green et al. (Pub.No.: 2019/0287517 A1) in view of BUEHLER et al. (Pub.No.: 2018/0131811 A1) and further in view of Joseph et al. (Pub.No.: 2003/0202643 A1).
Regarding claims 1, 11 and 16, Krishnapuram teaches a method, device and non-transitory computer-readable storage medium (see abstract), comprising:
analyzing, by a device, information related to a service request between a user and an agent of a service provider, the service request information comprising data related to interactions between the user and agent (reads on analyzing customer service quality, where “customer call service quality parameters” are identified from interaction data (e.g., call transcripts, metrics) and quantified for analysis, see [0024-0027]);
determining, by the device, based on the analysis, an intent of the user and a responsiveness by the agent, the intent comprising data related to functionality provided by the service provider, the responsiveness comprising data indicating mechanisms employed by the agent related to the intent (reads on the parameters that include measures such as Average Handling Time (AHT), Expressed Dissatisfaction Rate (EDR), First-Call Resolution (FCR), loyalty, call flow compliance, and communication skill, see [0024] also see [0026-0030]);
determining, by the device, based on the determined intent and responsiveness, a score for the service request (this reads on service quality can be a qualitative and a quantitative measure of performance. A service organization can measure its overall performance by measuring the two types of gaps based on several parameters measured from customer interactions. These parameters include: Conversion (successful, partially successful, unsuccessful), AHT (Average Call Handling Time), C-SAT (interaction rated on a scale by customer), EDR (Expressed Dissatisfaction rate), FCR Rate (First Call Resolution Rate), Net Promoter Score (Customer loyalty measure), Call flow (opening with correct greeting, covering all aspects during the call, giving/taking proper and complete information, following mandatory processes), Communication Skills (effective listening, personalized interaction with the customer, courteous, polite, professional, clarity in speech, good pacing, proper voice inflection, pronunciation, confidence, see [0009]), the score being a type of score that corresponds to the responsiveness data (reads on quantifying and correlating these service quality parameters using AI, NLP, statistical techniques, etc., which effectively results in a score that reflects the quality of the agent’s performance and whether the customer’s intent was addressed, see [0013 and 0026]); and
determining, by the device, whether the intent of the user was addressed during the service request based on the determined score (reads on determine aspects of service performance—e.g., did the agent follow the prescribed flow, are communication skills appropriate, and potentially whether the customer’s needs were met based on those metrics, see [0027]).
Krishnapuram features is already addressed in the rejection of claims 1, 11 and 16. Krishnapuram does not specifically teach “controlling, by the device, a result of the service request based on the determination of whether the intent was addressed, the control of the result corresponding to capabilities related to the functionality provided by the service provider”.
However, Green teaches routing customer service interactions, beginning with analyzing historical interactions between users and agents as discussed in [0002-0003 and 0012-0013]. Green further discloses that after monitoring the live interaction, the system identifies keywords/issues and selects a resolution resource from a pool (see [0006-0007] and [0011]), then the user may be connected to the potential resolution resource automatically, or connected in response to the user and/or the agent accepting the suggestion (see [0014]).
Thus, it would have been obvious to combine the analytics and scoring system of Green with the automatic routing/escalation mechanism of Krishnapuram to deliver analytical backbone and provide necessary outcome-control mechanism. Note that controlling the service request result is based on whether user intent is addressed or not.
Krishnapuram and Green features already addressed in the rejection of claims 1, 11 and 16. The combination of Krishnapuram and does not specifically teach “the control of the result comprising transferring the service request to another agent having a history of behaviors that includes a behavior other than the type of score that corresponds to the responsiveness data”.
However, BUEHLER teaches establishing a communication link between the given requestor and the dedicated agent (see [0007], [0024] and [0038]). Buehler teaches further teaches selecting an agent to assign to the given requestor includes retrieving a pool of agents from a data store, where the agents in the pool of agents are associated with the entity and where each agent in the pool of agents has a rating and rating is based on feedback from customers having previous interactions with the agent (see [0007]). Note that customer feedback score (i.e., rating) for a given agent is an average of feedback scores provided by customers during previous interactions with the agent and stored as a composite feedback score on the agent record database (see [0078-0080] and [0095-0097]). Further, BUEHLER teaches the rating for each agent may be adjusted based on different factors. For example, the rating may be adjusted based on at least one of an amount of time since an agent has interacted with a customer, a number of customers assigned to an agent, an amount or a percentage of missed calls amongst the calls routed to the agent. Adjusting the agent rating using the amount of time since the agent has interacted with a customer provides load balancing amongst the agents in the applicable pool of agents (see [0079-0081]).
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 incorporate the feature of routing requests to agents with a history of a good performance/scoring (i.e., positive score), as taught by BUHLER into the combination of Krishnapuram and Green in order to enhances customer’s experience and improve call center performance in general.
Krishnapuram, Green and Buehler features already discussed in the rejection of claim 1, 11 and 16, including analyzing the service interaction, determining the user’s intent and agent’s responsiveness, determining a corresponding score, controlling the result of the service request, and transferring the request to another agent selected using historical agent information.
The combination of references does not specifically teach “the type of score corresponding to a modification of an account of the user, by the agent, that is contrary to the intent of the user” as recited in independent claims 1, 11 and 16.
However, Joseph teaches slamming, namely changing a customer’s telephone-service provider without the customer’s permission (see [0002] and [0010]), and cramming, namely adding unauthorized products, services, or changes to a customer’s account without customer authorization (see [0002] and [0011]). Joseph further teaches correcting changes to a customer account that were not authorized by the customer (see [0013]).
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 configure the scoring system of Krishnapuram, as modified by Green and Buehler, to include a score type corresponding to the unauthorized account-modification conduct taught, by Joseph, because such a modification would enable the system to identify and quantify improper agent conduct affecting customer’s account and thereby improve the detection and handling of service requests involving slamming or cramming.
Regarding claims 2, 12 and 17, the combination of Krishnapuram, Green, BUEHLER and Joseph teach wherein the control of the result comprises transferring the service request to another agent, wherein the transfer enables further interaction data to be generated and analyzed (reads on escalation to another resources, see [0046-0047]).
Claims 3, 13 and 18 recite “wherein the result comprises a modification of an account of the user with the service provider”. The limitation of “modification of an account of the user” believed to be inherent if not obvious within the teachings of Krishnapuram and Green because emphasizes measuring whether customer intent (e.g., resolution of service/billing/technical issue) was addressed (see [0027] and [0057]). This inherently touches on service provider functions like account management. Also, Green teaches escalation to resolution resources, which could reasonably include an agent empowered to perform account changes.
Regarding claim 4, the combination of Krishnapuram, Green, BUEHLER and Joseph teach:
analyzing the determined responsiveness based on the determined intent (see Krishnapuram [0024-0027]); and
determining the score based on the analysis of the determined responsiveness (see Krishnapuram [0009, 0013 and 0026]).
Regarding claim 5, the combination of Krishnapuram, Green, BUEHLER and Joseph teach:
determining the type of the score for the service request based on the analysis of the determined responsiveness, wherein the type of score corresponds to the interactions data between the user and the agent (see [0012-0013] and [0024-0029]).
Regarding claim 6, the combination of Krishnapuram, Green, BUEHLER and Joseph teach:
retrieving, from a database, information related to the user, the user information comprising historical data related to activities of the user (see Green [0032, 0057 and 0092]); and
retrieving, from the database, information related to the agent, the agent information comprising historical data related to activities of the agent (see Green [0032]).
Regarding claim 7, the combination of Krishnapuram, Green, BUEHLER and Joseph teach wherein the determination of the intent of the user and the responsiveness of the agent are respectfully based on the user information and agent information (see Green [0012-0014]).
Regarding claim 8, the combination of Krishnapuram, Green, BUEHLER and Joseph teach wherein the database from which the user information is retrieved is a user database (see Green [0032, 0057 and 0092]).
Regarding claim 9, the combination of Krishnapuram, Green, BUEHLER and Joseph teach wherein the database from which the agent information is retrieved is an agent database (see Green [0032, 0057 and 0092]).
Regarding claim 10, the combination of Krishnapuram, Green, BUEHLER and Joseph teach wherein the interactions data comprises an indication of the result of the service request, the result comprising an action performed on behalf of the service provider in response to the intent of the user (this reads on interaction data FCR, dissatisfaction, compliance metrics that indicates the result of the service request, which corresponds to action taken in response to the user’s intent, see Krishnapuram [0026-0027]).
Dependent claims 14 and 19 are rejected for the same reasons addressed in dependent claims 4 and 5, respectively.
Dependent claims 15 and 20 are rejected for the same reasons addressed in dependent claims 6 and 7, respectively.
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
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