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 .
Status of the Claims
Claims 21-36 and 39-43 are all the claims pending in the application.
Claims 21, 28, 35, and 40 are amended.
Claims 41-43 are new.
Claims 37 and 38 are cancelled.
Claims 21-36 and 39-43 are rejected.
The following is a Final Office Action in response to amendments and remarks filed October 15, 2025.
Response to Arguments
Regarding the 103 rejections, the rejections are withdrawn because the cited references do not teach all the newly amended limitations. Please see below for the new 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.
Examiner Note
Please note, claims 41-43 were rejected under 35 USC 103 only because the claims a method claims that recite contingent limitations with a condition precedent that is not required to occur, see MPEP 2111.04.II. If these claims were rewritten to require the condition precedent occur, the claims would have been indicated as allowable if rewritten into independent form.
For example, if claim 41 was amended as (emphasized) “…wherein [[if]] a category of the complex analytics rule indicates a specific mobile phone issue for a given customer tier value and a given customer sentiment value, [[then]] and in response, the complex analytics rule indicates to provide a model article for troubleshooting the specific mobile phone issue from a resource database of the at least one model to the customer service representative.
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) 21-26, 28-36, and 39-43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adrian et al, US Pub. 2017/0178145, herein referred to as "Adrian" in view of Cecchi et al, US Pub. No. 2016/0269554, herein referred to as "Cecchi"; further in view of O’Connor et al, US Pub. No. 2015/0358463, herein referred to as “O’Connor”, further in view of Henry, US Pub. No. 2018/0374029, herein referred to as “Henry”.
Regarding claim 21, Adrian teaches:
receiving a plurality of historic interactions as interaction data structures and interaction metadata (stores customer service tickets in repository, ¶[0043] and Fig. 1; see also ¶[0035] noting customer service issue information includes context data like name of the customer)
an internal contact data field (customer service context information includes role of an IT customer service agent working on the IT customer service issue (e.g., front-end agent, back-end agent/technician, engineer, etc.), ¶[0037]; see also ¶[0038] discussing restricting resource searching to only those resources that are appropriate for that context),
customer identification data fields (customer service issue information includes name of the customer, ¶¶[0035], [0052]),
and a representative electronic resource data field listing computerized resource modules used by a customer service representative associated with the internal contact data field for the historic interaction (tracks tools as they are used to resolve (or attempt to resolve) particular IT customer service issues in customer service tickets, ¶[0046]; see also ¶[0064] noting system counts how usage of resources used when resolving an issue);
and generating a success indicator for each computerized resource module included in the representative electronic resource data field (uses successful resolution of customer support tickets to assess ratings of resources, ¶[0028], and tracks resolution of solution and selections and prompts documentation of actions taken to resolve issue, ¶[0056]);
wherein the success indicator includes a degree of success associated with using the computerized resource module for the corresponding interaction data structure (uses successful resolution of customer support tickets to assess ratings of resources, ¶[0028], and tracks resolution of solution and selections and prompts documentation of actions taken to resolve issue, ¶[0056]);
generating at least one model configured to create at least one analytics rule based on and generated success indicator (builds model to identify people or groups that are knowledgeable on different topics, ¶[0047]; see also ¶¶[0021], [0049] discussing dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource),
wherein the at least one analytics rule causes at least one of a plurality of computerized resource modules to be selected to be accessed by a central analytics engine in connection with receipt of a new interaction (searches resources, ¶[0063], and assigns relevancy scores, ¶[0067], to find resources relevant to customer service issue, ¶[0073]; see also Fig. 5 summarizing process; and e.g., ¶¶[[0088]-[0089] discussing processors),
further wherein the plurality of computerized resource modules are computerized applications (searched resources include knowledge article repository, customer service tool repository, ¶[0043], content configuration engine, ¶[0039], and resources include external websites (e.g., technical society websites, academic sites, etc.), ¶[0026]; see also Fig. 1; and ¶[0045] discussing indexing and searching databases with applications);
and using the at least one model to create at least one analytics rule (builds model to identify people or groups that are knowledgeable on different topics, ¶[0047]; see also ¶¶[0021], [0049] discussing dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource);
monitoring the system for new interaction data structures and automatedly updating the at least one model when a new interaction data structure is received such that the at least one model includes the new interaction data structure when creating analytics rules (updates relevancy scoring based on selections of resources, ¶[0075]);
and using the updated at least one model to create at least one new analytics rule (uses dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049]).
However, Adrian does not teach but Cecchi does teach:
receiving a plurality of historic interactions as interaction data structures (customer communicates with customer service representative, e.g., ¶[0020]; see also ¶[0040] discussing analyzing past help queries),
as interaction data structures, each interaction data structure including a plurality of fields, including an interaction data field (communication between customer and customer service representative includes chats, text messages and emails, ¶¶[0016], [0019]),
performing agent desktop use analytics and text analytics or voice analytics on each interaction data structure (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035] and analyzes text of communications from customer, e.g., ¶¶[0023]-[0025]),
wherein the agent desktop use analytics include monitoring use of agent desktops (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035]),
and receiving a set of data pertaining to use of agent desktops associated with each interaction data structure of the interaction data structures (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035]; see also ¶[0074] discussing desktops)
generating a customer sentiment based on the agent desktop use analytics, at least one of the plurality of fields in the interaction data structure, and the text analytics or voice analytics (analyzes words and phrases used by customers, ¶[0026], and detects growing levels of irritability/anger, ¶[0036]);
and generate a success indicator for each computerized resource module included in the representative electronic resource data field based on the customer sentiment (monitors interaction of customer and customer service representative, ¶[0035] and determines if new representative is needed, ¶[0046]),
generating at least one model configured to create at least one analytics rule using machine learning techniques based on the interaction data structures, generated customer sentiment, and generated success indicator (trains classifier for analyzing text to determine interaction styles of customers and customer service representatives, ¶¶[0028]-[0030]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
However the combination of Adrian and Cecchi does not teach but O’Connor does teach:
and generating a success indicator for each computerized resource module included in the representative electronic resource data field based on the customer sentiment the agent desktop use analytics, at least one of the plurality of fields in the interaction data structure, and the text analytics or voice analytics (determines satisfaction of customer based on emotion from the communication between the customer and the resources, ¶[0079]; see also e.g., ¶¶0056], [0074] discussing voice and text analytics)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian and Cecchi with the emotional monitoring of O’Connor 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 users would likely be interested in ensuring the customer service is effective when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze customer satisfaction, e.g., as taught by O’Connor.
However the combination of Adrian, Cecchi and O’Connor does not teach but Henry does teach:
automatedly continually updating the at least one model when a new interaction data structure is received based on the analysis of interaction data structures (model is updated over multiple time periods using selection data, e.g., Abstract, ¶[0011])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 22, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 21 and Henry further teaches:
performing customer value analytics on each interaction data structure and generating a customer value level (customers are classified by urgency level, ¶[0019]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 23, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 22 and Henry further teaches:
wherein the customer value level is also used in generating the at least one model (uses urgency level to train various machine learning models, ¶[0019]; see also ¶[0029] and Fig. 2 discussing using urgency model to compute overall score).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 24, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 21 and Adrian further teaches:
receiving a new interaction data structure and using the at least one model with the new interaction data structure to predict at least one analytics rule to apply to the new interaction data structure (updates relevancy scoring based on selections of resources, ¶[0075], and uses dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049]).
Regarding claim 25, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 21 and Cecchi further teaches:
wherein the at least one analytics rule is a complex analytics rule corresponding to at least one of the interaction data fields and at least one of the customer sentiment or the success indicator (monitors interaction of customer and customer service representative, ¶[0035] and determines if new representative is needed, ¶[0046]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
Regarding claim 26, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 21 and Adrian further teaches:
wherein the at least one analytics rule is a complex analytics rule corresponding to at least two of the following: a customer tier value, a customer sentiment, a customer feedback score, a type of interaction, a computerized resource module rating, or a set of keywords (determines ranked list of resources based on relevancy score, ¶[0073] and keywords, ¶[0065]).
Regarding claim 28, Cecchi teaches:
an internal contact data field (customer service context information includes role of an IT customer service agent working on the IT customer service issue (e.g., front-end agent, back-end agent/technician, engineer, etc.), ¶[0037]; see also ¶[0038] discussing restricting resource searching to only those resources that are appropriate for that context),
and customer identification data fields (customer context information includes name of customer, ¶[0035]);
wherein the at least one analytics rule identifies one or more computerized resource modules of a plurality of computerized resource modules to automatedly execute in real-time while completing the real-time interaction (dynamically updates multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049]; see also ¶¶[0067]-[0068] discussing assigning relevancy scoring),
further wherein the plurality of computerized resource modules are computerized applications (searched resources include knowledge article repository, customer service tool repository, ¶[0043], content configuration engine, ¶[0039], and resources include external websites (e.g., technical society websites, academic sites, etc.), ¶[0026]; see also Fig. 1; and ¶[0045] discussing indexing and searching databases with applications);
executing, by a central analytics engine, in real-time, the one or more computerized resource modules identified by the at least one analytics rule based on the applying above to complete the real-time interaction (results include knowledge articles, website, IT customer service tool included in a tools repository, etc. ¶¶[0026], [0043]);
generating a success indicator for each computerized resource module used to complete the real-time interaction (uses successful resolution of customer support tickets to assess ratings of resources, ¶[0028], and tracks resolution of solution and selections and prompts documentation of actions taken to resolve issue, ¶[0056]);
and monitoring for the real-time interaction and updating the at least one model when the real-time interaction data structure is received such that the at least one model includes the real-time data structure to create analytics rules (updates relevancy scoring based on selections of resources, ¶[0075]);
and using the updated at least one model to create at least one new analytics rule (uses dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049])..
However, Adrian does not teach but Cecchi does teach:
receiving a real-time interaction and associated interaction metadata in real-time (customer communicates with customer service representative, e.g., ¶[0020], and gathers customer information, ¶[0039]);
populating a real-time interaction data structure with the real-time interaction and associated interaction metadata (stores customer communications, ¶[0018], and stores customer information, ¶[0038]),
where the real-time interaction data structure includes a plurality of fields including an interaction data field (communication between customer and customer service representative includes chats, text messages and emails, ¶¶[0016], [0019]),
performing text analytics or voice analytics on the real-time interaction data structure (analyzes text of communications from customer, e.g., ¶¶[0023]-[0025]);
generating a customer sentiment based on at least one of the plurality of fields in the real-time interaction data structure and the text analytics or voice analytics (analyzes words and phrases used by customers, ¶[0026], and detects growing levels of irritability/anger, ¶[0036]);
inputting the real-time interaction data structure and generated customer sentiment into a trained model to predict at least one analytics rule to be applied to the real-time interaction (uses trained classifier, ¶[0028], to determine interaction style, ¶[0029]),
applying the at least one analytics rule predicted by the trained model to the real-time interaction data structure and generated customer sentiment (matches customer and customer service representative based on interaction style, ¶[0034]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
However the combination of Adrian and Cecchi does not teach but O’Connor does teach:
generating a success indicator for each computerized resource module used to complete the real-time interaction, wherein the success indicator includes a decree of success associated with using the computerized resource module for the corresponding interaction data structure (determines satisfaction of customer based on emotion from the communication between the customer and the resources, ¶[0079]; see also e.g., ¶¶0056], [0074] discussing voice and text analytics)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian and Cecchi with the emotional monitoring of O’Connor 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 users would likely be interested in ensuring the customer service is effective when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze customer satisfaction, e.g., as taught by O’Connor.
However the combination of Adrian, Cecchi and O’Connor does not teach but Henry does teach:
automatedly continually updating the at least one model when a new interaction data structure is received based on the analysis of interaction data structures (model is updated over multiple time periods using selection data, e.g., Abstract, ¶[0011])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 29, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 28 and Henry further teaches:
performing customer value analytics on the real-time interaction data structure and generating a customer value level (customers are classified by urgency level, ¶[0019]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 30, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 29 and Henry further teaches:
wherein the customer value level is also input into the trained model and used by the trained model to predict at least one of a plurality of analytics rules to be applied to the real-time interaction (machine learning model determines urgency, ¶[0019], and to determine selection score, ¶[0032]; see also ¶¶[0042], [0044] discussing calculating performance and reward scores)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 31, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 28 and Adrian further teaches:
using agent desktop analytics and the one or more computerized resource modules run for the real-time interaction in the fields of the real-time interaction data structure in a representative electronic resource data field (selects resources for resolving customer issue, e.g.. ¶[0067], [0073]; see also e.g., ¶[0054] discussing types of resources).
Regarding claim 32, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 31 and Adrian further teaches:
wherein the success indicator is based on the customer sentiment, the agent desktop use analytics, and fields in the real-time interaction data structure (monitors interaction of customer and customer service representative, ¶[0035] and determines if new representative is needed, ¶[0046]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
Regarding claim 33, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 32 and Adrian further teaches:
using the updated at least one model on a new real-time interaction to predict at least one analytics rule including the new analytics rule to apply to the new real-time interaction (updates relevancy scoring based on selections of resources, ¶[0075], and uses dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049]).
Regarding claim 34, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 33 and Adrian further teaches:
using the updated at least one model to update the at least one analytics rule (iteratively updates process for ranking resources based on additional context information being gathered, ¶[0060] and Fig 5).
Regarding claim 35, Adrian teaches:
a memory comprising computer readable instructions; a processor configured to read the computer readable instructions that when executed causes the system to (memory, instructions, and processors, ¶¶[0088]-[0089]):
receive a plurality of historic interactions as interaction data structures and interaction metadata (stores customer service tickets in repository, ¶[0043] and Fig. 1; see also ¶[0035] noting customer service issue information includes context data like name of the customer)
an internal contact data field (customer service context information includes role of an IT customer service agent working on the IT customer service issue (e.g., front-end agent, back-end agent/technician, engineer, etc.), ¶[0037]; see also ¶[0038] discussing restricting resource searching to only those resources that are appropriate for that context),
customer identification data fields (customer service issue information includes name of the customer, ¶¶[0035], [0052]),
and a representative electronic resource data field listing computerized resource modules used by a customer service representative associated with the internal contact data field for the historic interaction (tracks tools as they are used to resolve (or attempt to resolve) particular IT customer service issues in customer service tickets, ¶[0046]; see also ¶[0064] noting system counts how usage of resources used when resolving an issue);
and generate a success indicator for each computerized resource module included in the representative electronic resource data field (uses successful resolution of customer support tickets to assess ratings of resources, ¶[0028], and tracks resolution of solution and selections and prompts documentation of actions taken to resolve issue, ¶[0056]);
wherein the success indicator includes a degree of success associated with using the computerized resource module for the corresponding interaction data structure (uses successful resolution of customer support tickets to assess ratings of resources, ¶[0028], and tracks resolution of solution and selections and prompts documentation of actions taken to resolve issue, ¶[0056]);
generate at least one model configured to create at least one analytics rule based on and generated success indicator (builds model to identify people or groups that are knowledgeable on different topics, ¶[0047]; see also ¶¶[0021], [0049] discussing dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource),
wherein the at least one analytics rule causes at least one of a plurality of computerized resource modules to be selected to be accessed by a central analytics engine in connection with receipt of a new interaction (searches resources, ¶[0063], and assigns relevancy scores, ¶[0067], to find resources relevant to customer service issue, ¶[0073]; see also Fig. 5 summarizing process; and e.g., ¶¶[[0088]-[0089] discussing processors),
further wherein the plurality of computerized resource modules are computerized applications (searched resources include knowledge article repository, customer service tool repository, ¶[0043], content configuration engine, ¶[0039], and resources include external websites (e.g., technical society websites, academic sites, etc.), ¶[0026]; see also Fig. 1; and ¶[0045] discussing indexing and searching databases with applications);
and use the at least one model to create at least one analytics rule (builds model to identify people or groups that are knowledgeable on different topics, ¶[0047]; see also ¶¶[0021], [0049] discussing dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource);
monitor the system for new interaction data structures and automatedly updating the at least one model when a new interaction data structure is received such that the at least one model includes the new interaction data structure when creating analytics rules (updates relevancy scoring based on selections of resources, ¶[0075]);
and use the updated at least one model to create at least one new analytics rule (uses dynamically updating multi-factor context search to identify and change relevant resources by conducting iterative searches of the set of available resource, ¶¶[0021], [0049]).
However, Adrian does not teach but Cecchi does teach:
receive a plurality of historic interactions as interaction data structures (customer communicates with customer service representative, e.g., ¶[0020]; see also ¶[0040] discussing analyzing past help queries),
as interaction data structures, each interaction data structure including a plurality of fields, including an interaction data field (communication between customer and customer service representative includes chats, text messages and emails, ¶¶[0016], [0019]),
perform agent desktop use analytics and text analytics or voice analytics on each interaction data structure (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035] and analyzes text of communications from customer, e.g., ¶¶[0023]-[0025]),
wherein the agent desktop use analytic includes monitoring use of agent desktops (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035]),
and receive a set of data pertaining to use of agent desktops associated with each interaction data structure of the interaction data structures (monitors the interaction as the communication between the matched customer and customer service representative progresses, ¶[0035]; see also ¶[0074] discussing desktops)
generate a customer sentiment based on the agent desktop use analytics, at least one of the plurality of fields in the interaction data structure, and the text analytics or voice analytics (analyzes words and phrases used by customers, ¶[0026], and detects growing levels of irritability/anger, ¶[0036]);
and generate a success indicator for each computerized resource module included in the representative electronic resource data field based on the customer sentiment (monitors interaction of customer and customer service representative, ¶[0035] and determines if new representative is needed, ¶[0046]),
generate at least one model configured to create at least one analytics rule using machine learning techniques based on the interaction data structures, generated customer sentiment, and generated success indicator (trains classifier for analyzing text to determine interaction styles of customers and customer service representatives, ¶¶[0028]-[0030]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
However the combination of Adrian and Cecchi does not teach but O’Connor does teach:
and generating a success indicator for each computerized resource module included in the representative electronic resource data field based on the customer sentiment the agent desktop use analytics, at least one of the plurality of fields in the interaction data structure, and the text analytics or voice analytics (determines satisfaction of customer based on emotion from the communication between the customer and the resources, ¶[0079]; see also e.g., ¶¶0056], [0074] discussing voice and text analytics)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian and Cecchi with the emotional monitoring of O’Connor 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 users would likely be interested in ensuring the customer service is effective when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze customer satisfaction, e.g., as taught by O’Connor.
However the combination of Adrian, Cecchi and O’Connor does not teach but Henry does teach:
automatedly continually updating the at least one model when a new interaction data structure is received based on the analysis of interaction data structures (model is updated over multiple time periods using selection data, e.g., Abstract, ¶[0011])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 36, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 35 and Henry further teaches:
perform customer value analytics on each interaction data structure and generating a customer value level (customers are classified by urgency level, ¶[0019]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the urgency model of Henry 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 addressing urgent customer issues first would likely improve the overall customer service and accordingly would have modified Adrian, Cecchi and O’Connor to utilize the urgency model of Henry.
Regarding claim 39, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 35 and Cecchi further teaches:
wherein the at least one analytics rule is a complex analytics rule corresponding to at least one of the interaction data field and at least one of the customer sentiment or the success indicator (monitors interaction of customer and customer service representative, ¶[0035] and determines if new representative is needed, ¶[0046]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
Regarding claim 40, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 35 and Adrian further teaches:
wherein the at least one analytics rule is a complex analytics rule corresponding to at least two of the following: a customer tier value, a customer sentiment, a customer feedback score, a type of interaction, a computerized resource module rating, or a set of keywords (selects resources based on type of issue customer is having and knowledge article ratings and customer service survey ratings, ¶[0028]).
Regarding claim 41, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 26 the combination further teaches:
wherein if a category of the complex analytics rule indicates a specific mobile phone issue for a given customer tier value and a given customer sentiment value, then the complex analytics rule indicates to provide a model article for troubleshooting the specific mobile phone issue from a resource database of the at least one model to the customer service representative (these limitations do not substantially further limit the scope of the claims because it is a contingent limitation in a method claim with a condition precedent that is not required to occur, see MPEP 2111.04.II. That is, the claims do not require the complex analytics rule indicates a specific mobile phone issue and thus do not require providing a model article).
Regarding claim 42, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 26 the combination further teaches:
wherein if a category of the complex analytics rule indicates a specific mobile phone issue for a given customer tier value, a given customer sentiment, and a negative customer feedback score, then the complex analytics rule indicates to provide a customer service representative with an electronic resource to identify and fix the specific mobile phone issue (these limitations do not substantially further limit the scope of the claims because it is a contingent limitation in a method claim with a condition precedent that is not required to occur, see MPEP 2111.04.II. That is, the claims do not require the complex analytics rule indicates a specific mobile phone issue and thus do not require providing a customer service representative with an electronic resource).
Regarding claim 43, the combination of Adrian, Cecchi, O’Connor and Henry teaches all the limitations of claim 26 the combination further teaches:
wherein if a category of the complex analytics rule indicates a specific mobile phone issue for a high-tier customer tier value and an unfavorable customer sentiment, then the complex analytics rule indicates to provide an open scheduling electronic resource for a nearest shop for scheduling of an appointment (these limitations do not substantially further limit the scope of the claims because it is a contingent limitation in a method claim with a condition precedent that is not required to occur, see MPEP 2111.04.II. That is, the claims do not require the complex analytics rule indicates a specific mobile phone issue and thus do not require providing an open scheduling electronic resource).
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Adrian, Cecchi and O’Connor further in view of Lewis-Hawkins, US Pub. No. 2010/0100490, herein referred to as “Lewis-Hawkins”.
Regarding claim 27, the combination of Adrian, Cecchi and O’Connor teaches all the limitations of claim 21 and Adrian further teaches:
wherein the plurality of computerized resource modules are computerized electronic applications (searched resources include knowledge article repository, customer service tool repository, ¶[0043], content configuration engine, ¶[0039], and resources include external websites (e.g., technical society websites, academic sites, etc.), ¶[0026]; see also Fig. 1; and ¶[0045] discussing indexing and searching databases with applications).
However Adrian does not teach but Cecchi does teach
computerized electronic applications include voice analytic applications (converts online audio and video communications to text, ¶¶[0016], [0022]),
text analytic applications (analyzes text of communications from customer, e.g., ¶¶[0023]-[0025]),
quality assurance applications (system is used for determining customer satisfaction, ¶[0017]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the tracking resources used by customer service agents of Adrian with the customer service system of Cecchi 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 matching customers and customer service representatives based on interaction styles, i.e., as taught by Cecchi, would likely improve efficiency when resolving customer service issues, as in Adrian, and accordingly would have modified Adrian to analyze interaction styles.
However the combination of Adrian, Cecchi and O’Connor does not teach but Lewis-Hawkins does teach:
and scheduling applications (monitors conversations to schedule follow services, e.g., ¶[0014], by monitoring chats with customer, ¶[0023]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the resource tracking based customer service system of Adrian, Cecchi and O’Connor with the monitoring of Lewis-Hawkins because Lewis-Hawkins explicitly suggests using the system maximize service representative time by permitting the handling of multiple customers simultaneously by a single representative, ¶[0014]; see also MPEP 2143.I.G.
Conclusion
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.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626