DETAILED ACTION
Status of Claims
This action is in reply to Pre-Appeal conferences request filed on 12/30/25 and Pre-Appeal conference decision mailed on 2/20/26. Claims 1-18 are pending and examined.
Claim Rejections - 35 USC § 103
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.
Claims 1-3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210083994 A1 (Pan) in view of US 20210136207 A1 (Adibi) in view of
As to claim 1,
Pan teaches,
a plurality of chatbots, each of the plurality of machine learning models corresponding to a respective chatbot of the plurality of chatbots (FIG. 4, item 424 ¶ 113 “bot classifier model”),
a computing platform comprising a processor, a non-transitory computer-readable memory communicatively coupled to the processor, and machine-readable instructions stored in the memory that, when executed by the processor, cause the processor of the computing platform to (FIG. 26, item 2600, 2604, 2610, ¶ 268 “the processing units in processing subsystem 2604 can execute instructions stored in system memory 2610”),
receive a query comprising a content from a graphical user interface of a user of a client device as part of a conversation between the user of the client device and the computing platform (FIG. 1, item 110, 112, ¶ 55 “a user may provide a user input 110 to the digital assistant 106, which may provide a response 112 to the user input 110”, ¶ 64 “The conversation can include a combination of text or audio user inputs 110 provided by the user and responses 112 provided by the skill bots 116 by way of the digital assistant 106”),
route the query to one or more selected models of the plurality of machine learning models based on the content of the query such that the query is routed to one or more selected chatbots of the plurality of chatbots associated with the at least one sub-category identified as corresponding to the query (¶ 100 “master bot 114 determines if the input utterance explicitly identifies a skill bot 116 using its invocation name. If an invocation name is present in the input utterance, then it is treated as an explicit invocation of the skill bot 116 corresponding to the invocation name. In such a scenario, the master bot 114 may route the input utterance to the explicitly invoked skill bot 116 for further handling”),
generate a response to the query, using the one or more selected models and corresponding one or more selected chatbots and at least one or more object notation objects, as part of the conversation between the user of the client device and the computing platform (¶ 59 “generating a response to be output to the user responsive to the utterance, causing the action to be performed when appropriate, generating a response to be output to the user responsive to the utterance, and outputting the response to the user. The NLU processing can include parsing the received input utterance to understand the structure and meaning of the utterance, refining and reforming the utterance to develop a form that is more easily parsed and understood (e.g., a logical form). Generating a response may include using NLG techniques. Thus, the natural language processing performed by a digital assistant 106 can include a combination of NLU and NLG processing”) [wherein the one or more notation objects are configured to …],
display the customized output for the user on the graphical user interface of the client device of the user (para. 58 “The digital assistant 106 may end the conversation by outputting information to the user indicating that the pizza has been ordered”, para. 59, para. 241 “display the data feeds and/or real-time events via one or more display devices of client computing devices”).
Pan does not teach,
parse the query to identify, using a first machine learning technique associated with a computing platform machine learning model, to identify a top level category corresponding to the query and to identify, using a second machine learning technique associated with the computing platform machine learning model, at least one sub-category of the top level category corresponding to the query,
[generate a response to the query …] wherein the one or more object notation objects are configured to receive one or more updates to dynamically and in real-time update associated ones of the one or more selected models without affecting other models and without code modification;
automatically generate, based on the conversation between the user of the client device and the computing platform and the response to the query, the customized output for the user, the customized output for the user separate from the response to the query.
however, Adibi teaches,
parse the query (para. 45 “The virtual agent to whom the customer 110 is routed may “listen” to the customer 110 by a speech engine (components 206, 210 and/or 212 and/or translation 324) processing the customer's speech. The processed speech may be forwarded to a speech adapter 316 within a virtual agent engine”) to identify, using a first machine learning technique associated with a computing platform machine learning model, to identify a top level category corresponding to the query and to identify, using a second machine learning technique associated with the computing platform machine learning model, at least one sub-category of the top level category corresponding to the query (para. 46 “Upon an assignment of a customer 110 to a virtual agent, the virtual agent engine 314 updates a mapping between the selected virtual agent and the customer 110. The mapping may be used to route communication between the customer 110 and the selected virtual agent. If the assigned virtual agent is able to satisfy the customer's needs, the virtual agent engine 314 may update a reporting database and delete the mapping. However, if the assigned virtual agent is unable to satisfy the customer's needs, the customer may be escalated to an agent 120 or supervisor” mapping to agent and/or supervisor is interpreted as identifying top-level category and associated at least one sub-category),
[generate a response to the query …] wherein the one or more object notation objects are configured to receive one or more updates to dynamically and in real-time update associated ones of the one or more selected models without affecting other models and without code modification (para. 46 “The virtual agent engine 314 assigns the customer 110 to a virtual agent and will manage the message flows between the virtual agent and the customer. In some implementations, the virtual agent engine 314 maintains a map of queues serviced by virtual agents, tracks virtual agent sessions for recording/reporting agent events in a set of system statistics, reads site configuration values to identify which agents are virtual and which chat queues are serviced by virtual agents, and/or processes escalation rules and assigns chats requiring escalation to an appropriate live agent chat queue. The virtual agent engine 314 may associate a particular customer, organization, product, category, etc. with certain virtual agents, each having its own personality, capabilities, etc. as described below. In some implementations, the virtual agent engine 314 may apply rules to select an appropriate virtual agent. The rules may account for a product category, (e.g., smartphone, exercise equipment, etc.), customer identity (e.g., a high value customer), geographic location, time of day, etc. The rules may escalate a customer to a live agent 120. Upon an assignment of a customer 110 to a virtual agent, the virtual agent engine 314 updates a mapping between the selected virtual agent and the customer 110. The mapping may be used to route communication between the customer 110 and the selected virtual agent. If the assigned virtual agent is able to satisfy the customer's needs, the virtual agent engine 314 may update a reporting database and delete the mapping”);
automatically generate, based on the conversation between the user of the client device and the computing platform and the response to the query, the customized output for the user (para. 72 “virtual agent may have authority to offer $300”), the customized output for the user separate from the response to the query.(para. 82 “virtual agent, who will answer the customer's calls or response to other multi-channel interactions with the contact center 150. That specific virtual agent will know the customer's preferences, address, age, family, etc. through information in the CRM 304. The virtual agent engine 314 will use the machine learning module 402 to learn from every conversation and interaction with the customer 110 to tailor the interactions to be specific customer”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the customer conversation response generator of Pan with customer tailored conversation agent of Adibi because customized agent improves customer response uptake by streamlining conversation response to customer context.
As per claim 2, combination of Pan and Berger teach all the limitations of claim 1.
Pan also teaches,
train the computing platform machine learning model corresponding to the computing platform to route queries to one or more of the plurality of machine learning models based on the respective content of the queries (¶ 110 “the classifier model 324 may include a rules-based model trained on training data 354 that includes examples utterances”),
route the query to one or more selected models of the plurality of machine learning models based on the computing platform machine learning model as trained (¶ 112 “the routing subsystem 320 … may route the input utterance 303 to that skill bot 116 for processing”).
As per claim 3, combination of Pan and Adibi teach all the limitations of claim 1.
Pan also teaches,
train each of the plurality of machine learning models on one or more topics to be associated with the corresponding chatbot of the plurality of chatbots (¶ 81 “The skill bot 116 is then trained based upon these specified intents”),
generate an association between the content of the query and the one or more topics for at least one chatbot of the plurality of chatbots (¶ 112 “determine …, for each skill bot 116, an associated confidence score indicating the likelihood that the skill bot 116 is most suitable for processing (i.e., is a best match for) the input utterance”),
route the query to the at least one chatbot based on the association and the respective trained machine learning model corresponding to the at least one chatbot (¶ 112).
As per claim 11, combination of Pan and Adibi teach all the limitations of claim 1.
Pan also teaches,
display the response to the query on the graphical user interface of the client device of the user (¶ 59 “outputting the response to the user”, ¶ 236 “A client device may provide an interface that enables a user of the client device to interact with the client device”).
Claims 4-10 and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Pan in view of Adibi in further view of US 20150178849 A1 (Berger).
As per claim 4, combination of Pan and Adibi teach all the limitations of claim 1.
combination of Pan and Adibi do not teach,
generate a quote as the customized output based on the conversation and the response to the query,
display the quote as the customized output on the graphical user interface of the client device of the user.
however, Berger teaches,
generate a quote as the customized output based on the conversation and the response to the query (¶ 84 “generate a quote”),
display the quote as the customized output on the graphical user interface of the client device of the user (¶ 31 “transmitting the quote for display”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 5, combination of Pan and Adibi teach all the limitations of claims 1 and 4.
combination of Pan and Adibi do not teach,
prior to display of the quote, generate a prompt to the user on the graphical user interface requesting approval to display the quote,
based on receiving approval from the user via the prompt on the graphical user interface, display the quote on the graphical user interface.
however, Berger teaches,
prior to display of the quote, generate a prompt to the user on the graphical user interface requesting approval to display the quote (FIG. 4A, item 454, 455, ¶ 82 “the consultative insurance engine 420 presents this responsive content”),
based on receiving approval from the user via the prompt on the graphical user interface, display the quote on the graphical user interface (FIG. 4A, items 457, 459, ¶ 85 “the user 410 may respond 456 (or 457) to the consultative content 435 in the display 439 about loan lease gap insurance by indicating a desire to purchase this coverage for his leased Honda Accord”, ¶ 88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 6, combination of Pan and Adibi teach all the limitations of claim 1.
combination of Pan and Adibi do not teach,
prior to display of the customized output, generate a prompt to the user on the graphical user interface requesting approval to display the customized output,
based on receiving approval from the user via the prompt on the graphical user interface, display the customized output on the graphical user interface.
Berger teaches,
prior to display of the customized output, generate a prompt to the user on the graphical user interface requesting approval to display the customized output (FIG. 4A, items 454, 455, ¶ 82),
based on receiving approval from the user via the prompt on the graphical user interface, display the customized output on the graphical user interface (FIG. 4A, item 457, 459, ¶ 85, 88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 7, combination of Pan and Adibi teach all the limitations of claim 1.
combination of Pan and Adibi do not teach,
prior to generation of the customized output, generate a prompt to the user on the graphical user interface requesting approval to generate the customized output.
however, Berger teaches,
prior to generation of the customized output, generate a prompt to the user on the graphical user interface requesting approval to generate the customized output (FIG. 4A, items 454, 455, ¶ 82).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 8, combination of Pan and Adibi teach all the limitations of claims 1 and 7.
combination of Pan and Adibi do not teach,
based on receiving approval from the user via the prompt on the graphical user interface, generate an information prompt to the user on the graphical user interface requesting information from the user,
based on the information from the user received via the information prompt, generate the customized output for the user.
however, Berger teaches,
based on receiving approval from the user via the prompt on the graphical user interface, generate an information prompt to the user on the graphical user interface requesting information from the user (FIG. 4A, items 454, 455, ¶ 82),
based on the information from the user received via the information prompt, generate the customized output for the user (FIG. 4A, item 457, 459, ¶ 85, 88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 9, combination of Pan and Adibi teach all the limitations of claim 1, 7 and 8.
combination of Pan and Adibi do not teach,
receive an acceptance or a declination from the user with respect to an offer for a service associated with the customized output,
based on receipt of the acceptance of the user with respect to the offer associated with the customized output, proceed to generate the service for the user,
display confirmation of the service to the user on the graphical user interface.
however, Berger teaches,
receive an acceptance or a declination from the user with respect to an offer for a service associated with the customized output (FIG. 4A, items 454, 455, ¶ 82),
based on receipt of the acceptance of the user with respect to the offer associated with the customized output, proceed to generate the service for the user (FIG. 4A, item 457, ¶ 85),
display confirmation of the service to the user on the graphical user interface (FIG. 4A, item 459, ¶ 88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 10, combination of Pan and Adibi teach all the limitations of claims 1, 7 and 8.
combination of Pan and Adibi do not teach,
generate a plurality of interface screens on the graphical user interface to display as the information prompt to the user on the graphical user interface requesting information from the user,
receive information from the user via the plurality of interface screens, each interface screen requesting information from the user to generate the customized output.
however, Berger teaches,
generate a plurality of interface screens on the graphical user interface to display as the information prompt to the user on the graphical user interface requesting information from the user (FIG. 4A, items 454, 455, 457, 459 ¶ 82, 85, 89),
receive information from the user via the plurality of interface screens, each interface screen requesting information from the user to generate the customized output (FIG. 4A, items 454, 455, 457, 459 ¶ 82, 85, 89).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 12, combination of Pan and Adibi teach all the limitations of claim 1.
combination of Pan and Adibi do not teach,
the query comprises an inquiry regarding a type of insurance, and wherein the response to the query comprises information regarding the type of insurance to address the inquiry.
however, Berger teaches,
the query comprises an inquiry regarding a type of insurance, and wherein the response to the query comprises information regarding the type of insurance to address the inquiry (¶ 29 “request quotes for additional lines of insurance”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 13, combination of Pan and Adibi teach all the limitations of claims 1 and 12.
combination of Pan and Adibi do not teach,
the type of insurance comprises collision insurance.
however, Berger teaches,
the type of insurance comprises collision insurance (¶ 29 “collision coverage”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 14, combination of Pan and Adibi teach all the limitations of claims 1, 12 and 13.
combination of Pan and Adibi do not teach,
the customized output comprises a quote for collision insurance.
however, Berger teaches,
the customized output comprises a quote for collision insurance (¶ 52 “presented in a pop-up frame 282 that explains collision and comprehensive coverage concepts”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 15, combination of Pan and Adibi teach all the limitations of claim 1.
combination of Pan and Adibi do not teach,
display the response to the query to the user on the graphical user interface,
generate a prompt for the user to determine whether the response to the query is an acceptable total response or an unacceptable total response,
display the prompt to the user,
receive a response to the prompt from the user,
when the response to the prompt is indicative that the response to the query is the unacceptable total response, send an assistance request for assistance information to an enterprise user device,
receive the assistance information based on the assistance request from the enterprise user device,
display the response to the query to the user on the graphical user interface based on the assistance information.
however, Berger teaches,
display the response to the query to the user on the graphical user interface (FIG. 4A, item 454, ¶ 82),
generate a prompt for the user to determine whether the response to the query is an acceptable total response or an unacceptable total response (FIG. 4A, item 439, ¶ 82 “major content no. 2”),
display the prompt to the user (FIG. 4A, item 454, ¶ 82),
receive a response to the prompt from the user (FIG. 4A, item 452, ¶ 80),
when the response to the prompt is indicative that the response to the query is the unacceptable total response, send an assistance request for assistance information to an enterprise user device (FIG. 4A, item 456, ¶ 84),
receive the assistance information based on the assistance request from the enterprise user device (FIG. 4A, item 453, ¶ 81),
display the response to the query to the user on the graphical user interface based on the assistance information (FIG. 4A, item 454, ¶ 82).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 16, combination of Pan and Adibi teach all the limitations of claims 1 and 15.
combination of Pan and Adibi do not teach,
generate a follow-up prompt for the user to determine whether the response to the query based on the assistance information is the acceptable total response or the unacceptable total response,
display the follow-up prompt to the user,
receive a response to the follow-up prompt from the user.
however, Berger teaches,
generate a follow-up prompt for the user to determine whether the response to the query based on the assistance information is the acceptable total response or the unacceptable total response (FIG. 4A, items 452, 456, ¶ 80, 84),
display the follow-up prompt to the user (FIG. 4A, item 454, ¶ 82),
receive a response to the follow-up prompt from the user (FIG. 4A, item 457, ¶ 85).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 17, combination of Pan and Adibi teach all the limitations of claims 1, 15 and 16.
combination of Pan and Adibi do not teach,
when the response to the prompt is indicative that the response to the query is the unacceptable total response, send another assistance request for further assistance information to the enterprise user device,
receive the further assistance information based on the another assistance request from the enterprise user device,
display the response to the query to the user on the graphical user interface based on the further assistance information.
however, Berger teaches,
when the response to the prompt is indicative that the response to the query is the unacceptable total response, send another assistance request for further assistance information to the enterprise user device (FIG. 4A, items 452, 456, ¶ 80, 84),
receive the further assistance information based on the another assistance request from the enterprise user device (FIG. 4A, item 453, ¶ 81),
display the response to the query to the user on the graphical user interface based on the further assistance information (FIG. 4A, item 454, ¶ 82).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
As per claim 18, combination of Pan and Adibi teach all the limitations of claims 1, 15, 16 and 17.
combination of Pan and Adibi do not teach,
when the response to the prompt is indicative that the response to the query is the acceptable total response, generate a completion notification for the response to the query,
send the completion notification to the enterprise user device,
display the completion notification on the graphical user interface of the client device,
receive the further assistance information based on the another assistance request from the enterprise user device,
display the response to the query to the user on the graphical user interface based on the further assistance information.
Berger teaches,
when the response to the prompt is indicative that the response to the query is the acceptable total response, generate a completion notification for the response to the query (FIG. 4A, items 454, 455, ¶ 82, 84),
send the completion notification to the enterprise user device (FIG. 4A, items 454, 455, ¶ 82, 84),
display the completion notification on the graphical user interface of the client device (FIG. 4A, items 454, 455, ¶ 82, 84),
receive the further assistance information based on the another assistance request from the enterprise user device (FIG. 4A, item 457, ¶ 85),
display the response to the query to the user on the graphical user interface based on the further assistance information (FIG. 4A, item 459, ¶ 88).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the customer conversation response generator of Pan with customer tailored conversation agent of Adibi with customized insurance quotes of Berger because customized insurance improves customer response uptake by specifically tailoring insurance quote conversations to customer context.
Conclusion
Reference made of record, not cited, pertinent to Applicant’s disclosure, includes NPL: Nuruzzaman M, Hussain OK. IntelliBot: A Dialogue-based chatbot for the insurance industry. Knowledge-Based Systems. 2020 May 21;196:105810. (Year: 2020).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROCK E TURK whose telephone number is (571)272-5626. The examiner can normally be reached Monday-Friday 9AM-5PM EST.
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/BROCK E TURK/Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692 June 2, 2026
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