DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action has been issued in response to Applicant’s Communication of application S/N 19/251,916 filed on June 27, 2025. Claims 1 to 19 are currently pending with the application.
Priority
The instant application is a continuation of application S/N 17/855,184, filed on June 30, 2022, now U.S. Patent No. 12,373,893, which claims priority from provisional applications with S/N 63/216,673, 63/216,706, 63/216,655, 63/216,696, and 63/216,720, filed on June 30, 2021. Applicant’s claim for the benefit of the prior-filed applications under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 21, 2026 was filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 to 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 to 19 of U.S. Patent 12,373,893. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the instant application are anticipated by the claims in the U.S. Patents.
Following mapping of claims 1 to 17 of Instant Application to claims 1 to 17 of U.S. Patent 12,373,893. Similar mapping applies to claims 18 and 19 of instant application, since they recite similar limitations.
Instant Application
U.S. Patent 12,373,893
1. A chatbot system configured to analyze query data, the chatbot system comprising: a plurality of machine learning models and a plurality of chatbots, each of the plurality of machine learning models corresponding to a respective chatbot of the plurality of chatbots; and 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, across at least one or more intelligent platforms, technical platforms, or combinations thereof: parse a query comprising a content to identify at least a category and at least a first sub- category and a second sub-category, each associated with the category corresponding to the query; route the query to at least two selected models of the plurality of machine learning models based on the content of the query and the at least the first sub-category and the second sub-category that are identified such that the query is routed to at least two selected chatbots of the plurality of chatbots, wherein at least a first portion of the query is routed to a first selected chatbot of the at least two selected chatbots based on the first sub-category as identified and at least a second portion of the query is routed to the a second selected chatbot different from the first selected chatbot based on the second sub-category as identified, wherein the first portion is the same as, partially the same as, or different from the second portion of the query; generate a response to the query, using the at least two selected models and corresponding at least two selected chatbots, as part of a conversation between a user of a client device and the computing platform; and train the at least two selected models of the plurality of machine learning models by generating an association between the response and the at least two selected chatbots.
1. A chatbot system configured to analyze query data, the chatbot system comprising: a plurality of machine learning models and a plurality of chatbots, each of the plurality of machine learning models corresponding to a respective chatbot of the plurality of chatbots; and 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, across at least one or more intelligent platforms, technical platforms, or combinations thereof: parse a query comprising a content to identify at least a category and at least a first sub-category and a second sub-category, each associated with the category corresponding to the query; route the query to at least two selected models of the plurality of machine learning models based on the content of the query and the at least the first sub-category and the second sub-category that are identified such that the query is routed to at least two selected chatbots of the plurality of chatbots, wherein the query is routed to a first selected chatbot of the at least two selected chatbots based on the first sub-category as identified and the query is routed to the a second selected chatbot different from the first selected chatbot based on the second sub-category as identified; generate a response to the query, using the at least two selected models and corresponding at least two selected chatbots, as part of a conversation between a user of a client device and the computing platform; display the response to the query; generate a prompt to the user on a graphical user interface of the client device requesting a rating of the response to the query; generate analytics based on the rating of the response to the query and historical data; display the analytics on a graphical user interface of an enterprise client device; and train the at least two selected models of the plurality of machine learning models by generating an association between the analytics and the at least two selected chatbots.
2. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: train a 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; and route the query to the at least two selected models of the plurality of machine learning models based on the computing platform machine learning model as trained.
2. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: train a 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; and route the query to the at least two selected models of the plurality of machine learning models based on the computing platform machine learning model as trained.
3. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; 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 ;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.
3. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; 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; 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.
4. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: identify, using a first machine learning technique associated with a first machine learning model for the computing platform, a top level category as the category corresponding to the query, the top level category being one of a plurality of categories, the top level category having a highest category ranking as a match to the query; and identify, using a second machine learning technique associated with a second machine learning model for the computing platform different from the first machine learning model, the second machine learning technique different from the first machine learning technique, a plurality of sub-categories corresponding to the query, and having a highest sub-category ranking the first sub-category and the second sub-category having respectively a first highest sub-category ranking and a second highest sub-category ranking as a match to the query.
4. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: identify, using a first machine learning technique associated with a first machine learning model for the computing platform, a top level category as the category corresponding to the query, the top level category being one of a plurality of categories, the top level category having a highest category ranking as a match to the query; and identify, using a second machine learning technique associated with a second machine learning model for the computing platform different from the first machine learning model, the second machine learning technique different from the first machine learning technique, a plurality of sub-categories corresponding to the query, and having a highest sub-category ranking the first sub-category and the second sub-category having respectively a first highest sub-category ranking and a second highest sub-category ranking as a match to the query.
5. The chatbot system of claim 4, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: identify the top level category and the first sub-category and the second sub-category based on a number of utterances corresponding to the query, labeled data corresponding to the query, unlabeled data corresponding to the query, or combinations thereof, wherein the top level category is associated with a type of insurance, and each of the first sub-category and the second sub- category is associated with one or more requirements for the type of insurance.
5. The chatbot system of claim 4, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: identify the top level category and the first sub-category and the second sub-category based on a number of utterances corresponding to the query, labeled data corresponding to the query, unlabeled data corresponding to the query, or combinations thereof, wherein the top level category is associated with a type of insurance, and each of the first sub-category and the second sub-category is associated with one or more requirements for the type of insurance.
6. The chatbot system of claim 1, wherein the analytics displayed on the graphical user interface of the enterprise client device comprise information regarding whether the user is a flight risk such that the user may discontinue a service based on the rating and the historical data.
6. The chatbot system of claim 1, wherein the analytics displayed on the graphical user interface of the enterprise client device comprise information regarding whether the user is a flight risk such that the user may discontinue a service based on the rating and the historical data.
7. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: receive a configuration update for a selected model of the plurality of machine learning models via an enterprise user device; and update the selected model based on the configuration update without affecting the other models of the plurality of machine learning models.
7. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: receive a configuration update for a selected model of the plurality of machine learning models via an enterprise user device; and update the selected model based on the configuration update without affecting the other models of the plurality of machine learning models.
8. The chatbot system of claim 7, wherein the configuration update comprises a JavaScript Object Notation (JSON) object created by a user of an enterprise client device, and the selected model is updated based on storing the JSON object such that additional coding is not required to update the selected model.
8. The chatbot system of claim 7, wherein the configuration update comprises a JavaScript Object Notation (JSON) object created by a user of an enterprise client device, and the selected model is updated based on storing the JSON object such that additional coding is not required to update the selected model.
9. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: generate a plurality of interface screens on a graphical user interface of the client device to display as an information prompt to the user on the graphical user interface requesting information from the user; and receive information from the user via the plurality of interface screens, each interface screen requesting information from the user to generate the response to the query.
9. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: generate a plurality of interface screens on a graphical user interface of the client device to display as an information prompt to the user on the graphical user interface requesting information from the user; and receive information from the user via the plurality of interface screens, each interface screen requesting information from the user to generate the response to the query.
10. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: receive the query comprising the content from a graphical user interface of the user of the client device as part of the conversation between the user of the client device and the computing platform; and display the response to the query to the user on the graphical user interface of the client device of the user.
10. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: receive the query comprising the content from a graphical user interface of the user of the client device as part of the conversation between the user of the client device and the computing platform; and display the response to the query to the user on the graphical user interface of the client device of the user.
11. The chatbot system of claim 1, wherein the query comprises an inquiry regarding a type of insurance, and the response to the query comprises information regarding the type of insurance to address the inquiry.
11. The chatbot system of claim 1, wherein the query comprises an inquiry regarding a type of insurance, and the response to the query comprises information regarding the type of insurance to address the inquiry.
12. The chatbot system of claim 11, wherein the type of insurance comprises collision insurance.
12. The chatbot system of claim 11, wherein the type of insurance comprises collision insurance.
13. The chatbot system of claim 12, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: generate a customized output comprising a quote for collision insurance based on the response to the query; and display the customized out comprising the quote on a graphical user interface of the client device.
13. The chatbot system of claim 12, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: generate a customized output comprising a quote for collision insurance based on the response to the query; and display the customized out comprising the quote on a graphical user interface of the client device.
14. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and display the response to the query to the user on a graphical user interface of the client device based on the assistance information.
14. The chatbot system of claim 1, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and display the response to the query to the user on a graphical user interface of the client device based on the assistance information.
15. The chatbot system of claim 14, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and receive a response to the follow-up prompt from the user.
15. The chatbot system of claim 14, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and receive a response to the follow-up prompt from the user.
16. The chatbot system of claim 15, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and display the response to the query to the user on the graphical user interface of the client device based on the further assistance information.
16. The chatbot system of claim 15, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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; and display the response to the query to the user on the graphical user interface of the client device based on the further assistance information.
17. The chatbot system of claim 15, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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 assistance information based on the assistance request from the enterprise user device; and display the response to the query to the user on the graphical user interface based on the assistance information.
17. The chatbot system of claim 15, wherein the machine-readable instructions further, when executed by the processor, cause the processor of the computing platform to: 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 assistance information based on the assistance request from the enterprise user device; and display the response to the query to the user on the graphical user interface based on the assistance information.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Rajagopal et al., (U.S. Publication No. 2020/0227026) is directed to using, training, building, and managing question and answer engines, to automatically generate responses, including identification and utilization of topics and subtopics for question routing. However, it does not disclose the distribution of portions of the query to at least two models, and the selection of two chatbots based on a first and second sub-category.
Conclusion
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169
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