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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/30/2026 has been entered.
Information Disclosure Statement
The information disclosure submitted on 2/16/2026 was filed after the mailing data of the first office
action. The /submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the
information disclosure statement is being considered by the examiner.
Status of claims
Claims 1-2, 7, 9-11, 15 and 17-19 are amended. Claim 16 is cancelled. Claims 1-15 and 17-21 are presented for examination.
Response to Arguments
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claims 1 -15 and 17-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-12 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ji ( US 20250103821) and further in view of Batina (US: 20240289361)
Regarding claim 1, Ji teaches an apparatus comprising: a memory; and a processor coupled to the memory, the processor configured to: output an initial prompt via a chatbot during a conversation within a chat window of a software application (initial query, Fig 6, Para 0082) , receive a response associated with a item via the chat window ( receives an initial response, Para 0082, 0085-0086) , execute a large language model (LLM) on the initial prompt and the response to generate and output a plurality of prompts ( follow up has more follow ups, Para 0092) associated with the item ( The query entering panel 630 displays three follow-up queries 640, 650, and 660, which are automatically generated by the query generation engine 450 based on the initial response 620 to the initial user query 610, Para 0082, Fig 6; object of interest is a product ) ; receive responses to the plurality of prompts via the chat window ( Following the first response, the query generation engine 450 can generate a second set of follow-up queries based on the first response to the first follow-up query for the user to select. If a user selects a second follow-up query from the second set of follow-up queries or types in the second follow-up query, the answer generation engine 440 can generate a response to the second follow-up query. This way, the user can interact with the communication platform to obtain the relevant information about the virtual communication session., Para 0092) ; determine a sequence of interest expressed with respect to the object based on an order of the plurality of prompts and the responses within the chat window ( The user selection or non-selection of the follow-up queries generated by the query generation engine 450 can be feedback to the second pre-trained generative AI model, Para 0091) ; and retrain the LLM to identify an intent with respect to the product in fewer rounds of communication via execution of the LLM on sequence of interest (retraining the model and This way, the user can interact with the communication platform to obtain the relevant information about the virtual communication session., Para 0088-0092, Fig 6-7)
Ji does not explicitly teach product however does not explicitly teach receive a response associated with an object of interest via the chat window
However, Batina in the same field of endeavor teaches receive a response associated with an object of interest via the chat window ( selecting of the images( object of interest) and LLM follow up based on the object selected , Fig 3)
Ji teaches creating a sequence of prompt based on user interest in a particular prompt based on interested content. Batina teaches the concept of picking the object of interest ( an image). Ji teaches an apparatus which is differed by the claimed apparatus by the substitution of the step of selecting an object instead of text. Batina teaches substituted step of user selecting an object of interest and LLM executes on that selection before effective filing date since the use of images and media may improve search quality, particularly where a strictly text-based interaction with a chatbot limits a user's ability to fully capture their desired search parameters and preferences. Ji step of selection of text can be substituted with images/objects of Batina and the results would have been predictable.
Regarding claim 2, Ji modified by Batina as above in claim 1, teaches wherein the processor is configured to receive a sequence of responses in response to a sequence of prompts, and determine the sequence of interest ( information user is interested in, Para 0013-0014, 0091-0092) based on an order of the sequence of prompts and an additional order the sequence of responses, respectively ( The answer generation engine 440 can generate the first response to the first follow-up query using the first pre-trained generative AI model, generally as described in FIG. 3 or at block 706. Following the first response, the query generation engine 450 can generate a second set of follow-up queries based on the first response to the first follow-up query for the user to select. If a user selects a second follow-up query from the second set of follow-up queries or types in the second follow-up query, the answer generation engine 440 can generate a response to the second follow-up query., Para 0092, Ji; Additional follow-up and similarity search based on the selected image, Fig 3, Batina)
Regarding claim 3, Ji modified by Batina as above in claim 2, teaches , wherein the processor is further configured to train the LLM to understand a correlation between a product identifier and a document content based execution of the LLM on the plurality of prompts and the responses (items user is interested in and the final response based on prompts, Fig 6-7, Ji; images and additional information ( attributes based on images), receiving, via a first user interface, a selection associated with an object; determining a first set of object attributes based on the selection; presenting, via a second user interface, a text prompt for a user to identify a subset of the first set of object attributes; receiving, via the second user interface, an indication of one or more preferred object attributes of the identified subset; and updating the first user interface to display content relating to objects associated with the one or more preferred object attributes., Para 0012)
Regarding claim 4, Batina as above in claim 1, teaches wherein the processor is configured to receive a conversation history from the chat window which includes identifiers of user dialogue and chatbot dialogue ( historical transactions, Para 0036) , and generate a prompt based on execution of the LLM on the conversation history and document content about the object of interest ( generate more images/object and its attributes based on previous transactions, Para 0036)
Regarding claim 7, Ji modified by Batina as above in claim 1, teaches wherein the processor is configured to extract content from a document stored within a database based on the responses , and display the extracted content via the chat window ( extract information, Fig 5-7, Ji; extract attributes, Batina)
Regarding claim 8, Ji modified by Batina as above in claim 7, teaches wherein the processor is further configured to generate a next prompt with respect to the prompt based on execution of the LLM on the extracted content from the document ( follow-up prompts, Para 0083-0092)
Regarding claim 9, arguments analogous to claim 1, are applicable.
Regarding claim 10, arguments analogous to claim 2, are applicable.
Regarding claim 11, arguments analogous to claim 3, are applicable.
Regarding claim 12, arguments analogous to claim 4, are applicable.
Regarding claim 15, arguments analogous to claim 7, are applicable.
Regarding claim 16, arguments analogous to claim 8, are applicable.
Regarding claim 17, arguments analogous to claim 1, are applicable.
Regarding claim 18, arguments analogous to claim 2, are applicable.
Regarding claim 19, arguments analogous to claim 3, are applicable.
Regarding claim 20, arguments analogous to claim 4, are applicable.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ji ( US 20250103821) and further in view of Batina (US: 20240289361) and further in view of Baeuml ( US 20230074406)
Regarding claim 21, Ji modified by Batina as above in claim 1, does not explicitly teach wherein the processor is configured to infer a familiarity level with respect to the object of interest based on the conversation, and generate the plurality of prompts with varying linguistic complexity based on the inferred familiarity level
However, Baeuml teaches the concept of to infer a familiarity level with respect to the object of interest based on the conversation, and generate the plurality of prompts with varying linguistic complexity based on the inferred familiarity level (the modified assistant outputs can be generated with various personalities in terms of both a vocabulary that is contextually adapted throughout the dialog session and in terms of prosodic properties utilized to audibly render the modified assistant output, thereby causing the modified assistant output to even further resonate with the user., Para 0019)
It would have been obvious having the teachings of Ji and Batina to further include the concept of Baeuml before effective filing date so the output resonate with the user ( Para 0019, Baeuml)
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ji ( US 20250103821) and further in view of Batina (US: 20240289361) and further in view of Brdiczka( US Pub: 20250068829)
Regarding claim 5, Ji modified by Batina as above in claim 1, teaches model runs in a operating environment and updated in real time (follow-up real time query, Fig 6-7, Ji; follow up items/attributes in real time, Batina)however does not explicitly teaches wherein the processor is further configured to deploy the LLM within a live runtime environment on a host platform, and log runtime data of the LLM as the LLM generates the runtime date within the live runtime environment
However, Brdiczka teaches wherein the processor is further configured to deploy the LLM within a live runtime environment on a host platform, and log runtime data of the LLM as the LLM generates the runtime date within the live runtime environment (deploy the tested ML model 2130 as the trained ML model 2130 in a production environment, and continuously monitor and maintain it, Para 0202, 0215)
It would have been obvious having the teachings of Ji modified by Batina to further include the concept of Brdiczka before effective filing date so the more accurate and tested model can be deployed for production use ( Para 0203, Brdiczka)
Regarding claim 6, Brdiczka as above in claim 5, teaches wherein the processor is further configured to retrain the LLM based on execution of the LLM on the logged runtime data ( log data, Para 0156, 0210; A sample datapoint in the training data captured accordingly is illustrated in FIG. 8. The training dataset obtained from the logging is updated periodically, as new composite fonts are logged, for example, each month, each day, each week, etc. By leveraging the dataset of logged composite fonts and their associated font properties, the operating environment 1100 is trained to generate personalized composite fonts using transformer architecture, Para 0151)
Regarding claim 13, arguments analogous to claim 5, are applicable.
Regarding claim 14, arguments analogous to claim 6, are applicable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Phan Hai can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654