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 .
Response to Amendment
Claims 1, 5, 12-13 and 18-19 are amended. Claims 1-19 are presented for examination.
Response to Arguments
Applicant arguments filed on 3/30/2026 have been reviewed. Following are the response:
Applicant noted that “Burris is directed to a leasing AI platform that classifies messages and generates responses based on message categories. As described in Burris, "once message classification engine 111 determines a category corresponding to the message 140, action module 112 may generate appropriate responses or follow-ups to the message 140 based on the category." (Burris, Paragraph [0031].) Burris teaches that the leasing AI platform maps each message classification into an action or sequence of actions to be performed by action module 112, and action module 112 may generate a response with a template-based message.” and Examiner acknowledges these remark.
Applicant argues “The Office Action indicates that Burris does not explicitly teach providing, using the chat module, the first communication and first machine communication as an input to a generative AI model or obtaining an output of the generative AI model. (Office Action, January 12, 2026, Page 4.) As such, Applicant respectfully submits that Burris cannot be properly interpreted as teaching the amended features of the claims, including "combining, using the chat module, the first communication with the first machine communication to form a combined communication," "providing combined communication as an input to a generative artificial intelligence (AI) model," and "obtaining an output of the generative AI model, the output comprising a natural language response generated based on the combined communication." However, Burris does teach combining, using the chat module, the first communication with the first machine communication to form a combined communication ( see for e.g. calendar is combined with the reply; wherein the calendar is the first communication from the support module Para 0045, 0050, 0058-0060). Burris fails to teach "providing combined communication as an input to a generative artificial intelligence (AI) model," and "obtaining an output of the generative AI model, the output comprising a natural language response generated based on the combined communication.", which is taught by a separate reference.
Applicant noted that “Khosla is directed to a natural language question answering service that utilizes an aggregator component and an LLM component. As described in Khosla, "the aggregator component 104 retrieves the passages from the search systems 124" and "may modify and supplement the question with the retrieved passages to form a prompt." (Khosla, Paragraph [0060].) Additionally, Khosla teaches that "LLM component 106 may receive the prompt from the aggregator component 104, the user context (optionally) from user context component 105, and generate one or more answers based on the prompt and the user context." (Khosla, Paragraph [0024].)”
Applicant argues “Khosla's architecture, however, involves an aggregator component that retrieves passages from search systems, but does not include a chat module that receives machine communications comprising results of support operations performed by a support module. Khosla's aggregator-to-LLM architecture is distinct from the claimed architecture where the chat module itself combines the original device communication with machine communication results from a support module to form a combined communication before providing it to the generative AI model. Specifically, in Khosla, "aggregator component 104 can retrieve passages from the search systems 124 based on the natural language question and create a prompt for the LLM component 106" where "the aggregator component 104 may analyze the natural language question by using string matching techniques (e.g., partial string matching, dense passage retrieval, etc.) to determine the meaning of the natural language question" and "then determine which of the search systems 124 the aggregator component 104 may retrieve passages from." (Khosla, Paragraph [0022].) Khosla further teaches that the aggregator component "may modify and supplement the question with the retrieved passages to form a prompt" where "the prompt may comprise selected passages and QA pairs for the LLM component 106 to provide an answer to." (Khosla, Paragraph [0060].)” However, examiner has relied on the Khosla for the concept of sending a “combined communication” for e.g. the context/template/information with the answer ( search) and send it to the LLM to generate a final response. Examiner has also provided the motivation to combine Burris with Khosla so to supplement, optimize, or otherwise modify the natural language query for better replies/results ( Para 0009, Khosla). Examiner has not relied on Khosla for the chat module concept.
Applicant further argues “ In contrast, the amended claims recite a chat module that routes the first
communication to a first support module "based on the determined category type," where the first support module performs support operations associated with the request, and the chat module then receives "a first machine communication from the first support module, the first machine communication comprising results of the first support operation."Khosla does not describe a chat module that combines a first communication with a first machine communication to form a combined communication for a generative AI model. Rather, the system in Khosla retrieves static passages and QA pairs from search systems (i.e., document retrieval), but does not receive results from support modules that perform dynamic support operations associated with a user request. The "passages" in Khosla are pre-existing documents retrieved based on similarity matching, not results generated by performing operations in response to a specific request. Accordingly, Khosla does not teach or suggest a chat module that routes communications based on determined category types to support modules, receives machine communications comprising results of support operations from those support modules, and combines the original communication with those results to form a combined communication for a generative AI model.” However, examiner has relied on Burris to teach the argued concept. Khosla was relied for the idea of the generated answer (combined communication) an input to the LLM which generates a further response based on validation, correction etc.
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.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claims 1-6, 8-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Burris ( US 20200043087) in view of Khosla ( US 20250005057)
Regarding claim 1, Burris teaches a method comprising: receiving, from a client device connected to a property management software system (PMSS) ( computing device, Fig 1), a first communication indicating a request pertaining to the PMSS ( receive message from the messages 110 to the computing device, Para 0024); determining a category type associated with the request ( message classification engine classify/categorize messages, Para 0026) ; routing, using a chat module, the first communication to a first support module based on the determined category type ( categorize the message, Para 0041) , wherein the first support module is configured to perform one or more support operations associated with the category type of the request (leasing AI platform maps message into a plurality of actions -- identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and at block 460, automatically executes the one or more actions, Para 0043-0048 ); performing, via the first support module, a first support operation associated with the request (action module performs action, Para 0053-0055, Fig 6-7; wherein the action module has plural support modules for e.g. legal requirement ( external database) , plural templates, calendar etc.);
receiving, at the chat module, a first machine communication from the first support module, the first machine communication comprising results of first support operation ( for e.g. leasing AI takes action and action is retrieved from support module for e.g. time, property, other prospects, Para 0045)
combining, using the chat module, the first communication with the first machine communication to form a combined communication ( for e.g. calendar is combined with the reply; wherein the calendar is the first communication from the support module Para 0045, 0050, 0058-0060) ;
and providing, the natural language response to the indicated request through a user interface (UI) of the client device ( provide the replies to the client, Fig 2, Fig 7a-d)
Burris does not explicitly teach providing, the combined communication as an input to a generative artificial intelligence (AI) model; obtaining an output of the generative Al model, the output comprising a natural language response generated based on the combined communication; and providing, the natural language response to the indicated request through a user interface (UI) of the client device
In the same field of endeavor Khosla teaches the combined communication as an input to a generative artificial intelligence (AI) model ( the LLM component 106 receives the prompt and user context and determines an answer to the natural language question, Para 0061, Fig 3) ; obtaining an output of the generative Al model, the output comprising a natural language response generated based on the combined communication ( At (8), the verifier component 108 determines if the answer was generated in error (e.g., hallucinated), Para 0068, Fig 3); and providing, the natural language response to the indicated request through a user interface (UI) of the client device send answer to the user, Fig 3)
It would have been obvious having the teachings of Burris to further include the concept of Khosla before effective date so to supplement, optimize, or otherwise modify the natural language query for better replies/results ( Para 0009, Khosla)
Regarding claim 2, Burris as above in claim 1, teaches wherein determining a category type associated with the request comprises: performing semantic analysis to generate semantic data indicative of a category type of the request, wherein performing semantic analysis to generate semantic data comprises extracting natural language entity data from the first communication ( semantic parsing for the determining the classification of message, Para 0039, Fig 5)
Regarding claim 3, Burris as above in claim 2, teaches wherein determining a category type associated with the request further comprises: determining an intention of the request based on the extracted entity data ( for e.g. showing or property questions based on location, Fig 5, 7d, Para 0003) , and matching the intention with a category type based on at least one of keyword matching based on a set of previously established keyword pairings, or an output of a classifier neural network ( One algorithm for classifying messages is the heuristic approach. In the heuristic approach, a large number (e.g., hundreds) of messages, such as emails or text messages, for example, are taken and statistics are accumulated regarding what text (e.g., keywords) is used in a message of a certain category., Para 0015, Fig 5)
Regarding claim 4, Burris as above in claim 1, teaches wherein the request comprises at least one of: a request to summarize a document, a request to provide instructions, a request to send a communication ( fig 7a-d) , a request to draft a document, a request to provide a report comprising data associated with the PMSS ( Para 0044) , a request to generate a marketing description, a request to perform an action within the PMSS, a request to receive a link to a prebuilt report, or a request to generate a response to one or more questions ( questions replies, Para 0050, 0055)
Regarding claim 5, Burris as above in claim 1, teaches wherein the first support operation comprises at least one of: retrieving data from a database, or invoking an external API (action module consults with the database for e.g., Para 0045-0048; action module also gathers templates, Para 0050-0052)
Regarding claim 8, Khosla as above in claim 5, teaches wherein invoking an external API comprises mapping the request to one or more external API calls via a previously generated external API schema ( invoke api based on usage history etc., Para 0064, 0070-0072; further claim 5 only requires one of database schema or api schema and examiner interpretation is the retrieving only the database schema )
Regarding claim 6, Burris as above in claim 5, teaches wherein retrieving data from a database comprises at least one of: retrieving one or more documents from an unstructured database associated with the PMSS, retrieving structured data from a structured database associated with the PMSS (calendar or legal document or template ( repository) Para 0045, 0047, 0050-0055)
Regarding claim 9, Burris as above in claim 1, teaches wherein the first machine communication is at least one of a response to one or more questions, data retrieved from a database, a follow-up question related to the request, or a confirmation that the one or more support operations have been performed ( response to a question, follow up, Para 0017, 0023, 0031, 0050-0051)
Regarding claim 10, Burris as above in claim 1, teaches wherein the response to the indicated request is at least one of a response to one or more questions, a follow-up question related to the request, or a confirmation that the one or more support operations have been performed ( questions/confirmation based on legal requirement, showing of the house etc., Para 0050-0055, Fig 6, Fig 7d)
Regarding claim 11, Burris as above in claim 1, teaches wherein the first support module is one of a plurality of support modules associated with a plurality of request category types, wherein each support module of the plurality of support modules is configured to perform support operations associated with one or more category types of the plurality of category types ( different actions based on the action modules based on the categories, Fig 7d; further action module retrieves data from database external servers, calendar etc. hence plurality of support modules)
Regarding claim 12, Burris modified by Khosla as above in claim 1, teaches routing, using the chat module, the first communication to a second support module configured to perform support operations associated with the category type of the request ( for e.g. invoking external database, Para 0045) ; performing, via the second support module, a second support operation associated with the request ( results from the external database, Para 0045) ; receiving, at the chat module, a second machine communication from the second support module associated with an output of the second support operation (action can be calendar, consulting database for legal answer, templates etc. and communicates back to the leasing platform for e.g. to display etc., Fig 7a-d) ; combining, using the chat module, the first communication with the second machine communication to form a second combined communication ( messages are combined with the information for e.g. times, etc. Para 0045) ; and providing, the second combined communication as a second input to the generative Al model ( provide all context from the message and the communication as an input to the LLM, Fig 3, Para 0061, 0064, Khosla)
Regarding claim 13, Burris modified by Khosla as above in claim 1, teaches performing, via the first support module, a second support operation associated with the request ( different support for e.g. calendar or legal answer, Para 0043, 0045-0050) ; receiving, at the chat module, a second machine communication from the first support module associated with an output of the second support operation ( AI leasing platform receives appropriate response, Fig 7a-d); combining, using the chat module, the first communication with the second machine communication to form a second combined communication ( messages are combined with the information for e.g. times, etc. Para 0045); and providing the second combined communication as a second input to the generative Al model. ( provide all context from the message and the communication as an input to the LLM, Fig 3, Para 0061, 0064, Khosla)
Regarding claim 14, Burris modified by Khosla as above in claim 1, teaches wherein the generative AI model has been trained on a corpus of text to create a foundation model ( trained AI, Fig 3, Khosla; trained machine learning model, Para 0020, Burris)
Regarding claim 17, Khosla as above in claim 1, teaches wherein a retrieval component of a retrieval-augmented generation (RAG) system provides context associated with the request to the generative AI model ( RAG techniques, Para 0029, 0079)
Regarding claim 18, arguments analogous to claim 1, are applicable. In addition, Burris teaches A system comprising: a memory device; and a processing device communicatively coupled to the memory device, wherein the processing device is to perform the steps in claim 1 ( Fig 1)
Regarding claim 19, arguments analogous to claim 1, are applicable. In addition, Burris teaches A non-transitory computer readable storage medium comprising instructions that, when executed by a processing device, causes the processing device to perform operations in claim 1. ( Para 0025)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Burris ( US 20200043087) and further in view of Khosla ( US 20250005057) and further in view of Relan (US 20220374420)
Regarding claim 7, Burris as above in claim 5, teaches wherein retrieving data from a database further comprises mapping the request to a database query via a previously generated database ( for e.g. the requirement document or calendar is already available and mapping would be based on users’ message, Para 0045-0047)
While Burris does not explicitly mention mapping to database schema
Relan teaches mapping to database schema ( a database is based on schema based on previous stored records etc., Para 0012, 0046)
It would have been obvious having the teachings of Burris and Khosla to further include the concept of Relan before effective filing date to provide most accurate response ( Para 0008, Relan)
Claim 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Burris ( US 20200043087) and further in view of Khosla ( US 20250005057) and further in view of Lev (US 20240370498 )
Regarding claim 15, Burris as above in claim 1, mention a custom trained AI model ( Para 0056-0057) and generative model trained on specific domain ( Para 0062, Khosla) Burris modified by Khosla does not explicitly teaches wherein the generative AI model has been fine-tuned on proprietary organizational data associated with property management
However, Lev teaches wherein the generative AI model has been fine-tuned on proprietary organizational data associated with property management (fine-tuned for a specific task, Para 0096; task can be real estate queries, Para 0120, 0123)
It would have been obvious having the teachings of Burris and Khosla to further include the concept of Lev before effective filing date since it’s a well-known concept of leverage gpt model for a specific task ( Para 0096, 0123, Lev)
Regarding claim 16, Burris as above in claim 1, teaches AI model is fined tuned ( Para 0056-0057- custom AI for real estate) and Khosla teaches generate AI ( trained for e specific task, Para 0062) however does not explicitly teach wherein the generative AI model has been fine-tuned for application to PMSSs
However, Lev teaches wherein the generative AI model has been fine-tuned for application to PMSSs (fine- tuned for a specific task, Para 0096; task can be real estate queries, Para 0120, 0123)
It would have been obvious having the teachings of Burris and Khosla to further include the concept of Lev before effective filing date since it’s a well-known concept of leverage gpt model for a specific task ( Para 0096, 0123, Lev)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11288322 – discusses API schema similar to claim 8
US 20230259821 – routing to different LLM model based on diagnostics.
US 20230316000 – teaches the combined communication is an input the LLM model and LLM generates a final response
THIS ACTION IS MADE FINAL. 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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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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