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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/13/2026 has been entered.
Claims 1, 15, and 18 are amended.
Double patenting rejection has been withdrawn in light of the Terminal Disclaimers filed
Claims 1-20 are pending in this application and are presented for examination on merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1, the limitations are directed towards processing the one or more questions by an orchestrator and multiple agents to generate the response to the one or more questions based on a corpus of information related to the website, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents, and wherein the one or more multimodal models include at least one model trained on an industry-specific dataset; is a process that, under its broadest reasonably interpretation, covers performance of these limitation in the mind but for the recitation of generic computer components. That is, other than reciting processing the one or more questions by an orchestrator and multiple agents to generate the response to the one or more questions based on a corpus of information related to the website, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents and wherein the one or more multimodal models include at least one model trained on an industry-specific dataset; nothing in the claim precludes these steps from practically being performed in the mind. For example, but for the limitations processing the one or more questions by an orchestrator and multiple agents to generate the response to the one or more questions based on a corpus of information related to the website, wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents; and wherein the one or more multimodal models include at least one model trained on an industry-specific dataset; If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites abstract ideas.
The judicial exception is not integrated into a practical application by additional elements. In particular, receiving, via the search interface, one or more questions regarding content associated with the website; displaying the response with one or more relevant sources from the corpus of information related to the website. is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of storing and reading data) such that it amounts to no more than mere instructions to apply the exception. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, receiving, via the search interface, one or more questions regarding content associated with the website; displaying the response with one or more relevant sources from the corpus of information related to the website. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional elements, receiving, via the search interface, one or more questions regarding content associated with the website; displaying the response with one or more relevant sources from the corpus of information related to the website. is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network e.g., using the internet to gather data, Symantec (see MPEP 2106.05(d))). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. To further elaborate, the receiving, via the search interface, one or more questions regarding content associated with the website; displaying the response with one or more relevant sources from the corpus of information related to the website. does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Claim 1 is not patent eligible.
Claims 15 and 18 recite similar limitations as in claim 1. Therefore claim 11 and 16 are rejected for the same reasons as set forth above. See claim 1 for analysis.
With respect to claims 2, 16 and 19, the limitations are directed to data validation agent determines the accuracy and reliability of the one or more relevant sources associated with the response. The elements directed to directed to data validation agent determines the accuracy and reliability of the one or more relevant sources associated with the response further elaborates the abstract of generating a response by multiple agents based on an input. The additional elements directed to data validation agent determines the accuracy and reliability of the one or more relevant sources associated with the response merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 2, 16 and 19, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claims 3, 17 and 20, the limitations are directed to displaying the response is presented using an intuitive non-complex human machine interface. The elements directed to displaying the response is presented using an intuitive non-complex human machine interface. further elaborates the abstract of generating a response by multiple agents based on an input. The additional elements to displaying the response is presented using an intuitive non-complex human machine interface merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 3 17 and 20, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claim 4, the limitations are directed to a visualization agent, wherein the visualization generates a depiction of the one or more relevant sources associated with the response. The elements directed to a visualization agent, wherein the visualization generates a depiction of the one or more relevant sources associated with the response further elaborates the abstract of generating a response by multiple agents based on an input. The additional a visualization agent, wherein the visualization generates a depiction of the one or more relevant sources associated with the response merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 4 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
With respect to claim 5, the limitations are directed to depiction includes at least one of a link, a passage, a summarization, a data visualization, a graph, or an image. The elements directed to depiction includes at least one of a link, a passage, a summarization, a data visualization, a graph, or an image further elaborates the abstract of generating a response by multiple agents based on an input. The additional depiction includes at least one of a link, a passage, a summarization, a data visualization, a graph, or an image merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 5 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
With respect to claim 6, the limitations are directed to a comprehension agent to generate a rationale for why it produced the response, and wherein displaying the response further comprises providing the rationale with the response. The elements directed to a comprehension agent to generate a rationale for why it produced the response, and wherein displaying the response further comprises providing the rationale with the response further elaborates the abstract of generating a response by multiple agents based on an input. The additional a comprehension agent to generate a rationale for why it produced the response, and wherein displaying the response further comprises providing the rationale with the response merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 6 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
With respect to claim 7 , the limitations are directed to website includes structured and unstructured data, and wherein the multiple agents include different retrieval agents for structured and unstructured data. The elements directed to website includes structured and unstructured data, and wherein the multiple agents include different retrieval agents for structured and unstructured data further elaborates the abstract idea of generating a response by multiple agents based on an input. The additional website includes structured and unstructured data, and wherein the multiple agents include different retrieval agents for structured and unstructured data merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 7 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
With respect to claim 8 the limitations are directed to the website includes a data model accessible using a model-driven architecture; and wherein the multiple agents include a type system retriever agent of the model-driven architecture to retrieve a subset of the data model. The elements directed to the website includes a data model accessible using a model-driven architecture; and wherein the multiple agents include a type system retriever agent of the model-driven architecture to retrieve a subset of the data model further elaborates the abstract of generating a response by multiple agents based on an input. The additional the website includes a data model accessible using a model-driven architecture; and wherein the multiple agents include a type system retriever agent of the model-driven architecture to retrieve a subset of the data model merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 8 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
With respect to claims 9, the limitations are directed to one or more questions includes a first question and a second question; and further comprising processing the second question by the orchestrator, wherein a second response is generated based on the first question or a first response to the first question. The elements directed to one or more questions includes a first question and a second question; and further comprising processing the second question by the orchestrator, wherein a second response is generated based on the first question or a first response to the first question further elaborates the abstract of generating a response by multiple agents based on an input. The additional presenting relevant information with predictive analysis from an enterprise environment. to merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 4 and 13, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respect to claims 10, the limitations are directed to collecting user feedback after displaying the response, wherein a feedback agent analyzes the user feedback to update the one or more multimodal models to refine accuracy and relevance of potential responses. The elements directed to collecting user feedback after displaying the response, wherein a feedback agent analyzes the user feedback to update the one or more multimodal models to refine accuracy and relevance of potential responses further elaborates the abstract of generating a response by multiple agents based on an input. The additional collecting user feedback after displaying the response, wherein a feedback agent analyzes the user feedback to update the one or more multimodal models to refine accuracy and relevance of potential responses to merely confine the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. Therefore, claim 4 and 13, do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tater et al. (US 2023/0131495) Filed on Oct. 22, 2021 in view of Zhu et al. (2019/0324780) Published on Oct. 24, 2019) and further in view of Mandapaka et al. (US 2024/0062080) provisional application filed on Aug. 19, 2022
As per Claims 1, 15 and 18 A method for generating a response using a website search interface comprising: receiving, via the search interface, one or more questions regarding content associated with the website; (See para.25 describing and orchestrator model to receive and input and determines which agent should be used to respond, analogous to interpreting a prompt; also see para.81 wherein the chat system is web based site as taught by Tater)
processing the one or more questions by an orchestrator and multiple agents to generate the response to the one or more questions based on a corpus of information related to the website, (See para.26 describing the broadcasting the task to different agents, each agent addresses a different context of the request, such as visualization agents, data export agents; as taught by Tater)
wherein the orchestrator employs one or more multimodal models to process or deconstruct a prompt into a series of instructions for different agents; (See par.25, the orchestrator 102 can be chatbot or voicebot functionalities for conversing with a user, chatbots are analogous to large language model; as taught by Tater)
displaying the response with one or more relevant sources from the corpus of information; (See para.28, wherein a response is provided by each agent; as taught by Tater)
Tater does not explicitly teach information related to the website; and wherein the one or more multimodal models include at least one model trained on an industry-specific dataset;
On the other hand, Zhu teaches information related to the website; (See para.27, wherein the systems the system is a web based system and in Para.4, 31 and 32, describing social networking websites are part of the system that incorporates the models and the Xbot chat system; as taught by Zhu)
and wherein the one or more multimodal models include at least one model trained on an dataset; (See para.72, wherein the multimodal is a specific language model such as work model which is analogous to industry model “ a work-specific language model 420 may be trained on data captured when the user is interacting with work-colleagues via a messaging interface or within a certain radius of his/her work address. Accordingly, when the user is at home, the assistant system 140 may utilize the home-specific language model 420, while when the user is at work, the assistant system 140 may utilize the work-specific language model 420” ; as taught by Zhu)
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 teachings of the cited references and modify the invention as taught by Tater, by including the teachings of Zhu relating web related information that is context specific to provide personalized results ( as taught by Zhu para.72)
Even though Zhu describes the process of training work related models which is analogous to industry specific model, Mandapaka is introduced to teach and wherein the one or more multimodal models include at least one model trained on an industry-specific dataset; (See para.41 The method of any preceding clause, further comprising: receiving, at the computer system, past industry-specific indicators; training, via the at least one processor using the industry-specific indicators, a machine learning multiplier model; receiving, at the computer system, current industry-specific indicators; and executing, via the at least one processor, the machine learning multiplier model using the current industry-specific indicators as input, resulting in a multiplier, wherein the final prediction is further generated using the multiplier; also see para.15 and para.35 describing a multi model structure as taught by Mandapaka)
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 teachings of the cited references and modify the invention as taught by Tater and Zhu, by including the teachings of Mandapaka relating to industry specific training datasets to produce results and predictions directly based on the multiplier to improve relevancy of predictions ( as taught by Mandapaka para.48)
As per Claims 2, 16 and 19, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches wherein the multiple agents include a data validation agent, wherein the data validation agent determines the accuracy and reliability of the one or more relevant sources associated with the response; (See para.41, wherein the MLP can optimizes the confidence in responses received by the agents; as taught by Tater)
As per Claims 3, 17 and 20, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches wherein displaying the response is presented using an intuitive non-complex human machine interface; (See Fig. 4B-4D, wherein the system is provided though an interface chatbot; as taught by Tater)
As per Claim 4, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches further comprising: wherein the multiple agents include a visualization agent, wherein the visualization generates a depiction of the one or more relevant sources associated with the response; (See Para.35 and fig.4D, wherein a visualization agent provides a visualization of data, as taught by Tater)
As per Claim 5, The method of claim 4, the combination of Tater, Zhu and Mandapaka teaches wherein the depiction includes at least one of a link, a passage, a summarization, a data visualization, a graph, or an image (See Para.35 and fig.4D, wherein a visualization agent provides a visualization of graph data, as taught by Tater)
As per Claim 6, The method of claim 1, further comprising: the combination of Tater, Zhu and Mandapaka teaches wherein the multiple agents include a comprehension agent to generate a rationale for why it produced the response, and wherein displaying the response further comprises providing the rationale with the response; (See para.66 wherein the intent suggestion is analogous to the rational of providing the response; as taught by Zhu)
As per Claim 7, The method of claim 1, wherein the corpus of information related to the website includes structured and unstructured data, and wherein the multiple agents include different retrieval agents for structured and unstructured data; (See para.244, wherein the responses retrieved can include unstructured data such as images and audio segments other types of data such as structured which include spreadsheets and tables; as taught by Zhu, also see para.30 in Tater describing the different types of agents that can be used for different to retrieve a different format of data as a response, as taught by Tater)
As per Claim 8, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches wherein the corpus of information related to the website includes a data model accessible using a model-driven architecture; and wherein the multiple agents include a type system retriever agent of the model-driven architecture to retrieve a subset of the data model; (See para.53-54 wherein the agents implement machine learning models to perform the tasks; as taught by Tater)
As per Claim 9, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches wherein the one or more questions includes a first question and a second question; and further comprising processing the second question by the orchestrator, wherein a second response is generated based on the first question or a first response to the first question; (See 22, the system actively learns from historical interactions to improve responses, which is analogous to having subsequent responses be based on prior ones; as taught by Tater)
As per Claim 10, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches user feedback after displaying the response, wherein a feedback agent analyzes the user feedback to update the one or more multimodal models to refine accuracy and relevance of potential responses.; (See para.46 and 48, wherein the system collects user feedback with respect to responses to learn and updated algorithms; as taught by Tater)
As per Claim 11, The method of claim 10, the combination of Tater, Zhu and Mandapaka teaches wherein the feedback agent updates the corpus of information and improve the relevance of the potential responses; (See para.46 and 48, wherein the system collects user feedback with respect to responses to learn and updated algorithms; as taught by Tater)
As per Claim 12, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches, wherein the series of instructions for different agents includes an instruction to utilize a computational tool to perform calculations on data from the corpus.; (See para.26, wherein agent can be computer implemented modules or services that employ machine learning trained to learn its skill with is own trained data, see para.66; as taught by Tater)
As per Claim 13, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches, wherein the series of instructions for different agents includes a task for processing text, images, or audio data optimized for the respective data type; (See para.43, wherein the chatbot can process natural language via texting or voice, the orchestrator then broadcasts the question to the agents; as taught by Tater)
As per Claim 14, The method of claim 1, the combination of Tater, Zhu and Mandapaka teaches, wherein at least one instruction includes an API agent using an API tool to make calls to an application program interface; (See para.34, wherein an API is uses to communication with devices.; as taught by Tater)
Response to Arguments
Applicant’s arguments with respect to the rejections raised have been considered but are moot in view of the new grounds of rejection.
Conclusion
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/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169