DETAILED ACTION
This office action is in response to the above identified application filed on April 13, 2026. The application contains claims 1-20.
Claims 19 and 20 are newly added
Claims 1, 2, 7, 8, and 13-18 are amended
Claims 1-20 are pending
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 Arguments
Applicant's arguments and amendments filed on April 13, 2026 have been fully considered and the objections and rejections are updated accordingly.
Claim Objections
In view of the amendments to the claims, the objections to the claims are withdrawn.
Claim Rejections - 35 USC § 112
In view of the amendments to the claims, the 35 USC § 112 rejections to claims 13-18 are withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to the new limitations introduced with the amendments are addressed with new prior art and rationale.
Please refer to the updated 35 U.S.C. 103 rejections as set forth below for details.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ragukumar et al. (US 20250200355 A1), in view of GANGWAR et al. (US 20220207066 A1), and in further view of Bigbee et al. (US 20220247774 A1).
With regard to claim 1,
Ragukumar teaches
generating, by a processor (Fig. 13: processor 1302), a plurality of contextual relationships that exist between data points in a dataset by applying a large language model (LLM) to the dataset (Fig. 3; [0059]: the embedding LLM 302 is configured to convert the tokenized data inputs into an embedding vector. The embeddings represent the meaning and context of the tokens in a high-dimensional vector space. This embedding process generates contextual relationships among tokens extracted from data inputs and represents the relationships in the embeddings);
determining, by the processor, at least one insight based on the dataset with the generated plurality of contextual relationships, by applying the LLM on the dataset with the generated plurality of contextual relationships (Fig. 3; [0061]: the completion LLM 306 is trained to understand context, generate coherent text, and perform various language-related tasks. Fig. 1; [0049]: The AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data. The extracted insights may include, for example, patterns within the data, correlated events in the data, anomalies within the data, and so forth);
Ragukumar does not teach
a method of generating an agent, the method comprising:
determining, by the processor, at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight;
receiving, by the processor, a question for the dataset;
enforcing, by the processor, at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
when the received question is associated with the determined at least one insight, applying, by the processor, the LLM on the determined at least one query for the associated determined at least one insight;
generating, by the processor, the agent by the LLM based on the determined at least one query; and
updating the LLM based on performance of the generated agent.
GANGWAR teaches
a method of generating an agent (Abstract: generate an executable bot application, wherein the executable bot application corresponds to “an agent”), the method comprising:
determining, by the processor, at least one query for each determined at least one insight, by applying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight (Fig. 6; [0075]: at step 604, through a machine learning (ML) model, process training data comprising the set of potential queries, the video frame responses corresponding to each of the set of potential queries, and the intent that is mapped to each of the set of a potential queries to generate a trained model, wherein generating the trained model determines a mapping between a query and an intent, e.g., “insight”);
receiving, by the processor, a question for the dataset (Fig. 6; [0075]: at step 606 process an end-user query);
when the received question is associated with the determined at least one insight, applying, by the processor, the LLM on the determined at least one query for the associated determined at least one insight (Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query);
generating, by the processor, the agent by the LLM based on the determined at least one query (Fig. 6; [0075]: at step 608, auto-generate, using the prediction engine, the executable bot application by the entity); and
updating the LLM based on performance of the generated agent ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated. The entity can also edit an existing potential query from the set of potential queries and associate a new or edited or the same corresponding video frame response to said edited potential query, based on which the trained model is updated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar to incorporate the teachings of GANGWAR to determine, by the processor, at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight, receive, by the processor, a question for the dataset, if the received question is associated with the determined at least one insight, apply, by the processor, the LLM on the determined at least one query for the associated determined at least one insight, generate, by the processor, the agent by the LLM based on the determined at least one query, and update the LLM based on performance of the generated agent. Doing so would facilitate self-generation of entity/user specific bots that can be customized with one or more entity-specific automated visual responses to user queries, and at the same time are computationally convenient and time-efficient for generation, without requirement of any external help/vendor and at the same time ensuring that effective/informative responses are transmitted to end-user queries for enhanced user experience as taught by GANGWAR ([0008]).
Ragukumar and GANGWAR do not teach
enforcing, by the processor, at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
Bigbee teaches
enforcing, by the processor, at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset (Fig. 3; [0059]: enforce a set of role-based access control permissions for accessing application data);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar and GANGWAR to incorporate the teachings of Bigbee to enforce at least one of role-based access control (RBAC)permissions or attribute-based access control (ABAC) permissions for accessing the dataset. Doing so would help reduce security risk associated with the application by changing permission for user access by disallowing the one or more actions that have been never performed by the user of a particular role or users of a particular organizational unit as taught by Bigbee ([0051]).
With regard to claim 2,
As discussed in claim 1, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the method of claim 1, further comprising when the received question is not associated with the determined at least one insight, generating, by the processor, a new query by the LLM for the associated determined at least one insight ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated).
With regard to claim 3,
As discussed in claim 1, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the method of claim 1, further comprising applying, by the processor, the generated agent on at least one received user query (Fig. 4; [0070]; Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query).
With regard to claim 4,
As discussed in claim 1, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the method of claim 1, wherein the dataset comprises a semantic layer of information of the dataset (Fig. 3A; [0066]: extract some information or features (308) and convert texts to sequences (318) are examples of a semantic layer).
With regard to claim 5,
As discussed in claim 4, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the method of claim 4, wherein the information is received from a dedicated database (Fig. 3; [0057]: the AI/ML engine 110 retrieves data from the index search database 106 and the domain-specific database 108).
With regard to claim 6,
As discussed in claim 5, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the method of claim 5, wherein the at least one insight is determined by a corresponding at least one query to the dedicated database (Fig. 1; [0049]: the AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data).
With regard to claim 7,
Ragukumar teaches
generate a plurality of contextual relationships that exist between data points in the dataset by applying a large language model (LLM) to the dataset (Fig. 3; [0059]: the embedding LLM 302 is configured to convert the tokenized data inputs into an embedding vector. The embeddings represent the meaning and context of the tokens in a high-dimensional vector space. This embedding process generates contextual relationships among tokens extracted from data inputs and represents the relationships in the embeddings);
determine at least one insight based on the dataset with the generated plurality of contextual relationships, by applying the LLM on the dataset with the generated plurality of contextual relationships (Fig. 3; [0061]: the completion LLM 306 is trained to understand context, generate coherent text, and perform various language-related tasks. Fig. 1; [0049]: The AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data. The extracted insights may include, for example, patterns within the data, correlated events in the data, anomalies within the data, and so forth);
Ragukumar does not teach
a system for generating an agent, the system comprising:
a server, comprising a dataset with a plurality of data points; and
a processor, in communication with the server, wherein the processor is configured to:
determine at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight;
receive a question for the dataset;
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
when the received question is associated with the determined at least one insight, apply the LLM on the determined at least one query for the associated determined at least one insight;
generate the agent by the LLM based on the determined at least one query; and
update the LLM based on performance of the generated agent.
GANGWAR teaches
a system for generating an agent (Abstract: generate an executable bot application, wherein the executable bot application corresponds to “an agent”), the system comprising:
a server, comprising a dataset with a plurality of data points (Fig. 1: centralized server. Fig. 2; [0054]: a plurality of datasets based on one or more pre-defined visual/video frame responses and pre-defined/potential queries received from the computing device (104), i.e., a plurality of data points); and
a processor (Fig. 8: processor 870), in communication with the server, wherein the processor is configured to:
determine at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight (Fig. 6; [0075]: at step 604, through a machine learning (ML) model, process training data comprising the set of potential queries, the video frame responses corresponding to each of the set of potential queries, and the intent that is mapped to each of the set of a potential queries to generate a trained model, wherein generating the trained model determines a mapping between a query and an intent, e.g., “insight”);
receive a question for the dataset (Fig. 6; [0075]: at step 606 process an end-user query);
if the received question is associated with the determined at least one insight, apply the LLM on the determined at least one query for the associated determined at least one insight (Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query);
generate the agent by the LLM based on the determined at least one query (Fig. 6; [0075]: at step 608, auto-generate, using the prediction engine, the executable bot application by the entity); and
update the LLM based on performance of the generated agent ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated. The entity can also edit an existing potential query from the set of potential queries and associate a new or edited or the same corresponding video frame response to said edited potential query, based on which the trained model is updated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar to incorporate the teachings of GANGWAR to determine, by the processor, at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight, receive, by the processor, a question for the dataset, if the received question is associated with the determined at least one insight, apply, by the processor, the LLM on the determined at least one query for the associated determined at least one insight, generate, by the processor, the agent by the LLM based on the determined at least one query, and update the LLM based on performance of the generated agent. Doing so would facilitate self-generation of entity/user specific bots that can be customized with one or more entity-specific automated visual responses to user queries, and at the same time are computationally convenient and time-efficient for generation, without requirement of any external help/vendor and at the same time ensuring that effective/informative responses are transmitted to end-user queries for enhanced user experience as taught by GANGWAR ([0008]).
Ragukumar and GANGWAR do not teach
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
Bigbee teaches
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset (Fig. 3; [0059]: enforce a set of role-based access control permissions for accessing application data);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar and GANGWAR to incorporate the teachings of Bigbee to enforce at least one of role-based access control (RBAC)permissions or attribute-based access control (ABAC) permissions for accessing the dataset. Doing so would help reduce security risk associated with the application by changing permission for user access by disallowing the one or more actions that have been never performed by the user of a particular role or users of a particular organizational unit as taught by Bigbee ([0051]).
With regard to claim 8,
As discussed in claim 7, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the system of claim 7, wherein the processor is further configured to generate a new query by the LLM for the associated determined at least one insight, when the received question is not associated with the determined at least one insight ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated).
With regard to claim 9,
As discussed in claim 7, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the system of claim 7, wherein the processor is further configured to apply the generated agent on at least one received user query (Fig. 4; [0070]; Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query).
With regard to claim 10,
As discussed in claim 7, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the system of claim 7, wherein the dataset comprises a semantic layer of information of the dataset (Fig. 3A; [0066]: extract some information or features (308) and convert texts to sequences (318) are examples of a semantic layer).
With regard to claim 11,
As discussed in claim 10, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the system of claim 10, wherein the information is received from a dedicated database (Fig. 3; [0057]: the AI/ML engine 110 retrieves data from the index search database 106 and the domain-specific database 108).
With regard to claim 12,
As discussed in claim 11, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the system of claim 11, wherein the at least one insight is determined by a corresponding at least one query to the dedicated database (Fig. 1; [0049]: the AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data).
With regard to claim 13,
Ragukumar teaches
generate a plurality of contextual relationships that exist between data points in a dataset by applying a large language model (LLM) to the dataset (Fig. 3; [0059]: the embedding LLM 302 is configured to convert the tokenized data inputs into an embedding vector. The embeddings represent the meaning and context of the tokens in a high-dimensional vector space. This embedding process generates contextual relationships among tokens extracted from data inputs and represents the relationships in the embeddings);
determine at least one insight based on the dataset with the generated plurality of contextual relationships, by applying the LLM on the dataset with the generated plurality of contextual relationships (Fig. 3; [0061]: the completion LLM 306 is trained to understand context, generate coherent text, and perform various language-related tasks. Fig. 1; [0049]: The AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data. The extracted insights may include, for example, patterns within the data, correlated events in the data, anomalies within the data, and so forth);
Ragukumar does not teach
a computer-readable medium comprising instructions which, when executed by a processor, cause the processor to:
determine at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight;
receive a question for the dataset;
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
when the received question is associated with the determined at least one insight, apply the LLM on the determined at least one query for the associated determined at least one insight;
generate an agent by the LLM based on the determined at least one query; and
update the LLM based on performance of the generated agent.
GANGWAR teaches
a computer-readable medium comprising instructions which, when executed by a processor (Fig. 8: processor 870), cause the processor to:
determine at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight (Fig. 6; [0075]: at step 604, through a machine learning (ML) model, process training data comprising the set of potential queries, the video frame responses corresponding to each of the set of potential queries, and the intent that is mapped to each of the set of a potential queries to generate a trained model, wherein generating the trained model determines a mapping between a query and an intent, e.g., “insight”);
receive a question for the dataset (Fig. 6; [0075]: at step 606 process an end-user query);
if the received question is associated with the determined at least one insight, apply the LLM on the determined at least one query for the associated determined at least one insight (Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query);
generate an agent by the LLM based on the determined at least one query (Fig. 6; [0075]: at step 608, auto-generate, using the prediction engine, the executable bot application by the entity); and
update the LLM based on performance of the generated agent ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated. The entity can also edit an existing potential query from the set of potential queries and associate a new or edited or the same corresponding video frame response to said edited potential query, based on which the trained model is updated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar to incorporate the teachings of GANGWAR to determine, by the processor, at least one query for each determined at least one insight, by app lying the LLM on the dataset with the generated plurality of contextual relationships and determined at least one insight, receive, by the processor, a question for the dataset, if the received question is associated with the determined at least one insight, apply, by the processor, the LLM on the determined at least one query for the associated determined at least one insight, generate, by the processor, the agent by the LLM based on the determined at least one query, and update the LLM based on performance of the generated agent. Doing so would facilitate self-generation of entity/user specific bots that can be customized with one or more entity-specific automated visual responses to user queries, and at the same time are computationally convenient and time-efficient for generation, without requirement of any external help/vendor and at the same time ensuring that effective/informative responses are transmitted to end-user queries for enhanced user experience as taught by GANGWAR ([0008]).
Ragukumar and GANGWAR do not teach
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset;
Bigbee teaches
enforce at least one of role-based access control (RBAC) permissions or attribute-based access control (ABAC) permissions for accessing the dataset (Fig. 3; [0059]: enforce a set of role-based access control permissions for accessing application data);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ragukumar and GANGWAR to incorporate the teachings of Bigbee to enforce at least one of role-based access control (RBAC)permissions or attribute-based access control (ABAC) permissions for accessing the dataset. Doing so would help reduce security risk associated with the application by changing permission for user access by disallowing the one or more actions that have been never performed by the user of a particular role or users of a particular organizational unit as taught by Bigbee ([0051]).
With regard to claim 14,
As discussed in claim 13, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the computer-readable medium of claim 13, wherein the processor is further configured to generate a new query by the LLM for the associated determined at least one insight, if the received question is not associated with the determined at least one insight ([0023]: the entity can add a new potential query to the set of potential queries and associate a corresponding video frame response to said new potential query, based on which the trained model is updated).
With regard to claim 15,
As discussed in claim 13, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the computer-readable medium of claim 13, wherein the processor is further configured to apply the generated agent on at least one received user query (Fig. 4; [0070]; Fig. 6; [0075]: at step 606, generate a prediction engine configured to process an end-user query and predict, from the plurality of intents, an intent associated with the end-user query).
With regard to claim 16,
As discussed in claim 13, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the computer-readable medium of claim 13, wherein the dataset comprises a semantic layer of information of the dataset (Fig. 3A; [0066]: extract some information or features (308) and convert texts to sequences (318) are examples of a semantic layer).
With regard to claim 17,
As discussed in claim 16, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the computer-readable medium of claim 16, wherein the information is received from a dedicated database (Fig. 3; [0057]: the AI/ML engine 110 retrieves data from the index search database 106 and the domain-specific database 108).
With regard to claim 18,
As discussed in claim 17, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
Ragukumar further teaches
the computer-readable medium of claim 17, wherein the at least one insight is determined by a corresponding at least one query to the dedicated database (Fig. 1; [0049]: the AI/ML engine 110 is configured to assess data stored in the index search database 106 and then extract insights from the data).
With regard to claim 19,
As discussed in claim 1, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the method of claim 1, further comprising:
executing, by the processor, the determined at least one query against at least one database storing at least a portion of the dataset to obtain a query result;
determining, by the processor, whether execution of the determined at least one query resulted in a failure or an unexpected query result;
in response to determining that execution resulted in the failure or the unexpected query result, providing feedback indicative of the failure or unexpected query result as input to the LLM and generating, by the processor via the LLM, a corrected query; and
re-executing the corrected query, and iteratively repeating the providing feedback, generating the corrected query, and the re-executing steps until a query result satisfies at least one acceptance criterion ([0068]-[0069]).
With regard to claim 20,
As discussed in claim 7, Ragukumar and GANGWAR and Bigbee teach all the limitations therein.
GANGWAR further teaches
the system of claim 7, wherein the processor is further configured to:
execute the determined at least one query against at least one database storing at least a portion of the dataset to obtain a query result;
determine whether execution of the determined at least one query resulted in a failure or an unexpected query result;
in response to determining that execution resulted in the failure or the unexpected query result, provide feedback indicative of the failure or unexpected query result as input to the LLM and generating, by the processor via the LLM, a corrected query; and
re-execute the corrected query, and iteratively repeat the providing feedback, generate the corrected query, and the re-execute steps until a query result satisfies at least one acceptance criterion ([0068]-[0069]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOQIN HU whose telephone number is (571)272-1792. The examiner can normally be reached on Monday-Friday 7:00am-3:30pm.
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/XIAOQIN HU/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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