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 .
The following action is in response to the amendment and remarks of 06/08/2026.
By the amendment, claims 1, 7, 10, 16 and 19 have been amended. Claims 8 and 17 have been canceled.
Claims 1-7, 9-16 and 18-20 are pending and have been considered below.
Response to Arguments
The drawings objection (Non-Final Rejection 03/12/2026) has been withdrawn in light of the replacement drawing and corresponding remarks.
Regarding the 35 USC 102(a)(1) rejection of claims 1-20 by Tanner, the Examiner partially agrees with Applicant’s remarks. Specifically, the Examiner agrees that while Tanner discloses mapping the configuration data to at least one parameter of the model by identifying an API, Tanner fails to disclose identifying the API from among a plurality of standard API configurations for the model (Remarks pages 12-13). However, the Examiner disagrees that Tanner fails to disclose aggregating data from a networked environment using generated software code of the model to forecast outcomes (Remarks page 13). Tanner discloses that the agent-based system model is a predictive analytic system for performing tasks for a user (pp. 9), broadly the task of a predictive analytic system is predicting outcomes. The task oriented most optimal agent-based system model is adapted to be executable through translation to execute on a particular computing device in a distributed environment (pp. 66), broadly generated software code of the model to gather data in a networked environment. The argument is not persuasive.
Due to Tanner failing to teach all the limitations of the amended claims, the 35 USC 102 rejection of claims 1-20 by Tanner has been withdrawn. On further search and consideration, the prior art of Tummala (US 2023/0359513) was found to meet the deficiencies of Tanner, as presented in the new grounds of rejection below.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 9-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tanner, Jr. et al., US 2011/0087625 A1 [“TANNER”] in view of Tummala et al., US 2023/0359513 A1 [“TUMMALA”].
Regarding claim 1, TANNER discloses a method for providing a canonical data model, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor via a graphical user interface, at least one request to generate at least one model, the at least one request including configuration data for the at least one model (¶13: receive request to generate agent from user including config data specifications, ¶9: agent-based system is a model, ¶15: user interface with display and inputs);
identifying, by the at least one processor, the canonical data model that corresponds to the at least one model, the canonical data model including at least one parameter (¶13: canonical model is identified for the specifications, including parameter ontology);
automatically mapping, by the at least one processor, the configuration data to the at least one parameter (¶13: mapping between the specification data sources and the ontology) by identifying at least one standard application programming interface (API) configuration for the at least one model based on the at least one parameter (¶22, ¶28);
automatically generating, by the at least one processor, software code for the at least one model based on a result of the mapping (¶14: select, generate and iterate the agent-based system, ¶66: optimal agent output executable code), wherein the software code is operable in a networked environment to access data (¶66: operable in a networked environment); and
outputting, by the at least one processor, the generated software code for the at least one model in response to the at least one request (¶14, ¶65: outputting the optimal agent-based system, ¶66: as optimal agent output executable code); and
aggregating, by the at least one processor via the outputted generated software code for the at least one model, data from the networked environment to forecast a plurality of outcomes (¶9: model is predictive analytic system for performing predictive tasks, broadly forecasting outcomes, ¶66: model is output as executable code for executing in a networked environment for accomplishing its task, ¶16: gather data from external data sources).
TANNER fails to disclose wherein the API configuration is from among a plurality of standard API configurations.
TUMMALA discloses methods for adapting and deploying canonical data models (¶11), an analogous art. In particular, TUMMALA discloses incorporating API configurations from a plurality of API configurations for the data model (¶69). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of TANNER and TUMMALA before them before the effective filing date of the claimed invention to combine the identifying API configurations form a plurality of API configurations of a canonical data model, as suggested by TUMMALA, with the identifying of the API configuration of TANNER. One would have been motivated to make this combination in order to provide modular structure allowing systems to adapt to new providers, as suggested by TUMMALA (¶69).
Regarding claim 2, TANNER and TUMMALA disclose the method of claim 1, and TANNER further discloses wherein the canonical data model relates to a predetermined data model that includes a standardized mapping of a plurality of entities and columns for a plurality of network components, the plurality of network components including at least one application and at least one application programming interface (¶21-27).
Regarding claim 3, TANNER and TUMMALA disclose the method of claim 1, and TANNER further discloses wherein, prior to identifying the canonical data model, the method further comprises:
determining, by the at least one processor using the configuration data, whether the requested at least one model corresponds to a previously generated concept (¶73: determine if a model has not been previously included in the request); and
identifying, by the at least one processor, the canonical data model when the requested at least one model does not correspond to the previously generated concept (¶73: forming the model in response to the request if no model has been included).
Regarding claim 4, TANNER and TUMMALA disclose the method of claim 3, and TANNER further discloses:
identifying, by the at least one processor, a previously generated model that corresponds to the previously generated concept when the requested at least one model corresponds to the previously generated concept (¶73: identifying a prior model included in the request); and
outputting, by the at least one processor, the previously generated model in response to the at least one request (¶73: omitting the mapping and modeling steps to output the included model for use in generating the agent-based system).
Regarding claim 5, TANNER and TUMMALA disclose the method of claim 1, and TANNER further discloses wherein automatically mapping the configuration data further comprises:
categorizing, by the at least one processor, at least one business context in the configuration data based on the at least one parameter (¶25-28: categorization of information in knowledge source in ontological mapping); and
determining, by the at least one processor, at least one downstream feed for the at least one model based on the at least one parameter (¶29).
Regarding claim 6, TANNER and TUMMALA disclose the method of claim 5, and TANNER further discloses wherein the at least one parameter includes standardized terminology for categorizing the at least one business context in the configuration data (¶25).
Regarding claim 7, TANNER and TUMMALA disclose the method of claim 5, and TANNER further discloses:
determining, by the at least one processor, at least one standard integration configuration for the at least one model based on the at least one parameter (¶65-67).
Regarding claim 9, TANNER and TUMMALA disclose the method of claim 1, and TANNER further discloses wherein the at least one model includes at least one from among a machine learning model, a mathematical model, a process model, and a data model (¶9).
Regarding claims 10-16 and 18, claims 10-16 and 18 recite limitations similar to claims 1-7 and 9, respectively, and are similarly rejected.
Regarding claims 19-20, claims 19-20 recite limitations similar to claims 1-2, respectively, and are similarly rejected.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141
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