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 original filing of 04/26/2023.
Claims 1-20 are pending and have been considered below.
Drawings
The drawings of 04/26/2023 are objected to under 37 CFR 1.83(a) because they contain details and features which are blurry and hard to read/comprehend (see at least Fig. 6).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tanner, Jr. et al., US 2011/0087625 A1 [“TANNER”]
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);
automatically generating, by the at least one processor, the at least one model based on a result of the mapping (¶14: select, generate and iterate the agent-based system); and
outputting, by the at least one processor, the at least one model in response to the at least one request (¶14, ¶65: outputting the optimal agent-based system).
Regarding claim 2, TANNER discloses the method of claim 1, 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 discloses the method of claim 1, 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 discloses the method of claim 3, further comprising:
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 discloses the method of claim 1, 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 discloses the method of claim 5, 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 discloses the method of claim 5, further comprising:
determining, by the at least one processor, at least one standard application programming interface configuration for the at least one model based on the at least one parameter (¶22, ¶28); and
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 8, TANNER discloses the method of claim 1, wherein automatically generating the at least one model further comprises:
automatically generating, by the at least one processor, software code for the at least one model based on the result of the mapping (¶66: optimal agent output executable code),
wherein the automatically generated software code is operable in a networked environment to access data and to forecast at least one outcome based on the accessed data (¶66: operable in a networked environment, ¶9: predictive analytic system).
Regarding claim 9, TANNER discloses the method of claim 1, 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 10-18, claims 10-18 recite limitations similar to claims 1-9, 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Carpenter; Richard Christopher
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US 8701128 B2
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US 10430164 B2
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US 11507069 B2
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US 12174802 B2
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Aigner; Werner et al.
US 20110153624 A1
DATA MODEL ACCESS CONFIGURATION AND CUSTOMIZATION
Krishnan; Pavitra et al.
US 20130325789 A1
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Carus; Alwin B. et al.
US 20160350283 A1
APPARATUS, SYSTEM AND METHOD FOR APPLICATION-SPECIFIC AND CUSTOMIZABLE SEMANTIC SIMILARITY MEASUREMENT
Dietrich; Michael et al.
US 20170220698 A1
CANONICAL DATA MODEL FOR ITERATIVE EFFORT REDUCTION IN BUSINESS-TO-BUSINESS SCHEMA INTEGRATION
Thunoli; Shyam Sunder et al.
US 20180253669 A1
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Lowe; Jonathan et al.
US 20180314622 A1
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Behzadi; Houman et al.
US 20190265971 A1
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US 20210158085 A1
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Jobér; Johan et al.
US 20210166171 A1
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Martin; Jamie L. et al.
US 20220027380 A1
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Donatelli; Alessandro et al.
US 20220188630 A1
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Polen; Michael et al.
US 20230109718 A1
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Gupta; Rahul
US 20230124593 A1
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Mazor; Yuval et al.
US 20230161564 A1
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Tummala; Aparna Kumar et al.
US 20230359513 A1
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Yang; Kevin et al.
US 20240086844 A1
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Crabtree Jason et al.
WO 2019113501 A1
TRANSFER LEARNING AND DOMAIN ADAPTATION USING DISTRIBUTABLE DATA MODELS
Dietrich, Michael, and Jens Lemcke. "A refined canonical data model for multi-schema integration and mapping." 2011 IEEE 8th International Conference on e-Business Engineering. IEEE, 2011.
Chiș, Andrei. "A modeling method for model-driven API management." Complex Systems Informatics and Modeling Quarterly 25 (2020): 1-18.
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