CTFR 18/624,614 CTFR 92061 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION This office action is in response to communication filed 3/23/2026. Claims 1-20 are currently pending and claims 1, 8, and 15 are the independent claims. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-4, 8-11, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (herein called Park) (US Patent 11,409,642 B2) and Sonawale et al. (herein called Sonawale) (US Patent 10,678,679 B1) in further view of Ward et al. (herein called Ward) (US PG Pub. 2013/0185240 A1) . As per claim 1, Park teaches: a system comprising: at least one hardware processor; and a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: accessing application program interface (API) metadata (col. 1 lines 40-53, col. 4 lines 54-61, col. 5 lines 17-35, col. 9 lines 25-35, col. 10 lines 15-20, 47-62, API metadata information is extracted from documentation corpus (access API metadata) and is used to generate/extract/determine/etc. API parameter values. ); dynamically generating an API test case based on the API metadata (col. 1 lines 53-55, col. 5 lines 50-56, col. 11 lines 1-10, col. 22 lines 1-10, API test endpoint represents test scenario (API test case) and API test endpoint is generated/configured/etc. based on API parameter values extracted/determined/etc. using API metadata (based on API metadata).); and testing the first instance of the computer software component by executing the API test case on the first instance of the computer software component (col.1 lines 56-57, col. 5 lines 57-67, col. 22 lines 55-67, API test endpoints/API test case/set/sequence of test API endpoints/test cases/etc. is executed, and as teaches that the API testing may be for an API of an instance of a computer software component, as seen below, it is obvious that executing the test API endpoints/test case is testing the first instance of the computer software component by executing the API test case on the first instance of the computer software component.). Park does not explicitly disclose that the API being tested is an API for an instance of a computer software component, and as such does not explicitly state, however Sonawale teaches: accessing application program interface (API) metadata for a first instance of a computer software component (col. 2 lines 16-60, col. 3 lines 10-25, col. 4 lines 44-60, API environments having testing activities performed include software/applications/etc. (API environment is instance of computer software component/software/application being tested) etc. and testing may include regression testing, validation testing, etc.. API architecture of API environment/software/application is determined by tracing/determining/etc. API paths of business flows and parameters of the APIs (access API metadata/architecture information/etc.) and stores information/metadata associated with API architecture in dictionary/storage/etc., and test cases are recommended/generated based on the API architecture information/metadata/etc..). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park such that that API testing is for computer software component/software/application, as conceptually taught by Sonawale, to create accessing application program interface (API) metadata for a first instance of a computer software component because these modifications allow for software/applications/software components having API to be tested, which is desirable as it increases the usability of the API tests/test cases/etc. by expanding their use to software/applications/software components thereby making the more desirable to users/developers/etc. while helping to ensure that software/applications/API/software components/etc. operate correctly/as desired and helping to prevent errors from occurring in software/applications/etc.. While Park teaches accessing API metadata, Park and Sonawale do not explicitly state that the metadata describes data model configuration, and as such do not explicitly state, however Ward teaches: the API metadata describing a data model configuration specific to the first instance, wherein different instances of the computer software component have different data model configurations (pars. [0020]-[0024], [0028], [0064], [0071], data models of applications have characteristics and properties corresponding to information/data etc. to be used in application/retrieved from data sources/services/etc. via APIs corresponding to the properties (API metadata describing data model configuration/properties/characteristics/etc. of application) and presented to user/etc., and users specify which data model an application uses and properties/characteristics of the data model. As the data model characteristics/properties correspond to data/information received from data sources/services/etc. and used by the application via APIs, the data model properties/characteristics is API metadata describing data model configuration/properties/characteristics specific to the application/first instance/etc., and as users specify which data model an application uses and the properties/characteristics of the selected data model it is obvious that different users of the application may select different data models/different properties for the data model/etc. and as such it is obvious that different instances of the application/software component may have different data model configurations/different data models/different properties/etc.. corresponding to different user selections.). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Park and Sonawale such that the API metadata describes a data model configuration, as conceptually taught by Ward, to create the API metadata describing a data model configuration specific to the first instance, wherein different instances of the computer software component have different data model configurations, because these modifications allow for users to customize their instance of the application in accordance with each user’s needs/desires/etc. so that the application operates as desired by users, thereby increasing user control over the application and increasing useability of the application for users. As per claim 2, Park further teaches: wherein the operations further comprise: accessing seed data (col. 5 lines 32-56, col. 10 lines 47-67, col. 12 lines 8-23, parameter values (seed data) are extracted from knowledge base server (access seed data) and are used to generate test API endpoints representing test scenarios/test cases (parameter values are seed data for/used to generate/etc. test API endpoints/test cases/test scenarios/etc.).); and wherein the dynamically generating includes dynamically generating the API test case based on the API metadata and the seed data (col. 5 lines 1-56, col. 11 lines 1-12, col. 21 lines 30-40, API metadata information (API metadata) includes API path, description, etc. which is used to determine parameter clusters for which parameter values (seed data) is extracted/determined/generated/accessed, and test API endpoints representing test scenario (API test case) is generated using parameter value, metadata information, etc. (dynamically generating API test case based on/using/etc. API metadata/metadata information and seed data/parameter value).). As per claim 3, Park further teaches: wherein the seed data is specified for the API test case being generated (col. 5 lines 1-5, 17-56, API metadata information includes API title, path, endpoint, parameter type, parameter description, etc., parameter clusters are generated based on the API metadata information which have a representative term that is used to query/search for and extract/determine/specify parameter values/seed data used to generate test API endpoint/test scenario/test case (specify/query for and extract/etc. seed data/parameter values for the API test case being generated).). As per claim 4, while Park teaches that the seed data may be specified (ex: col. 5 lines 1-5, 17-56), it does not explicitly state, however Sonawale teaches: wherein the seed data is specified for an entity for which the first instance is run (col. 2 lines 16-60, environments to perform testing includes applications/software/etc. having API, API architecture information includes API paths, parameters (seed data), etc. which is discovered/determined/etc. (specified), and stored, and are used to recommend/generate test cases for testing an environment. As the API paths, parameters/seed data, etc. are determined/discovered/specified and used to generated test cases for testing software, the seed data/parameters are specified/determined/etc. for an entity for which the first instance is run/software/application having the API/API architecture being tested by executing/running/etc. the test cases/test scenario on the API/etc.). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to add wherein the seed data is specified for an entity for which the first instance is run, as conceptually taught by Sonawale, into that of Park because these modifications allow for an effective method of determining/specifying/obtaining/etc. seed data for performing the testing in a manner that ensures the data used to generate the tests is intended for/correct/usable by/etc. the API in the software/application being tested, which is desirable as it helps ensure that tests/test cases/etc. are able to correctly test the API/software/etc. so that errors may be accurately determined and helping to ensure that the software/application/APIs execute correctly/as desired/etc.. As per claims 8-11, they recite methods having similar limitations as the systems of claims 1-4, respectively, and are therefore rejected for similar reasoning as claims 1-4, respectively, above. As per claims 15-18, they recite non-transitory machine-readable mediums having similar limitations as the systems of claims 1-4, respectively, and are therefore rejected for similar reasoning as claims 1-4, respectively, above . 07-21-aia AIA Claim s 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (herein called Park) (US Patent 11,409,642 B2), Sonawale et al. (herein called Sonawale) (US Patent 10,678,679 B1), Ward et al. (herein called Ward) (US PG Pub. 2013/0185240 A1), and Kanagovi et al. (herein called Kanagovi) (US PG Pub. 2025/0265179 A1), in further view of Chandel et al. (herein called Chandel) (US PG Pub. 2024/0403198 A1) . As per claim 5, Park does not explicitly state, however Sonawale teaches: wherein the operations further comprise: wherein the dynamically generating of the API test case includes using the trained machine learning model (col. 3 lines 58-65, col. 4 lines 6-20, col. 5 lines 46-56, artificial intelligence (trained machine learning model) is used to automatically recommend API test case permutations (use artificial intelligence/trained machine learning model to generate API test case) and perform API testing using the test case.). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to add wherein the dynamically generating of the API test case includes using the trained machine learning model, as conceptually taught by Sonawale, into that of Park because these modifications allow for a machine learning model/artificial intelligence/etc. to be used to generate/create/etc. the API test case, which is desirable as it save time and resources that would be spent by a developer/user/human having to manually generate/create/etc. the test cases, thereby making the creation/generation/etc. of the test cases more desirable to users. Park, Sonawale, and Ward do not explicitly state, however Kanagovi teaches: accessing test history data (pars. [0027], [0044], test case database stores information regarding historical test cases (test history data) which are used to tune/train/etc. machine learning model/large language model/LLM/etc. (access/use/etc. test history data/historical test cases to train LLM/machine learning model/etc.) to generate code for test case/script/etc..); and training a machine learning model using the test history data as training data (pars. [0027], [0031], [0044], [0056], machine learning model/large language model/LLM/etc. is tuned/trained using historical test cases (train/tune machine learning model using test history data/historical test cases as training data).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add accessing test history data; and training a machine learning model using the test history data as training data, as conceptually taught by Kanagovi, into that of Park, Sonawale, and Ward because these modifications allow for machine learning models to be effectively trained to generate test cases/scripts/etc. thereby saving time and resources that would be spent by a human user/developer having to develop the test cases/scripts/etc., thereby making them more desirable to users. Park, Sonawale, Ward and Kanagovi do not explicitly state, however Chandel teaches: the training comprising iterating among various weights that will be multiplied by various input variables and evaluating a loss function at each iteration, until the loss function is minimized (pars. [0039]-[0041], [0070], [0073], attention weights are used to provide context/relevance/etc. to values/tokens/dataset/etc. used to train neural models/machine learning/artificial intelligence and values/tokens are multiplied by the weight (various weights that will be multiplied by various input variables), and training neural model/AI/machine learning model/etc. includes iteratively training model/machine learning my making multiple passes over training dataset with each iteration including a loss calculation (evaluating loss function at each iteration) and updating the weights (iterating among various weights) until optimization algorithm finds values of parameters that minimizes the loss function (training comprising iterating among various weights that will be multiplied by various input variables and evaluating a loss function at each iteration, until the loss function converges/finds values that minimizes the loss function/converges on values that minimizes the loss function).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the training comprising iterating among various weights that will be multiplied by various input variables and evaluating a loss function at each iteration, until the loss function converges, as conceptually taught by Chandel, into that of Park, Sonawale, Ward and Kanagovi, because these modifications allow for an effective an efficient method of training the machine learning model/artificial intelligence/etc. thereby helping to ensure that the machine learning model/artificial intelligence operates correctly/as desired and provides correct/desired output/test cases/etc.. As per claims 12 and 19, they recite a method and non-transitory machine-readable medium, respectively, having similar limitations as the system of claim 5, and are therefore rejected for similar reasoning as claim 5, above . 07-21-aia AIA Claim s 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (herein called Park) (US Patent 11,409,642 B2), Sonawale et al. (herein called Sonawale) (US Patent 10,678,679 B1), Ward et al. (herein called Ward) (US PG Pub. 2013/0185240 A1), Kanagovi et al. (herein called Kanagovi) (US PG Pub. 2025/0265179 A1), and Chandel et al. (herein called Chandel) (US PG Pub. 2024/0403198 A1) in further view of Sen (US PG Pub. 2025/0077397 A1) . As per claim 6, Park, Sonawale, Ward, Kanagovi, and Chandel do not explicitly state, however Sen teaches: wherein the operations further comprise: dynamically retraining the machine learning model based on feedback from a user (pars. [0058], [0063], machine learning model/AI model generates/outputs/etc. test case/output/etc. which is displayed to user and user provided feedback/confirmation/indication of correctness or incorrectness/etc. which is used to retrain the model (retrain machine learning model based on feedback from a user).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add dynamically retraining the machine learning model based on feedback from a user, as conceptually taught by Sen, into that of Park, Sonawale, Ward, Kanagovi, and Chandel because these modifications allow for increased user control over the machine learning model by allowing a user to ensure that the model is producing desired output and, if needed, retraining the model to help ensure it produces desired output, which is desirable as it helps ensure the model operates as desired by users. As per claims 13 and 20, they recite a method and non-transitory machine-readable medium, respectively, having similar limitations as the system of claim 6, and are therefore rejected for similar reasoning as claim 6, above . 07-21-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (herein called Park) (US Patent 11,409,642 B2), Sonawale et al. (herein called Sonawale) (US Patent 10,678,679 B1), and Ward et al. (herein called Ward) (US PG Pub. 2013/0185240 A1) in further view of Hu et al. (herein called Hu) (US PG Pub. 2025/0094138 A1) . As per claim 7, while Sonawale teaches that machine learning models/artificial intelligence/etc. may be used to generate the test cases (ex: col. 3 lines 58-65, col. 4 lines 6-20, col. 5 lines 46-56, artificial intelligence/trained machine learning model is used to automatically recommend/generate/etc. API test case permutations and perform API testing using the test case), Park, Sonawale, and Ward do not explicitly state, however Hu teaches: wherein the dynamically generating the API test case includes accessing a dynamic prompt template (pars. [0024], [0058], [0083], [0090], template prompt is populated (access dynamic prompt template) to generate prompt to be provided to LLM/machine learning model to generate code.); generating a prompt based on the dynamic prompt template and the API metadata (pars. [0058], [0082]-[0083], [0090], code is retrieved/obtained and template prompt (dynamic prompt template) is populated with programming language, code, description, etc. (metadata/API metadata as seen in Park and Sonawale above) to generate a prompt that is provided to LLM/machine learning to generate code (generate prompt based on dynamic prompt template and API metadata).); and feeding the prompt to a large language model (LLM) to generate code for inclusion in the API test case (pars. [0058], [0083], [0093], [0095], prompt is input/provided/etc. to LLM/artificial intelligence (machine learning/AI generating test case code from Sonawale) which executes on the prompt to generate code (feeding the prompt to LLM/machine learning model to generate code for inclusion in the API test case).). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add accessing a dynamic prompt template; generating a prompt based on the dynamic prompt template and the API metadata; and feeding the prompt to a large language model (LLM) to generate code for inclusion in the API test case, as conceptually taught by Hu, into that of Park, Sonawale, and Ward because these modifications allow for an effective method of using artificial intelligence/machine learning/large language model/etc. to generate code/test cases/etc., which is desirable as it saves time and resources that would be spent by a human/user/programmer manually developing/generating the test cases/code/etc., thereby making them more desirable to users. As per claim 14, it recites a method having similar limitations as the system of claim 7, and is therefore rejected for similar reasoning as claim 7, above. Response to Arguments Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. As per the 103 arguments on pg. 7 par. 1-pg of the remarks that Park et al. (herein called Park) (US Patent 11,409,642 B2) and Sonawale et al. (herein called Sonawale) (US Patent 10,678,679 B1) do not teach all amended features/limitations of the amended independent claims and none of the other references cited with respect to the dependent clams correct the deficiencies of Park and Sonawale, and therefore the amended independent claims and their respective dependent claims are allowable, the examiner would like to point out that the new reference Ward et al. (herein called Ward) (US PG Pub. 2013/0185240 A1) is currently relied upon to correct the deficiencies of Park and Sonawale with respect to the amended independent claims, and therefore the arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Therefore, the examiner finds these arguments unpersuasive and maintains that the rejection under 35 USC 103 is proper. Conclusion 07-40 AIA 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 DOUGLAS M SLACHTA whose telephone number is (571)270-0653. The examiner can normally be reached Monday-Friday 6:30am-4pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DOUGLAS M SLACHTA/Examiner, Art Unit 2193 Application/Control Number: 18/624,614 Page 2 Art Unit: 2193 Application/Control Number: 18/624,614 Page 3 Art Unit: 2193 Application/Control Number: 18/624,614 Page 4 Art Unit: 2193 Application/Control Number: 18/624,614 Page 5 Art Unit: 2193 Application/Control Number: 18/624,614 Page 6 Art Unit: 2193 Application/Control Number: 18/624,614 Page 7 Art Unit: 2193 Application/Control Number: 18/624,614 Page 8 Art Unit: 2193 Application/Control Number: 18/624,614 Page 9 Art Unit: 2193 Application/Control Number: 18/624,614 Page 10 Art Unit: 2193 Application/Control Number: 18/624,614 Page 11 Art Unit: 2193 Application/Control Number: 18/624,614 Page 12 Art Unit: 2193 Application/Control Number: 18/624,614 Page 13 Art Unit: 2193 Application/Control Number: 18/624,614 Page 14 Art Unit: 2193 Application/Control Number: 18/624,614 Page 15 Art Unit: 2193 Application/Control Number: 18/624,614 Page 16 Art Unit: 2193 Application/Control Number: 18/624,614 Page 17 Art Unit: 2193
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