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 .
DETAILED ACTION
This action is in response to the application filed on 10/17/2024.
Claims 1-20 are pending.
Examiner’s Note
Please note that Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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(s) 1-3, 6-7, 10-12 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over USPN 20230138180 to Vasvan et al. in view of USPN 20180196739 to Li et al.
Per claim 1:
Vasvan discloses:
1. A computer-implemented method comprising:
receiving a request to generate a test strategy for a new feature of a software solution (20230138180 paragraph [0010] “the testing system may receive a selection of a software product and test input data identifying inputs of a test case for the software product”), wherein the received request includes a specification of the new feature (paragraph [0010] “generate a test case plan that includes the test data and test steps and may generate test scripts for the test case plan based on the software product”);
consecutively generating one or more sections defined at a test strategy template for the test strategy (paragraph [0011] “automatically generates and executes a test case plan for a software product”), wherein consecutively generating the one or more sections comprises:
invoking, for each section, a large language model in a conversational mode for generating test strategy data for each section of the test strategy template (paragraph [0045] “using a machine learning model in connection with generating and executing a test case plan (i.e., template) for a software product”), wherein invoking the large language model comprises providing data comprising technical documentation relevant for the respective section (paragraph [0017] “testing system 115 executes a first NLP technique for analyzing unstructured documents. For example, the testing system 115 may analyze unstructured text input via the chat interface (e.g., information identifying a software product and/or test input data)”), data for the respective section from legacy strategies, and requests relevant to the respective section as defined in the test strategy template (paragraph [0046] “set of observations may be obtained from training data (e.g., historical data), such as data gathered during one or more processes… the machine learning system may receive the set of observations (e.g., as input) from the user device 105”); and
executing the generated test strategy for the new feature of the software solution to obtain output data (paragraph [0038] “testing system 115 automatically causes the software product to execute the test scripts to generate test results”), the output data including monitoring data for performance of the new features (paragraph [0051] “monitor performance of the selected trained model in the production environment… a new feature or a variable added to an application may cause new unexpected data input… validation platform may monitor the same metrics that were used to select the model on an ongoing basis to ensure that performance of the selected trained model is sustained as expected”), wherein the performance is determined based on criteria of the generated test strategy (paragraph [0052] “validation platform may utilize a common environment to generate and maintain these metrics for newly trained models, as well as models in production”).
Vasvan does not explicitly disclose generating a specification of the test strategy by concatenating the sections defined in the test strategy template.
However, Li discloses in an analogous computer system generating a specification of the test strategy (paragraph [0024] “test objectives are attached to satisfy certain coverage criteria at the requirements-level, such as requirements coverage (e.g., all requirements are activated), logic condition coverage (e.g., logic conditions in the requirements are activated at certain level), etc.”) by concatenating the sections defined in the test strategy template (paragraph [0029] “automatic test case generator unit 140 receives such and, from that, generates test cases, according to one or more strategies (e.g., requirements coverage strategy, logic condition coverage strategy, input masking strategy, data completeness analysis strategy, event strategy, list strategy, decomposition and equation strategy, equivalence class strategy, boundary value analysis strategy, robustness strategy, timing strategy, etc.)”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the method of generating a specification of the test strategy by concatenating the sections defined in the test strategy template as taught by Li into the method of generating test cases using machine learning model as taught by Vasvan. The modification would be obvious because of one of ordinary skill in the art would be motivated to add/incorporate the features of generating a specification of the test strategy by concatenating the sections defined in the test strategy template to provide an efficient technique so that automatically generated test cases and/or test procedures derived from the high-level software requirements can help reduce the cost introduced by manual test case generation and review activities as suggested by Li (paragraph [001-0003]).
Per claim 2:
Vasvan discloses:
2. The method of claim 1, wherein the received request further includes at least one of: i) documents describing the feature, ii) a list of previously technical issues and respective one or more provided solutions for each of the technical issues, wherein the previous technical issues are identified for features of the software solution that are different from the new feature (Since this appears to be Markush type language requiring at a minimum just one from the list, Vasavan teaches paragraph [0017] “testing system 115 executes a first NLP technique for analyzing unstructured documents… the testing system 115 may analyze unstructured text input via the chat interface (e.g., information identifying a software product and/or test input data)”).
Per claim 3:
Vasvan discloses:
3. The method of claim 1, wherein consecutively generating each section of the section comprises:
in response to generating a first section of the sections defined in the test strategy template, invoking the large language model to generate a second consecutive section based on a prompt including the generated first section (paragraph [0018] “testing system 115 executes a token-based NLP technique, such as a technique using regular expressions, to identify the test input data”).
Per claim 6:
Vasvan discloses:
6. The method of claim 1, wherein the specification of the new feature is provided in a predefined format (paragraph [0047] “the machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data, and/or by receiving input from an operator”) .
Per claim 7:
Vasvan discloses:
7. The method of claim 1, wherein the generated test strategy template includes sections defined for at least one of a testing approach, methodologies, tools, and criteria necessary for comprehensive testing of the new feature (Since this appears to be Markush type language requiring at a minimum just one from the list, Vasavan teaches paragraph [0031] “test case plan may include information identifying portions of the test data associated with each test step included in a particular iteration of test steps”).
Claims 10-12 and 15-16 is/are the apparatus/system claim corresponding to method claims 1-3 and 6-7 respectively and rejected under the same rational set forth in connection with the rejection of claims 6-7 respectively, as noted above.
Claims 19-20 is/are the medium/product claim corresponding to method claims 1-2 respectively and rejected under the same rational set forth in connection with the rejection of claims 1-2 respectively, as noted above.
Allowable Subject Matter
Claims 4, 8, 13 and 17 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claims 5, 9, 14 and 18 are objected by virtue of their respective dependencies on claims 4, 8, 13 and 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Related cited arts:
Beller, Moritz, et al. "Developer testing in the ide: Patterns, beliefs, and behavior." IEEE Transactions on Software Engineering 45.3 (2017): pp. 261-284.
Jackson, Daniel, and Craig A. Damon. "Elements of style: Analyzing a software design feature with a counterexample detector." ACM SIGSOFT Software Engineering Notes 21.3 (1996): pp. 239-249.
Claessen, Koen, and John Hughes. "QuickCheck: a lightweight tool for random testing of Haskell programs." Proceedings of the fifth ACM SIGPLAN international conference on Functional programming. 2000. pp. 268-279.
US 10989757 - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving requirements documentation for an application through a user interface (UI), analyzing the requirements documentation to extract terminologies based on an entity term corpus; categorizing the extracted terminologies based on a corpus of known terms; generating a semantic graph from standardized statements constructed from the categorized extracted terminologies; generating a process flow map for the application by identifying processes of the application and a respective relationship between each process from the semantic graph; generating a test scenario map of test scenarios for the application from the process flow map and the semantic graph; and providing the test scenario map to a tester through the UI.
US 20170337116 - Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a p_method for testing applications. A connection is made by a test development device to a source device. User interactions with various components of an application executing at the source device are detected by the test development device. A p_method corresponding to each user interaction with the various components of the application is identified by the test development device and within code of the application or underlying OS framework code. Contextual information is extracted from each identified p_method that corresponding to the component with which the user interaction occurred. A test script is generated by the test development device based on the user interactions and the contextual information extracted from the identified p_methods. The test script is automatically run on a test device that differs from the source device.
US 20180285246 - A system and method for utilizing metadata of a client computer in form of transactions and/or software operations (e.g., actions) in an enterprise system hosted by a host entity, such as in a cloud environment for testing the software operations is disclosed. The host entity stores metadata (e.g., response time, delay, processing time, usage) about the transactions in a database for the client computer system's actions. The host entity then uses the stored metadata to automatically generate a test script based on actual user interactions with the software operations of the enterprise resource planning system for specific data range and environment in order to test functionality of a plurality of software operations in the enterprise resource planning systems being operated by users of the client computers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Satish Rampuria whose telephone number is 571-272-3732. The examiner can normally be reached on Monday-Friday from 8:30 AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do, can be reached at telephone number 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Satish Rampuria/Primary Examiner, Art Unit 2193
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