Detailed Office 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 .
Status of the Claims
Claims 1-5, 7-8, 11-16, 18-19, and 21-25 are rejected
Claim Rejections - 35 USC § 103
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 1-5, 7-8, 11-16, 18-19, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Agrawal et al. (U.S. Publication No. 2025/0147832 A1), hereinafter referred to as Agrawal, in view of Sen et al. (U.S. Publication No. 2025/0077399 A1), hereinafter referred to as Sen, in further view of Sharda et al. (U.S. Publication No. 2015/0269062 A1), hereinafter referred to as Sharda.
Regarding Claim 1, Agrawal teaches:
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured:
to generate a first data structure at least in part by parsing a support ticket, the support ticket comprising information characterizing one or more defects encountered while operating an information technology asset; ([0036]; [0037]; regarding, “Based on the error message, the context associated with the error, and/or the instructions, the system may generate a prompt for an LLM.”);
to process at least portions of the first data structure utilizing a machine learning model to generate a second data structure, the second data structure specifying a given sequence of test steps for a given test scenario configured for testing of at least one of the one or more defects; ([0048]; regarding, “The system may transmit the prompt to the LLM, and receive an output from the LLM. The output may include an explanation of the error message, and a suggested fix for the error.”);
Agrawal fails so explicitly disclose but Sen teaches:
to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework, each of the application programming interface calls being associated with a functional code test unit of a test code database of the test automation framework; ([0097]; regarding, “the sequence of steps (step definition) may include mappings between each step/scenario within the software test and the code function to be executed by the script. The step definition can be thought of as the automation script.”);
and executing the application programming interface calls of the test automation framework mapped to the given sequence of test steps to execute the given test scenario on the configured test bed having the selected one or more test bed characteristics; (Sen, [0071]; regarding, “the GenAI model described herein may be trained based on custom-defined prompts designed to draw out specific attributes associated with a software test, automation script, vulnerability, or the like… a user may input a software test description, such as the requirements that the software test will be expected to perform when testing a software program.”; [0081]; regarding, “the GenAI model 524 may be trained on a large corpus of software tests and software test documentation and can receive a description of a software test, such as the requirements of the test, and generate a software test document that includes… expected results…”; [0048]; regarding, “the framework connector can connect the test execution service 130 to the proper testing framework… based on a user request or in an automated way… Each framework may create test scripts by developing required code or running commands in the test environment of the respective framework.”;
and responsive to verifying that the given test scenario successfully reproduces said at least one of the one or more defects, to add the given test scenario to a test repository of the test automation framework. (Sen, [0081]; regarding, “the GenAI model 524 may be trained on a large corpus of software tests and software test documentation and can receive a description of a software test, such as the requirements of the test, and generate a software test document that includes… expected results…”; [0063]; regarding, “The GenAI model 322 may learn mappings/connections between requirements associated with a software test and the components included within the software test case and can thus create software test cases from a description of the requirements of the software test case. When fully trained, the model may be stored within the model repository 323”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Agrawal with the teachings of Sen. Doing so can improve test development efficiency and enables seamless AI-powered test design process integration (Sen, [0041]).
Agrawal in view of Sen fails to explicitly disclose but Sharda teaches:
to verify whether the given test scenario successfully reproduces said at least one of the one or more defects, wherein verifying whether the given test scenario successfully reproduces said at least one of the one or more defects comprises:
determining an operating environment of the information technology asset for at least one time at which said at least one of the one or more defects was encountered; ([0017]; regarding, “A user may specify requirements, constraints, or other features of a test bed for a particular software test in metadata attached to a file, document, or other data specifying the particular software test. The test bed requirements can include, for example, hardware and/or software requirements, networking constraints, and/or data defining a test engine framework for the test.”; [0035]; regarding, “if the test bed requirements are stored in metadata 126 attached to the test data 122, the test requirement engine 122 can detect the metadata 126 and extract the test bed requirements from the metadata 126.”);
selecting, based at least in part on the determined operating environment, one or more test bed characteristics; (Fig. 1, [0024]; regarding, “the test bed requirements are specified in metadata 126… In the illustrated example, the metadata 126 specifies a number of CPUs (3), an amount of memory (64 GB), and test engine framework number 17.”);
configuring a test bed with the selected one or more test bed characteristics; ([0018]; regarding, “The test system can extract the test bed requirements from the metadata and compare the requirements to the characteristics of a set of available test beds. If a particular test bed satisfies or meets the test bed requirements, the test system may cause the test to be performed using the particular test bed.”);
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Agrawal and Sen with the teachings of Sharda. Doing so can allow Software tests can be performed more accurately and more effectively (Sharda, [0009]).
Regarding Claim 2, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein parsing the support ticket comprises extracting a natural language description of a root cause of at least one of the one or more defects encountered while operating the information technology asset. (Agrawal, [0057]; regarding, “A Prompt (or “Natural Language Prompt” or “Model Input”)… A prompt may be provided to an LLM which the LLM can use to generate a response (or “model output”).”; [0070]; regarding, “The prompt generation module 110 may generate a prompt that includes an error message indicating a code error and context associated with the error…”; [0036]).
Regarding Claim 3, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein generating the first data structure comprises selecting, from at least one repository of a test management environment, one or more additional support tickets and one or more existing test scenarios generated for testing of one or more additional defects identified in the one or more additional support tickets. (Sen, [0047]; regarding, “the system includes a test execution service 130 that hosts a software testing environment and can execute software tests stored in a test repository 132. In the example embodiments, the test execution service 130 can execute a test in any of multiple different frameworks/different programming languages”; [0049]; regarding, “the GenAI model 120 may include a data store 122 with a repository of software tests, automation scripts, source code, vulnerability code, software patches, and the like.”).
Regarding Claim 4, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 3 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein processing the first data structure utilizing the machine learning model comprises utilizing the selected one or more additional support tickets and one or more existing test scenarios for adapting the machine learning model to a testing context of the support ticket. (Sen, [0063]; regarding, “The GenAI model 322 may learn mappings/connections between requirements associated with a software test and the components included within the software test case and can thus create software test cases from a description of the requirements of the software test case.”; [0064]; regarding, “the training process may use executional results that have already been generated/output by the GenAI model 322 in a live environment (including any customer feedback, etc.)”).
Regarding Claim 5, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein the machine learning model comprises a large language model. (Agrawal, [0037]; regarding, “Based on the error message, the context associated with the error, and/or the instructions, the system may generate a prompt for an LLM.”).
Regarding Claim 7, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein determining the operating environment of the information technology asset comprises determining at least one of a hardware and a software configuration of the information technology asset at the time when said at least one of the one or more defects was encountered. (Sharda, [0017]; regarding, “A user may specify requirements, constraints, or other features of a test bed for a particular software test in metadata attached to a file, document, or other data specifying the particular software test. The test bed requirements can include, for example, hardware and/or software requirements, networking constraints, and/or data defining a test engine framework for the test.”; [0035]; regarding, “if the test bed requirements are stored in metadata 126 attached to the test data 122, the test requirement engine 122 can detect the metadata 126 and extract the test bed requirements from the metadata 126.”).
Regarding Claim 8, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein determining the operating environment of the information technology asset comprises determining one or more workloads which were running on the information technology asset at the time when said at least one of the one or more defects was encountered. (Agrawal, [0038]; regarding, “the system may receive or access a log that includes error messages indicating one or more errors in code. The log may be generated by a software when implementing an application or a service (e.g., running a data processing pipeline, compiling a software package, or the like). The log may include information related to a code error (also referred to as “an error”), such as an error message that informs the error, a type of the error (e.g., compile time error, run time error, a syntax error, an overflow error, or the like), the name and or file-path of the code associated with the error, timestamps associated with the error, or other information related to the error.”).
Regarding Claim 11, Agrawal in view of Sen in further view of Sharda the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein adding the given test scenario to the test repository of the test automation framework comprises assigning a priority to the given test scenario, the priority assigned to the given test scenario being based at least in part on the support ticket. (Sen, [0076]; regarding, “The content within the prompts and the ordering of the prompts can cause the GenAI model 422 to generate software tests.”; [0125]; regarding, “generating a plurality of testing elements based on the execution of a generative artificial intelligence (GenAI) model on the description of the requirements of the software program and a repository of test cases.”).
Regarding Claim 12, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein the at least one processing device is further configured to determine whether the given test scenario has any testing gaps for said at least one of the one or more defects. (Sen, [0082]; regarding, “a user may submit a list of requirements or features of the software program that the user wishes to test.”; [0083]; regarding, “The requirements may include a list of software functions, components, etc., that are to be tested and a description of the activities to be performed to carry out the test and what should happen when the activities are carried out.”; [0059]; regarding, “For example, a user may input a confirmation that the test case generated by a GenAI model is correct or includes incorrect content. This information may be added to the results of execution and stored within a log 225.”).
Regarding Claim 13, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 12 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein the at least one processing device is further configured,
responsive to determining that the given test scenario has one or more testing gaps for said at least one of the one or more defects, to update the first data structure and re-process the updated first data structure utilizing the machine learning model to generate an updated second data structure; (Sen, [0113]; regarding, “The logged data may be input to a machine learning model 832, which is trained to identify new features for a software application, such as new buttons or controls on the user interface, new methods, new APIs, new code modules, etc.”);
Regarding Claim 14, Agrawal in view of Sen in further view of Sharda teaches the apparatus of claim 1 as cited above. Agrawal in view of Sen in further view of Sharda further teaches:
wherein the at least one processing device is further configured, responsive to determining that the given test scenario does not successfully reproduce said at least one of the one or more defects, to update the support ticket with at least one (i) additional root cause information for said at least one of the one or more defects and (ii) results of execution of the given test scenario. (Sen, [0059]; regarding, “For example, a user may input a confirmation that the test case generated by a GenAI model is correct or includes incorrect content. This information may be added to the results of execution and stored within a log 225.”; [0113]; regarding, “The logged data may be input to a machine learning model 832, which is trained to identify new features for a software application, such as new buttons or controls on the user interface, new methods, new APIs, new code modules, etc.”; [0111]; regarding, “During the testing processes, the testing software 822 may log the testing results into a log database 828, including the tests performed, the outputs, the pass/fail status, and the like.”).
Claims 15-16 and 18-19 are rejected under 35 U.S.C. 103 under the same ground of rejection as claims 1 and 5 respectively.
Claims 21-22, and 24 are rejected under 35 U.S.C 103 under the same grounds of rejection as claim 13.
Claims 23 and 25 are rejected under 35 U.S.C 103 under the same grounds of rejection as claim 12 and 14 respectively.
Response to Arguments
Applicant’s arguments filed 02/09/2026 with respect to claims have been considered.
Applicant’s arguments with respect to the previous rejection on independent Claim 1,
and similarly Claims 15 and 18, have been considered and a new grounds of rejection has been
provided addressing the newly claimed matter. Please see the above detailed rejection of the
newly recited subject matter.
Newly cited references Sharda and in combination with Agrawal and Sen teaches verify whether the given test scenario successfully reproduces said at least one of the one or more defects, wherein verifying whether the given test scenario successfully reproduces said at least one of the one or more defects comprises: determining an operating environment of the information technology asset for at least one time at which said at least one of the one or more defects was encountered; selecting, based at least in part on the determined operating environment, one or more test bed characteristics; configuring a test bed with the selected one or more test bed characteristics;
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEW GUSTAFSON whose telephone number is (571)272-5273. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/M.D.G./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
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