DETAILED ACTION
Remarks
Applicant presents a request for continued examination filed 10 April 2026 responsive to the 12 December 2025 final Office action (the “Final Office Action”) as well as the 20 February 2026 advisory action.
With the request:
claims 1-5, 7-12, 14-18 and 21 are amended;
Claim 19 is cancelled.
New claim 22 is added.
Claims 1-18 and 21-22 are pending. Claims 1, 8 and 15 are the independent claims.
Any unpersuasive arguments are addressed in the “Response to Arguments” section below.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures 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 their entirety 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.
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 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.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). In particular, the specification provides no clear antecedent basis for:
the term “structurally coupled” in claims 1, 8, 15 and 21 or tests data structurally coupled to test steps as recited in those claims; and
the term “jointly process” in claim 22.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 and 21-22 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As to claim 1, the claim refers to “the test data structurally coupled to respective test steps”. There is insufficient support in the originally filed specification for these features. Applicant points to paragraphs [0030], [0031], [0089], [0096-0102] and Figure 8B as support but none of these portions of the originally filed specification describe what is claimed. They only show test steps displayed with associated test data, which is not the same as what is claimed.
As to claims 2-7, 21 and 22, the claims are dependent on claim 1 but do not cure the deficiencies of that claim. Accordingly, they are rejected for the same reasons.
Further as to claim 22, the claim recites that:
…the processor is further configured to extract components from the at least one code module and jointly process the text description and the components extracted from the at least one code module to generate the sequence of steps.
There is insufficient support in the originally filed specification for these elements as well. Applicant again points to paragraphs [0030], [0031], [0089], [0096-0102] and Figure 8B as support but none of these portions of the originally filed specification describe what is claimed. In particular, none of these paragraphs refers to extracting any components from any code module and “jointly” processing that information and any text-based description. Paragraph [0030]: refers to parsing source code to extract information as training data but not extracting that information from an identified code module. Furthermore, it does not describe “jointly processing” the extracted training data and any text-based description. Nor does any other portion of the originally filed specification.
As to claim 8, the claim includes the same new matter as claim 1 and is rejected for the same reasons.
As to claims 9-14, the claims are dependent on claim 8 but do not cure the deficiencies of that claim. Accordingly, they are rejected for the same reasons.
As to claim 15, the claim includes the same new matter as claim 1 and is rejected for the same reasons.
As to claims 16-18, the claims are dependent on claim 15 but do not cure the deficiencies of that claim. Accordingly, they are rejected for the same reasons.
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, 8, 12, 15, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara et al. (US 2025/0077391) (art of record – hereinafter Carrara) in view of Deakin (US 2024/0411673) (art of record – hereinafter Deakin) and Palyekar et al. (US 2018/0217921) (art made of record – hereinafter Palyekar)
As to claim 1, Carrara discloses an apparatus comprising:
a memory; (e.g., Carrara, par. [0054]: memory 220 can be a computer-readable storage medium storing computer-executable instructions and/or information for performing the functions described herein) and
a processor coupled to the memory and configured (e.g., Carrara, par. [0049]: processors 218, and memory 220 can be coupled to one another; par. [0054]: the one or more processors 218 can perform the one or more functions described herein) to:
receive a request from a software program which comprises a text-based description, (e.g., Carrara, abstract: the system uses generative AI to translate plain language requests into control code or other aspects of an industrial control project; Fig. 2 and associated text, par. [0075]: In the example scenario, generative AI component 10 can assist a developer in generating control code 908. The user can submit plain language requests to generate control code 908. To this end, user interface 204 can include a chat interface that allows the user to exchange chat-based communication with the IDE system. For example, if the user submits, a plain language request formatted as the statement “I need a control program for a web tension control system”, the generative AI component 210 can submit this request to the generative AI model 226 [So the AI model receives text-based requests from the AI component (software program). The user interface 204 (software component) is also separate from the other components, so any components using a request input to that interface implicitly receives the request from that interface as well]; par. [0140]: Fig. 19 illustrates an example methodology 1900 for generating test scripts. At 1904, a request is received via a chat interface to generate test scripts)
identify at least one code module included in source code of the software program stored in the memory; (e.g., Carrara, par. [0113]: model 226 can identify smart objects 422 that are included as part of control code 908 [software program stored in the memory]; par. [0076]: a programming language in which the control code 908 should be written “(e.g., ladder logic, structure text, functional block diagram)” [textual code in a programming language is source code]).
generate, by executing a generative artificial intelligence (GenAI) model on the at least one code module a software test comprising a sequence of test steps for testing functionality of the at least one code module, and test data (e.g., Carrara, par. [0141]: the system formulates, based on the analysis of the control code [at least one software module] using the generative AI model, test scenarios to be executed against portions of the control code in order to validate the code. At 1908, the IDE system can generate, using the trained generative AI model, test scripts for executing the test scenarios on the control code. A given test script can define a testing routine in terms of a sequencing of simulated inputs [test data] to be fed to a portion of the control code, and expected responses [also test data] of the code the simulated inputs; par. [0113]: generative IA model 226. The model 226 can identify smart objects 422 [also code modules] that are included as part of control code 908 and generate test scripts 1202 for executing tests on these smart object instances)
execute the software test and the test data on the software program (e.g., Carrara, par. [0142]: at 1910, the test scripts are executed to validate proper operation of the control code [software program]; par. [0141]: a given test script can define a testing routine in terms of a sequencing of simulated inputs [test data] to be fed to a portion of the control code) using a testing software application running in a testing environment of the memory (e.g., Carrara, par. [0053]: project testing component 212 can be configured to execute testing scripts; par. [0049]: IDE system 202 can include a project testing component 212. Components can comprise software instructions stored on memory 220) to generate test results (e.g., Carrara, par. [0142]: at 1912, a determination is made as to whether the project is validated based on the response of the system project to execution of the test scripts).
Carrara does not explicitly disclose to: generate, by executing a generative artificial intelligence (GenAI) model on the text-based description a software test and test data structurally coupled to respective test steps in the sequence; and the structurally coupled test data.
However, in an analogous art, Deakin discloses to:
generate, by executing a generative artificial intelligence (GenAI) model on the text-based description to generate a software test and test data (e.g., Deakin, par. [0009]: generating data sets for the test scenarios using generative AI based on the natural language description of expected behaviors; par. [0031]: natural language descriptions may be utilized as input to the AI for generation of the test code; par. [0028]: writing test scenarios with expected behavior in natural language is well-suited for submission to generative AI to create the necessary test code).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text-based description request and GenAI model of Carrara such that the GenAI model is executed on the request to generate tests and test data, as taught by Deakin, as Deakin would provide the advantage of a means of verifying the code meets specified expected behaviors. (See Deakin, par. [0032]). Carrara also suggests the combination because Carrara describes using requests in the form of text-based descriptions and processing them to generate its responses. (See Carrara, pars. [0075], [0081]).
Further, in an analogous art, Paleykar discloses:
test data structurally coupled to respective test steps in the sequence; (e.g., Paleykar, par. [0052]: the “link” button is used to link [structurally couple] existing test data to a new test step; Fig. 1U and associated text, par. [0062]: the report contains details related to each test step [the steps are in a sequence as shown, note that the term “step” itself implies a sequence as well]) and
the structurally coupled test data (see immediately above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the test data and sequence of test steps of Carrara/Deakin to include structurally linking test data to respective test steps, as taught by Paleykar, as Paleykar would provide the advantage of a means of identifying which data is used to execute each step and a means of reusing test data for different test steps. (See Paleykar, pars. [0048], [0062]).
As to claim 5, Carrara/Deakin/Paleykar discloses the apparatus of claim 1 (see rejection of claim 1 above), Carrara further discloses:
wherein the processor is configured to display at least one prompt on a user interface based on execution of the GenAI model, receive at least one response to the at least one prompt, and generate the test data based on execution of the GenAI model on the at least one prompt and the at least one response (e.g., Carrara, par. [0141]: in response to the request received at step 1904, the system formulates, using the generative AI model, test scenarios [test data] to be executed; par. [0121]: the plain language request or query 1406; par. [0122]: the prompt enhancement component 214 can word any query responses 1402 to the original query 1406—including responses 1402 prompting for additional information from the user [prompts, the additional information provided by the user in response being responses to those prompts] to assist the generative AI model 226 in arriving at the user’s desired result or responses 1402 that are believed to answer the users’ queries; par. [0076]: prompts generated by the generative AI component 210 and presented to the user by the user interface can be formatted as plain language questions asking the user for the required information).
As to claim 8, it is a method claim whose limitations are substantially the same as those of claim 1. Accordingly, it is rejected for substantially the same reasons.
As to claim 12, it is a method claim whose limitations are substantially the same as those of claim 5. Accordingly, it is rejected for substantially the same reasons.
As to claim 15, it is a medium claim whose limitations are substantially the same as those of claim 1. Accordingly, it is rejected for substantially the same reasons.
Further limitations, disclosed by Carrara include:
a computer-readable medium comprising instructions stored therein which when executed by a processor cause a computer (e.g., Carrara, par. [0158], [0174]) to perform (see rejection of claim 1 above).
As to claim 21, Carrara/Deakin/Paleykar discloses the apparatus of claim 1 (see rejection of claim 1 above), Carrara further discloses:
wherein the processor is configured to execute a test step included in the sequence of test steps using the test data e.g., Carrara, par. [0141: at 1908, the IDE system can generate, using the trained generative AI model, test scripts for executing the test scenarios on the control code; par. [0141]: a given test script can define a testing routine in terms of a sequencing of simulated inputs [sequence of steps] to be fed to a portion of the control code; par. [0108]: execution of the test scripts 1202 can involve feeding test inputs 1212 to the control code according to a sequence defined by the test scripts).
Carrara does not explicitly disclose the test data structurally coupled to the test step.
However, in an analogous art, Paleykar discloses:
the test data structurally coupled to the test step (e.g., Paleykar, par. [0052]: the “link” button is used to link [structurally couple] existing test data to a new test step; Fig. 1U and associated text, par. [0062]: the report contains details related to each test step [the steps are in a sequence as shown, note that the term “step” itself implies a sequence as well]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the test data and sequence of test steps of Carrara/Deakin to include structurally linking test data to respective test steps, as taught by Paleykar, as Paleykar would provide the advantage of a means of identifying which data is used to execute each step and a means of reusing test data for different test steps. (See Paleykar, pars. [0048], [0062]).
Claims 2-4, 9-11 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara (US 2025/0077391) in view of Deakin (US 2024/0411673) in view of Palyekar (US 2018/0217921) in further view of Kumar et al. (US 2022/0091968) (art of record – hereinafter Kumar)
As to claim 2, Carrara/Deakin/Palyekar discloses the apparatus of claim 1 (see rejection of claim 1 above), but does not explicitly disclose wherein the processor is further configured to train the GenAI model to generate the test data based on historical testing test data stored in the memory
However, in an analogous art, Kumar discloses:
wherein the processor is further configured to train the GenAI model to generate the test data based on historical testing test data stored in the memory (e.g., Kumar, par. [0053]: the ML model 300 can process the text and images [test data] to produce test cases 700, test data 750. The corrected test case and test data information is provided back to the ML model 300 for further training the ML model for better results going forward [the data used by Kumar is necessarily stored in memory, as all data is]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ai model and testing test data of Carrara/Deakin/Palyekar, to include training the GenAI model to generate test data based on the historical test data stored in memory, as taught by Kumar, as Kumar would provide the advantage of a means of training the model further for better future results. (See Kumar, par. [0053]).
As to claim 3, Carrara/Deakin/Palyekar discloses the apparatus of claim 1 (see rejection of claim 1 above), but does not explicitly disclose wherein the processor is further configured to display the software test and the test data via a user interface, and receive feedback about the
test data via the user interface.
However, in an analogous art, Kumar discloses:
wherein the processor is further configured to display the software test and the test data via a user interface, and receive feedback about the test data via the user interface (e.g., Kumar, par. [0053]: test cases 700 [software tests] and test data 750 [test data] can be in human-readable form and allows tester 1200 to review and provide feedback to the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the testing test data of Carrara, to include displaying the testing test data via a user interface, and receive feedback about the testing test data via the user interface, as taught by Kumar, as Kumar would provide the advantage of a means of training the model further for better future results. (See Kumar, par. [0053]).
As to claim 4, Carrara/Deakin//Palyekar/Kumar discloses the apparatus of claim 3 (see rejection of claim 3 above), but Carrara/Deakin does not explicitly disclose wherein the processor is further configured to update the memory to include the software test, the test data and the feedback about the test data, and retrain the GenAI model based on execution of the GenAI model on the test data and the feedback.
However, in an analogous art, Kumar discloses:
wherein the processor is further configured to update the memory to include the software test, the test data and the feedback about the test data, and retrain the GenAI model based on execution of the GenAI model on the test data and the feedback (e.g., Kumar, par. [0053]: test cases 700 [software tests] and test data 750 [test data] can be in human-readable form and allows tester 1200 to review and provide feedback to the system. The corrected test case and test data information [necessarily stored in memory] is provided back to the ML model 300 for future reference and training the model 300 [executing the model on the data that trains it] for better results going forward; par. [0062]: tester can review the information in Test Cases 700 and Test Data 750 and make corrections or additions to them. All such edits [feedback] are recorded by the Feedback 1300 that may be used to retrain the existing models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the memory, software tests and test data taught by of Carrara, to include updating the memory to include the software test, the test data and the feedback about the test data, and retrain the GenAI model based on execution of the GenAI model on the test data and the feedback, as taught by Kumar, as Kumar would provide the advantage of a means of obtaining better results in the futures. (See Kumar, par. [0053]).
As to claim 9, it is a method claim whose limitations are substantially the same as those of claim 2. Accordingly, it is rejected for substantially the same reasons
As to claim 10, it is a method claim whose limitations are substantially the same as those of claim 3. Accordingly, it is rejected for substantially the same reasons.
As to claim 11, it is a method claim whose limitations are substantially the same as those of claim 4. Accordingly, it is rejected for substantially the same reasons.
As to claim 16, it is a medium claim whose limitations are substantially the same as those of claim 2. Accordingly, it is rejected for substantially the same reasons.
As to claim 17, it is a medium claim whose limitations are substantially the same as those of claim 3. Accordingly, it is rejected for substantially the same reasons.
As to claim 18, it is a medium claim whose limitations are substantially the same as those of claim 4. Accordingly, it is rejected for substantially the same reasons.
Claims 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Carrara (US 2025/0077391) in view of Deakin (US 2024/0411673) in view of Palyekar (US 2018/0217921) in further view of Enokido et al. (US 6,243,835) (art of record – hereinafter Enokido).
As to claim 6, Carrara/Deakin/Palyekar closes the apparatus of claim 1 (see rejection of claim 1 above), but does not explicitly disclose wherein the processor is configured to display an input field via a user interface, receive a search input term via the input field, and identify the software program based on the search input term.
However, in an analogous art, Enokido discloses:
wherein the processor is configured to display an input field via a user interface, receive a search input term via the input field and identify the software program based on the search input term (e.g., Enokido, Fig. 19 and associated text, col. 8 ll. 8-16: on an entry screen for entering the name of a program for which part of the test specification is to be created, the program is designated by using a corresponding program ID. Then the table is searched by using the program ID as a key. The record is found to be a relevant one and retrieved to obtain the program name).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Carrara, to include displaying an input field via a user interface, receive a search input term via the input field and identifying a software program to be tested based on the search input term, as taught by Enokido, as Enokido would provide the advantage of a finding the program to be tested. (See Enokido, col. 8 ll. 8-16).
As to claim 7, Carrara/Deakin/Palyekar/Enokido discloses the apparatus of claim 6 (see rejection of claim 6 above), but Carrara does not explicitly disclose wherein the processor is further configured to identify a subset of test data within the memory that corresponds to the software program, and execute the software test of the software program based on the subset of test data via the testing software application.
However, in an analogous art, Palyekar discloses:
wherein the processor is further configured to identify a subset of test data within the memory that corresponds to the software program, and execute the software test of the software program based on the subset of test data via the testing software application (e.g., Palyekar, par. [0037]: testing of software application [software program] functionalities; par. [0052]: test data ID is used for searching existing test data [necessarily in memory]; par. [0048]: executing test cases at the testing unit 112. Each test case comprise[s] multiple test steps; par. [0053]: test data is used for executing specific test steps; par. [0077]: the present invention may be embodied as a computer program product [i.e., the aforementioned testing unit is software, i.e., a testing software application]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and software test of Carrara/Deakin to include identifying a subset of test data within the memory that corresponds to the software program, and execute the software test of the software program based on the subset of test data via the testing software application, as taught by Palyekar, as Palyekar would provide the advantage of a means of executing steps of the software test with the appropriate corresponding test data. (See Palyekar, par. [0048]).
As to claim 13, it is a method claim whose limitations are substantially the same as those of claim 6. Accordingly, it is rejected for substantially the same reasons.
As to claim 14, it is a medium claim whose limitations are substantially the same as those of claim 7. Accordingly, it is rejected for substantially the same reasons.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Carrara (US 2025/0077391) in view of Deakin (US 2024/0411673) in view of Palyekar (US 2018/0217921) in further view of Zimerman et al. (US 2026/0037414) (art made of record – hereinafter Zimerman).
As to claim 22, Carrera/Deakin/Palyekar discloses the apparatus of claim 1 (see rejection of claim 1 above) but does not explicitly disclose wherein the processor is further configured to extract components from the at least one code module and jointly process the text-based description and the components extracted from the at least one code module to generate the sequence of test steps.
However, in an analogous art, Zimerman discloses:
wherein the processor is further configured to extract components from the at least one code module and jointly process the text-based description and the components extracted from the at least one code module to generate the sequence of test steps (e.g., Zimerman, Fig. 6 and associated text, par. [0052]: upon receiving feedback to at least one question from the user, analyzer 108 can analyze the feedback [text-based description, text based because the user answers questions with text as shown in Figure 6]. The test code generator can generate one or more tests for testing the code sections having clear intents “(either initially, or after being clarified with the user)”; par. [0089]: after analyzing the user’s feedback, it is identified that the user has provided clear intent which enables the ML model to generate the tests [sequence of test steps because the tests generated are software functions as shown, e.g., by element 606 in Figure 6B]; par. [0123]: the user can reply with the component of interest, e.g., the BankAccount class; par. [0078]: a set of code sections [components] in the software code to be covered can be selected [extracted, in the sense that are obtained from the larger whole]; par. [0128]: the model can examine the code, identify the sections which are not covered by any tests and determine for which code sections the new tests should be generated [the sections and text-based description are “jointly processes” in the sense that they are both processed]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text-based description, code module and sequence of steps of Carrera/Deakin/Palyekar to include extracting components from the code module and jointly processing the text-based description and the components extracted from the at least one code module to generate the sequence of test steps, as taught by Zimerman, as Zimerman would provide the advantage of means of testing behavior of individual sections of the module as specified by a user. (See Zimerman, par. [0052], [0085]).
Conclusion
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/TODD AGUILERA/Primary Examiner, Art Unit 2192
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