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 .
This Office Action is in response to Request for Continued Examination filed on June 12, 2026.
Claims 1-20 are pending.
Claims 1, 9 and 14 have been amended.
Response to Amendment
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claims 1, 9 and 14 state “adding the test case and the other test case to a testing framework” in lines 13, 18 and 14 respectively. In the interest of consistency, it is recommended that this be amended to “adding the test case and the another test case to a testing framework”.
Claims 2-8, 10-13 and 15-20 depend on the objected to claims and do not resolve the deficiencies and thus, are objected to for at least the same reasons.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-8 are directed to methods and fall within the statutory category of processes; Claims 9-13 are directed to a system and fall within the statutory category of machines; and Claims 14-20 are directed to a non-transitory computer readable medium and fall within the statutory category of articles of manufacture. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1 and 14: The limitation “processing the user interaction data of the application to derive a pattern, wherein the processing of the user interaction data includes extracting nouns and verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate user interaction data and mentally process, with or without the use of pen and paper, the user interaction data of the application to derive a pattern and extract nouns and verb phrases. The limitation “subsequent to the processing, analyzing the user interaction data to identify test data and a test scenario based on the pattern”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate user interaction data and mentally analyze, the user interaction data to identify test data and a test scenario based on the pattern subsequent to processing the user interaction data.
The limitation “generating a feedback on the test data and the test scenario based on the user interaction data”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate test data and a test scenario based on user interaction data and mentally generate a feedback on the test data and the test scenario based on the user interaction data. The limitation “generating a test case based on the test data and the test scenario, wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate test data and a test scenario and mentally generate, with or without the use of pen and paper, a test case based on the test data and the test scenario wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases.
The limitation “generating another test case based on the feedback”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate feedback and mentally generate another test case based on the feedback. The limitation “adding the test case and the other test case to a testing framework”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a test case and other test case and mentally add, with or without the use of pen and paper, the test case and the other test case to a testing framework.
Claim 9: The limitation “processing the telemetry data of the application and the user interaction data to derive a pattern, wherein the processing of the telemetry data and the user interaction data includes extracting nouns and verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate telemetry data and user interaction data and mentally process, with or without the use of pen and paper, the telemetry data of the application and the user interaction data to derive a pattern wherein the processing includes extracting nouns and verb phrases. The limitation “subsequent to the processing, analyzing the telemetry data and the user interaction data to identify test data and a test scenario based on the pattern”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate telemetry data and user interaction data and mentally analyze, the telemetry data and the user interaction data to identify test data and a test scenario based on the pattern subsequent to the processing.
The limitation “generating a feedback on the test data and the test scenario based on the user interaction data”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate test data and a test scenario based on user interaction data and mentally generate a feedback on the test data and the test scenario based on the user interaction data. The limitation “generating a test case based on the test data and the test scenario, wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate test data and a test scenario and mentally generate, with or without the use of pen and paper, a test case based on the test data and the test scenario wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases.
The limitation “generating another test case based on the feedback”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate feedback and mentally generate another test case based on the feedback. The limitation “adding the test case and the other test case to a testing framework”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a test case and other test case and mentally add, with or without the use of pen and paper, the test case and the other test case to a testing framework.
Therefore, Yes, claims 1, 9 and 14 recite judicial exceptions.
The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1 and 14: The judicial exception is not integrated into a practical application. In particular, the claim recites the following additional elements –“a processor” and “A non-transitory computer-readable medium to store instructions that are executable to perform operations comprising” which are merely recitations of generic computing components and functions being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, claims 1 and 14 recite the following additional elements – “collecting, by a processor, user interaction data associated with an application” which is merely a recitation of insignificant data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application and will also be addressed below in Step 2B as also being Well-Understood, Routine and Conventional.
Claim 9: The judicial exception is not integrated into a practical application. In particular, the claim recites the following additional elements –“a processor” and “a memory storing instructions that when executed cause the processor to perform..” which are merely recitations of generic computing components and functions being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)) which does not integrate a judicial exception into practical application. Further, claim 9 recites the following additional elements – “collecting telemetry data and user interaction data associated with an application” which is merely a recitation of insignificant data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application and will also be addressed below in Step 2B as also being Well-Understood, Routine and Conventional.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 9 and 14 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into a practical application.
Step 2B:
Claims 1, 9 and 14: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and mere instructions to apply an exception which do not amount to significantly more than the abstract idea. Moreover, the recitations of insignificant data gathering activity as also Well-Understood, Routine and Conventional. See at least MPEP § 2106.05(d)(II) “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. That is, in the instant claims these limitations merely receive or transmit/provide data which is Well-Understood, Routine and Conventional.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, Claims 1, 9 and 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claims 2, 10 and 15, they recite additional abstract idea recitations of “further comprising generating trace data” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate an application, just as in the independent claims above, mentally generate, with or without the use of pen and paper, trace data. Further, claims 2, 10 and 15 do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 2, 10 and 15 also fails both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 2, 10 and 15 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claims 3 and 17, they recite additional abstract idea recitations of “further comprising generating an application user interaction based on the user interaction data” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate user interaction data, just as in the independent claims above, mentally generate, with or without the use of pen and paper, an application user interaction based on the user interaction data. Further, claims 3 and 17 do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 3 and 17 also fails both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 3 and 17 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claims 4 and 18, they recite additional element of “wherein the application user interaction includes a user input” which is merely an insignificant data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application and is also Well-Understood, Routine and Conventional. See at least MPEP § 2106.05(d)(II) “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. That is, in the instant claims these limitations merely receive or transmit/provide data which is Well-Understood, Routine and Conventional. Further, claims 4 and 18 do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 4 and 18 also fails both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 4 and 18 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claims 5, 12 and 20, they recite additional abstract idea recitations of “further comprising linking the application user interaction to another application user interaction” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate application user interactions, just as in the independent claims above, mentally link, with or without the use of pen and paper, an application user interaction to another application user interaction. Further, claims 5, 12 and 20 do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 5, 12 and 20 also fails both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 5, 12 and 20 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claims 6, 13 and 16, they recite additional element of “wherein the test case includes at least one step based on the test scenario” which is merely recitations of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application and does not amount to significantly more. Further, claims 6, 13 and 16 do not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 6, 13 and 16 also fails both Step 2A prong 2, thus the claims are directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 6, 13 and 16 do not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claim 7, it recites additional abstract idea recitations of “determining a repeatability limit for the application user interaction” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate an application user interaction, just as in the independent claims above, mentally determine, with or without the use of pen and paper, a repeatability limit for the application user interaction. Further, claim 7 does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 7 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claim 8, it recite additional element of “collecting telemetry data associated with the application” which is merely an insignificant data gathering activity (see MPEP § 2106.05(g)) which does not integrate a judicial exception into practical application and is also Well-Understood, Routine and Conventional. See at least MPEP § 2106.05(d)(II) “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. That is, in the instant claims these limitations merely receive or transmit/provide data which is Well-Understood, Routine and Conventional. Further, claim 8 does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 8 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claim 11, it recites additional abstract idea recitations of “wherein the operations further comprise generating an application user interaction based on the telemetry data” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate telemetry data, just as in the independent claims above, mentally generate, with or without the use of pen and paper, an application user interaction based on the telemetry data. Further, claim 11 does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 11 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 11 does not recite patent eligible subject matter under 35 U.S.C. § 101.
With regard to claim 19, it recites additional abstract idea recitations of “wherein the application user interaction further comprise extracting a scenario type” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think about and observe, judge and evaluate application user interaction, just as in the independent claims above, mentally extract, with or without the use of pen and paper, a scenario type. Further, claim 19 does not recite any further additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 19 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 19 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Therefore, Claims 1-20 do not recite patent eligible subject matter under 35 U.S.C. §101.
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, 3-6, 14, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Raghavan et al. (US 2015/0227452) and in further view of Dsouza (US 2018/0267887)
With respect to Claim 1, Cser disclose:
collecting, by a processor, user interaction data associated with an application; (a recorder (e.g., a software snippet) is added to the application and the recorder records user actions on the application graphical user interface, Paragraph 80)
processing the user interaction data of the application to derive a pattern, (providing test case data in a textual representation of user interaction with the graphical user interface (user interaction data) and segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (pattern), Paragraph 70; using a language model approach to learn patterns of user interaction with a graphical user interface of an application, Paragraph 4; the fine-tuned model is trained to recognize sequences of user actions (pattern), Paragraph 79) wherein the processing of the user interaction data includes [extracting words]; (segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (extracting phrases), Paragraph 70)
subsequent to the processing, analyzing the user interaction data to identify test data and a test scenario based on the pattern; (recognize patterns (e.g., typical and/or predominant patterns) of user interaction with the application. In particular, the fine-tuned model is trained to recognize sequences of user actions (test data/test scenario) to predict sequences of user actions that find use in producing test cases. Further, the fine-tuned model is trained to recognize acceptable user inputs produced by user actions (test data), Paragraph 79; the application-specific training dataset (user interaction data) is used to produce a probabilistic model describing user action sequences, (test scenario) types of data input by a user, and/or values of data input by a user (test data), Paragraph 80)
and generating a test case based on the test data and the test scenario; (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81; the trained GPT model may generate any data type matching the learned inputs and may generate in or out of bound values as part of the test case generation (test data/test scenario), Paragraph 96)
and adding the test case to a testing framework. (the action of a test script (testing framework) step (test case) is performed on a plurality of the ranked list of elements and the outcome of the test step is evaluated, Paragraph 92; outputting a test script (testing framework) describing a sequence of user actions performed on element of a graphical user interface of an application (e.g., a test case) (adding the test case to a testing framework), Paragraph 96)
Cser does not disclose:
[extracting words] includes extracting nouns and verb phases;
generating a feedback on the test data and the test scenario based on the user interaction data;
generating another test case based on the feedback;
adding the other test case to a testing framework;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Raghavan et al. disclose:
generating a feedback on the test data and the test scenario based on the user interaction data; (the test scenarios may also be processed by a risk management module 126 to associate a risk index (feedback) with each of the test scenarios wherein the risk index is indicative of the criticality and the priority of the test scenario (feedback), Paragraph 45)
generating another test case based on the feedback; (if the risk rating is complete, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to convert all path flows of the business processes into test cases without performing any optimization. In another example, if the risk rating is high, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include all business processes which have a high rating and their child processes in the test cases. Additionally the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include at least one path having low and/or medium risk ratings., Paragraphs 46-47)
adding the other test case to a testing framework; (In one implementation, the set of test cases, for each of the identified test scenarios, may be optimized (other test case). Thereafter, keyword driven pseudo automated test scripts are generated for executing the test cases (adding the other test case to a testing framework), Paragraph 23)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Raghavan et al. into the teaching of Cser to include generating a feedback on the test data and the test scenario based on the user interaction data, generating another test case based on the feedback and adding the other test case to a testing framework in order to be able to optimize test scenarios by indicating a criticality and priority of the test scenario using a risk index which can help generate test cases based on a risk rating. (Raghavan et al., Paragraphs 45-47)
Cser and Raghavan et al. do not disclose:
[extracting words] includes extracting nouns and verb phases;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Dsouza discloses:
[extracting words] includes extracting nouns and verb phases; (NLP parsing can be used in step 220 to identify and extract the noun phrases (NP) and verb phrases (VP) from the test steps, Paragraph 29)
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases; (In step 230, method 200 can convert the actions that are to be executed (e.g., the NP and VP) identified in step 220 into actionable test execution script (test case(s)), Paragraph 30; As an example, a test step “Click on the Submit button” can be broken down into the verb “Mouse click” (step) and the noun “Submit button” (test objective), Paragraph 29)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Dsouza into the teaching of Cser and Raghavan et al. to include [extracting words] includes extracting nouns and verb phases and wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases in order to automatically convert test step actions into actionable test automation scripts. (Dsouza, Paragraph 15)
With respect to Claim 3, all the limitations of Claim 1 have been addressed above; and Cser further discloses:
further comprising generating an application user interaction based on the user interaction data. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model (user interaction data). In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 4, all the limitations of Claim 3 have been addressed above; and Cser further discloses:
wherein the application user interaction includes a user input. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model. In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) (user input) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 5, all the limitations of Claim 3 have been addressed above; and Cser further discloses:
further comprising linking the application user interaction to another application user interaction. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model. In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) (sequence means a chain of multiple application user interactions (linking)) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 6, all the limitations of Claim 1 have been addressed above; and Cser further discloses:
wherein the test case include at least one step based on the test scenario. (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (at least one step based on the test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81)
With respect to Claim 14, Cser discloses:
collecting user interaction data associated with an application; (a recorder (e.g., a software snippet) is added to the application and the recorder records user actions on the application graphical user interface, Paragraph 80)
processing the user interaction data of the application to derive a pattern, (providing test case data in a textual representation of user interaction with the graphical user interface (user interaction data) and segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (pattern), Paragraph 70; using a language model approach to learn patterns of user interaction with a graphical user interface of an application, Paragraph 4; the fine-tuned model is trained to recognize sequences of user actions (pattern), Paragraph 79) wherein the processing of the user interaction data includes [extracting words]; (segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (extracting phrases), Paragraph 70)
subsequent to the processing, analyzing the user interaction data to identify test data and a test scenario based on the pattern; (recognize patterns (e.g., typical and/or predominant patterns) of user interaction with the application. In particular, the fine-tuned model is trained to recognize sequences of user actions (test data/test scenario) to predict sequences of user actions that find use in producing test cases. Further, the fine-tuned model is trained to recognize acceptable user inputs produced by user actions (test data), Paragraph 79; the application-specific training dataset (user interaction data) is used to produce a probabilistic model describing user action sequences, (test scenario) types of data input by a user, and/or values of data input by a user (test data), Paragraph 80)
and generating a test case based on the test data and the test scenario. (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81; the trained GPT model may generate any data type matching the learned inputs and may generate in or out of bound values as part of the test case generation (test data), Paragraph 96)
and adding the test case to a testing framework. (the action of a test script step is performed on a plurality of the ranked list of elements and the outcome of the test step is evaluated, Paragraph 92; outputting a test script describing a sequence of user actions performed on element of a graphical user interface of an application (e.g., a test case) (adding the test case to a testing framework), Paragraph 96)
Cser does not disclose:
[extracting words] includes extracting nouns and verb phases;
generating a feedback on the test data and the test scenario based on the user interaction data;
generating another test case based on the feedback;
adding the other test case to a testing framework;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Raghavan et al. disclose:
generating a feedback on the test data and the test scenario based on the user interaction data; (the test scenarios may also be processed by a risk management module 126 to associate a risk index (feedback) with each of the test scenarios wherein the risk index is indicative of the criticality and the priority of the test scenario (feedback), Paragraph 45)
generating another test case based on the feedback; (if the risk rating is complete, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to convert all path flows of the business processes into test cases without performing any optimization. In another example, if the risk rating is high, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include all business processes which have a high rating and their child processes in the test cases. Additionally the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include at least one path having low and/or medium risk ratings., Paragraphs 46-47)
adding the other test case to a testing framework; (In one implementation, the set of test cases, for each of the identified test scenarios, may be optimized (other test case). Thereafter, keyword driven pseudo automated test scripts are generated for executing the test cases (adding the other test case to a testing framework), Paragraph 23)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Raghavan et al. into the teaching of Cser to include generating a feedback on the test data and the test scenario based on the user interaction data, generating another test case based on the feedback and adding the other test case to a testing framework in order to be able to optimize test scenarios by indicating a criticality and priority of the test scenario using a risk index which can help generate test cases based on a risk rating. (Raghavan et al., Paragraphs 45-47)
Cser and Raghavan et al. do not disclose:
[extracting words] includes extracting nouns and verb phases;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Dsouza discloses:
[extracting words] includes extracting nouns and verb phases; (NLP parsing can be used in step 220 to identify and extract the noun phrases (NP) and verb phrases (VP) from the test steps, Paragraph 29)
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases; (In step 230, method 200 can convert the actions that are to be executed (e.g., the NP and VP) identified in step 220 into actionable test execution script (test case(s)), Paragraph 30; As an example, a test step “Click on the Submit button” can be broken down into the verb “Mouse click” (step) and the noun “Submit button” (test objective), Paragraph 29)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Dsouza into the teaching of Cser and Raghavan et al. to include [extracting words] includes extracting nouns and verb phases and wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases in order to automatically convert test step actions into actionable test automation scripts. (Dsouza, Paragraph 15)
With respect to Claim 16, all the limitations of Claim 14 have been addressed above; and Cser further discloses:
wherein the test case include at least one step based on the test scenario. (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (at least one step based on the test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81)
With respect to Claim 17, all the limitations of Claim 14 have been addressed above; and Cser further discloses:
wherein the operations further comprise generating an application user interaction based on the user interaction data. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model (user interaction data). In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 18, all the limitations of Claim 17 have been addressed above; and Cser further discloses:
wherein the application user interaction includes a user input. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model. In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) (user input) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 20, all the limitations of Claim 17 have been addressed above; and Cser further discloses:
wherein operations further comprise linking the application user interaction to another application user interaction. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model (user interaction data). In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) (sequence means a chain of multiple application user interactions (linking)) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Raghavan et al. (US 2015/0227452) in view of Dsouza (US 2018/0267887) and in further view of Cathro (US 2011/0289360).
With respect to Claim 2, all the limitations of Claim 1 have been addressed above; and Cser, Raghavan et al. and Dsouza do not disclose:
further comprising generating trace data.
However, Cathro discloses:
further comprising generating trace data. (generating code-tracing data, Paragraph 2)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cathro into the teaching of Cser, Raghavan et al. and Dsouza to include generating trace data in order to improve fault detection in software. (Cathro, Paragraph 2, lines 1-5)
With respect to Claim 15, all the limitations of Claim 14 have been addressed above; and Cser, Raghavan et al. and Dsouza do not disclose:
wherein the operations further comprise generating trace data.
However, Cathro discloses:
wherein the operations further comprise generating trace data. (generating code-tracing data, Paragraph 2)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cathro into the teaching of Cser, Raghavan et al. and Dsouza to include wherein the operations further comprise generating trace data in order to improve fault detection in software. (Cathro, Paragraph 2, lines 1-5)
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Raghavan et al. (US 2015/0227452) in view of Dsouza (US 2018/0267887) and in further view of Almy (US 6,609,216).
With respect to Claim 7, all the limitations of Claim 3 have been addressed above; and Cser, Raghavan et al. and Dsouza do not disclose:
further comprising determining a repeatability limit for the application user interaction.
However, Almy discloses:
further comprising determining a repeatability limit for the application user interaction. (the repetitions loop runs the test case sequence (application user interaction) repetitively for a desired number of repetitions (repeatability limit), Column 4, lines 26-39)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Almy into the teaching of Cser, Raghavan et al. and Dsouza to include determining a repeatability limit for the application user interaction in order to help optimize a test case sequence. (Almy, Column 2, lines 8-22)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Raghavan et al. (US 2015/0227452) in view of Dsouza (US 2018/0267887) and in further view of Bhatnagar et al. (US 12,273,255).
With respect to Claim 8, all the limitations of Claim 7 have been addressed above; and Cser, Raghavan et al. and Dsouza do not disclose:
further comprising collecting telemetry data associated with the application.
However, Bhatnagar et al. disclose:
further comprising collecting telemetry data associated with the application. (using telemetry data (collecting telemetry data) from a production version of the network service (application) to identify classes of testable behaviors, Abstract, lines 1-8)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Bhatnagar et al. into the teaching of Cser, Raghavan et al. and Dsouza to include collecting telemetry data associated with an application in order to help generate test cases that adapt test coverage to observed behaviors. (Bhatnagar et al., Abstract, lines 1-4)
Claims 9 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Bhatnagar et al. (US 12,273,255) in view of Raghavan et al. (US 2015/0227452) and in further view of Dsouza (US 2018/0267887).
With respect to Claim 9, Cser disclose:
a processor; and (see Figure 4; electronic processor 420)
a memory storing instructions that when executed cause the processor to perform operations including: (see Figure 4; memory 430)
collecting, by a processor, user interaction data associated with an application; (a recorder (e.g., a software snippet) is added to the application and the recorder records user actions on the application graphical user interface, Paragraph 80)
processing the user interaction data of the application to derive a pattern, (providing test case data in a textual representation of user interaction with the graphical user interface (user interaction data) and segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (pattern), Paragraph 70; using a language model approach to learn patterns of user interaction with a graphical user interface of an application, Paragraph 4; the fine-tuned model is trained to recognize sequences of user actions (pattern), Paragraph 79) wherein the processing of the user interaction data includes [extracting words]; (segmenting the test case data in a textual representation into sentences, and tokenizing the sentences into words or subwords (extracting phrases), Paragraph 70)
subsequent to the processing, analyzing the user interaction data to identify test data and a test scenario based on the pattern; (recognize patterns (e.g., typical and/or predominant patterns) of user interaction with the application. In particular, the fine-tuned model is trained to recognize sequences of user actions (test scenario) to predict sequences of user actions that find use in producing test cases. Further, the fine-tuned model is trained to recognize acceptable user inputs produced by user actions (test data), Paragraph 79; the application-specific training dataset (user interaction data) is used to produce a probabilistic model describing user action sequences, (test scenario) types of data input by a user, and/or values of data input by a user (test data), Paragraph 80)
and generating a test case based on the test data and the test scenario. (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81; the trained GPT model may generate any data type matching the learned inputs and may generate in or out of bound values as part of the test case generation (test data), Paragraph 96)
and adding the test case to a testing framework. (the action of a test script step is performed on a plurality of the ranked list of elements and the outcome of the test step is evaluated, Paragraph 92; outputting a test script describing a sequence of user actions performed on element of a graphical user interface of an application (e.g., a test case) (adding the test case to a testing framework), Paragraph 96)
Cser does not disclose:
collecting telemetry data associated with an application;
processing the telemetry data of the application to derive a pattern, wherein the processing of the telemetry data includes extracting nouns and verb phrases;
generating a feedback on the test data and the test scenario based on the user interaction data;
generating another test case based on the feedback;
adding the other test case to a testing framework;
[extracting words] includes extracting nouns and verb phases;
subsequent to the processing, analyzing the telemetry data to identify test data and a test scenario based on the pattern;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Bhatnagar et al. disclose:
collecting telemetry data associated with an application; (using telemetry data (collecting telemetry data) from a production version of the network service (application) to identify classes of testable behaviors, Abstract, lines 1-8)
processing the telemetry data of the application to derive a pattern, (the behavior classes may be identified 544 (pattern) from the telemetry data at this stage, Column 15, lines 56-58) wherein the processing of the telemetry data includes [extracting words]; (the behaviors extracted (extracting words) from the telemetry data are stored in a behavior store 550. In some embodiments, the behaviors may be stored in a behavior specification language as discussed in connection with FIG. 4. In some embodiments, the extracted behaviors from different environments 510 are aggregated into sets of environment-neutral behaviors 552 by removing any environment-specific variables (domain names, IP address, etc.) from the behaviors. (extracting words), Column 16, lines 26-39)
subsequent to the processing, analyzing the telemetry data to identify test data and a test scenario based on the pattern; (the extracted behaviors from different environments 510 are aggregated into sets of environment-neutral behaviors 552 by removing any environment-specific variables (domain names, IP address, etc.) from the behaviors. The set of environment-neutral behaviors represents a common set of testable behaviors for a single type of network service. The last stage of the process augments the environment-neutral behaviors of the network service (test data) into a set of environment-specific test cases that are executable on each of the environments 510 (test scenario), Column 16, lines 26-53)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Bhatnagar et al. into the teaching of Cser to include collecting telemetry data associated with an application, processing the telemetry data of the application to derive a pattern and subsequent to the processing, analyzing the telemetry data to identify test data and a test scenario based on the pattern in order to help generate test cases that adapt test coverage to observed behaviors. (Bhatnagar et al., Abstract, lines 1-4)
Cser and Bhatnagar et al. do not disclose:
generating a feedback on the test data and the test scenario based on the user interaction data;
generating another test case based on the feedback;
adding the other test case to a testing framework;
[extracting words] includes extracting nouns and verb phases;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Raghavan et al. disclose:
generating a feedback on the test data and the test scenario based on the user interaction data; (the test scenarios may also be processed by a risk management module 126 to associate a risk index (feedback) with each of the test scenarios wherein the risk index is indicative of the criticality and the priority of the test scenario (feedback), Paragraph 45)
generating another test case based on the feedback; (if the risk rating is complete, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to convert all path flows of the business processes into test cases without performing any optimization. In another example, if the risk rating is high, the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include all business processes which have a high rating and their child processes in the test cases. Additionally the risk management module 126 instructs the test scenario identification module 120 and the test case generation module 122 to include at least one path having low and/or medium risk ratings., Paragraphs 46-47)
adding the other test case to a testing framework; (In one implementation, the set of test cases, for each of the identified test scenarios, may be optimized (other test case). Thereafter, keyword driven pseudo automated test scripts are generated for executing the test cases (adding the other test case to a testing framework), Paragraph 23)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Raghavan et al. into the teaching of Cser and Bhatnagar et al. to include generating a feedback on the test data and the test scenario based on the user interaction data, generating another test case based on the feedback and adding the other test case to a testing framework in order to be able to optimize test scenarios by indicating a criticality and priority of the test scenario using a risk index which can help generate test cases based on a risk rating. (Raghavan et al., Paragraphs 45-47)
Cser, Bhatnagar et al. and Raghavan et al. do not disclose:
[extracting words] includes extracting nouns and verb phases;
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases;
However, Dsouza discloses:
[extracting words] includes extracting nouns and verb phases; (NLP parsing can be used in step 220 to identify and extract the noun phrases (NP) and verb phrases (VP) from the test steps, Paragraph 29)
wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases; (In step 230, method 200 can convert the actions that are to be executed (e.g., the NP and VP) identified in step 220 into actionable test execution script (test case(s)), Paragraph 30; As an example, a test step “Click on the Submit button” can be broken down into the verb “Mouse click” (step) and the noun “Submit button” (test objective), Paragraph 29)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Dsouza into the teaching of Cser, Bhatnagar et al. and Raghavan et al. to include [extracting words] includes extracting nouns and verb phases and wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases in order to automatically convert test step actions into actionable test automation scripts. (Dsouza, Paragraph 15)
With respect to Claim 11, all the limitations of Claim 9 have been addressed above; and Cser, Raghavan et al. and Dsouza further disclose:
wherein the operations further comprise generating an application user interaction based on the [data]. (Cser, generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model (user interaction data). In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
Cser, Raghavan et al. and Dsouza do not disclose:
[data] is telemetry data
However, Bhatnagar et al. disclose:
data is telemetry data (using telemetry data to identify classes of testable behaviors which are used to generate test cases, Abstract, lines 1-8)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Bhatnagar et al. into the teaching of Cser, Raghavan et al. and Dsouza to include telemetry data in order to help generate test cases to adapt test coverage to observed behaviors. (Bhatnagar et al., Abstract, lines 1-4)
With respect to Claim 12, all the limitations of Claim 11 have been addressed above; and Cser further discloses:
wherein the operations further comprise linking the application user interaction to another application user interaction. (generating program code (e.g., a test script) from the token sequence produced by the fine-tuned model (user interaction data). In particular, the token sequence may be converted to a particular simulated user interaction (e.g., comprising a sequence of user actions on application graphical user interface elements) (sequence means a chain of multiple application user interactions (linking)) on an application for use in generating executable code in one or more languages that can perform the simulated user interaction on the application graphical user interface, Paragraph 95)
With respect to Claim 13, all the limitations of Claim 9 have been addressed above; and Cser further discloses:
wherein the test case include at least one step based on the test scenario. (perform test case generation (e.g., comprising a predicted sequence of user actions and probabilities associated with each user action) (at least one step based on the test scenario) for a specific application and to predict the probabilities of data types and values entered into the application, Paragraph 77; sequences of user actions are input to the fine-tuned model to generate a large number of outputs describing user test cases for the application, Paragraph 81)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Bhatnagar et al. (US 12,273,255) in view of Raghavan et al. (US 2015/0227452) in view of Dsouza (US 2018/0267887) and in further view of Cathro (US 2011/0289360).
With respect to Claim 10, all the limitations of Claim 9 have been addressed above; and Cser, Bhatnagar et al., Raghavan et al. and Dsouza do not disclose:
further comprising generating trace data.
However, Cathro discloses:
further comprising generating trace data. (generating code-tracing data, Paragraph 2)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cathro into the teaching of Cser, Bhatnagar et al., Raghavan et al. and Dsouza to include generating trace data in order to improve fault detection in software. (Cathro, Paragraph 2, lines 1-5)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Cser (US 2024/0320131) in view of Raghavan et al. (US 2015/0227452) in view of Dsouza (US 2018/0267887) and in further view of Talukdar et al. (US 2020/0104241).
With respect to Claim 19, all the limitations of Claim 17 have been addressed above; and Cser, Raghavan et al. and Dsouza do not disclose:
wherein the application user interaction further comprise extracting a scenario type.
However, Talukdar et al. disclose:
wherein the application user interaction further comprise extracting a scenario type. (creating automation tests by extracting a scenario type from the feature file (application user interaction) such as positive (login to a system successfully) or negative scenario (incorrect username or password), Paragraphs 23-24)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Talukdar et al. into the teaching of Cser, Raghavan et al. and Dsouza to include wherein the application user interaction further comprise extracting a scenario type in order to help describe the use case to be tested. (Talukdar et al., Paragraph 24)
Response to Arguments
Applicant’s arguments with respect to the §103 rejections of 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.
Applicant's arguments filed May 26, 2026 with respect to the §101 rejection have been fully considered but they are not persuasive.
In the Remarks, Applicant argues:
Claims 1-20 are eligible under the first prong of Step 2A Prong One because the features recited in the claims are not directed to a law of nature, natural phenomenon, or an abstract idea. In particular, claims 1-20 do not recite mathematical concepts, certain methods of organizing human activity, or mental processes as the Examiner asserts. The courts consider a mental process that can be performed in the human mind or by using a pen and paper to be an abstract idea. On the other hand, a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance, when the human mind is not equipped to perform the claim limitation(s). Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. Memorandum (Reminders on Evaluating Subject Matter Eligibility of Claims under 35 USC § 101, 2025 Memo), issued by the USPTO on August 4, 2025. The claims of the current application recite detailed steps that are implemented via one or more processors that cannot practically be performed in the human mind. For example, claim 1 as amended recites processing the user interaction data of the application to derive a pattern, wherein the processing of the user interaction data includes extracting nouns and verb phrases. In addition, the amended claim 1 recites generating a test case based on the test data and the test scenario, wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases and adding the test case to a testing framework.
Examiner’s Response:
Applicant argues that “[t]he claims of the current application recite detailed steps that are implemented via one or more processors that cannot practically be performed in the human mind”. The Examiner respectfully disagrees. The limitation “processing the user interaction data of the application to derive a pattern, wherein the processing of the user interaction data includes extracting nouns and verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate user interaction data and mentally process, with or without the use of pen and paper, the user interaction data of the application to derive a pattern and extract nouns and verb phrases. Further, the claim limitation “generating a test case based on the test data and the test scenario, wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate test data and a test scenario and mentally generate, with or without the use of pen and paper, a test case based on the test data and the test scenario wherein the test case includes a noun of the extracted nouns as a test objective, and wherein the test case includes a step based on a verb of the extracted verb phrases.
Further still, the limitation “adding the test case to a testing framework”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a test case and mentally add, with or without the use of pen and paper, the test case to a testing framework. Thus, claim 1 has been identified to recite judicial exceptions under Step 2A, Prong 1.
Therefore, for at least the reasons set forth above, the rejection under 35 U.S.C. §101 is proper and thus, maintained.
In the Remarks, Applicant argues:
Claims 1-20 are eligible under the first prong of Step 2A Prong Two because the features recited in the claims integrate any alleged abstract idea into a practical application. Examiners evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. In computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field. The examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field and evaluate the claim to ensure it reflects the disclosed improvement. Memorandum (Reminders on Evaluating Subject Matter Eligibility of Claims under 35 USC § 101, 2025 Memo), issued by the USPTO on August 4, 2025.
In the current application, the specification provides that data collected, processed, and analyzed can be used to generate feedback on a test scenario and/or test data. The feedback may then be used to generate one or more test cases. See Specification, paragraph [0034]. Further, the feedback may be used to derive test configuration and scenario prioritization. In addition, the feedback may be used to generate test cases, which may be used to standardize the test data prioritization ranking. The generated test cases can be used to test other applications. See Specification, paragraph [0036]. Claim 1, as amended, recites generating a feedback on the test data and the test scenario based on the user interaction data. In addition, claim 1 as amended recites generating another test case based on the feedback and adding the test case and the other test case to a testing framework. Accordingly, claim 1 as amended reflects an improvement in the functioning of a computer or another technology or technical field.
Examiner’s Response:
The Examiner respectfully disagrees. As can be seen in the updated §101 rejection to claim 1 above, the Examiner has interpreted amended claim limitations of “generating a feedback on the test data and the test scenario based on the user interaction data”, “generating another test case based on the feedback” and “adding the test case and the other test case to a testing framework” under Step 2A, Prong 1 for being part of the abstract idea. These limitations can be reasonably interpreted as being directed to a mental process that can be performed in the human mind and/or by using a pen and paper. Therefore, these newly added claim limitations were not analyzed under Step 2A, Prong 2 as being additional elements (since they are part of the abstract idea) and thus, cannot show improvement to the functioning of a computer or another technology or technical field.
In the Remarks, Applicant argues:
Independent claims 9 and 14, as amended, recite similar features to those of claim 1.
Therefore, claims 9, and 14 are patent eligible and allowable, as are the remaining claims, at least by virtue of their dependence.
Examiner’s Response:
Please see response to arguments above with respect to claim 1.
Conclusion
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/LANNY N UNG/ Primary Examiner, Art Unit 2197
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