DETAILED ACTION
Remarks
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 filed in response to Applicant’s Request for Continued Examination dated December 9, 2025. Claims 1, 9, and 17 are currently amended and claims 1-5, 7-13, 15-22 remain pending in the application and have been fully considered by Examiner.
Applicant's arguments with respect to the prior art rejections have been considered, but are moot in view of the new grounds of rejection presented herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 9, 2025, has been entered.
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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 15, and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 7, lines 6-8 recite, with emphasis added, “modifying, by transfer learning circuitry, the scenario generation model based on the first context setting and the first context-based testing scenario data, wherein the modified scenario generation model generates …” and parent claim 1 recites “modifying a second model parameter of the scenario generation model based on the first context setting and the first context parameter to produce a modified scenario generation model … using the modified scenario generation model ….” In claim 7, “the modified scenario generation model” is ambiguous because it could mean any of the following: (1) the modified scenario generation model that necessarily results from “modifying, by transfer learning circuitry, the scenario generation model based on the first context setting and the first context-based testing scenario data”, as recited in claim 7; (2) “the modified scenario generation model” recited in parent claim that 1 is produced by “modifying a second model parameter of the scenario generation model based on the first context setting and the first context parameter”; or (3) both of these (i.e., they are the all same “modified scenario generation model”). The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted claim 7 as reciting “wherein producing the modified scenario generation model further comprises modifying, by transfer learning circuitry, the scenario generation model based on the first context setting and the first context-based testing scenario data, wherein the modified scenario generation model generates ….”
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, 5, 7, 8, 9, 11, 13, 15, 16, 17, 19, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Vidal et al. (US 20210073110 A1, hereinafter Vidal) in view of Kuris et al. (US 20210042214 A1, hereinafter Kuris) and Clement et al. (US 20230161567 A1, hereinafter Clement).
With respect to claim 1, Vidal discloses A method comprising:
receiving, by communications hardware, an application and circumstantial data associated with the application, wherein the circumstantial data indicates a business context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0043], a user of an automated test platform initiates exploration of an AUT [application] as described herein by providing access information for the AUT (402) which might include … the code of the AUT itself (e.g., uploaded to the platform); [0011], analytics data [circumstantial data] for the AUT are received. The analytics data represent use of the AUT by a population of user; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior may be derived from analytics data [circumstantial data] (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users; see also [0061].);
extracting, by advanced testing circuitry, a first context setting based on the circumstantial data associated with the application, wherein the first context setting comprises a first context parameter related to a context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior [first context setting comprises a first context parameter related to a context in which the application is applied] may be derived [extracting] from analytics data [circumstantial data] (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of user.);
a scenario generation model trained using training data generated within the business context (e.g., Figs. 1-5 and associated text, e.g. [0022], a machine-learning (ML) approach called reinforcement learning is used to govern the way in which the AI agent explores an environment, i.e., an AUT. “Reinforcement learning” [training] refers to machine learning techniques in which software agents take actions in an environment to maximize some kind of cumulative reward; [0024], A QA engineer would be able to upload analytics data to the test generation platform, and that data could be used to guide and reward the AI agent [a scenario generation model trained using training data generated within the business context]; see also Abstract, [0044], [0046], and [0061].);
modifying a second model parameter of the scenario generation model based on the first context setting and the first context parameter to produce a modified scenario generation model (e.g., Figs. 1-5 and associated text, e.g., [0023], But once a result is captured, the reward [second model parameter] for performing that action is reduced or eliminated, correspondingly reducing the agent's tendency to spend resources on that action when it encounters that state (or similar states) again [modified scenario generation model]; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [based on the first context setting and the first context parameter].);
generating, by the advanced testing circuitry and using the modified scenario generation model, one or more first context-based testing scenarios (e.g., Figs. 1-4 and associated text, e.g., Abstract, An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states; [0066], The sequence of interactions for the identified application flow is then selected as a candidate for being the basis for a test in the test suite; [0067], Test code is derived for each test candidate (520). The test code embodies the sequence of interactions with the AUT for that flow, e.g., a collection of test scripts that perform the interactions of the sequence [first context-based testing scenarios]; see also [0044], [0067-68], [0073-74], and claim 1.); and
testing, by the advanced testing circuitry, the application using an advanced testing model, wherein the advanced testing model uses the one or more first context-based testing scenarios (e.g., Figs. 1-6E and associated text, e.g., [0003], methods, apparatus, systems, platforms, and computer program products are provided that support automated testing of applications; [0074] FIGS. 6A-6E are examples of screenshots with which a developer or QA engineer might interact when creating and running a test suite for a web application as enabled by the present disclosure.).
Vidal does not appear to disclose the following, which is taught in analogous art, Kuris: testing, by preliminary testing circuitry, the application using a preliminary testing model (e.g., Figs. 1-4 and associated text, e.g., [0009], As a result, an ALM quality assurance effort implements ongoing testing of the computer product throughout the lifecycle of the computer product. The quality assurance efforts can involve identifying customer requirements for a product, identifying use cases, producing a test plan for the product testing using the use cases, executing the test plan, verifying results meet expectations, identifying issues, resolving identified issues, etc; see also [0012] and [0028].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Vidal with the invention of Kuris, such that test plans are executed and modified in an iterative and ongoing manner during the product’s lifecycle, because it “enhances accuracy, performance and efficiency of computer systems,” as suggested by Kuris (see [0014]).
Vidal does not appear to disclose the following, which is taught in analogous art, Clement: fixing a first model parameter of (e.g., Figs. 1-4B along with associated text, e.g., [0013], A deep learning model may be generated for … generating unit test cases; [0011], freezing the model's parameters; see also Abstract and [0004].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Vidal with the invention of Clement such that certain model parameters are frozen because “Often in fine-tuning, all the parameters of the deep learning model are updated when the model is trained on the downstream task … This becomes impractical when the model utilizes a large number of parameters and there are limited computing resources,” as suggested by Clement (see [0002]).
With respect to claim 9, Vidal discloses An apparatus (e.g., Figs. 2-3 and associated text, e.g., [0003], apparatus … are provided that support automated testing of applications.) comprising:
communications hardware configured to receive an application and circumstantial data associated with the application, wherein the circumstantial data indicates a business context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0043], a user of an automated test platform initiates exploration of an AUT [application] as described herein by providing access information for the AUT (402) which might include … the code of the AUT itself (e.g., uploaded to the platform); [0011], analytics data [circumstantial data] for the AUT are received. The analytics data represent use of the AUT by a population of user; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior may be derived from analytics data (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users; see also [0061].);
and
advanced testing circuitry configured to: extract a first context setting based on the circumstantial data associated with the application, wherein the first context setting comprises a first context parameter related to a context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior [first context setting comprises a first context parameter related to a context in which the application is applied] may be derived [extract] from analytics data [circumstantial data] (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of user.);
a scenario generation model trained using training data generated within the business context (e.g., Figs. 1-5 and associated text, e.g. [0022], a machine-learning (ML) approach called reinforcement learning is used to govern the way in which the AI agent explores an environment, i.e., an AUT. “Reinforcement learning” [training] refers to machine learning techniques in which software agents take actions in an environment to maximize some kind of cumulative reward; [0024], A QA engineer would be able to upload analytics data to the test generation platform, and that data could be used to guide and reward the AI agent [a scenario generation model trained using training data generated within the business context]; see also Abstract, [0044], [0046], and [0061].);
modify a second model parameter of the scenario generation model based on the first context setting and the first context parameter to produce a modified scenario generation model (e.g., Figs. 1-5 and associated text, e.g., [0023], But once a result is captured, the reward [second model parameter] for performing that action is reduced or eliminated, correspondingly reducing the agent's tendency to spend resources on that action when it encounters that state (or similar states) again [modified scenario generation model]; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [based on the first context setting and the first context parameter].);
generate, using the modified scenario generation model, one or more first context-based testing scenarios (e.g., Figs. 1-4 and associated text, e.g., Abstract, An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states; [0066], The sequence of interactions for the identified application flow is then selected as a candidate for being the basis for a test in the test suite; [0067], Test code is derived for each test candidate (520). The test code embodies the sequence of interactions with the AUT for that flow, e.g., a collection of test scripts that perform the interactions of the sequence [first context-based testing scenarios]; see also [0044], [0067-68], [0073-74], and claim 1.); and
test the application using an advanced testing model, wherein the advanced testing model uses the one or more first context-based testing scenarios (e.g., Figs. 1-6E and associated text, e.g., [0003], methods, apparatus, systems, platforms, and computer program products are provided that support automated testing of applications; [0074] FIGS. 6A-6E are examples of screenshots with which a developer or QA engineer might interact when creating and running a test suite for a web application as enabled by the present disclosure.).
Vidal does not appear to disclose the following, which is taught in analogous art, Kuris: preliminary testing circuitry configured to test the application using a preliminary testing model (e.g., Figs. 1-4 and associated text, e.g., [0009], As a result, an ALM quality assurance effort implements ongoing testing of the computer product throughout the lifecycle of the computer product. The quality assurance efforts can involve identifying customer requirements for a product, identifying use cases, producing a test plan for the product testing using the use cases, executing the test plan, verifying results meet expectations, identifying issues, resolving identified issues, etc; see also [0012] and [0028].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Vidal with the invention of Kuris, such that test plans are executed and modified in an iterative and ongoing manner during the product’s lifecycle, because it “enhances accuracy, performance and efficiency of computer systems,” as suggested by Kuris (see [0014]).
Vidal does not appear to disclose the following, which is taught in analogous art, Clement: fix a first model parameter of (e.g., Figs. 1-4B along with associated text, e.g., [0013], A deep learning model may be generated for … generating unit test cases; [0011], freezing the model's parameters; see also Abstract and [0004].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Vidal with the invention of Clement such that certain model parameters are frozen because “Often in fine-tuning, all the parameters of the deep learning model are updated when the model is trained on the downstream task … This becomes impractical when the model utilizes a large number of parameters and there are limited computing resources,” as suggested by Clement (see [0002]).
With respect to claim 17, Vidal discloses An apparatus (e.g., Figs. 2-3 and associated text, e.g., [0003], apparatus … are provided that support automated testing of applications.) comprising:
means for receiving an application and circumstantial data associated with the application, wherein the circumstantial data indicates a business context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0043], a user of an automated test platform initiates exploration of an AUT [application] as described herein by providing access information for the AUT (402) which might include … the code of the AUT itself (e.g., uploaded to the platform); [0011], analytics data [circumstantial data] for the AUT are received. The analytics data represent use of the AUT by a population of users; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior may be derived from analytics data (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users; see also [0061].);
means for extracting, by advanced testing circuitry, a first context setting based on the circumstantial data associated with the application, wherein the first context setting comprises a first context parameter related to a context in which the application is applied (e.g., Figs. 1-5 and associated text, e.g., [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior [first context setting comprises a first context parameter related to a context in which the application is applied] may be derived [extracting] from analytics data [circumstantial data] (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users.);
means for a scenario generation model trained using training data generated within the business context (e.g., Figs. 1-5 and associated text, e.g. [0022], a machine-learning (ML) approach called reinforcement learning is used to govern the way in which the AI agent explores an environment, i.e., an AUT. “Reinforcement learning” [training] refers to machine learning techniques in which software agents take actions in an environment to maximize some kind of cumulative reward; [0024], A QA engineer would be able to upload analytics data to the test generation platform, and that data could be used to guide and reward the AI agent [a scenario generation model trained using training data generated within the business context]; see also Abstract, [0044], [0046], and [0061].);
means for modifying a second model parameter of the scenario generation model based on the first context setting and the first context parameter to produce a modified scenario generation model (e.g., Figs. 1-5 and associated text, e.g., [0023], But once a result is captured, the reward [second model parameter] for performing that action is reduced or eliminated, correspondingly reducing the agent's tendency to spend resources on that action when it encounters that state (or similar states) again [modified scenario generation model]; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [based on the first context setting and the first context parameter].);
means for generating, using the modified scenario generation model, one or more first context-based testing scenarios (e.g., Figs. 1-4 and associated text, e.g., Abstract, An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states; [0066], The sequence of interactions for the identified application flow is then selected as a candidate for being the basis for a test in the test suite; [0067], Test code is derived for each test candidate (520). The test code embodies the sequence of interactions with the AUT for that flow, e.g., a collection of test scripts that perform the interactions of the sequence [first context-based testing scenarios]; see also [0044], [0067-68], [0073-74], and claim 1.); and
means for testing the application using an advanced testing model, wherein the advanced testing model uses the one or more first context-based testing scenarios (e.g., Figs. 1-6E and associated text, e.g., [0003], methods, apparatus, systems, platforms, and computer program products are provided that support automated testing of applications; [0074] FIGS. 6A-6E are examples of screenshots with which a developer or QA engineer might interact when creating and running a test suite for a web application as enabled by the present disclosure.).
Vidal does not appear to disclose the following, which is taught in analogous art, Kuris: means for testing the application using a preliminary testing model (e.g., Figs. 1-4 and associated text, e.g., [0009], As a result, an ALM quality assurance effort implements ongoing testing of the computer product throughout the lifecycle of the computer product. The quality assurance efforts can involve identifying customer requirements for a product, identifying use cases, producing a test plan for the product testing using the use cases, executing the test plan, verifying results meet expectations, identifying issues, resolving identified issues, etc; see also [0012] and [0028].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Vidal with the invention of Kuris, such that test plans are executed and modified in an iterative and ongoing manner during the product’s lifecycle, because it “enhances accuracy, performance and efficiency of computer systems,” as suggested by Kuris (see [0014]).
Vidal does not appear to disclose the following, which is taught in analogous art, Clement: fixing a first model parameter of (e.g., Figs. 1-4B along with associated text, e.g., [0013], A deep learning model may be generated for … generating unit test cases; [0011], freezing the model's parameters; see also Abstract and [0004].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Vidal with the invention of Clement such that certain model parameters are frozen because “Often in fine-tuning, all the parameters of the deep learning model are updated when the model is trained on the downstream task … This becomes impractical when the model utilizes a large number of parameters and there are limited computing resources,” as suggested by Clement (see [0002]).
With respect to claims 3, 11, and 19, Vidal also discloses wherein testing the application using the advanced testing model determines one or more advanced faults of the application (e.g., Figs. 1-6E and associated text, e.g., [0074], FIGS. 6A-6E are examples of screenshots with which a developer or QA engineer might interact when creating and running a test suite for a web application as enabled by the present disclosure; [0077], a failed test run will surface each application state for which content and/or style differences from the baseline are detailed, e.g., specific missing elements, new elements, or changed elements are identified. Upon viewing these results, a user might agree that a true failure is represented.), wherein the one or more advanced faults are related to the first context setting (Id., particularly, a failed test run will surface each application state for which content and/or style differences from the baseline are detailed; see also [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [first context setting]; Abstract, An AI agent guided by reinforcement learning [related to the first context setting] explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite; [0067], The test code embodies the sequence of interactions with the AUT for that flow.).
With respect to claims 5, 13, and 21, Vidal also discloses receiving, by the communications hardware, first context-based testing scenario data (e.g., Figs. 1-5 and associated text, e.g., [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior may be derived from analytics data (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users. A QA engineer would be able to upload analytics data to the test generation platform, and that data [first context-based testing scenario data] could be used to guide and reward the AI agent; see also Abstract, [0022-23], [0044], and [0046].); and training, by the advanced testing circuitry, the scenario generation model using the first context-based testing scenario data (e.g., Figs. 1-5 and associated text, e.g., [0024], A QA engineer would be able to upload analytics data to the test generation platform, and that data could be used to guide and reward the AI agent [training, by the advanced testing circuitry, the scenario generation model]; see also Abstract, [0022-23], [0044], and [0046].).
With respect to claims 7, 15, and 22, Vidal also discloses receiving, by the communications hardware, a second context setting, first context-based testing scenario data, and second context-based testing scenario data (e.g., Figs. 1-5 and associated text, e.g., [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior. Such behavior [second context settings] may be derived from analytics data (e.g., Google Analytics) that represents use of a version of the AUT or another similar application by a population of users. A QA engineer would be able to upload analytics data to the test generation platform, and that data [first and second context-based testing scenario data] could be used to guide and reward the AI agent; see also Abstract, [0022-23], [0044], and [0046].);
training, by the advanced testing circuitry, the scenario generation model using the second context-based testing scenario data (e.g., Figs. 1-5 and associated text, e.g., [0024], A QA engineer would be able to upload analytics data to the test generation platform, and that data [first and second context-based testing scenario data] could be used to guide and reward the AI agent [training, by the advanced testing circuitry, the scenario generation model]; see also Abstract, [0022-23], [0044], and [0046].); and
modifying, by transfer learning circuitry, the scenario generation model based on the first context setting and the first context-based testing scenario data (e.g., Figs. 1-5 and associated text, e.g., [0023], But once a result is captured, the reward for performing that action is reduced or eliminated, correspondingly reducing the agent's tendency to spend resources on that action when it encounters that state (or similar states) again [modifying the scenario generation model]; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [based on the first context setting and the first context-based testing scenario data].), wherein the modified scenario generation model generates the one or more first context-based testing scenarios based on the first context setting and the second context setting (e.g., Figs. 1-4 and associated text, e.g., Abstract, An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states; [0024], some rewards may also or alternatively be based on how the behavior of the AI agent conforms to typical or expected user behavior [based on the first context setting and the second context setting]; [0066], The sequence of interactions for the identified application flow is then selected as a candidate for being the basis for a test in the test suite; [0067], Test code is derived for each test candidate (520). The test code embodies the sequence of interactions with the AUT for that flow, e.g., a collection of test scripts that perform the interactions of the sequence [first context-based testing scenarios]; see also [0044], [0067-68], [0073-74], and claim 1.).
With respect to claims 8 and 16, Vidal also discloses wherein (i) the application comprises one or more service components (e.g., Figs. 1-5 and associated text, e.g., [0036], the AUT is a web application; [0043], a user of an automated test platform initiates exploration of an AUT as described herein by providing access information for the AUT (402) which might include the code of the AUT itself (e.g., uploaded to the platform)), and Kuris further teaches and (ii) testing the application using the preliminary testing model comprises testing the one or more service components (e.g., Figs. 1-4 and associated text, e.g., [0009], an ALM quality assurance effort implements ongoing testing of the computer product throughout the lifecycle of the computer product. The quality assurance efforts can involve identifying customer requirements for a product, identifying use cases, producing a test plan for the product testing using the use cases, executing the test plan, verifying results meet expectations, identifying issues, resolving identified issues, etc; see also [0012] and [0028].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Vidal with the invention of Kuris for the same reason set forth above.
Claims 2, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vidal in view of Kuris and Clement, as applied to claim 1, 9, and 17 above, and further in view of Herrin et al. (US 10565093 B1, hereinafter Herrin).
With respect to claims 2, 10, and 18, Kuris further teaches wherein testing the application using the preliminary testing model determines one or more preliminary faults of the application (e.g., Figs. 1-4 and associated text, e.g., [0009], executing the test plan, verifying results meet expectations, identifying issues, resolving identified issues, etc; [0049], ; tool can capture the results (e.g., passed, failed, in progress, not started, etc.) of performing the test cases and record the results in data store 240; see also [0012] and [0028].), .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Vidal with the invention of Kuris for the same reason set forth above.
Vidal as modified does not appear to disclose the following, which is taught in analogous art, Herrin: wherein the method further comprises: determining, by the preliminary testing circuitry, one or more resolutions for the one or more preliminary faults of the application (e.g., Figs. 1-2 and 4 along with associated text, e.g., col. 11:39-33, The machine learning model may indicate that a particular stage (e.g., test) in the continuous delivery pipeline has failed … The machine learning model may recommend code fixes; see also col. 12:11-20.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Vidal with the invention of Herrin, such that fixes are determined, because it can provide the benefit of “effectively limiting the failures in the continuous delivery pipeline,” as suggested by Herrin (see col. 1:61-62).
Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vidal in view of Kuris and Clement, as applied to claim 3, 11, and 19 above, and further in view of Raman et al. (US 10073763 B1, hereinafter Raman).
With respect to claims 4, 12, and 20, Vidal also discloses generating, by the advanced testing circuitry, resolve the one or more advanced faults (e.g., Figs. 1-5 and associated text, [0077], a failed test run will surface each application state for which content and/or style differences from the baseline are detailed, e.g., specific missing elements, new elements, or changed elements are identified. Upon viewing these results, a user might agree that a true failure is represented and fix the code to address that error); .
Vidal as modified does not appear to disclose the following, which is taught in analogous art, Raman: one or more recommendations to … and providing, by the communications hardware, the one or more recommendations to a user (e.g., Fig. 8 and associated text, e.g., col. 16:44-57, dynamic defect analyzer engine 127 analyzes the defect found through the execution of the sequence test case and other test cases run against the tested application or system. The dynamic defect analyzer engine 127 determines recommendation resolutions … This determined recommendation … are passed users 112 by way of the control center 120.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Vidal with the invention of Raman because users could not only save time by automatically generating a recommended resolution, they could also leverage their experience and expertise to decide if they like the resolution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Durelli et al., “Machine Learning Applied to Software Testing: A Systematic Mapping Study” teaches using machine learning algorithms to create test cases.
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/STEPHEN D BERMAN/ Examiner, Art Unit 2192
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