DETAILED ACTION
Claims 1-23 are pending. Claims 1, 13 and 22 have been amended.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the applicant’s response received on 12/29/2025, for the after-final office action mailed on 12/23/2025.
Examiner’s Notes
Examiner has cited particular columns and line numbers, paragraph numbers, or figures 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 from the applicant, in preparing the responses, to fully consider the references in 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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Response to Arguments
Applicant's arguments filed 07/10/2025 regarding rejection made under 35 U.S.C. § 103 have been fully considered but are not persuasive.
Applicant argues Sathia doesn’t teach “the improvements including at least one of implementing the computer application with higher efficiency, or implementing the computer application with lower complexity,” see applicant’s remarks pp. 22-23. Examiner respectfully disagrees as Sathis teaches the improvements including at least one of implementing the computer application with higher efficiency, or implementing the computer application with lower complexity (see Sathia paragraph [0005], “The present disclosure describes a test system and method that addresses at least some of the shortcomings of conventional testing methods and systems identified above. In some embodiments, systems and methods are provided for ad-hoc batch testing of API tests starting with an API inventory (e.g., a listing of API references), test and/or validation data, and a dynamic framework (e.g., a test engine) for monitoring and testing various APIs (including SOAP and RESTful APIs). The API inventory system is more easily and efficiently developed compared to individual test scripts because it is a higher-level system that is untangled from specific variations of the software code used to write the APIs, and the dynamic testing framework provides several opportunities for optimizing and monitoring API test execution”).
Applicant's arguments filed 12/29/2025 regarding rejection made under 35 U.S.C. § 101 have been fully considered and are persuasive.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-9, 13-19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Subbarayan et al. (US-PGPUB-NO: 2019/0114417 A1) hereinafter Subbarayan, in further view of Ang (US-PGPUB-NO: 2021/0406711 A1), Muguda (US-PGPUB-NO: 2014/0282626 A1), Dennis et al. (US-PAT-NO: 11,315,092 B1) hereinafter Dennis, Anwar et al. (US-PGPUB-NO: 2022/0156633 A1) hereinafter Anwar and Sathianarayanan et al. (US-PGPUB-NO: 2023/0065572 A1) hereinafter Sathia.
As per claim 1, Subbarayan teaches a system for autonomous testing of a computer application, comprising: a non-transitory computer-readable medium configured to store instructions; and at least one processor configured to execute the instructions to perform operations comprising: receiving analytic data associated with at least two application programming interface (API) flows (see Subbarayan paragraph [0033], showing analysis server analyzing API traffic and see Subbarayan paragraph [0060-0061], showing a first set of API calls and a second set of API calls), wherein an API flow of the at least two API flows comprises at least one API (see Subbarayan paragraph [0033], showing API calls which are identified within the API traffic (i.e., API flow)); determining response data of the at least one API by inputting the analytic data to a prediction model determined based on a first machine learning technique, the prediction model configured to predict the response data as output in response to the analytic data as input (see Subbarayan paragraph [0052], showing a machine learning model receiving an API call (i.e., analytic data) from the API flow and being able predict a sequence of API calls (i.e., response data) and “In some such implementations, the ML model 253 can be provided an input of a second API call received from the client device and the ML model 253 can identify the set of parameters including a predicted number of API calls between the first API call and the second API call. In some implementations the set of parameters identified by the ML model 253 can include a predicted time period between the first API call and the second API call”) training the prediction model using historical analytic data and at least one API output of the at least one API of the at least two API flows (see Subbarayan paragraph [0060], showing the training of ML model using a first set of API calls which are before a first time (i.e., from the past, i.e., historical) to predict sequences of API calls to use for a second set of API calls).
Subbarayan teaches training a ML model using a first set of API calls but does not explicitly teach training the prediction model using historical analytic data and at least one API output of the at least one API of the at least two API flows. However, Ang teaches training the prediction model using historical analytic data and at least one API output of the at least one API of the at least two API flows (see Ang paragraph [0062], showing the machine learning model being trained off the data being received related to the web API call which contains historical data and a sequence of call operations (i.e., output)).
Subbarayan and Ang are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies with Ang’s teaching of web application programming interface calls predictions to incorporate using historical data along with the output of the web API calls to better predict the outcomes of API calls being used in the future.
Subbarayan modified with Ang does not explicitly teach determining a subset of the at least two API flows based on the response data and input data representing at least one of a priority level or a risk level of the at least two API flows, wherein the risk level includes data representing a proneness to error. However, Muguda teaches determining a subset of the at least two API flows based on the response data and input data representing at least one of a priority level or a risk level of the at least two API flows, wherein the risk level includes data representing a proneness to error (see Muguda paragraph [0096], showing a priority value being assigned to a data set of a corresponding channels (i.e, API flows), using said data to select one channel (i.e., API flow) over another based on the comparing of the priority value).
Subbarayan, Ang and Muguda are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies and Ang’s teaching of web application programming interface calls predictions with Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic to incorporate letting a subset of API traffic pass based on a risk factor / level in prevent any security issues or malicious code/activity in a system.
Subbarayan, Ang and Muguda do not explicitly teach wherein the at least one API output comprises at least one of message data, error-case data, or error type data to determine inferred response data. However, Dennis teaches wherein the at least one API output comprises at least one of message data, error-case data, or error type data to determine inferred response data (see Dennis [column 11, lines 21-32], showing an API providing a response message that includes data representing an operation code which indicates an outcome of the request message (whether success or failure)).
Subbarayan, Ang, Muguda and Dennis are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions and Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic with Dennis’ teaching of an ATM system that uses APIs to interface between two systems to incorporate API outputs responses being related to the messages and whether or not a successful or unsuccessful communication was established.
Subbarayan, Ang, Muguda and Dennis do not explicitly teach updating at least one parameter of the prediction model based on differences between the response data and the inferred response data to reduce differences; iteratively updating the prediction model based on the differences until a predetermined condition is met. However, Anwar teaches updating at least one parameter of the prediction model based on differences between the response data and the inferred response data to reduce differences (see Anwar paragraph [0054], showing the update parameters that have changed by a certain threshold between consecutive updates (i.e., reponse data and inferred response data); iteratively updating the prediction model based on the differences until a predetermined condition is met and terminating iteratively updating the prediction model when the predetermined condition is met (see Anwar paragraph [0055], showing iteratively updating only if the change in the size of the model parameter is greater than the difference threshold (i.e., update is being made until difference threshold is not greater signifying a condition being met to stop updating).
Subbarayan, Ang, Muguda, Dennis and Anwar are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions, Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic and Dennis’ teaching of an ATM system that uses APIs to interface between two systems with Anwar’s teaching of performing federated learning efficiently by optimizing the size of updates between devices within the system according to model performance to incorporate updating a model based on differences of two datasets.
Subbarayan modified with Ang, Muguda, Dennis and Anwar do not explicitly teach determining, based on the response data predicted by the prediction model, to output the at least one API for performing a test based on at least one of testing data, testing environment, or testing time and executing the subset of the at least two API flows for implementing the computer application using a customizable testing scheme, wherein the executing results is faster for the at least two API flows, the faster adjustments resulting in improvements to the computer application, the improvements including at least one of implementing the computer application with higher efficiency, or implementing the computer application with lower complexity. However, Sathia teaches determining, based on the response data predicted by the prediction model, to output the at least one API for performing a test based on at least one of testing data, testing environment, or testing time (see Sathia paragraph [0086], showing a test payload is generated by determining an API reference which includes a signature or declaration (i.e., testing data) which corresponds to the test) and executing the subset of the at least two API flows for implementing the computer application using a customizable testing scheme, wherein the executing results is faster adjustments for the at least two API flows, the faster adjustments resulting in improvements to the computer application (see Sathia paragraph [0042], showing multiple API tests being conducted based on execution of test scenarios (i.e., testing scheme)) the improvements including at least one of implementing the computer application with higher efficiency, or implementing the computer application with lower complexity (see Sathia paragraph [0005], “The present disclosure describes a test system and method that addresses at least some of the shortcomings of conventional testing methods and systems identified above. In some embodiments, systems and methods are provided for ad-hoc batch testing of API tests starting with an API inventory (e.g., a listing of API references), test and/or validation data, and a dynamic framework (e.g., a test engine) for monitoring and testing various APIs (including SOAP and RESTful APIs). The API inventory system is more easily and efficiently developed compared to individual test scripts because it is a higher-level system that is untangled from specific variations of the software code used to write the APIs, and the dynamic testing framework provides several opportunities for optimizing and monitoring API test execution”).
Subbarayan, Ang, Muguda, Dennis, Anwar and Sathia are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions, Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic, Dennis’ teaching of an ATM system that uses APIs to interface between two systems and Anwar’s teaching of performing federated learning efficiently by optimizing the size of updates between devices within the system according to model performance with Sathia’s teaching of enabling ad-hoc batch testing of APIs without relying on individual test scripts to incorporate the determination of API references in order to obtain testing payloads without the use of testing scripts.
As per claim 2, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein, when the at least one API comprises two or more APIs, the at least two API flows further comprises a sequence of the at least one API (see Subbarayan paragraph [0046], showing the identification of sequences within API transactions) and a scheme for exchanging metadata between the at least one API (see Subbarayan paragraph [0045], showing API traffic data which is associated with raw data log being transmitted via an application layer protocol (i.e., scheme for exchanging metadata).
As per claim 3, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the analytic data comprises at least one of input- field data representing a characteristic of an input field of the at least one API (see Subbarayan paragraph [0044], showing the extraction of data regarding predetermined set data parameters for the API calls which is used to identify specific indicators associated with API traffic such as context of API calls), status data representing whether the at least one API succeeds in the execution, or validity data representing whether an internal conflict exists in the at least one API (see Subbarayan paragraph [0044], showing an indication of compromise corresponding to one or more API).
As per claim 4, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the response data comprises at least one of: message data representing successful or failed execution of the at least one API, error- cause data representing a cause of the failed execution of the at least one API, or error type data representing a type of the cause (see Subbarayan paragraph [0085], showing outputting results regarding errors in connection with an API which is reported in an aggregated summary).
As per claim 5, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the first machine learning technique comprises a supervised learning technique (see Subbarayan paragraph [0050], showing the ML model can be a supervised model).
As per claim 7, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the operations further comprise: determining the at least two API flows in response to receiving an input API flow comprising a plurality of APIs (see Subbarayan paragraph [0033], showing API calls which are identified within the API traffic (i.e., API flow)) and specification data associated with of the plurality of APIs (see Subbarayan paragraph [0044], showing the extraction of data regarding predetermined set data parameters for the API calls which is used to identify specific indicators associated with API traffic such as context of API calls), wherein the plurality of APIs comprises the at least one API (see Subbarayan paragraph [0044], showing an indication of compromise corresponding to one or more API); and outputting the at least two API flows for execution (see Subbarayan paragraph [0053], showing an outlier detector which receives output from the ML model a detects the outliers (i.e., the subset of API traffic) in the API traffic).
As per claim 8, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the at least two API flows comprises all API flows capable of implementing the computer application (see Subbarayan paragraph [0018], showing API calls can all be associated with a single application), and wherein each API flow of the at least two API flows has a different sequence or composition of the plurality of APIs (see Subbarayan paragraph [0018], showing sequence of API calls which can be sent to different destinations).
As per claim 9, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia teaches wherein the operations further comprise: updating the at least two API flows in response to receiving data representing a change in the input API flow (see Subbarayan paragraph [0066], showing the ML model generating an update to the dictionary of symbols / API transactions arising from normal activity).
As per claims 13-19. These are the method claims to system claims 1-4 and 7-9, respectively. Therefore, they are rejected for the same reasons as above.
As per claim 22, this is the computer-readable medium claim to system claim 1. Therefore, it is rejected for the same reasons as above.
Claims 6, 10-12, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Subbarayan (US-PGPUB-NO: 2019/0114417 A1), Ang (US-PGPUB-NO: 2021/0406711 A1), Muguda (US-PGPUB-NO: 2014/0282626 A1), Dennis (US-PAT-NO: 11,315,092 B1), Anwar (US-PGPUB-NO: 2022/0156633 A1) and Sathia (US-PGPUB-NO: 2023/0065572 A1), in further view of Arguelles et al. (US-PAT-NO: 10,452,522 B1) hereinafter Arguelles.
As per claim 6, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia does not explicitly teach wherein the operations further comprise: determining, based on the response data, whether to perform a test on the at least one API; and based on a determination to perform the test on the at least one API, outputting the at least one API for performing the test. However, Arguelles teaches wherein the operations further comprise: determining, based on the response data, whether to perform a test on the at least one API and based on a determination to perform the test on the at least one API (see Arguelles [column 4, lines 66-67 and column 5, lines 1-10], showing testing parameters (i.e., response data) dictating which API calls to test), outputting the at least one API for performing the test (see Arguelles [column 5, lines 5-15], showing testing parameters invoking a single API call).
Subbarayan, Ang, Muguda, Dennis, Anwar, Sathia and Arguelles are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions, Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic, Dennis’ teaching of an ATM system that uses APIs to interface between two systems, Anwar’s teaching of performing federated learning efficiently by optimizing the size of updates between devices within the system according to model performance and Sathia’s teaching of enabling ad-hoc batch testing of APIs without relying on individual test scripts with Arguelles teaches of dynamically generating synthetic data used to test a web service to incorporate invoking an API call to test a web service or an application for better testing coverage or said web service / application.
As per claim 10, Subbarayan modified with Ang, Muguda, Dennis, Anwar, Sathia and Arguelles teaches wherein the operations further comprise: in response to receiving the analytic data and the at least two API flows, generating test data for executing the at least two API flows (see Arguelles [column 5, lines 5-20], showing synthetic data being generated to test a sequence of API calls); and determining execution data of the at least two API flows by executing the at least two API flows using the test data (see Arguelles [column 5, lines 54-61], showing the invocation of API calls with test data based on rules to expose web services), wherein the execution data comprises an execution result of the at least two API flow and at least one API output of the at least one API of the at least two API flows (see Arguelles [column 5, lines 15-25], showing the invocation of API calls with the synthetic data).
Subbarayan, Ang, Muguda, Dennis, Anwar, Sathia and Arguelles are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions, Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic, Dennis’ teaching of an ATM system that uses APIs to interface between two systems, Anwar’s teaching of performing federated learning efficiently by optimizing the size of updates between devices within the system according to model performance and Sathia’s teaching of enabling ad-hoc batch testing of APIs without relying on individual test scripts with Arguelles teaches of dynamically generating synthetic data used to test a web service to incorporate invoking an API call to test a web service or an application for better testing coverage or said web service / application.
As per claims 11, Subbarayan modified with Ang, Muguda, Dennis, Anwar, Sathia and Arguelles teaches wherein the operations further comprise: storing the execution data in a database (see Subbarayan paragraph [0051], showing the ML model outputting databases.
As per claim 12, Subbarayan modified with Ang, Muguda, Dennis, Anwar, Sathia and Arguelles teaches wherein the operations further comprise: in response to receiving the execution data (see Arguelles [column 5, lines 15-25], showing the invocation of API calls with the synthetic data), determining the analytic data by inputting the execution data to a clustering model determined based on a second machine learning technique (see Subbarayan paragraph [0050], showing the use of different machine learning models such as neural network model, random forest model, Bayesian network model, a clustering model and the like).
As per claims 20 and 21, these are the method claims to system claims 10 and 12, respectively. Therefore, they are rejected for the same reasons as above.
Claim 23 are rejected under 35 U.S.C. 103 as being unpatentable over Subbarayan (US-PGPUB-NO: 2019/0114417 A1), Ang (US-PGPUB-NO: 2021/0406711 A1), Muguda (US-PGPUB-NO: 2014/0282626 A1), Dennis (US-PAT-NO: 11,315,092 B1), Anwar (US-PGPUB-NO: 2022/0156633 A1) and Sathia (US-PGPUB-NO: 2023/0065572 A1), in further view of Corbin, II et al. (US-PGPUB-NO: 2018/0211177 A1) hereinafter Corbin.
As per claim 23, Subbarayan modified with Ang, Muguda, Dennis, Anwar and Sathia do not explicitly teach wherein the number of API flows within the subset stabilizes after a predetermined number of iterations, terminating the iterations. However, Corbin teaches wherein the number of API flows within the subset stabilizes after a predetermined number of iterations, terminating the iterations (see Corbin paragraph [0241], showing a stop criterion based on a predetermined value for stabilizing iterations).
Subbarayan, Ang, Muguda, Dennis, Anwar, Sathia and Corbin are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Subbarayan’s teaching of inputting an API call to a machine learning model in order to predict anomalies, Ang’s teaching of web application programming interface calls predictions, Muguda’s teaching of application programing interface traffic and caching and enabling equitable bandwidth distribution of the API traffic, Dennis’ teaching of an ATM system that uses APIs to interface between two systems, Anwar’s teaching of performing federated learning efficiently by optimizing the size of updates between devices within the system according to model performance and Sathia’s teaching of enabling ad-hoc batch testing of APIs without relying on individual test scripts with Corbin’s teaching of generating Bayes net content to incorporate stabilization of data after a predetermined amount of iterations in order to optimize the API traffic being taught in Subbarayan and Muguda.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lincoln et al. (US-PGPUB-NO: 2019/0243692 A1) teaches application programming interface call data analyzed for a user to identify a relationship between API input data and API output data.
Djosic et al. (US-PGPUB-NO: 2021/0152555 A1) teaches detecting unauthorized activity.
Sriharsha (US-PGPUB-NO: 2022/0036177 A1) teaches a data field extraction by a data intake and query system.
Roy et al. (US-PAT-NO: 10,740,164 B1) teaches API assessment for a plurality of APIs and automatically healing risk identified APIs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LENIN PAULINO whose telephone number is (571)270-1734. The examiner can normally be reached Week 1: Mon-Thu 7:30am - 5:00pm Week 2: Mon-Thu 7:30am - 5:00pm and Fri 7:30am - 4:00pm EST.
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/LENIN PAULINO/Examiner, Art Unit 2197
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197