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 .
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1:
line 15: “the plurality of processes” lacks proper antecedent basis.
Claim 4:
line 2: “the category of processes” lacks proper antecedent basis.
Claim 9:
line 1: insert --of-- before “the plurality of transactions”.
Claim 10:
line 18: “the plurality of processes” lacks proper antecedent basis.
Claim 12:
line 3: “the category of processes” lacks proper antecedent basis.
Claim 13:
line 1: insert --wherein-- before “one or more transactions”.
Claim 15:
line 15: “the plurality of processes” lacks proper antecedent basis.
Claim 17:
line 4: “the category of processes” lacks proper antecedent basis.
Claim 18:
lines 1-2: “the one or more transactions” lacks proper antecedent basis.
Claims 2-8, 11, 14, 16, 19, and 20 are objected to for being dependent on claims 1, 10, and 15.
Appropriate correction is required.
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 (i.e., changing from AIA to pre-AIA ) 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, 2, 7, 8, 9, 10, 11, 14, 15, 16, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al. (US20230077510, Goel hereinafter) in view of Rinke et al. (US20170212937, Rinke hereinafter) and Sen (US20250077396).
Regarding claim 1, Goel discloses: A computer implemented method performing automated software test generation (Goel, see paragraph [0021], “The artificial intelligence-based autonomous continuous testing platform 104 can function to generate and execute test scripts using artificial intelligence and/or machine learning (collectively referred to as either artificial intelligence or machine learning) continuously and autonomously…”) comprising:
retrieving (“obtain logs 244”), by a first software component (“log extraction engine 208”), from one or more applications (“application instances”), a plurality of process flows (“ERP transaction logs”) (Goel, see paragraph [0041], “The log extraction engine 208 is intended to represent an engine that obtain logs 244 from remote systems. In a specific implementation, the logs 244 can include transaction logs, such as ERP transaction logs.”), (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances…”);
receiving, from the (Goel, see paragraph [0045], “…the model-based test analytics engine 212 can generate the autonomous test scripts 256 based on one or more custom configuration models 252 and one or more user journeys 254 (discussed further elsewhere herein) to be tested (e.g., autonomously tested)…”); and
executing the plurality of test scripts in a software test automation system to test the plurality of processes in the one or more applications (Goel, see paragraph [0056], “…the test agent engine 218 executes the autonomous test scripts 256 to test one or more user journeys 254, although autonomous test scripts 256 can test other types of software and/or hardware.”).
Goel does not appear to distinctly disclose:
extracting, by a second software component, task data for each of the plurality of process flows, said extracting comprising
retrieving a process identification for each of the plurality of process flows and
using the process identifications to retrieve the task data from the applications,
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows;
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Rinke discloses:
extracting (“derive”), by a second software component (“process sensor”), task data (“process data”) for each of the plurality of process flows (“executed processes”) (Rinke, see paragraph [0080], “Thus, a process sensor is adapted to derive process data from the raw data comprising the digital traces of the executed processes, and to generate from the derived process data the event log.”), said extracting comprising
retrieving a process identification for each of the plurality of process flows (Rinke, see paragraph [0135], “In this more complex process sensor three tables are involved: the invoice table “BSEG” and two change log tables “CDPOS” and “CDHDR”. Since the unique ID is retrieved from the invoice table “BSEG”, the process step from the table “CDPOS” and the timestamp from the table “CDHDR” these three tables have to be related within this process sensor.”) and
using the process identifications to retrieve the task data from the applications (Rinke, see paragraph [0100], “…The consecutive join over (possibly) multiple tables from the unique identifier Δ to the process step table Π allows to retrieve the process step name π from a transformation using the fields from all joined tables.”), and
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows (Rinke, see paragraph [0077], “A set of process steps which a process element can pass during its process. Not every step has to be fulfilled by the process element.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include extracting, by a second software component, task data for each of the plurality of process flows, retrieving a process identification for each of the plurality of process flows, using the process identifications to retrieve the task data from the applications, and wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows as taught by Rinke for the result of ensuring complete task data for each process flow.
Goel as modified does not appear to distinctly disclose:
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Sen discloses:
converting the task data into one or more JSON objects (Sen, see paragraph [0084], “…the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”);
sending the one or more JSON objects to a large language model (Sen, see paragraph [0127], “…in 1011, the method may include receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device. In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”), (Sen, see paragraph [0042], “…the GenAI model may be a large language model (LLM), such as a multimodal large language model…”); and
receiving, from the large language model, a plurality of test scripts (Sen, see paragraph [0048], “…the GenAI model 120 may generate automation scripts for executing software test cases in an automated manner…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include converting the task data into one or more JSON objects, sending the one or more JSON objects to a large language model, and receiving, from the large language model, a plurality of test scripts as taught by Sen for the result of turning the extracted process data into runnable test scripts.
Regarding claim 2, Goel discloses:
wherein the application is part of an enterprise application software system (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances. For example, the remote systems 106 can include Oracle systems, SAP systems, Salesforce systems, business intelligence systems, custom systems, and/or a combination thereof…”).
Regarding claim 7, Goel as modified does not appear to distinctly disclose:
storing a plurality of test automation scripts in a repository;
integrating the test automation scripts in the repository into the large language model; and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts using the large language model.
However, Sen discloses:
storing a plurality of test automation scripts in a repository (Sen, see paragraph [0079], “…the host platform 520 also includes a script database 528 that stores automation scripts that enable the software tests to be performed/executed in an automated manner.”);
integrating the test automation scripts in the repository into the large language model (Sen, see paragraph [0091], “…the GenAI model 624 may be trained on a large corpus of automation scripts, software tests, scripting best practices, and the like, and can receive a description of a software test, such as the requirements of the test, and generate a list of features to be performed by the software test.”), (Sen, see paragraph [0093], “…the GenAI model 624 may ingest training materials from a repository of software test s 626, a database of test data 627, a repository of automation scripts 628, or the like, as well as the requirements input by the user on the user interface 612.”); and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts using the large language model (Sen, see paragraph [0127], “…In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include storing a plurality of test automation scripts in a repository, integrating the test automation scripts in the repository into the large language model, and processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts using the large language model as taught by Sen for the result of allowing scripts to run in the existing automation system without having to be rewritten.
Regarding claim 8, Goel discloses:
wherein extracting the task data for each of the plurality of process flows comprises communicating, by the second software component, with a data mining software system (Goel, see paragraph [0088], “…The artificial intelligence-based autonomous continuous testing platform 502 can create thousands of autonomous tests in minutes with its transaction logs and configuration mining technology to highlight deviations and recommendations from optimal tests with gap analysis performed by the artificial intelligence-based autonomous continuous testing platform 502.”), (Goel, see paragraph [0090], “…this input 1.1 can be sent to a data transformation layer 508 where this data can be processed across multiple processes, such as O2C, P2P, R2R, and/or the like, via a process transformation configuration repository 510 via ERP specific XML files/queries…”),
wherein the data mining software system is configured to receive data from the one or more applications (Goel, see paragraph [0089], “…The log extraction connector 504 can obtain the data extracted through an XML file/query from a data extraction connector repository 505 of the artificial intelligence-based autonomous continuous testing platform 502, and connects through separate ERP specific “LEC's,” such as SAP LEC/SFDC LEC/Custom LEC, and extracts the ERP transaction logs.”).
Regarding claim 9, Goel discloses:
wherein at least one of the plurality of test scripts causes the user interface element to be invoked during a test on a backend server running the one or more applications.
(Goel, see paragraph [0023], “…Functionality of the remote systems 106 can be performed by one or more servers (e.g., a cloud-based server) and/or other computing devices. When referring to the remote systems 106 herein, such a reference can refer to the remote system itself (e.g., the server device and/or server software) and/or any applications executing on the remote system 106…”), (Goel, see paragraph [0059], “…the test agent engine 218 can function to execute tests and interact with application technology layer/UI layer and perform the required actions on the application as per the test case and capture all the logs and the execution data and then converts it into the test reports which can be viewed in different relevant formats by customers…”).
Goel does not appear to distinctly disclose:
wherein one or more the plurality of transactions specify a user interface element. However, Sen discloses:
wherein one or more the plurality of transactions specify a user interface element (Sen, see paragraph [0051], “…the test case is a test of four separate text boxes on a graphical user interface of a software application that receives four input characters, each corresponding to a 16-digit payment card number.”), (Sen, see paragraph [0052], “The test case also includes additional components, including four input activities to be performed during testing, as indicated in the four cells in column 154…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein one or more the plurality of transactions specify a user interface element as taught by Sen for the result of allowing the user to test applications.
Regarding claim 10, Goel discloses: A computer system comprising: at least one processor; at least one non-transitory computer-readable medium storing computer-executable instructions that, when executed by the at least one processor, cause the computer system to perform a method (Goel, see paragraph [0010], “…a computer system will include a processor, memory, non-volatile storage, and an interface…”), (Goel, see paragraph [0016], “…An engine can include hardware, firmware, or software embodied in a computer-readable medium for execution by the processor…”) comprising:
retrieving, by a first software component, from one or more applications, a plurality of process flows (Goel, see paragraph [0041], “The log extraction engine 208 is intended to represent an engine that obtain logs 244 from remote systems. In a specific implementation, the logs 244 can include transaction logs, such as ERP transaction logs.”), (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances…”);
receiving, from the (Goel, see paragraph [0045], “…the model-based test analytics engine 212 can generate the autonomous test scripts 256 based on one or more custom configuration models 252 and one or more user journeys 254 (discussed further elsewhere herein) to be tested (e.g., autonomously tested)…”); and
executing the plurality of test scripts in a software test automation system to test the plurality of processes in the one or more applications (Goel, see paragraph [0056], “…the test agent engine 218 executes the autonomous test scripts 256 to test one or more user journeys 254, although autonomous test scripts 256 can test other types of software and/or hardware.”).
Goel does not appear to distinctly disclose:
extracting, by a second software component, task data for each of the plurality of process flows, said extracting comprising
retrieving a process identification for each of the plurality of process flows and
using the process identifications to retrieve the task data from the applications,
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows;
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Rinke discloses:
extracting (“derive”), by a second software component (“process sensor”), task data (“process data”) for each of the plurality of process flows (“executed processes”) (Rinke, see paragraph [0080], “Thus, a process sensor is adapted to derive process data from the raw data comprising the digital traces of the executed processes, and to generate from the derived process data the event log.”), said extracting comprising
retrieving a process identification for each of the plurality of process flows (Rinke, see paragraph [0135], “In this more complex process sensor three tables are involved: the invoice table “BSEG” and two change log tables “CDPOS” and “CDHDR”. Since the unique ID is retrieved from the invoice table “BSEG”, the process step from the table “CDPOS” and the timestamp from the table “CDHDR” these three tables have to be related within this process sensor.”) and
using the process identifications to retrieve the task data from the applications (Rinke, see paragraph [0100], “…The consecutive join over (possibly) multiple tables from the unique identifier Δ to the process step table Π allows to retrieve the process step name π from a transformation using the fields from all joined tables.”), and
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows (Rinke, see paragraph [0077], “A set of process steps which a process element can pass during its process. Not every step has to be fulfilled by the process element.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include extracting, by a second software component, task data for each of the plurality of process flows, retrieving a process identification for each of the plurality of process flows, using the process identifications to retrieve the task data from the applications, and wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows as taught by Rinke for the result of ensuring complete task data for each process flow.
Goel as modified does not appear to distinctly disclose:
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Sen discloses:
converting the task data into one or more JSON objects (Sen, see paragraph [0084], “…the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”);
sending the one or more JSON objects to a large language model (Sen, see paragraph [0127], “…in 1011, the method may include receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device. In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”), (Sen, see paragraph [0042], “…the GenAI model may be a large language model (LLM), such as a multimodal large language model…”); and
receiving, from the large language model, test scripts (Sen, see paragraph [0048], “…the GenAI model 120 may generate automation scripts for executing software test cases in an automated manner…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include converting the task data into one or more JSON objects, sending the one or more JSON objects to a large language model, and receiving, from the large language model, test scripts as taught by Sen for the result of turning the extracted process data into runnable test scripts.
Regarding claim 11, Goel discloses:
wherein the application is part of an enterprise application software system (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances. For example, the remote systems 106 can include Oracle systems, SAP systems, Salesforce systems, business intelligence systems, custom systems, and/or a combination thereof…”).
Regarding claim 14, Goel as modified does not appear to distinctly disclose:
storing a plurality of test automation scripts in a repository;
integrating the test automation scripts in the repository into the LLM; and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts.
However, Sen discloses:
storing a plurality of test automation scripts in a repository (Sen, see paragraph [0079], “…the host platform 520 also includes a script database 528 that stores automation scripts that enable the software tests to be performed/executed in an automated manner.”);
integrating the test automation scripts in the repository into the LLM (Sen, see paragraph [0091], “…the GenAI model 624 may be trained on a large corpus of automation scripts, software tests, scripting best practices, and the like, and can receive a description of a software test, such as the requirements of the test, and generate a list of features to be performed by the software test.”), (Sen, see paragraph [0093], “…the GenAI model 624 may ingest training materials from a repository of software test s 626, a database of test data 627, a repository of automation scripts 628, or the like, as well as the requirements input by the user on the user interface 612.”); and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts (Sen, see paragraph [0127], “…In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include storing a plurality of test automation scripts in a repository, integrating the test automation scripts in the repository into the LLM, and processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts as taught by Sen for the result of allowing scripts to run in the existing automation system without having to be rewritten.
Regarding claim 15, Goel discloses: A non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one processor, perform a method (Goel, see paragraph [0010], “…a computer system will include a processor, memory, non-volatile storage, and an interface…”), (Goel, see paragraph [0016], “…An engine can include hardware, firmware, or software embodied in a computer-readable medium for execution by the processor…”) comprising:
retrieving, by a first software component, from one or more applications, a plurality of process flows (Goel, see paragraph [0041], “The log extraction engine 208 is intended to represent an engine that obtain logs 244 from remote systems. In a specific implementation, the logs 244 can include transaction logs, such as ERP transaction logs.”), (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances…”);
receiving, from the (Goel, see paragraph [0045], “…the model-based test analytics engine 212 can generate the autonomous test scripts 256 based on one or more custom configuration models 252 and one or more user journeys 254 (discussed further elsewhere herein) to be tested (e.g., autonomously tested)…”); and
executing the plurality of test scripts in a software test automation system to test the plurality of processes in the one or more applications (Goel, see paragraph [0056], “…the test agent engine 218 executes the autonomous test scripts 256 to test one or more user journeys 254, although autonomous test scripts 256 can test other types of software and/or hardware.”).
Goel does not appear to distinctly disclose:
extracting, by a second software component, task data for each of the plurality of process flows, said extracting comprising
retrieving a process identification for each of the plurality of process flows and
using the process identifications to retrieve the task data from the applications,
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows;
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Rinke discloses:
extracting (“derive”), by a second software component (“process sensor”), task data (“process data”) for each of the plurality of process flows (“executed processes”) (Rinke, see paragraph [0080], “Thus, a process sensor is adapted to derive process data from the raw data comprising the digital traces of the executed processes, and to generate from the derived process data the event log.”), said extracting comprising
retrieving a process identification for each of the plurality of process flows (Rinke, see paragraph [0135], “In this more complex process sensor three tables are involved: the invoice table “BSEG” and two change log tables “CDPOS” and “CDHDR”. Since the unique ID is retrieved from the invoice table “BSEG”, the process step from the table “CDPOS” and the timestamp from the table “CDHDR” these three tables have to be related within this process sensor.”) and
using the process identifications to retrieve the task data from the applications (Rinke, see paragraph [0100], “…The consecutive join over (possibly) multiple tables from the unique identifier Δ to the process step table Π allows to retrieve the process step name π from a transformation using the fields from all joined tables.”), and
wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows (Rinke, see paragraph [0077], “A set of process steps which a process element can pass during its process. Not every step has to be fulfilled by the process element.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include extracting, by a second software component, task data for each of the plurality of process flows retrieving a process identification for each of the plurality of process flows, using the process identifications to retrieve the task data from the applications, and wherein the task data comprises, for each process flow, a plurality of transactions forming a particular process flow of the plurality of process flows as taught by Rinke for the result of ensuring complete task data for each process flow.
Goel as modified does not appear to distinctly disclose:
converting the task data into one or more JSON objects;
sending the one or more JSON objects to a large language model; and
However, Sen discloses:
converting the task data into one or more JSON objects (Sen, see paragraph [0084], “…the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”);
sending the one or more JSON objects to a large language model (Sen, see paragraph [0127], “…in 1011, the method may include receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device. In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”), (Sen, see paragraph [0042], “…the GenAI model may be a large language model (LLM), such as a multimodal large language model…”); and
receiving, from the large language model, test scripts (Sen, see paragraph [0048], “…the GenAI model 120 may generate automation scripts for executing software test cases in an automated manner…”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include converting the task data into one or more JSON objects, sending the one or more JSON objects to a large language model, and receiving, from the large language model, test scripts as taught by Sen for the result of turning the extracted process data into runnable test scripts.
Regarding claim 16, Goel discloses:
wherein the application is part of an enterprise application software system (Goel, see paragraph [0023], “The remote systems 106 can function to execute application instances, such as Enterprise Resource Planning (ERP) application instances. For example, the remote systems 106 can include Oracle systems, SAP systems, Salesforce systems, business intelligence systems, custom systems, and/or a combination thereof…”).
Regarding claim 19, Goel as modified does not appear to distinctly disclose:
storing a plurality of test automation scripts in a repository;
integrating the test automation scripts in the repository into the LLM; and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts.
However, Sen discloses:
storing a plurality of test automation scripts in a repository (Sen, see paragraph [0079], “…the host platform 520 also includes a script database 528 that stores automation scripts that enable the software tests to be performed/executed in an automated manner.”);
integrating the test automation scripts in the repository into the LLM (Sen, see paragraph [0091], “…the GenAI model 624 may be trained on a large corpus of automation scripts, software tests, scripting best practices, and the like, and can receive a description of a software test, such as the requirements of the test, and generate a list of features to be performed by the software test.”), (Sen, see paragraph [0093], “…the GenAI model 624 may ingest training materials from a repository of software test s 626, a database of test data 627, a repository of automation scripts 628, or the like, as well as the requirements input by the user on the user interface 612.”); and
processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts (Sen, see paragraph [0127], “…In 1012, the method may include generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include storing a plurality of test automation scripts in a repository, integrating the test automation scripts in the repository into the LLM, and processing the one or more JSON objects based, at least in part, on the plurality of test automation scripts as taught by Sen for the result of allowing scripts to run in the existing automation system without having to be rewritten.
Regarding claim 20, Goel discloses:
wherein extracting the task data for each of the plurality of process flows comprises communicating, by the second software component, with a data mining software system (Goel, see paragraph [0088], “…The artificial intelligence-based autonomous continuous testing platform 502 can create thousands of autonomous tests in minutes with its transaction logs and configuration mining technology to highlight deviations and recommendations from optimal tests with gap analysis performed by the artificial intelligence-based autonomous continuous testing platform 502.”), (Goel, see paragraph [0090], “…this input 1.1 can be sent to a data transformation layer 508 where this data can be processed across multiple processes, such as O2C, P2P, R2R, and/or the like, via a process transformation configuration repository 510 via ERP specific XML files/queries…”),
wherein the data mining software system is configured to receive data from the one or more applications (Goel, see paragraph [0089], “…The log extraction connector 504 can obtain the data extracted through an XML file/query from a data extraction connector repository 505 of the artificial intelligence-based autonomous continuous testing platform 502, and connects through separate ERP specific “LEC's,” such as SAP LEC/SFDC LEC/Custom LEC, and extracts the ERP transaction logs.”).
Claims 3, 4, 5, 6, 12, 13, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Goel, Rinke, and Sen as applied to claim 1, 10, and 15 above, and further in view of Kushnir et al. (US20240362157, Kushnir hereinafter).
Regarding claim 3, Goel as modified does not appear to distinctly disclose:
determining a top N process flows of the plurality of process flows.
However, Kushnir discloses:
determining a top N process flows of the plurality of process flows (Kushnir, see paragraph [0149], “…The menu item 1031 can be a drop down menu 1101 that provides selectable menu options, such as all transactions, top ten (10) transactions, and top twenty (20) transactions…”), (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include determining a top N process flows of the plurality of process flows as taught by Kushnir for the result of limiting the building and running of tests for only the most relevant process flows.
Regarding claim 4, Goel as modified does not appear to distinctly disclose:
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes.
However, Kushnir discloses:
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes (Kushnir, see paragraph [0026], “…The determination engine can enable the graphic user interface heatmap to depict the most used transaction in the software system along with test automation details…”), (Kushnir, see paragraph [0140], “Dialogs/executions as a weight can include a number of executions of a transaction. In this regard, a greater weight can be given to a transaction in accordance with a number of executions…”), (Kushnir, see paragraph [0151], “The heatmap 1001 can include a first process 1040 with one or more elements or shapes 1041, 1042, 1043, 1044, and 1045 (e.g., squares) representing transactions and/or tests thereof…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes as taught by Kushnir for the result of prioritizing the building and running of tests for only the most relevant process flows over less relevant process flows.
Regarding claim 5, Goel discloses:
wherein the plurality of process flows are variant processes for performing a particular job (Goel, see paragraph [0048], “…the model-based test analytics engine 212 can function to utilize neural networks and/or other deep learning technology to combine data and generate relevant user journeys (RUJs) that need to be tested autonomously along with their multiple variants and data combinations…”).
Regarding claim 6, Goel does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
the transaction activities comprising a process identifications, an activity name, a code fragment, and a transaction name, and
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Rinke discloses:
the transaction activities comprising a process identifications, an activity name, (Rinke, see paragraph [0097], “With this requirement a process sensor S can be defined as a tuple of the data set mentioned before with a unique identifier I, a process step P, an order attribute T and further attributes V…), (Rinke, see paragraph [0098], “This n-tuple is evaluated during the execution of the sensor S and the results consists of an event log with these columns.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include the transaction activities comprising a process identifications and an activity name, as taught by Rinke for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Sen discloses:
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions (Sen, see paragraph [0084], “…In response, the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions as taught by Sen for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities, and
However, Kushnir discloses:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”), (Kushnir, see paragraph [0140], “…the determination engine can determine ‘how many “dialog steps” were performed, per one dialog step of one screen in SAP systems…”), and
the transaction activities comprising (Kushnir, see paragraph [0142], “…the determination engine can determine development objects of individual parts of advanced business application programming (ABAP) application, such as screens, functional modules, tables, domain, data elements, fields, classes…”), (Kushnir, see paragraph [0145], “…The test cases can be identified using transaction labels associated therewith. For instance, the test cases can be identified using SAP transaction or SAP programs labels.”), (Kushnir, see paragraph [0156], “The second process 1050 of the heatmap 1001 of FIG. 10 can be divided as follows: the element 1051 can provide a change purchase order at a 6% size, the element 1052 can provide a cash journal at a 3% size, and the element 1053 can provide an account line at an 3% size to total a 12% area of the heatmap 1001…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities and the transaction activities comprising a code fragment and a transaction name as taught by Kushnir for the result of allowing the script to point to the appropriate transaction.
Regarding claim 12, Goel discloses:
wherein the plurality of process flows are variant processes for performing a particular job (Goel, see paragraph [0048], “…the model-based test analytics engine 212 can function to utilize neural networks and/or other deep learning technology to combine data and generate relevant user journeys (RUJs) that need to be tested autonomously along with their multiple variants and data combinations…”).
Goel does not appear to distinctly disclose:
determining a top N process flows of the plurality of process flows,
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes.
However, Kushnir discloses:
determining a top N process flows of the plurality of process flows (Kushnir, see paragraph [0149], “…The menu item 1031 can be a drop down menu 1101 that provides selectable menu options, such as all transactions, top ten (10) transactions, and top twenty (20) transactions…”), (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”),
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes (Kushnir, see paragraph [0026], “…The determination engine can enable the graphic user interface heatmap to depict the most used transaction in the software system along with test automation details…”), (Kushnir, see paragraph [0140], “Dialogs/executions as a weight can include a number of executions of a transaction. In this regard, a greater weight can be given to a transaction in accordance with a number of executions…”), (Kushnir, see paragraph [0151], “The heatmap 1001 can include a first process 1040 with one or more elements or shapes 1041, 1042, 1043, 1044, and 1045 (e.g., squares) representing transactions and/or tests thereof…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include determining a top N process flows of the plurality of process flows and wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes as taught by Kushnir for the result of prioritizing the building and running of tests for only the most relevant process flows over less relevant process flows.
Regarding claim 13, Goel does not appear to distinctly disclose:
one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
the transaction activities comprising a process identifications, an activity name, a code fragment, and a transaction name, and
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Rinke discloses:
the transaction activities comprising a process identifications, an activity name, (Rinke, see paragraph [0097], “With this requirement a process sensor S can be defined as a tuple of the data set mentioned before with a unique identifier I, a process step P, an order attribute T and further attributes V…), (Rinke, see paragraph [0098], “This n-tuple is evaluated during the execution of the sensor S and the results consists of an event log with these columns.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include the transaction activities comprising a process identifications and an activity name as taught by Rinke for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Sen discloses:
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions (Sen, see paragraph [0084], “…In response, the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions as taught by Sen for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities, and
However, Kushnir discloses:
one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”), (Kushnir, see paragraph [0140], “…the determination engine can determine ‘how many “dialog steps” were performed, per one dialog step of one screen in SAP systems…”), and
the transaction activities comprising (Kushnir, see paragraph [0142], “…the determination engine can determine development objects of individual parts of advanced business application programming (ABAP) application, such as screens, functional modules, tables, domain, data elements, fields, classes…”), (Kushnir, see paragraph [0145], “…The test cases can be identified using transaction labels associated therewith. For instance, the test cases can be identified using SAP transaction or SAP programs labels.”), (Kushnir, see paragraph [0156], “The second process 1050 of the heatmap 1001 of FIG. 10 can be divided as follows: the element 1051 can provide a change purchase order at a 6% size, the element 1052 can provide a cash journal at a 3% size, and the element 1053 can provide an account line at an 3% size to total a 12% area of the heatmap 1001…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities and the transaction activities comprising a code fragment and a transaction name as taught by Kushnir for the result of allowing the script to point to the appropriate transaction.
Regarding claim 17, Goel discloses:
wherein the plurality of process flows are variant processes for performing a particular job (Goel, see paragraph [0048], “…the model-based test analytics engine 212 can function to utilize neural networks and/or other deep learning technology to combine data and generate relevant user journeys (RUJs) that need to be tested autonomously along with their multiple variants and data combinations…”).
Goel does not appear to distinctly disclose:
determining a top N process flows of the plurality of process flows,
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes.
However, Kushnir discloses:
determining a top N process flows of the plurality of process flows (Kushnir, see paragraph [0149], “…The menu item 1031 can be a drop down menu 1101 that provides selectable menu options, such as all transactions, top ten (10) transactions, and top twenty (20) transactions…”), (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”),
wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes (Kushnir, see paragraph [0026], “…The determination engine can enable the graphic user interface heatmap to depict the most used transaction in the software system along with test automation details…”), (Kushnir, see paragraph [0140], “Dialogs/executions as a weight can include a number of executions of a transaction. In this regard, a greater weight can be given to a transaction in accordance with a number of executions…”), (Kushnir, see paragraph [0151], “The heatmap 1001 can include a first process 1040 with one or more elements or shapes 1041, 1042, 1043, 1044, and 1045 (e.g., squares) representing transactions and/or tests thereof…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include determining a top N process flows of the plurality of process flows and wherein the top N process flows of the plurality of process flows are the top N most frequently used process flows for the category of processes as taught by Kushnir for the result of prioritizing the building and running of tests for only the most relevant process flows over less relevant process flows.
Regarding claim 18, Goel does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
the transaction activities comprising a process identifications, an activity name, a code fragment, and a transaction name, and
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Rinke discloses:
the transaction activities comprising a process identifications, an activity name, (Rinke, see paragraph [0097], “With this requirement a process sensor S can be defined as a tuple of the data set mentioned before with a unique identifier I, a process step P, an order attribute T and further attributes V…), (Rinke, see paragraph [0098], “This n-tuple is evaluated during the execution of the sensor S and the results consists of an event log with these columns.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include the transaction activities comprising a process identifications and an activity name as taught by Rinke for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities,
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions.
However, Sen discloses:
wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions (Sen, see paragraph [0084], “…In response, the system described herein inputs the steps on the user interface 512 into a new software test 552, which includes the steps shown on the user interface, including the step 542, the step 544, the step 546, and the step 548, as well as the order in which the sequence of steps are in. For example, the host platform 520 may generate a document, an XML file, a JSON, etc., describing the steps.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein the one or more JSON objects specify the process identifications, the activity, the code fragment, and the transaction name for the plurality of transactions as taught by Sen for the result of allowing the script to point to the appropriate transaction.
Goel as modified does not appear to distinctly disclose:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities, and
However, Kushnir discloses:
wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities (Kushnir, see paragraph [0151], “…from the transformed data and test results, each element or shape of any process 1040, 1050, and 1060 (e.g., representing a transaction/process of SAP system instances) includes one or more credentials, such as a size (e.g., final rating) and a color.”), (Kushnir, see paragraph [0140], “…the determination engine can determine ‘how many “dialog steps” were performed, per one dialog step of one screen in SAP systems…”), and
the transaction activities comprising (Kushnir, see paragraph [0142], “…the determination engine can determine development objects of individual parts of advanced business application programming (ABAP) application, such as screens, functional modules, tables, domain, data elements, fields, classes…”), (Kushnir, see paragraph [0145], “…The test cases can be identified using transaction labels associated therewith. For instance, the test cases can be identified using SAP transaction or SAP programs labels.”), (Kushnir, see paragraph [0156], “The second process 1050 of the heatmap 1001 of FIG. 10 can be divided as follows: the element 1051 can provide a change purchase order at a 6% size, the element 1052 can provide a cash journal at a 3% size, and the element 1053 can provide an account line at an 3% size to total a 12% area of the heatmap 1001…”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an artificial intelligence-based system for autonomous testing as taught by Goel to include wherein one or more transactions of the plurality of transactions forming a particular process comprise a plurality of transaction activities and the transaction activities comprising a code fragment and a transaction name as taught by Kushnir for the result of allowing the script to point to the appropriate transaction.
Conclusion
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Joshua Tran whose telephone number is (571)272-5460.
The examiner can normally be reached on M-F 9-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Hyung Sough can be reached on (571)272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA TRAN/
Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
