DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed June 2nd, 2026, has been entered. Claims 1 and 3-9 remain
pending in the application. Claim 2 has been canceled. Applicants’ amendments to the claims have overcome each 112(b) rejection previously set forth in the non-final action mailed March 5th, 2026. Examiner further acknowledges applicants’ amendment to the claims to no longer invoke 35 U.S.C. 112(f) in claim 8.
Claim Objections
Claim 3 is objected to because of the following informalities: Claim 3 recites "wherein the adapting the at least one test case.." Examiner suggests amending to “wherein adapting the at least one test case..”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1, 8 and 9 recite(s):
ascertaining a deviation specification which indicates a difference between at least two versions of the software including a current version of the software and a previous version of the software;
ascertaining an error specification about an error that occurred during an execution of the at least one software test of the software, wherein the error occurred during execution of the at least one software test in the current version of the software;
carrying out an evaluation of the deviation specification and the error specification with respect to a correlation of the error that has occurred and the difference between the at least two versions of the software;
generating an adaptation specification based on the carried out evaluation, wherein the adaptation specification specifies at least one item of information for adapting at least one test case and/or test code of the at least one software test to eliminate the error caused by the difference between the at least two versions of the software;
and adapting the at least one test case and/or test code of the at least one software test based on the adaptation specification, wherein the adapting includes eliminating the error in the at least one software test during an ongoing execution of the at least one software test.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 1 is a method
Yes. Claim 8 is a machine
Yes. Claim 9 is a manufacture
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The limitation of "evaluation", as drafted in #3 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "method for automated software tests", "device for data processing", or "non-transitory computer-readable medium", nothing in the claim element precludes the step from being performed by a person on paper.
The limitation of "generating an adaption", as drafted in #4 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "method for automated software tests", "device for data processing", or "non-transitory computer-readable medium", nothing in the claim element precludes the step from being performed by a person on paper.
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The "adapting" limitation in #5 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "adapting... based on the adaption specification" in the context of this claim encompasses merely changing or modifying test code according to suggestions made by an evaluation. See in the MPEP §§2106.05(f).
The "ascertain" limitations in #1-2 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “ascertain” in the context of this claim encompasses mere data gathering. See in the MPEP §§ 2106.05(g).
Additionally, the claims recite the following additional element:
device for data processing, non-transitory computer readable storage medium
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Additionally, with regards to #1-2 above, per MPEP 2106.05(d)(ll), the courts have recognized the following computer function(s) as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim(s) 3 recite(s):
wherein the adapting the at least one test case and/or test code of the software test is carried out at least partially automatically based on the adaptation specification, wherein the adaptation adapting includes removing and/or adapting the at least one test case and/or test code of the software test which refers to associated with elements of the current version of the software that have been removed from the previous version of the software, wherein the elements include to-be-tested functions and/or elements of a user interface and/or objects of the software.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 3 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #6 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "adapting the at least one test case and/or test code" in the context of this claim encompasses merely configuring software. See in the MPEP §§2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 4 recite(s):
wherein a classification of the difference between the at least two versions is carried out in the evaluation, for detecting code changes in the software that are likely to cause the error to occur
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 4 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The limitation, as drafted in #7 above, under its broadest reasonable interpretation, covers performance of the mind, but for generic computer parts. That is, other than reciting "a method for automated adaption", nothing in the claim element precludes the step from being performed by a person on paper.
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 5 recite(s):
wherein the evaluation and/or the generation of the adaptation specification is based at least in part on machine learning.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 5 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #8 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, "the evaluation and/or generation of the adaption specification is based at least in part on machine learning" in the context of this claim encompasses merely using a machine learning to complete a task. See in the MPEP §§2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Claim(s) 6 recite(s):
ascertaining training data, wherein the training data include example deviation specifications about a respective difference between at least two versions of the software and example error specifications about a respective error during execution of the software test of the software, and wherein the training data include annotation data that provide reference adaptation specifications which specify the at least one item of information for adapting the software test to resolve the respective error;
initializing weights of the machine learning model;
and carrying out a training process to optimize the weights of the machine learning model based on the training data, wherein the training process uses a loss function that minimizes a difference between data generated by the machine learning model and the annotation data.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 6 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The limitation of "carrying out a training process", as drafted in #11 above, under its broadest reasonable interpretation, covers performance of the mind, but for general computer parts. That is, other than reciting the process is handled by machine-learning, nothing in the claim element precludes the step from performed on paper by a person.
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ascertaining limitations in #9 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, ascertaining training data in the context of this claim encompasses mere data gathering. See in the MPEP §§ 2106.05(g).
The initializing limitation in #10 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, initializing in the context of this claim encompasses merely setting values for variables. See in the MPEP §§2106.05(f).
Additionally, the claims recite the following additional element:
machine learning model is provided, wherein the machine learning model is trained ... for the evaluating and/or the generating of the adaptation specification
The element that is recited in the claims are stated at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using generic computer component. See the MPEP §§ 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
Additionally, with regards to #9 above, per MPEP 2106.05(d)(ll), the courts have recognized the following computer function(s) as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim(s) 7 recite(s):
wherein continuous learning of the machine learning model is provided during repeated execution of the evaluation and/or the generation, based on the basis of the thereby ascertained deviation and error specifications, retraining in which the ascertained deviation and error specifications are incorporated into the training data, and/or by incremental learning.
Step 1: are the claims to a process, machine, manufacture, or a composition of matter?
Yes. Claim 7 is a method
Step 2A, Prong I; Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
Step 2A Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitation in #12 above. As claimed and under BRI, is an additional element that is mere instructions to apply an exception. For example, continuous learning of a machine learning model in the context of this claim encompasses merely providing data to an MLM. See in the MPEP §§2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide the inventive step.
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.
Claim(s) 1, 3-4, and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20130151906 A1 (hereinafter referred to as D'Alterio) in view of US 20200371903 A1 (hereinafter referred to as Straub) .
Regarding claim 1, D'Alterio teaches:
A method for automated adaptation of at least one software test of a software, comprising the following steps: ascertaining a deviation specification which indicates a difference between at least two versions of the software including a current version of the software and a previous version of the software ([Abstract], D'Alterio shows “a plurality of test cases each one for exercising a set of corresponding exercised software components. A corresponding method comprises the steps of receiving an indication of each failed test case whose current execution has failed” Fig. 3 (335) Para. [83], D'Alterio shows whenever any current execution of the test is to be analyzed, the change resolver 330 determines the software components and the test cases that have been changed by comparing the files of the repositories 325 and 320; the change resolver 330 saves an indication of these changed software components into a repository 335 and an indication of these changed test cases into a repository 340”);
ascertaining an error specification about an error that occurred during an execution of the at least one software test of the software, wherein the error occurred during execution of the at least one software test in the current version of the software (Fig. 3 (360) Para. [27], D'Alterio shows “the execution of the test case has failed (and a result of this failed test case is set to a failed value) when an unexpected outcome is obtained (i.e., the actual outcome does not match the expected outcome, the software product has crashed, it has entered an endless loop or any other improper operating condition). A result of (the execution of) the test is defined by the results of all its test cases (for example, listed in a corresponding test report)”);
carrying out an evaluation of the deviation specification and the error specification with respect to a correlation of the error that has occurred and the difference between the at least two versions of the software (Para. [83-87], D'Alterio shows “the change resolver 330 saves an indication of these changed software components into a repository 335 and an indication of these changed test cases into a repository 340. the test case resolver 345 saves an indication of these exercised software components of each test case into a repository 350. A preparation engine 355 accesses the repository 335 and the repository 350. the preparation engine 355 determines whether it is suspect (i.e., the corresponding suspicion attribute has the suspect value) by comparing its exercised software components (from the repository 350) with the changed software components (from the repository 335); the preparation engine 355 saves an indication of the suspect failed test cases into a repository 360. A correlation engine 365 accesses the repository 340 and the repository 360; moreover, it receives the current result 315c and extracts the result of the previous execution of the test (denoted as previous result 315p) from the repository 315. The correlation engine 365 classifies the failed test cases (from the current result 315c) according to their suspicion attributes (determined from the repository 360), change attributes (determined from the repository 340) and regression attributes (determined from the previous result 315p). The correlation engine 365 then generates a test report 370 by updating the current result 315c accordingly (for example, by assigning the color of the corresponding class to each failed test case)”);
D'Alterio does not disclose:
generating an adaptation specification based on the carried out evaluation, wherein the adaptation specification specifies at least one item of information for adapting at least one test case and/or test code of the at least one software test to eliminate the error caused by the difference between the at least two versions of the software;
and adapting the at least one test case and/or test code of the at least one software test based on the adaptation specification, wherein the adapting includes eliminating the error in the at least one software test during an ongoing execution of the at least one software test.
However, in the analogous art of automated , Straub teaches:
generating an adaptation specification based on the carried out evaluation, wherein the adaptation specification specifies at least one item of information for adapting at least one test case and/or test code of the at least one software test to eliminate the error caused by the difference between the at least two versions of the software (Para. [63], Straub shows “In implementations, such assessment, analysis, and introspection may allow software testing system 100 to suggest to users to increase or decrease the number of tests, increase or decrease the number of software process flows 324, modify individual nodes 304 or software process flows 324, or otherwise adapt software testing of an application to meet or adapt to a variety of different testing criteria, external system variations, end user requests, system variations, etc.” Para. [25], Straub shows “The software testing system may be configured to adapt testing as needed, either pre or post production, to allow, for example, user interactions to be used to provide feedback on which additional tests to add and/or subtract automatically to a testing sequence. Moreover, data gathering techniques such as crowd source data sampling, statistical sampling, and the like, may be employed as additional input to the system. The resulting adaptation may be used to help prevent software failures for software in use, or yet to be implemented, as well as help prevent failures due to software regression, where failures occur in software once tested and approved due to, for example, updates in the software application, also referred to herein as “software” or “application.” Examiner notes the citation above shows suggesting to users to modify and adapt software testing. Examiner further notes the citations above show adapting in response to updates to the software (current version) to prevent errors from occurring due to software regression causing implemented functions (previous version) to malfunction);
and adapting the at least one test case and/or test code of the at least one software test based on the adaptation specification, wherein the adapting includes eliminating the error in the at least one software test during an ongoing execution of the at least one software test (Para. [32], Straub shows “For example, during a software testing process, software testing engine 116 may compare user software interactions relative to other defined software interactions and/or one or more base interaction models in order to suggest and/or automatically enable variations to software testing and/or the testing flows and processes” Para. [25], Straub shows “The software testing system may be configured to adapt testing as needed, either pre or post production, to allow, for example, user interactions to be used to provide feedback on which additional tests to add and/or subtract automatically to a testing sequence. Moreover, data gathering techniques such as crowd source data sampling, statistical sampling, and the like, may be employed as additional input to the system. The resulting adaptation may be used to help prevent software failures for software in use, or yet to be implemented, as well as help prevent failures due to software regression, where failures occur in software once tested and approved due to, for example, updates in the software application, also referred to herein as “software” or “application.” Para. [7], Straub shows “Such adjustable error tolerance may be used to set tolerance thresholds that when crossed, for example, add additional tests, remove tests, modify tests, change a sequence of testing, etc., relative to criteria such as processor efficiency needs, false alarm tolerance, frequency of errors, types of errors, and the like” Examiner notes the citations above show adapting these software tests during an ongoing testing process and doing this in order to prevent errors from software regression, which is caused by updates to a software (current version) causing implemented functions (previous version) to incorrectly function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Straub into the teachings of D'Alterio to implement “generating an adaptation specification based on the carried out evaluation, wherein the adaptation specification specifies at least one item of information for adapting at least one test case and/or test code of the at least one software test to eliminate the error caused by the difference between the at least two versions of the software; and adapting the at least one test case and/or test code of the at least one software test based on the adaptation specification, wherein the adapting includes eliminating the error in the at least one software test during an ongoing execution of the at least one software test”. The modification would have been obvious as one of ordinary skill in the art would be motivated to use the adaptation to prevent software failures caused by software regression (Straub, Para. [25]).
Regarding claim 3, D'Alterio as modified teaches claim 1 as cited above, but does not disclose:
The method according to claim 1, wherein the adapting the at least one test case and/or test code of the software test is carried out at least partially automatically based on the adaptation specification, wherein the adaptation adapting includes removing and/or adapting the at least one test case and/or test code of the software test which refers to associated with elements of the current version of the software that have been removed from the previous version of the software, wherein the elements include to-be-tested functions and/or elements of a user interface and/or objects of the software
However, in the analogous art of automated , Straub teaches:
wherein the adapting the at least one test case and/or test code of the software test is carried out at least partially automatically based on the adaptation specification, wherein the adaptation adapting includes removing and/or adapting the at least one test case and/or test code of the software test which refers to associated with elements of the current version of the software that have been removed from the previous version of the software, wherein the elements include to-be-tested functions and/or elements of a user interface and/or objects of the software (Para. [6], Straub shows “automatically generating, deploying, modifying, and monitoring software tests and testing flows employed to test software applications. As described in more detail herein, one or more signals may be accepted to initiate generation of and vary software application tests and testing flows based on several criteria including monitoring a software application process flows processing one or more defined input and output tests to determine which parts of the software application are being tested, which parts of the software application are not being tested” Para. [32], Straub shows “ For example, during a software testing process, software testing engine 116 may compare user software interactions relative to other defined software interactions and/or one or more base interaction models in order to suggest and/or automatically enable variations to software testing and/or the testing flows and processes” Para. [25], Straub shows “The software testing system may be configured to adapt testing as needed, either pre or post production, to allow, for example, user interactions to be used to provide feedback on which additional tests to add and/or subtract automatically to a testing sequence. Moreover, data gathering techniques such as crowd source data sampling, statistical sampling, and the like, may be employed as additional input to the system. The resulting adaptation may be used to help prevent software failures for software in use, or yet to be implemented, as well as help prevent failures due to software regression, where failures occur in software once tested and approved due to, for example, updates in the software application, also referred to herein as “software” or “application.” Para. [7], Straub shows “Such adjustable error tolerance may be used to set tolerance thresholds that when crossed, for example, add additional tests, remove tests, modify tests, change a sequence of testing, etc., relative to criteria such as processor efficiency needs, false alarm tolerance, frequency of errors, types of errors, and the like).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Straub into the teachings of D'Alterio as modified to implement "wherein the adapting the at least one test case and/or test code of the software test is carried out at least partially automatically based on the adaptation specification, wherein the adaptation adapting includes removing and/or adapting the at least one test case and/or test code of the software test which refers to associated with elements of the current version of the software that have been removed from the previous version of the software, wherein the elements include to-be-tested functions and/or elements of a user interface and/or objects of the software. The modification would have been obvious as one of ordinary skill in the art would be motivated The modification would have been obvious as one of ordinary skill in the art would be motivated to use the adaptation to prevent software failures caused by software regression (Straub, Para. [25]).
Regarding claim 4, D’Alterio as modified teaches claim 1 as cited above and teaches:
wherein a classification of the difference between the at least two versions is carried out in the evaluation (Para. [121] D'Alterio shows “the suspicion attribute is indicative of a change to the corresponding exercised software components since a previous execution of the failed test case), for detecting code changes in the software that are likely to cause the error to occur (Para. [123] D'Alterio shows retrieving a suspicion attribute of each failed test case comprises the following operations. An indication is retrieved of the exercised software components of each failed test case. An indication is
retrieved of a set of changed software components of the software product that have been changed since the previous execution of the failed test cases. The suspicion attribute of each failed test case is determined according to a comparison between the corresponding exercised software components and the changed software components”).
With regards to claim 8, it is a machine claim having similar limitations as cited in claim 1 above. Thus, claim 8 is also rejected under the same rationale as cited in the rejection of claim 1 above.
With regards to claim 9, it is a non-transitory computer product claim having similar limitations as cited in claim 1 above. Thus, claim 9 is also rejected under the same rationale as cited in the rejection of claim 1 above.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20130151906 A1 (hereinafter referred to as D'Alterio) in view of US 20200371903 A1 (hereinafter referred to as Straub) in further view of US 20200019493 A1 (hereinafter referred to as Ramakrishna).
Regarding claim 5, D'Alterio as modified teaches claim 1 as cited above, but does not disclose:
wherein the evaluation and/or the generation of the adaptation specification is based at least in part on machine learning
However, in the analogous art of automated software testing, Ramakrishna teaches:
wherein the evaluation and/or the generation of the adaptation specification is based at least in part on machine learning (Para. [18], Ramakrishna shows “the software deployment platform may process the software code and the software code change, with a model (e.g., a machine learning model)”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of D'Alterio as modified into the teachings of Ramakrishna to implement “wherein the evaluation and/or the generation of the adaptation specification is based at least in part on machine learning ”. The modification would have been obvious as one of ordinary skill in the art would be motivated to detect patterns and/or trends undetectable to human analysts or systems using less complex techniques (Ramakrishna, Para. [22]).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20130151906 A1 (hereinafter referred to as D'Alterio) in view of US 20200371903 A1 (hereinafter referred to as Straub) in further view of US 20200019493 A1 (hereinafter referred to as Ramakrishna) and US 20220091968 A1 (hereinafter referred to as Kumar).
Regarding claim 6, D'Alterio as modified teaches claim 5 as cited above, but does not disclose:
wherein, a machine learning model is provided, wherein the machine learning model is trained by the following steps for the evaluating and/or the generating of the adaptation specification:
ascertaining training data, wherein the training data include example deviation specifications about a respective difference between at least two versions of the software
and example error specifications about a respective error during execution of the software test of the software,
and wherein the training data include annotation data that provide reference adaptation specifications which specify the at least one item of information for adapting the software test to resolve the respective error;
initializing weights of the machine learning model; and carrying out a training process to optimize the weights of the machine learning model based on the training data, wherein the training process uses a loss function that minimizes a difference between data generated by the machine learning model and the annotation data
However, in the analogous art of automated software testing, Ramakrishna teaches:
wherein, a machine learning model is provided, wherein the machine learning model is trained by the following steps for the evaluating and/or the generating of the adaptation specification (Para. [18], Ramakrishna shows “the software deployment platform may process the software code and the software code change, with a model (e.g., a machine learning model)”):
ascertaining training data, wherein the training data include example deviation specifications about a respective difference between at least two versions of the software (Para. [19], Ramakrishna shows “the software deployment platform may separate the historical software code change information into a training set, a validation set, a test set, and/or the like. In some implementations, the software deployment platform may train the machine learning model using, for example, an unsupervised training procedure and based on the training set of the historical software code change information”)
and example error specifications about a respective error during execution of the software test of the software (Para. [48], Ramakrishna shows “the software deployment platform may automatically link all software code changes and defects to an original software change request”),
initializing weights of the machine learning model; and carrying out a training process to optimize the weights of the machine learning model based on the training data, wherein the training process uses a loss function that minimizes a difference between data generated by the machine learning model and the annotation data (Para. [20] Ramakrishna shows “the software deployment platform may use a logistic regression classification technique to determine a categorical outcome (e.g., that the historical software code change information indicates that particular tasks are recommended for particular software code changes). Additionally, or alternatively, the software deployment platform may use a naïve Bayesian classifier technique. In this case, the software deployment platform may perform binary recursive partitioning to split the historical software code change information into partitions and/or branches, and use the partitions and/or branches to perform predictions (e.g., that the historical software code change information indicates that particular tasks are recommended for particular software code changes). Based on using recursive partitioning, the software deployment platform may reduce utilization of computing resources relative to manual, linear sorting and analysis of data points, thereby enabling use of thousands, millions, or billions of data points to train the machine learning model, which may result in a more accurate model than using fewer data points.” Examiner notes that to use the logistic regression technique or others, initializing weight (setting parameters is inherent, even if it includes default values))
In addition, in the analogous art of automated generati, Kumar teaches:
and wherein the training data include annotation data that provide reference adaptation specifications which specify the at least one item of information for adapting the software test to resolve the respective error (Para. [53], Kumar shows Test Cases 700 and Test Data 750 can be in human-readable form and allows Tester 1200 to review and provide feedback to the system. The corrected test case and test data information is provided back to the ML Model 300 for future reference and training the ML Model 300);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramakrishna into the teachings of D'Alteri to implement “wherein, a machine learning model is provided, wherein the machine learning model is trained by the following steps for the evaluating and/or the generating of the adaptation specification: ascertaining training data, wherein the training data include example deviation specifications about a respective difference between at least two versions of the software and example error specifications about a respective error during execution of the software test of the software, initializing weights of the machine learning model; and carrying out a training process to optimize the weights of the machine learning model based on the training data, wherein the training process uses a loss function that minimizes a difference between data generated by the machine learning model and the annotation data”. The modification would have been obvious as one of ordinary skill in the art would be motivated to enable the use of thousands, millions, or billions of data points to train the machine learning model, which may result in a more accurate model than using fewer data points (Ramakrishna, Para. [20]).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kumar into the teachings of D'Alterio as modified to implement “wherein the training data include annotation data that provide reference adaptation specifications which specify the at least one item of information for adapting the software test to resolve the respective error”. The modification would have been obvious as one of ordinary skill in the art would be motivated improve the machine learning model for better results going forward (Kumar, Para. [53]).
Regarding claim 7, D'Alterio as modified teaches claim 6 as cited above, but does not disclose:
wherein continuous learning of a machine learning model is provided during repeated execution of the evaluation and/or the generation, based on the basis of the thereby ascertained deviation and error specifications, retraining in which the ascertained deviation and error specifications are incorporated into the training data, and/or by incremental learning
However, in the analogous art of automated software testing, Ramakrishna teaches:
wherein continuous learning of the machine learning model is provided during repeated execution of the evaluation and/or the generation, based on the basis of the thereby ascertained deviation and error specifications, retraining in which the ascertained deviation and error specifications are incorporated into the training data, and/or by incremental learning (Para. [48], Ramakrishna shows “the software deployment platform may automatically link all software code changes and defects to an original software change request.” Para. [19], Ramakrishna shows “the software deployment platform may train the machine learning model using, for example, an unsupervised training procedure and based on the training set of the historical software code change information.” Para. [56]. Ramakrishna shows “the software deployment platform may provide automatic defect creation. In such implementations, the software deployment platform may capture test execution results, may create defects for failed test scripts, and may search for a next sequence of steps for passing test scripts”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of D'Alterio as modified into the teachings of Ramakrishna to implement “wherein continuous learning of a machine learning model is provided during repeated execution of the evaluation and/or the generation, based on the basis of the thereby ascertained deviation and error specifications, retraining in which the ascertained deviation and error specifications are incorporated into the training data, and/or by incremental learning”. The modification would have been obvious as one of ordinary skill in the art would be motivated to enable the use of thousands, millions, or billions of data points to train the machine learning model, which may result in a more accurate model than using fewer data points (Ramakrishna, Para. [20]).
Response to Arguments
Applicant's arguments regarding 35 U.S.C 112 filed 6/02/2026 have been fully considered and they are persuasive.
Applicant's arguments regarding 35 U.S.C 101 filed 6/02/2026 have been fully considered but they are not persuasive.
Applicant argues on Pg. 6, that applicants’ amendment to claim 1 regarding the limitation “adapting the at least one test case and/or test code of the at least one software test based on the adaptation specification, wherein the adapting includes eliminating the error in the at least one software test during an ongoing execution of the at least one software test.” Applicant states with this amendment, the independent claims are directed to patent-eligible subject matter. Examiner notes that the amendments to the independent claim are not enough for the claims to be directed towards patent-eligible subject matter. See 35 U.S.C 101 rejection above for further details.
Applicant argues on Pg. 7, that “the human mind is not equipped to practically eliminate an error in a software test, much less during an ongoing execution”. Examiner notes while a human mind cannot practically eliminate an error while a software test is ongoing, debugging is a task humans often are required to eliminate software errors using results gained from software tests. Examiner further notes that the adapting occurs during an ongoing execution, however “generating an adaption specification based on the carried out evaluation” is considered a mental process as a person can create an adaption specification using a pen and paper if needed to specify how an error can be eliminated.
Applicant further argues on Pg. 7-8, that the claims integrate the purported judicial exception into a practical application under Prong Two. Examiner disagrees and directs applicant to 35 U.S.C 101 rejection above for examiners reasoning behind the rejection.
For these reasons above, the examiner finds these arguments unpersuasive and maintains that the rejection under 35 U.S.C. 101 is proper.
Applicant's arguments regarding 35 U.S.C 103 filed 6/02/2026 have been fully considered but they are not persuasive.
Applicant argues on Pg. 9-10, that D’Alterio in view of Ramakrishna does not teach claim 1 as amended. As Ramakrishna teaches the modification of software code, instead of test cases and test scripts. However, Straub teaches: (Para. [32], Straub shows “For example, during a software testing process, software testing engine 116 may compare user software interactions relative to other defined software interactions and/or one or more base interaction models in order to suggest and/or automatically enable variations to software testing and/or the testing flows and processes” Para. [25], Straub shows “The software testing system may be configured to adapt testing as needed, either pre or post production, to allow, for example, user interactions to be used to provide feedback on which additional tests to add and/or subtract automatically to a testing sequence. Moreover, data gathering techniques such as crowd source data sampling, statistical sampling, and the like, may be employed as additional input to the system. The resulting adaptation may be used to help prevent software failures for software in use, or yet to be implemented, as well as help prevent failures due to software regression, where failures occur in software once tested and approved due to, for example, updates in the software application, also referred to herein as “software” or “application.” Para. [7], Straub shows “Such adjustable error tolerance may be used to set tolerance thresholds that when crossed, for example, add additional tests, remove tests, modify tests, change a sequence of testing, etc., relative to criteria such as processor efficiency needs, false alarm tolerance, frequency of errors, types of errors, and the like” Examiner notes the citations above show modification of test cases based on evaluations made, in order to eliminating errors from the code).
The 103 rejection for claims 8-9 is maintained as they recite similar limitations to claim 1 above and the rejection to claim 1 is maintained as well.
The 103 rejection for claims 3-7 is maintained as they have not been amended/ recite similar limitations that have been rejected before and the rejection to claim 1 is maintained as well.
For these reasons above, the examiner finds these arguments unpersuasive and maintains that the rejection under 35 U.S.C. 103 is proper.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220391312 A1 – this art teaches non-application related defects and modifying test filters to remove these tests
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEERICK A MALIK whose telephone number is (571)272-8110. The examiner can normally be reached Mon-Thurs, 7-5.
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/Z.A.M./Examiner, Art Unit 2193
/Chat C Do/Supervisory Patent Examiner, Art Unit 2193
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