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 .
DETAILED ACTION
1. This Office Action is in response to the application filed on 04/02/2026. Claims 1-10 are pending in this application. Claims 1, 7, 8, 9 and 10 are independent claims.
Claim Rejections - 35 USC § 101
2. 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.
3. Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim 8 does not fall within at least one of the four categories of patent eligible subject matter because a model for predicting coverage information of the claim 8 is subject to software per se as the model can be interpreted as software program or algorithm with BRI in light of the specification.
4. Claims 1-6 and 9 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1 and 9 and 10 are corresponding to one of four statutory categories including method, system, and method respectively under step 1. These claims 1, 9 and 10 similarly recite “providing at least one target program to be tested; providing program inputs for the at least one provided target program for executing at least one predetermined test case in the at least one provided target program based on black box fuzzing; predicting coverage information based on the provided program inputs, wherein the coverage information specifies an effect in the at least one provided target program which results from the execution of the at least one predetermined test case; and using the predicted coverage information as feedback for the software test”.
The limitation of the claims 1, 9 and 10 of “predicting coverage information based on the provided program inputs, wherein the coverage information specifies an effect in the at least one provided target program which results from the execution of the at least one predetermined test case” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “predicting”. For example, a human may predict coverage information based on the provided program inputs, wherein the coverage information specifies an effect in the at least one provided target program which results from the execution of the at least one predetermined test case, which covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 9 and 10 recite additional elements such as “providing at least one target program to be tested”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 9 and 10 recite additional elements such as “providing program inputs for the at least one provided target program for executing at least one predetermined test case in the at least one provided target program based on black box fuzzing”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 1, 9 and 10 recite additional elements such as “using the predicted coverage information as feedback for the software test”.
Examiner would like to point out that with the broad reasonable interpretation, this element amount to “apply it” under MPEP § 2106.05(f): Mere Instructions to Apply an Exception, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 2 recites additional elements such as “the effect is a code coverage including a line coverage, and/or a branch coverage and/or a path coverage, and specifies source code of the at least one provided target program which is executed during the execution of the at least one predetermined test case”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 3 recites additional elements such as “the model results from training using training test cases and their effect on a target program”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
This judicial exception is not integrated into a practical application. In particular, the claim 4 recites additional elements such as “the at least one predetermined test case is executed using black box fuzzing, wherein direct access to a source code of the at least one provided target program for ascertaining the effect is prevented, wherein a fuzzer is provided which receives feedback regarding the effect via the predicted coverage information”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to field of use under MPEP § 2106.05(h): Field of Use and Technological Environment, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Step 2A Prong 2 and 2B.
The limitation of the claim 5 of “generating at least one new test case based on the at least one predetermined test case and the predicted coverage information, wherein the new test case is optimized for increasing the effect including a code coverage, in the at least one provided target program” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “generating [designing] a test case”. For example, a human may generate at least one new test case based on the at least one predetermined test case and the predicted coverage information, wherein the new test case is optimized for increasing the effect including a code coverage, in the at least one provided target program, which covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1.
The limitation of the claim 6 of “the black box fuzzing is transformed into gray box fuzzing by using the predicted coverage information” as a drafted is a mental process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind such as “transforming [changing] a black box fuzzing into a gray box fuzzing”. For example, a human may transform the black box fuzzing is transformed into gray box fuzzing by using the predicted coverage information, which covers performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1.
Dependent claims 2-6 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-6 are also rejected for incorporating the deficiency of their independent claim 1.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claims 1, 2, 4, 5, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hu (US PGPub 20240134781), in view of Okazaki (US PGPub 20190391910), and further in view of Hekmatpour (US PGPub 20020002698).
As per Claim 1, Hu teaches of a method for taking feedback into account in a software test, comprising the following steps: providing at least one target program to be tested; (Par 5, generating a plurality of testcases; executing a target program based on each of the generated testcases to obtain a plurality of execution results)
providing program inputs for the at least one provided target program for executing at least one predetermined test case in the at least one provided target program based on black box fuzzing; (Par 3, Black-box fuzzing test is a technology of testing a test target (such as, a program) as a black box without considering its internal architecture. The basic idea of the black-box fuzzing test is to take a set of random data as an input to the test target and monitor any abnormality during execution of the test target, and further locate defects in the test target by recording the input data that caused the abnormality.)
wherein the coverage information specifies an effect in the at least one provided target program which results from the execution of the at least one predetermined test case; and (Par 25, there is provided a black-box fuzzing testing method, comprising: generating a plurality of testcases; executing a target program based on each of the generated testcases to obtain a return value and an execution time for each of the generated testcases; filtering out at least one of the generated test cases based on the return value and the execution time for each of the generated cases to generate a subset of testcases; and determining a code coverage of the target program based on the subset of test cases.)
Hu does not specifically teach, however Okazaki teaches of predicting coverage information based on the provided program inputs, (Par 42-43, In the unit test in the general program development, a white box test is often performed in which a test case is set so that coverage for the entire source code approaches 100% as far as possible. The test case referred to here is a combination of inputs to the program. It is possible to increase the coverage by selecting the combination of inputs so that all of the paths of the program are passed as much as possible.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add predicting coverage information based on the provided program inputs, as conceptually seen from the teaching of Okazaki, into that of Hu because this modification can help enable faster and more efficient testing by determining test inputs for executing target code paths.
Neither Hu nor Okazaki specifically teaches, however Hekmatpour teaches of using the predicted coverage information as feedback for the software test. (Par 45, Test specification optimizer 204 also receives feedback from a coverage estimator 212. The coverage estimator 212, as described in greater detail below, analyzes test description 210 against an overall design coverage model and identifies various indicia of functional coverage with respect to the integrated circuit. Coverage estimator 212 provides feedback to test specification optimizer 204 regarding the potential coverage achievable by the test description 210.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add using the predicted coverage information as feedback for the software test, as conceptually seen from the teaching of Hekmatpour, into that of Hu and Okazaki because this modification can help identifying untested code paths, reducing bugs, and optimizing test suites while improving test efficiency and reducing redundant tests.
As per Claim 2, Hu further teaches of the method according to claim 1, wherein the effect is a code coverage including a line coverage, and/or a branch coverage and/or a path coverage, and specifies source code of the at least one provided target program which is executed during the execution of the at least one predetermined test case. (Par 25, determining a code coverage of the target program based on the subset of test cases. Par 26, The code coverage of the test target may be determined based on the total number of paths of the current test target and the number of new paths that have been discovered. In addition, embodiments of the present disclosure may also be applied to the black-box fuzzing testing of SSD firmware.)
As per Claim 4, Hu further teaches of the method according to claim 1, wherein the at least one predetermined test case is executed using black box fuzzing, wherein direct access to a source code of the at least one provided target program for ascertaining the effect is prevented, wherein a fuzzer is provided which receives feedback regarding the effect via the predicted coverage information. (Par 14, there is provided a black-box fuzzing testing apparatus, comprising: a testcase execution unit configured to generate a plurality of testcases and execute a target program based on the generated testcases to obtain execution results; a path management unit configured to determine, using the execution results, a plurality of execution paths of the target program; and a feedback management unit configured to determine a code coverage of the target program from the execution paths. Par 53, If there is a newly generated path, it is added to a corresponding node, and an increase of the path may feedback an increase of the test coverage.)
As per Claim 5, Hu further teaches of the method according to claim 1, further comprising: generating at least one new test case based on the at least one predetermined test case and the predicted coverage information, wherein the new test case is optimized for increasing the effect including a code coverage, in the at least one provided target program. (Par 8, The black-box fuzzing testing method may further comprise: mutating the given testcase to generate new testcases for testing the target program if the first execution path is determined to be a new execution path; and recording an association relationship between the given testcase and the new testcases in a testcase relationship table.)
Re Claim 9, it is the system claim, having similar limitations of claim 1. Thus, claim 9 is also rejected under the similar rationale as cited in the rejection of claim 1.
Re Claim 10, it is the product claim, having similar limitations of claim 1. Thus, claim 10 is also rejected under the similar rationale as cited in the rejection of claim 1.
8. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Hu (US PGPub 20240134781), in view of Okazaki (US PGPub 20190391910), in view of Hekmatpour (US PGPub 20020002698), and further in view of Pearson (US Patent 12072790).
As per Claim 3, none of Hu, Okazaki and Hekmatpour specifically teaches, however Pearson teaches of the method according to claim 1, wherein the coverage information is predicted by a model, wherein the model results from training using training test cases and their effect on a target program. (Col 13, lines 36-45, Additionally or alternatively, the mutation test system 104 may train the machine-learned model 400 based on code coverage data, such as the code coverage results (e.g., classes, functions, and lines covered) associated with different test cases in the suite. In various examples, the mutation test system 104 also may train the machine-learned model 400 based on mutation rules 118, input from developers/testers, code annotations, and/or determined attributes associated with particular portions of the source code.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the coverage information is predicted by a model, wherein the model results from training using training test cases and their effect on a target program, as conceptually seen from the teaching of Pearson, into that of Hu, Okazaki and Hekmatpour because this modification can help identifying untested code paths, reducing bugs, and optimizing test suites while improving test efficiency and reducing redundant tests.
9. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hu (US PGPub 20240134781), in view of Okazaki (US PGPub 20190391910), in view of Hekmatpour (US PGPub 20020002698), and further in view of Soukup (US PGPub 20230367704).
As per Claim 6, none of Hu, Okazaki and Hekmatpour specifically teaches, however Soukup teaches of the method according to claim 1, wherein the black box fuzzing is transformed into gray box fuzzing by using the predicted coverage information. (Par 46, Simultaneously, grey-box fuzzing overcomes black-box fuzzing randomness while generating a large number of test cases quickly. Hence, the grey-box approach addresses three testing challenges: the system's complexity and size by avoiding intensive code analysis, outsourcing by limiting the knowledge about the system, and input and output fluctuation by creating a massive number of inputs.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add the black box fuzzing is transformed into gray box fuzzing by using the predicted coverage information, as conceptually seen from the teaching of Soukup, into that of Hu, Okazaki and Hekmatpour because this modification can help identifying untested code paths, reducing bugs, and optimizing test suites while improving test efficiency and reducing redundant tests.
10. Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Cecchetti (US PGPub 20160350211), in view of Zorian (US Patent 11829692).
As per Claim 7, Cecchetti teaches of a training method for a model for predicting coverage information for a software test to expand black box fuzzing with feedback provided by the coverage information, the training method comprising the following steps: providing the trained model for expanding the black box fuzzing. (Par 3, One family of fuzzers are commonly known as blackbox fuzzers, which generally require no information about the software target that will be tested. Par 18, One family of fuzzers is commonly known as blackbox fuzzers, which generally require no information about the software target that will be tested. A blackbox fuzzer generally operates outside the process space of a software target, hereinafter ‘target’. Blackbox fuzzers can have models to assist in producing data for a target, but typically a blackbox fuzzer has no direct knowledge of how the target is executing. A blackbox fuzzer can be, in some embodiments, used from a different machine than the target. An example of a blackbox fuzzer can be Peach™ that can, for example, run on a laptop to test a network router.)
Cecchetti does not specifically teach, however Zorian teaches of providing training data, wherein the training data specify training test cases and their effect on a target program to be tested; (Col 1, line 51- Col 2, line 2, Training data may be collected based on a set of test-case configurations for each IC design in a set of IC designs. The training data may include a set of features extracted from each IC design, and a count of test cycles required for achieving a target test coverage for each test-case configuration. An ML model may be trained using the training data to obtain a trained ML model. The trained ML model may be used to predict a set of ranked test-case configurations for a given IC design based on features extracted from the given IC design. The highest ranked test-case configuration in the set of ranked test-case configurations may correspond to an optimal test-case configuration. In some embodiments, for each suboptimal test-case configuration in the set of ranked test-case configurations, the trained ML model may predict an increase in a count of test cycles required for achieving the target test coverage relative to the optimal test-case configuration.)
training the model for predicting the coverage information based on the provided training data, wherein the coverage information specifies the effect; and (Col 1, lines 52-60, The training data may include a set of features extracted from each IC design, and a count of test cycles required for achieving a target test coverage for each test-case configuration. An ML model may be trained using the training data to obtain a trained ML model. The trained ML model may be used to predict a set of ranked test-case configurations for a given IC design based on features extracted from the given IC design. Col 4, lines 53-61, Next, an ATPG tool may be used to generate test patterns and calculate the required test cycles for each test case TC-1 through TC-N (at 310). The same test coverage (T) may be used as the targeted test coverage for all test cases in all designs. The term “test coverage” may refer to the degree to which the RTL description of the IC design is executed when the IC design is tested using an ATPG tool. Different test cases may require a different number of test cycles to achieve the same test coverage.)
Therefore, it would have been obvious for one of the ordinary skill in the art before the effective filing date of the claimed invention to add providing training data, wherein the training data specify training test cases and their effect on a target program to be tested and training the model for predicting the coverage information based on the provided training data, wherein the coverage information specifies the effect, as conceptually seen from the teaching of Zorian, into that of Cecchetti because this modification can help significantly enhance operational efficiency and risk management by optimizing test code coverage.
Re Claim 8, it is the system claim, having similar limitations of claim 7. Thus, claim 8 is also rejected under the similar rationale as cited in the rejection of claim 7.
Conclusion
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/JAE U JEON/Primary Examiner, Art Unit 2193