DETAILED ACTION
This action is responsive to the application Remarks and Claim Amendments on June 22, 2026.
Claims 16, 20-21, 24-25 and 29 have been amended.
Claims 16-30 are pending and are presented to examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Response to Amendments
The objection of the drawings (Figures 1-3) is withdrawn in view of applicant’s amendments.
The objection of the specification (Abstract of the Disclosure) is withdrawn in view of applicant’s amendments.
The objection of claims 24-30 is withdrawn in view of applicant’s amendments.
Response to Arguments
Rejections under 35 U.S.C. § 102(a)(1) and § 103 — Chopra and Chopra in view of Dundigalla. Applicant argues that amended independent claims 16 and 24 distinguish over Chopra because Chopra does not disclose “analyzing the distribution comprises determining what number of data values are allocated for execution of a given condition and identifying the conditions for which the data values are not present,” and because Chopra selects predefined test cases to achieve a code-coverage goal rather than analyzing, transforming, or producing a dataset of data values.
Applicant’s arguments are persuasive with respect to Chopra. Chopra is directed to selecting a minimum set of predefined test cases that exercises a target percentage of code coverage, and Chopra’s coverage bitmap and coverage counters record whether code-level functions, conditions, or statements are executed; Chopra does not disclose determining the number of data values allocated to a given condition, identifying conditions for which no data value is present, or rebalancing input test data to produce synthesized test data. Accordingly, the rejection of claims 16-20 and 23-28 under 35 U.S.C. § 102(a)(1) as anticipated by Chopra, and the rejection of claims 21-22 and 29-30 under 35 U.S.C. § 103 over Chopra in view of Dundigalla, are withdrawn.
However, Applicant’s amendment necessitated new grounds of rejection. Upon further consideration and an updated search, the amended limitations are addressed by the newly applied prior art, as set forth in the rejections below. In particular, the limitation that Applicant identifies as absent from Chopra — “determining what number of data values are allocated for execution of a given condition and identifying the conditions for which the data values are not present” — is taught by Kosuda. Kosuda analyzes to what extent the input values cover the equivalence classes (the recited conditions) of each parameter, sets an equivalence-class flag for each input value so as to determine how many input values are allocated to each equivalence class (condition), and highlights and reports the equivalence classes (and equivalence-class combinations) that are not tested — i.e., the conditions for which no data value is present (see Kosuda, paragraphs [0149], [0154], [0156], [0157], [0180], [0185]; and the rejection of claim 16 below).
Applicant’s further contention that the prior art does not create, transform, or produce a dataset is addressed by Shang and Prasad. Shang rebalances an imbalanced dataset by counting the number of data points in each class and oversampling the under-represented class according to a computed oversampling ratio, thereby producing a rebalanced (synthesized) dataset covering the condition for which data values were lacking (Shang, paragraphs [0041], [0048]-[0050], [0053]); and Prasad iteratively generates additional test data for the conditions identified as not covered and continues until the required structural coverage is achieved (Prasad, paragraphs [0015], [0067], [0077]-[0078]). The combination therefore teaches rebalancing the input test data, based on the analyzed distribution, to produce synthesized test data having an overall coverage equal to or greater than the predefined test case coverage, as set forth in the rejection of claim 16 below. Applicant’s arguments, which are directed solely to Chopra and Dundigalla, do not address the newly applied combination of Kosuda, Shang, and Prasad and are therefore moot as to the present grounds.
Dependent claims. Applicant argues that dependent claims 17-20, 23, and 25-28 (and claims 21-22 and 29-30) are allowable by virtue of their dependency on the independent claims, and that Dundigalla does not cure Chopra’s deficiency. Because the independent claims stand rejected over the newly applied art, the dependent claims are not allowable on the basis of dependency alone, and each is separately rejected as set forth in the corresponding rejections below. Dundigalla is retained only as to claims 21-22 and 29-30 (for the production-data and sensitive-data limitations); it is not relied upon for the amended distribution-analysis limitation.
In view of the foregoing, Applicant’s arguments have been fully considered but are not persuasive in view of the new grounds of rejection set forth below, which were necessitated by Applicant’s amendment. The application is not in condition for allowance. Accordingly, this action is properly made FINAL. See MPEP § 706.07(a).
Claim Objections
Claims 18-19, 21, 26-27 and 29 are objected to because of the following informalities: Claim 18 (and similar for claim 26) recites the limitation “validating the input test data; [[and]] or” in line 7. Please replace “and” to –or-- as indicated in bold. Appropriate correction is required.
Claim 19 (and similar for claim 27) recites the limitation “creating the plurality of test cases; [[and]] or” in line 3. Please replace “and” to –or-- as indicated in bold. Appropriate correction is required. Claim 21 (and similar for claim 29) recites “wherein the input test data has the same schema, data type and statistical properties as production data and is one of: the production data, an obfuscated subset of the production data, or mock data.”. Please amend the claim language as indicated in bold. Appropriate correction is required. Claim 27 recites the limitation “access a record of pre-created test cases that is stored at the data repository, wherein the data repository is communicably coupled to the at least one processor” in lines 4-5. Please add “at least one” after “the” as indicated in bold. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 16-20, 23-28 are rejected under 35 U.S.C. 103 as being unpatentable over Kosuda (US Pub. No. 2012/0167059) in view of Shang et al. (US Pub. No. 2022/0172007, hereinafter Shang) and further in view of Prasad et al. (US Pub. No. 2010/0175052, hereinafter Prasad).
With respect to claim 16 (Currently Amended), Kosuda teaches a method for creating a synthesized test data having a predefined test case coverage, the method comprising: (see Abstract; paragraphs [0013], [0039], [0195]). Kosuda is directed to a method for evaluating, and indicating how to attain, the coverage of a software test of test data, computing the coverage achieved by the input values and providing information about what kind and combination of test cases should be additionally performed to improve the coverage ([0039], [0195]) — i.e., a method directed to obtaining test data having a predefined (target) test coverage. processing a query for extracting a plurality of test cases and for determining a data storage location of input test data corresponding to the plurality of test cases (see Abstract; paragraphs [0053], [0054], [0070]-[0071], [0093]-[0101]). The analysis-target setting information entered on the analysis-target setting screen (the recited query) is processed so that the trace-log extraction component extracts, from the trace-log storage device, the target trace log containing the plurality of tests ([0053], [0070]), and the parameters/input fields appearing on the designated screen are extracted ([0054], [0071]). The target-server, target-PC and screen-URI fields of the query designate which stored trace log and which input values are to be drawn ([0093]-[0101]), thereby determining the data storage location of the input test data corresponding to the plurality of tests (test cases). extracting the input test data from a data repository and executing the plurality of test cases on the input test data for determining individual test case coverage percentages of the input test data (see paragraphs [0053], [0055], [0056], [0157], [0160]-[0161]). The input values (input test data) are extracted from the trace-log storage device (the recited data repository) and the test-data generation component generates the test data therefrom ([0053], [0055]). The single-factor analysis component then analyzes, for each parameter (each test case), to what extent the input values cover the values that should be input, and computes the equivalence-class coverage ratio per parameter expressed in percentage ([0056], [0157], [0160]-[0161]) — that per-parameter coverage ratio is the recited individual test case coverage percentage of the input test data. determining an overall test case coverage of the input test data, based on the individual test case coverage percentages of the input test data (see paragraphs [0160], [0162]). The single-factor analysis result (summary) screen computes the equivalence-class coverage and equivalence-class coverage ratio of all parameters, obtained by adding up the equivalence-class coverage of all parameters ([0162]). This all-parameter coverage ratio is the recited overall test case coverage, determined based on (aggregated from) the individual per-parameter coverage percentages. when the overall test case coverage of the input test data is less than the predefined test case coverage, identifying one or more test cases amongst the plurality of test cases for which individual test case coverage percentage of the input test data is less than a predefined value (see paragraphs [0039], [0156], [0157], [0160]-[0161], [0195]). Kosuda compares the coverage achieved by the input values against the values that should be input (the predefined target), and, where the coverage is incomplete, highlights for each parameter the equivalence-class ranges/boundary values that are not tested ([0156]) and reports the parameters whose individual coverage ratio falls short of full coverage ([0157], [0160]-[0161]), and indicates which additional test cases must be performed to improve the coverage ([0039], [0195]). Thus, when the overall coverage is below the desired (predefined) coverage, Kosuda identifies the particular parameters (test cases) whose individual coverage percentage is below the target value. analyzing a distribution of the input test data, with respect to conditions in the one or more test cases, wherein analyzing the distribution comprises determining what number of data values are allocated for execution of a given condition and identifying the conditions for which the data values are not present (see paragraphs [0149], [0154], [0156], [0157], [0180], [0184], [0185]). With respect to the recited analyzing a distribution with respect to conditions, the single-factor analysis sorts the input values into the equivalence-class ranges and boundary values of each parameter and builds a parameter-value list distributing the values across those equivalence classes ([0154]); each equivalence class is a recited condition, so this sorting is the analysis of the distribution of the input test data with respect to the conditions. With respect to determining what number of data values are allocated for execution of a given condition, Kosuda sets an equivalence-class flag for each input value indicating which equivalence class it satisfies ([0149]) and reports the equivalence-class coverage indicating how many equivalence classes there are and how many are finished with a test ([0157]), and the two-dimensional analysis tabulates the number of input values/tests falling in each combination (e.g., the total number of test times) ([0184]) — thereby determining how many input values (data values) are allocated to each equivalence class (condition). With respect to identifying the conditions for which the data values are not present, Kosuda highlights in red the equivalence-class range or boundary value that is not tested ([0156]) and reports the untested-combination cells and the untested-combination total ([0180], [0185]); an untested equivalence class is a condition for which no input value (data value) is present, so this directly identifies the conditions for which the data values are not present. Kosuda is silent to disclose, however, in an analogous art, Shang teaches: rebalancing the input test data based on the distribution of the input test data, for producing the synthesized test data, wherein the input test data is rebalanced for covering the conditions in the one or more test cases [[in a manner that an overall test case coverage of the synthesized test data would be equal to or greater than the predefined test case coverage.]] (see paragraphs [0041], [0048], [0049], [0050], [0053]). Shang determines the distribution of data points across a first class and a second class by counting the number of data points in each class ([0048], [0050]), and upon determining that the classes are imbalanced (one class under-represented relative to the other) rebalances the data set by oversampling the under-represented class according to a computed oversampling ratio, producing a rebalanced sample set having additional data points for the under-represented class ([0041], [0049], [0053]). Oversampling the under-represented class based on the imbalance in the distribution reads on rebalancing the input test data based on the distribution of the input test data to produce the synthesized (rebalanced) test data, wherein the rebalanced data covers the condition (class) for which data values were lacking.
It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to modify the method of Kosuda, which identifies the conditions for which the data values are not present, by rebalancing the input test data through oversampling of the under-represented condition(s) to produce a synthesized data set as taught by Shang, in order to ensure that a sufficient number of data values is available for the under-covered conditions so that those conditions can be adequately exercised during testing (Shang, paragraphs [0040]-[0041]).
Kosuda in view of Shang is silent to disclose, however, in an analogous art, Prasad teaches in a manner that an overall test case coverage of the synthesized test data would be equal to or greater than the predefined test case coverage (see paragraphs [0015], [0067], [0077]-[0078], [0122]). Prasad iteratively generates additional test data for the conditions identified as not yet covered and continues until the structural coverage criterion is satisfied — additional test data has to be generated until acceptable structural coverage is achieved ([0015]) — beginning each iteration with a condition from the set of uncovered conditions and stopping when all conditions are covered ([0077]-[0078]), and providing feedback on coverage compliance and iterating in case of non-compliance ([0067], [0122]). Thus, Prasad teaches continuing the production of test data so that the resulting (synthesized) test data attains an overall coverage equal to or greater than the required (predefined) coverage.
It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to further configure the rebalancing of Kosuda in view of Shang to continue until the overall test case coverage of the synthesized test data is equal to or greater than the predefined test case coverage, as taught by Prasad, in order to guarantee that the synthesized test data achieves the required level of coverage before testing and thereby reduce the number of undetected defects and the time and effort of manually preparing such data (Prasad, paragraphs [0015], [0024]).
With respect to claim 17 (Previously Presented), the combination of Kosuda and Prasad teaches wherein the predefined test case coverage lies in a range of 80 percent to 99.9 percent, and wherein the predefined value lies in a range of 60 percent to 99.9 percent (Kosuda expresses the test coverage as a coverage ratio in percentage that is to be maximized toward the values that should be covered ([0160]-[0162]), and Prasad expresses the coverage to be attained as a percentage that is driven toward 100% ([0019], [0130], [0132]); the predefined test case coverage and the predefined value are therefore recognized in the combination as result-effective variables, namely the target degrees of test coverage to be attained. It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to set the predefined test case coverage within a range of 80 percent to 99.9 percent and the predefined value within a range of 60 percent to 99.9 percent, since it is not inventive to discover the optimum or workable ranges of a result-effective variable by routine experimentation, in order to set the required level of test coverage to the reliability needs of the application under test, absent a showing that the claimed ranges achieve unexpected results (see MPEP § 2144.05 II).
With respect to claim 18 (Previously Presented), Kosuda in view of Prasad is silent to disclose, however, in an analogous art, Shang teaches wherein the step of rebalancing the input test data comprises at least one of: [[altering a portion of the input test data; adding new test data to the input test data;]] adjusting the distribution of the input test data across different conditions in the plurality of test cases; [[validating the input test data; and removing redundancy in the input test data]]. (see Shang, paragraphs [0041], [0053]). The claim recites “at least one of” the listed operations, so teaching one of the listed operations satisfies the limitation; Shang oversamples the under-represented class according to a computed oversampling ratio, thereby redistributing and supplying data points across the classes ([0041], [0053]), which reads on adjusting the distribution of the input test data across different conditions in the plurality of test cases.
It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to perform the rebalancing of Kosuda in view of Prasad by adjusting the distribution of the input test data across different conditions in the plurality of test cases through oversampling, as taught by Shang, in order to ensure that a sufficient number of data values is allocated to the under-covered conditions so that those conditions can be adequately exercised during testing (Shang, paragraphs [0040]-[0041]). With respect to claim 19 (Previously Presented), Kosuda teaches further comprising at least one of: [[creating the plurality of test cases;]] and accessing a record of pre-created test cases that is stored at the data repository (see Kosuda, paragraphs [0053], [0060], [0070]). The claim recites “at least one of,” so teaching one alternative suffices; Kosuda accesses the stored trace logs of previously-executed tests from the trace-log storage device ([0053], [0070]) and stores/accesses the generated test data in the test-data storage component ([0060]), which reads on accessing a record of pre-created test cases that is stored at the data repository. With respect to claim 20 (Currently Amended), Kosuda teaches further comprising generating a test case coverage report indicative of at least one of: the individual test case coverage percentages, the overall test case coverage, [[the one or more test cases, a portion of the input test data which covers a given test case, a portion of the query which indicates a given test case, a coverage status of each condition of a given test case.]] (see Kosuda, paragraphs [0058], [0152], [0157], [0159]-[0162]). The claim recites “at least one of,” so teaching one indicated item suffices; the single-factor analysis result screen and summary screen are output and indicate the equivalence-class coverage ratio per parameter (the individual test case coverage percentages) and the equivalence-class coverage ratio of all parameters (the overall test case coverage) ([0152], [0159]-[0162]), which reads on the recited test case coverage report.
With respect to claim 23 (Previously Presented), Kosuda teaches further comprising storing the synthesized test data at the data repository (see Kosuda, paragraph [0060]). Kosuda stores the generated test data in the test-data storage component (the recited data repository) ([0060]); in the combination, the test data so stored is the synthesized test data produced by the rebalancing of Shang and Prasad as set forth for claim 16, so the combination teaches storing the synthesized test data at the data repository.
With respect to claim 24, claim 24 recites limitations similar to claim 16 and differs only in that the recited functions are presented as A system for creating a synthesized test data having a predefined test case coverage, the system comprising at least one processor configured to perform the functions. Kosuda teaches a system comprising at least one processor configured to perform the recited functions (the coverage measurement apparatus whose components are realized by a CPU loading and executing a program, paragraph [0052]; CPU 90a, FIG. 19, paragraph [0205]); Shang teaches one or more processors ([0027]); and Prasad teaches processing means ([0048]). Claim 24 is otherwise rejected for the same reasons set forth for claim 16 above. With respect to claim 25, claim 25 recites limitations similar to claim 17 and is rejected for the same reasons set forth for claim 17 above.
With respect to claim 26, claim 26 recites limitations similar to claim 18 and is rejected for the same reasons set forth for claim 18 above. With respect to claim 27, claim 27 recites limitations similar to claim 19 and is rejected for the same reasons set forth for claim 19 above. With respect to claim 28, claim 28 recites limitations similar to claim 20 and is rejected for the same reasons set forth for claim 20 above.
Claims 21-22 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Kosuda (US Pub. No. 2012/0167059) in view of Shang et al. (US Pub. No. 2022/0172007, hereinafter Shang) in view of Prasad et al. (US Pub. No. 2010/0175052, hereinafter Prasad) and further in view of Dundigalla et al. (US Pub. No. 2022/0035730, hereinafter Dundigalla – previously presented).
With respect to claim 21 (Currently Amended), Kosuda in view of Shang in view of Prasad is silent to disclose, however, in an analogous art, Dundigalla teaches wherein the input test data has same schema, data type and statistical properties as production data and is one of: the production data[[, an obfuscated subset of the production data, mock data.]] (see Dundigalla, paragraphs [0037], [0043], [0046], [0076]). Dundigalla generates the testing data from production data of the types processed by the application in production (e.g., user name, account information, transaction history) ([0043], [0076]), the testing data being generated from sanitized production data ([0037]); the testing data thus has the same schema, data type and statistical properties as the production data, and being a sanitized (obfuscated) subset of the production data reads on the recited “obfuscated subset of the production data.”
It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to source the input test data of Kosuda in view of Shang in view of Prasad from production data, or an obfuscated subset of the production data, as taught by Dundigalla, in order to test the application with data having the same schema, data type and statistical properties as real production data and thereby obtain higher-quality, realistic test results (Dundigalla, paragraphs [0040], [0047]).
With respect to claim 22 (Previously Presented), Kosuda in view of Shang in view of Prasad is silent to disclose, however, in an analogous art, Dundigalla teaches further comprising removing sensitive data from the input test data (see Dundigalla, paragraphs [0035], [0043], [0076]). Dundigalla sanitizes the testing data to remove all sensitive information (e.g., personally identifiable information) from the testing data before it is introduced into the testing environment ([0035], [0043]), and replaces sensitive fields to generate sanitized testing data ([0076]) — i.e., removing sensitive data from the input test data.
It would have been obvious to one of ordinary skill in the art at the time the invention was made before the effective filing date of the claimed invention to remove sensitive data from the input test data of Kosuda in view of Shang and Prasad, as taught by Dundigalla, in order to prevent disclosure of sensitive/PII information during testing and to comply with applicable data-protection requirements (Dundigalla, paragraphs [0035], [0043]).
With respect to claim 29, claim 29 recites limitations similar to claim 21 and is rejected for the same reasons set forth for claim 21 above.
With respect to claim 30, claim 30 recites limitations similar to claim 22 and is rejected for the same reasons set forth for claim 22 above.
Conclusion
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 ANIBAL RIVERACRUZ whose telephone number is (571)270-1200. The examiner can normally be reached Monday-Friday 9:30 AM-6:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S Sough can be reached at 5712726799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANIBAL RIVERACRUZ/Primary Examiner, Art Unit 2192
Prosecution Insights