DETAILED ACTION
Remarks
Applicant presents a communication dated 25 February 2026 in response to the 2 December 2025 non-final Office action (the “Previous Action”).
With the communication:
claims 1, 4-5, 8-9, 11-12, 15-16 and 18 are amended;
claims 3, 10 and 17 are cancelled;
claims 21-23 are added.
Claims 1-2, 4-9, 11-16 and 18-23 are pending. Claims 1, 8 and 15 are the independent claims.
Any unpersuasive arguments are addressed in the “Response to Arguments” section below. Any new ground(s) of rejection were necessitated by Applicant’s amendments.
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 .
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
Applicant has incorporated the features of cancelled claims 3, 10 and 17 into independent claims 1, 8 and 15 and asserts that Chen does not address a predicted execution time of a given test case “on a particular IT asset” being predicted using at least one actual execution time of the given test case “on one or more different IT assets” than the particular asset. (Remarks p. 10 pars. 4-5).
Examiner respectfully points out, however, that Chen was never cited as teaching predicting execution time of a test case on a particular IT asset using actual execution time of the test case on one or more different IT assets. Xia is cited as teaching those features, not Chen. It would have been obvious to that prediction such that it is made using a prediction function generated using a regression analysis of historical execution data comprising feature values for the test case, as taught by Chen. Using such a prediction function would provide the advantages of machine learning, which enables a computer to learn from the data to make the predictions without being explicitly programmed to do so and improve its performance over time. (See, e.g., “What is Machine Learning? Definition, Types, Tools & More” at p. 1 last par. – p. 2 par. 5).
Applicant’s arguments are thus unpersuasive.
Applicant’s arguments with respect to the remaining claims by virtue of their dependence from claims 1, 8 or 15 are unpersuasive for the same reasons.
Claim Rejections - 35 USC § 112
The Previous Action’s § 112 rejections are withdrawn in view of Applicant’s claim amendments unless reproduced herein below.
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.
Claims 1-2, 4, 6-9, 11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Sathe (US 2006/0010448) (art of record – hereinafter Sathe) in view of Xia et al. “Scheduling Functional Regression Tests for IBM DB2 Products” (art of record – hereinafter Xia) in view of Chen et al. (CN 107203469) (art of record – hereinafter Chen).
NOTE: Chen is not in English. Citations herein refer to the English machine translation of Chen in the file record.
As to claim 1, Sathe discloses a method, comprising:
a plurality of information technology (IT) assets, of an IT infrastructure, that execute one or more of the plurality of test cases; (e.g., Sathe, abstract: tests to be run on the network test system [IT infrastructure]; par. [0008]: test objects [IT assets] are special purpose computers where the actual physical resources need to run the test are located; par. [0048]: the test controller 51 runs the tests [test cases] on the test objects 41)
obtaining information characterizing an execution time of one or more of the plurality of test cases on one or more of the plurality of IT assets, wherein at least one execution time of a given one of the plurality of test cases on a particular one of the plurality of IT assets comprises at least one predicted execution time, wherein the at least one predicted execution time is predicted using at least one actual execution time of the given test case (e.g., Sathe, par. [0009]: a baseline, or a prediction of the time each test is likely to take to complete [execution time], depending on a pattern each test has followed historically; par. [0049]: updating the test baseline information 49 with actual run times [execution times]. Updating the test baseline information 49 with actual run times improves the accuracy of time prediction for future runs of the tests)
automatically generating, using the information characterizing the execution time of the one or more test cases on the one or more IT assets, a schedule for additional executions of at least a subset of the plurality of test cases on respective ones of the plurality of IT assets; (e.g., Sathe, par. [0037]: using the available test baseline information 37, the scheduler 31 creates a plurality of testing schedules; par. [0021]: the test controller runs the tests on the test objects according to the selected schedule) and
initiating one or more automated actions based at least in part on the schedule; (e.g., Sathe, par. [0021]: the scheduler selects one of the schedules and communicates the selected schedule to the test controller. After receiving the selected schedule, the test controller runs the tests on the test objects according to the selected schedule)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (e.g., Sathe, par. [0037]: the scheduler 31 is a computer [computers necessarily comprise a processor coupled to memory]).
Sathe does not explicitly disclose: obtaining information characterizing a plurality of test cases that evaluate one or more software issues related to a software application; obtaining information characterizing a plurality of information technology (IT) assets, of an IT infrastructure, that execute one or more of the plurality of test cases; wherein the at least one predicted execution time is predicted using at least one actual execution time of the given test case on one or more different IT assets than the particular IT asset, and wherein the at least one predicted execution time of the given test case is predicted using a prediction function generated using a regression analysis of historical execution data comprising features values for the given test case executing on the one or more different IT assets than the particular IT asset.
However, in an analogous art, Xia discloses:
obtaining information characterizing a plurality of test cases that evaluate one or more software issues related to a software application; (e.g., Xia, p. 1 right col. Sec. 1: regression testing (FRT) assures that, when new functions are added or the design is changed, the product has not regressed [an issue]. The DBT Regression Test Team conducts the FRT for DB2 UDB products [a software application]; p. 1 left par. Abstract: test jobs [test cases]; p. 6 left col. Sec. 5.1 par. 2: job information determines a job’s characteristics [information characterizing a test case] and is used to decide the similarity of jobs; p. 6 right col. Sec. 5.2 pars. 1-2: we compare the job with cases in the CaseBase. Equal jobs have the same characteristics)
obtaining information characterizing a plurality of information technology (IT) assets, of an IT infrastructure, that execute one or more of the test cases (e.g., Xia, p. 2 right col last par. – p. 2 par. 2: the Team conducts the testing on a grid [IT infrastructure], which comprises about 300 machines [IT assets] with different configurations; p. 4 left col. last par: machines running jobs [test cases, see above]; p. 6 left col. Sec. 5.1 par. 3: machine information [information characterizing a plurality of IT assets] contains the machine characteristics of slaves)
wherein the at least one predicted execution time is predicted using at least one actual execution time of the given test case on one or more different IT assets than the particular IT asset; (e.g., Xia, p. 3 right col. par. 2: new jobs’ run times are estimated [predicted] according to “similar” jobs” that have run in the past; p. 7 left col. Figure 5.1 [see algorithm]: for each for each similar Job get the average actual run time and adjust the estimated run time due to the difference in machine characteristics; p. 2 left col. par. 4: with a large grid it is unlikely that the same job will be executed on the same machine multiple times; p. 7 left col. p. 6 left col. last par.: similar jobs have the same job characteristics but run on different machines) and the given test case executing on the one or more different IT assets than the particular IT asset (see immediately above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the execution of test case tasks on a particular IT asset of an IT infrastructure using a predicted execution time of the test case taught by Sathe by obtaining information characterizing a plurality of test cases that evaluate one or more software issues related to a software application; obtaining information characterizing the plurality of information technology (IT) assets and wherein the at least one predicted execution time is predicted using at least one actual execution time of the given test case on one or more different IT assets than the particular IT asset, as taught by Xia, as Xia would provide the advantages of a means of detecting regressions of a software product (see Xia, p. 1 right col. last par) and a means of providing accurate execution time predictions. (See Xia, p. 8 right col. last par.).
Further still, in an analogous art, Chen discloses:
wherein the at least one predicted execution time of the given test case is predicted using a prediction function generated using a regression analysis of historical execution data comprising feature values for the given test case executing (e.g., Chen, pars. [0018-0019]: we used the features collected by CSmith as follows Address characteristics, such as the number of times the address of a structure or variable is addressed [this number being a feature value]; par. [0033]: a set of test programs [test cases] was collected as a training set, and their execution time was recorded. All the above features were extracted, and the recorded time was used as a label [the labeled features being historical execution data comprising feature values for the given test case]. Similar to the training capability model, the first step is to normalize each dimension of the features in the training set, and then use a Gaussian process to build a regression model, i.e., a time model [prediction function, training this regression model being regression analysis]. The trained time model is used to predict the actual execution time of the new test program [given test case]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the execution time prediction based on historical execution on one or more different IT assets than the particular asset taught by Sathe/Xia to include predicting the execution time using a prediction function generated using a regression analysis of the historical execution data that includes feature values for the given test case historically executing, as taught by Chen, as Chen would provide a means of performing the prediction using machine learning. (See Chen, par. [0043]). Machine learning would be able to improve over time and learn the prediction function from the features used to train it.
As to 2, Sathe/Xia/Chen discloses the method of claim 1, (see rejection of claim 1 above) Sathe further discloses:
wherein the schedule one or more of reduces a total execution time of the subset of the plurality of test cases and increases a utilization of the plurality of IT assets (e.g., Sathe, par. [0002]: a method and apparatus to optimize resource usage in a scheduled testing system; par. [0038]: maximizing usage of the test objects [IT assets] during the testing interval).
As to claim 4, Sathe/Xia/Chen discloses the method of claim 1 (See rejection of claim 1 above) but Sathe/Xia does not explicitly disclose further comprising transforming the historical execution data to generate training data used to generate the prediction function, wherein the transforming the historical execution data comprises one or more of: cleaning at least some of the historical execution data, integrating at least some of the historical execution data and standardizing at least some of the historical execution data.
However, in an analogous art, Chen discloses:
further comprising transforming the historical execution data to generate training data used to generate the prediction function, wherein the transforming the historical execution data comprises one or more of: cleaning at least some of the historical execution data, integrating at least some of the historical execution data and standardizing at least some of the historical execution data (e.g., Chen, par. [0033]: a set of test programs [test cases] was collected as a training set, and their execution time was recorded. All the above features were extracted, and the recorded time was used as a label. Similar to the training capability model, the first step is to normalize [standardize] each dimension of the features in the training set [historical execution data], and then use a Gaussian process to build a regression model, i.e., a time model [prediction function]; The trained time model is used to predict the actual execution time of the new test program).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the historical execution data of Sathe/Xia to include transforming that data by standardizing it to generate training data used to generate the prediction function, as taught by Chen, as Chen would provide a means of normalizing the data to a particular scale. (See Chen, par. [0059]).
As to claim 6, Sathe/Xia/Chen discloses the method of claim 1 (see rejection of claim 1 above), Sathe further discloses:
wherein the automatically generating the schedule to execute the at least the subset of the plurality of test cases employs a processor-based scheduling optimizer (e.g., Sathe, par. [0037]: the scheduler is a computer [i.e., is processor based]; par. [0002]: to optimize resource usage in a scheduled testing system; Sathe, par. [0021]: the scheduler creates testing schedules. Then the scheduler selects one of the schedules and communicates the selected schedule to the test controller. After receiving the selected schedule, the test controller runs the tests on the test objects according to the selected schedule).
As to claim 7, Sathe/Xia/Chen discloses the method of claim 1 (see rejection of claim 12 above), but Sathe does not explicitly disclose wherein a total execution time to execute the at least the subset of the plurality of test cases comprises a maximum one of a plurality of sums of the execution times of the at least the subset of the plurality of test cases on the respective ones of the IT assets.
However, in an analogous art, Xia discloses:
wherein a total execution time to execute the at least the subset of the plurality of test cases comprises a maximum one of a plurality of sums of the execution times of the at least the subset of the plurality of test cases on the respective ones of the IT assets (e.g., Xia, p. 10 left col Sec. 6.3 par. 1: for each set of jobs [test cases, see above] we shuffle and run them [on the IT assets, see above] three times. The performance metric is the total run time [sum of execution times] of the jobs tested; p. 10 left col. Sec. 6.3 Table 6.2 and last par.: we select the worst run time [maximum run time of the plurality] for the CAS dispatcher).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Sathe by incorporating determining a total execution time to execute the at least the subset of the plurality of test cases that comprises a maximum one of a plurality of sums of the execution times of the at least the subset of the plurality of test cases on the respective ones of the IT assets, as taught by Xia, as Xia would provide the advantage of a means of determining a worst run time of the scheduler and identifying the relative performance of that run time. (See Xia, p. 10 Table 6.2 and p. 10 left col. last par. – right col. par. 1).
As to claim 8, it is an apparatus claims having limitations substantially the same as claim 1. Accordingly, it is rejected for substantially the same reasons. Further limitations, disclosed by Sathe, include:
an apparatus comprising:
at least one processing device comprising a processor coupled to a memory; (e.g., Sathe, par. [0037]: the scheduler 31 is a computer [computers necessarily comprise a processor coupled to memory])
the at least one processing device being configured to (see rejection of claim 1 above).
As to claim 9, it is an apparatus claim having limitations substantially the same as those of claim 2. Accordingly, it is rejected for substantially the same reasons.
As to claim 11, it is an apparatus claim having limitations substantially the same as those of claim 4. Accordingly, it is rejected for substantially the same reasons.
As to claim 13, it is an apparatus claim having limitations substantially the same as those of claim 6. Accordingly, it is rejected for substantially the same reasons.
As to claim 14, it is an apparatus claim having limitations substantially the same as those of claim 7. Accordingly, it is rejected for substantially the same reasons.
Claims 5, 12, 15-16 and 18-20 is rejected under 35 U.S.C. 103 as being unpatentable over Sathe (US 2006/0010448) in view of Xia (“Scheduling Functional Regression Tests for IBM DB2 Products”) in view of Chen (CN 107203469) in view of Banavar (US 2005/0267770).
As to claim 5, Sathe/Xia/Chen discloses the method of claim 1 (see rejection of claim 1 above), and further discloses test case execution (see rejection of claim 1 above) but does not explicitly disclose wherein the prediction function is used to populate one or more missing entries of a test case execution time matrix.
However, in an analogous art, Banavar discloses:
wherein the prediction function is used to populate one or more missing entries of a execution time matrix (e.g., Banavar, par. [0039]: execution duration predictor 150 stores predictions of the duration of each task’s execution in a Duration database. A format of the Duration Database 175 is shown in Fig 10. Each entry in the database represents statistics of the time it takes a given user to perform a given task).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the test execution time prediction by a prediction function of Sathe/Xia/Chen to include using that function populate missing entries of an execution time matrix, as taught by Banavar, as Banavar would provide the advantage of a means of persisting prediction data in a database table. (See Banavar, par. [0039]).
As to claim 12, it is an apparatus claim having limitations substantially the same as those of claim 5. Accordingly, it is rejected for substantially the same reasons.
As to claim 15, it is a non-transitory processor-readable storage medium claim having limitations substantially the same as those of claim 1. Those limitations, including the following steps are taught by or obvious in view of the Sathe/Xia/Chen for the reasons set forth above.
Sathe/Xia/Chen does not explicitly disclose a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the steps.
However, in an analogous art, Banavar dislcoses:
a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the steps (e.g., Banavar, par. [0065]: software components including instructions for performing the methodologies described herein may be stored in one or more memory devices “(e.g., ROM, fixed or removable memory),” loaded in part or in whole “(e.g., into RAM)” and executed by a CPU).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Sathe/Xia/Chen to include a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the steps, as taught by Banavar, as Banavar would provide the advantage of a means of implementing the steps via software executing in a computer. (See Banavar, pars. [0065], Figure 6 and associated text).
As to claim 16, it is a medium claim having limitations substantially the same as those of claim 2. Accordingly, it is rejected for substantially the same reasons.
As to claim 18, it is a medium claim having limitations substantially the same as those of claim 4. Accordingly, it is rejected for substantially the same reasons.
As to claim 19, it is a medium claim having limitations substantially the same as those of claim 6. Accordingly, it is rejected for substantially the same reasons.
As to claim 20, it is a medium claim having limitations substantially the same as those of claim 7. Accordingly, it is rejected for substantially the same reasons.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Sathe (US 2006/0010448) in view of Xia (“Scheduling Functional Regression Tests for IBM DB2 Products”) in view of Chen (CN 107203469) in further view of Ramalingam et al. (US 10,133,775) (art made of record – hereinafter Ramalingam) and Wells et al. (US 2024/0150307) (art made of record – hereinafter Wells).
As to claim 21, Sathe/Xia/Chen discloses the method of claim 1 (see rejection of claim 1 above), and further discloses the given test case (see rejection of claim 1 above) but does not explicitly disclose wherein the regression analysis of the historical execution data fits an execution time curve of the particular IT asset for the given test case to obtain one or more parameters values of the prediction function using a designated confidence level.
However, in an analogous art, Ramalingam discloses:
wherein the regression analysis of the historical execution data fits an execution time curve of the particular IT asset for the given software to obtain one or more parameters values of the prediction function (e.g., Ramalingam, col. 12 ll. 60-66: additional data may be incorporated into execution time data 210. The additional data may include data for parameter(s) 706 describing characteristics of the data storage system(s) 124 where the data queries 104 [software] previously executed; col. 5 ll. 30-32: module 113 performs a regression analysis to generate the model(s); col. 9 ll. 4-7: the model 116 may be generated by fitting a curve to the combined query [software] codes and execution time data; col. 13 ll. -29: the model may be expressed as a mathematical formula, where Te is the estimated execution time of a data query 104, P1 is a first additional parameter such as the memory capacity of the data storage system(s) where the data query is to be executed; col. 13 ll. 56-58: the model 116 may be used to generate predicted query execution time 122 across different data storage systems 124 [IT assets], such as on differing server clusters).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the regression analysis predicting an execution time of a given test case, such that the regression analysis fits an execution time curve of the particular IT asset for the given software whose execution time being predicted, to obtain one or more parameters values of the prediction function, as taught by Ramalingam, as Ramalingam would provide the advantage of a means of generating a model that best describes the historical execution data. (See Ramalingam, col. 9 ll. 19-26, 33-35).
Further, in an analogous art, Wells discloses:
wherein the regression analysis fits [a] curve using a designated confidence level (e.g., Wells, par. [0163]: the data processing system may determine a confidence level in the fitted curve. That is, the system may check whether the curve 286 is a good it for the data points. The system may assess the goodness of fit of the curve 286 using a defined tunable threshold such as a minimum acceptable R 2 or adjusted R 2 metric. However, any other suitable technique may be used to assure the confidence (i.e., goodness) of the fitted curve).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the regression analysis fitting a curve of Sathe/Xia/Chen/Ramalingam, such that the regression analysis the curve using a designated confidence level, as taught by Wells, as Wells would provide the advantage of a means of assuring a sufficiently good curve. (See Wells, par. [0163]).
As to claim 22, it is an apparatus claim having limitations substantially the same as those of claim 21. Accordingly, it is rejected for substantially the same reasons.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Sathe (US 2006/0010448) in view of Xia (“Scheduling Functional Regression Tests for IBM DB2 Products”) in view of Chen (CN 107203469) in view of Banavar (US 2005/0267770) in further view of Ramalingam (US 10,133,775) and Wells (US 2024/0150307).
As to claim 23, it is a medium claim having limitations substantially the same as those of claim 21. Accordingly, it is rejected for substantially the same reasons.
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 TODD AGUILERA whose telephone number is (571)270-5186. The examiner can normally be reached M-F 11AM - 7:30PM EST.
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/TODD AGUILERA/Primary Examiner, Art Unit 2192
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