DETAILED ACTION
Remarks
Applicant presents a request for continued examination dated 18 June 2026 in response to the 9 April 2026 final Office action (the “Previous Action”), as well as the 8 June 2026 advisory action.
Claims 1, 8 and 15 are amended.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 18 June 2026 has been entered.
37 C.F.R. § 1.121
It is noted for the record that Applicant’s claim listing is not compliant with 37 C.F.R. § 1.121 because shows the word “maximizes” struck thorough as if that language is newly deleted, when it was in fact already deleted from them claim with Applicant’s previous amendments. The claims are nonetheless examined in the interests of compact prosecution.
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 asserts with respect to claims 1, 8 and 15 that page 8 of the Previous Action “alleges, without support, that ‘it would have been obvious (from Xia) 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.’” (Remarks, p. 9 par. 5 – p. 10 par. 4).
Examiner respectfully disagrees with Applicant’s characterization of the Office action. The cited portion explicitly refers to regression analysis of historical data comprising features values for the test case “as taught by Chen”, not Xia. (See the Previous Action at p. 8 item 12 par. 3). Xia was never cited as teaching regression analysis as argued.
Applicant’s remaining arguments with are moot in view of the new ground(s) of rejection below, necessitated by Applicant’s amendments.
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.
Claims 1-2, 4-9, 11-16 and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
As to claim 1, the claim recites:
A method, comprising:
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;
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 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 feature values for the given test case executing on the one or more different IT assets than the particular IT asset, wherein the feature values for the historical execution data comprise an execution time and one or more characteristics of the one or more different IT assets;
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; and
initiating one or more automated actions based at least in part on the schedule;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
Although a process is claimed (Step 1), under the broadest reasonable interpretation in light of the specification the above underlined elements recite a mental process because they describe a process performable by the human mind with aid of pen and paper. Note that the prediction “function” can be a mathematical equation per page 11 lines 9-12 of the specification. The claim therefore recites an abstract idea. (Step 2A Prong 1).
None of the additional elements integrate the judicial exception into a practical application. (Step 2A Prong 2).
Reference to generating the schedule “automatically” and the method as being performed by “at least one processing device comprising a processor coupled to memory” only amounts to mere instructions to implement the abstract idea on a computer. See M.P.E.P. § 2106.05(f).
Looking at the claim limitations as an ordered combination yields the same conclusion as that reached when looking at the elements individually. Their collective function is merely to implement the abstract idea using a generic computer.
The claim does not include additional elements that amount to significantly more than the judicial exception for substantially the same reasons discussed above with respect to a practical application. (Step 2B).
As to claim 2, the features of this claim do not integrate the abstract idea into a practical application or amount to significantly more at least because the schedule is not actually used to reduce any exaction time or increase utilization of IT assets. The claim (via claim 1) only requires initiation an unspecified action based “in part” on the schedule. Such an action could merely include, for example, approving the schedule or generating a report.
As to claims 4-5, 7 and 21, the features of these claims do not integrate the abstract idea into a practical application or amount to significantly more at least because they only further describe the abstract idea itself.
As to claims 6, the features of this claim do not integrate the abstract idea into a practical application or amount to significantly more at least because the claim only refers to generating the schedule “to execute” the test cases. This language does not require actual execution of the test cases using the schedule. Reference to an “optimizer” does not require any particular optimization of the schedule either or optimization at all. And referring to the optimizer as “processor-based” only amounts to mere instructions to implement the abstract idea on a computer. See M.P.E.P. § 2106.05(f).
As to claim 8, the claim recites the same abstract idea as claim 1 and does not include additional elements that integrate abstract idea into a practical application or amount to significantly more than the abstract idea for substantially the same reasons. The addition of “at least one processing device comprising a processor coupled to a memory” and “the at least one processing device configured to implement” the steps of the method only amount to mere instructions to implement this step of the abstract idea using a generic computer. See M.P.E.P. § 2106.05(f).
As to claims 9, 11-14 and 22, the features of this claim do not integrate the abstract idea into a practical application or amount to significantly more for reasons substantially the same as those set forth above with respect to claims 2, 4-7 and 21.
As to claim 15, the claim recites the same abstract idea as claim 1 and does not include additional elements that integrate abstract idea into a practical application or amount to significantly more than the abstract idea for substantially the same reasons. The addition of “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 only amounts to mere instructions to implement this step of the abstract idea using a generic computer. See M.P.E.P. § 2106.05(f).
As to claims 16, 18-20 and 23, the features of this claim do not integrate the abstract idea into a practical application or amount to significantly more for reasons substantially the same as those set forth above with respect to claims 2, 4, 6-7 and 23.
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, 13-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sathe (US 2006/0010448) (art of record – hereinafter Sathe), Xia et al. “Scheduling Functional Regression Tests for IBM DB2 Products” (art of record – hereinafter Xia), Chen et al. (CN 107203469) (art of record – hereinafter Chen) and Ramalingam et al. (US 10,133,775) (art of record – hereinafter Ramalingam).
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, wherein the feature values for the historical execution data comprise an execution time and one or more characteristics of the one or more different IT assets.
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 making more accurate predictions when executing test cases on different machines. (See Xia, p. 8 left col. par. 2, p. 8 right col. par. 1).
Further, 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 learn the prediction function from the features used to train it and make predictions without the need for explicitly programming to do so. (See Crabtree, “What is Machine Learning? Definition, Types, Tools & More”, already of record at p. 1 last par. – p. 2 par. 1).
Further, in an analogous art, Ramalingham discloses:
wherein the feature values for the historical execution data comprise an execution time and one or more characteristics of the one or more different IT assets (e.g., Ramalingham, col. 8 ll. 40-43: each record of the combined query cost an execution time data 210 may comprise a tuple of cost 206 and execution time 208; col. 12 l. 60 – col. 13 l. 3: additional data may be incorporated into the tuples of the combined query cost and execution time data 210. The additional data may include data for additional parameter(s) 706 describing characteristics of the storage systems(s) 124 [IT assets] where the data queries 104 previously [historically] executed. The data for additional parameters may include memory capacity of the storage system(s) 124; processing resources of the data storage system(s) 124; col. 13 ll. 12-13: the model 116 may be generated based on the combined query cost and execution data 210 as modified to include data for the additional parameters 706; col. 12 ll. 16-17: the model 116 may be determined based on regression analysis; col. 12 ll. 24-26: the model 116 may be used to determine the predicted query execution time 122 of the data query 104; col. 8 ll. 53-57: due to differences in physical hardware between data storage system 204(1) and 204(2), executing the same data query 104 may have different predicted query execution time 122).
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 regression analysis execution data of Sathe/Xia/Chen to include feature values comprising an execution time and one or more characteristics of the one or more different IT assets, as taught by Ramalingham, as Ramalingham would provide the advantage of a means of providing a regression model that enables a more accurate prediction of execution time when executing on systems of varied characteristics. (See Ramalingham, col. 13 ll. 36-40).
As to 2, Sathe/Xia/Chen/Ramalingam 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/Ramalingam 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/Ramalingam 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/Ramalingam 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.
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 those steps.
However, in an analogous art, Ramalingam discloses:
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., Ramalingam, col. 16 ll. 10-25: one or more non-transitory computer-readable storage media having instructions that may be used to program a computer to perform processes or methods described herein).
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 Ramalingam, as Ramalingam would provide the advantage of a means of programming a computer to perform the steps. (See Ramalingam, col. 16 ll. 10-25).
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 5 and 12 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 view of Ramalingam (US 10,133,775) in further view of Banavar et al. (US 2005/0267770) (art of record – hereinafter Banavar).
As to claim 5, Sathe/Xia/Chen/Ramalingam 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.
Claims 21-23 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 view of Ramalingam (US 10,133,775) in further view of Wells et al. (US 2024/0150307) (art of record – hereinafter Wells).
As to claim 21, Sathe/Xia/Chen/Ramalingam 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 Sathe/Xia/Chen 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.
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
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/TODD AGUILERA/Primary Examiner, Art Unit 2192
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