DETAILED ACTION
This action is in response to the filing 04/27/2026. Claims 1-20 are pending and have been fully considered.
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 .
Status of the Claims
Claims 1-20 are rejected under 35 U.S.C. 103.
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, 8-9, and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Kaitha (U.S. PGPub No. 20210049093) in view of Gao et al. (U.S. PGPub No. 20220198340).
Regarding Claim 1, Kaitha teaches,
A method for testing servers, comprising: constructing an artificial intelligence (AI) framework system (see test system (100) comprising, at least, intelligent configuration generator (114) within a learning engine (302) [0055; Fig. 1]; also see test environment (120) comprising servers (122) [0018; Fig. 1]);
packaging a test script for testing the servers and putting the test script into the AI framework system (a test script is executed at the test environment (120) and may execute on the servers ("testing servers") [0022]);
receiving first test results from the AI framework system and generating a database for storing the first test results (after a test script is executed, the intelligent rule generator (114) may store the results in a database (128) [0031]);
[…]
executing the test task with the AI framework system (where the second modified version of the test script ("test task") is executed [0026]);
and outputting second test results for the test task to the database (after each test script is executed, the intelligent rule generator (114) may store the results in a database (128) [0031]).
While Kaitha discloses that each test case includes test tasks [0022] and that a modified version of a test script may be provided following user feedback [0026], Kaitha does not appear to disclose and Gao teaches,
collecting, from the first test results, test parameters associated with prior test executions (where a test is executed [steps 618-622 of Fig. 6A with corresponding text: 0105-0107]; after execution of the test, test configuration and results are stored in test history data (320) [step 624 of Fig. 6A with corresponding text: 0108]; a predictive model is trained on test history data (320) [step 638 of Fig. 6B with corresponding text: 0115]; a test configuration includes a parameter value for each test parameter [0075]; the Examiner notes that, therefore, test parameters associated with prior executions are collected);
setting up a neural network using the test parameters to predict a test execution metric for a given test task (a predictive model is trained ("set up") on test history data ("test parameters") (320) [step 638 of Fig. 6B with corresponding text: 0115]; the model type may be a neural network [0078]; the predictive model is trained to predict "target variables" [step 640 of Fig. 6B with corresponding text: 0118]; a target variable may indicate, at least, how much execution time will be required for a new test configuration ("test execution metric") [0085]);
receiving a test task for testing the servers (a test objective function is received from a user interface [0083] where the test objective function includes, at least, specifying model execution characteristics such as test execution time [0084]);
adjusting execution of the test task based on the predicted test execution metric generated by the neural network (test configurations in subsequent iterations are selected based on the predicted scores and used to define the next current set of test configurations [step 641 of Fig. 6B with corresponding text 0120]);
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 server-testing method including user-modification of tests as disclosed by Kathia to incorporate trained neural network-based modification of tests for specific objectives as disclosed by Gao. The resulting combination allows for an optimized test generation process that drives tests to better achieve test objectives faster [Gao; 0019-0020].
Regarding Claim 2, Gao teaches,
The method of claim 1, further comprising: constructing a user interface for receiving the test task (a test objective function is received from a user interface [0083] where the test objective function includes, at least, specifying model execution characteristics such as test execution time [0084]))
and wherein the test execution metric comprises a test execution time (the predictive model is trained to predict "target variables" [step 640 of Fig. 6B with corresponding text: 0118]; a target variable may indicate, at least, how much execution time will be required for a new test configuration [0085]).
The same motivation for Claim 1 also applies to Claim 2.
Regarding Claims 8, see all teachings with respect to Claim 1, above, and that Kaitha teaches,
A computer system comprising one or more processors and one or more memories storing computer instructions, wherein the one or more processors are configured to execute the computer instructions to perform operations comprising: … ([0081])
Claim 9 depends from Claim 8 and is rejected by the same grounds of rejection under 35 U.S.C. 103 as being unpatentable over Katia in view of Gao for the same teachings as Claim 2, above.
Regarding Claim 15, see all teachings with respect to Claim 1, above, and that Kaitha teaches,
A non-transitory computer-readable storage medium storing computer instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising: ([0092]) …
Claim 16 depends from Claim 15 and is rejected by the same grounds of rejection under 35 U.S.C. 103 as being unpatentable over Katia in view of Gao for the same teachings as Claim 2, above.
Claims 3-7, 10-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaitha in view of Gao, further in view of Wang et al. (U.S. PGPub No. 20070112695).
Regarding Claim 3, Kaitha discloses,
The method of claim 1, wherein constructing the AI framework system comprises: … adding, to the AI framework, … algorithms that support ... machine learning (where intelligent rule generator (114) may be within a learning engine (302) [0055] and the learning engine may employ machine learning algorithms [0062]);
While Gao discloses use of a neural network [0078], Kaitha in view of Gao do not appear to disclose and Wang teaches,
creating a system file defining an AI environment for an AI framework to run (where the (H)FNN (fuzzy neural network) may be embodied as software [0056]);
adding, to the AI framework, libraries and algorithms that support fuzzy logic and machine learning (where a fuzzy neural network may be embodied on a computer (302) [0055]; computer (302) includes a memory storing a program library ("that support…") [0051]; further, the fuzzy neural network (FNN) may be trained with learning algorithms [0047]);
and building a system image by incorporating fuzzy logic algorithms and machine learning models defined in the AI framework (where the FNN is fuzzy rules imbedded in a neural network [0041]; where the FNN may be trained with the learning algorithms [0047]; where computer (302) storing program libraries [0051] may determine the arrangement and structure of the FNNs [0059]).
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 server testing system employing neural networks of Kaitha in view of Gao to include a fuzzy neural network system used to perform classification of Wang. The resulting combination using fuzzy logic improves (input to output) classification accuracy [Wang; 0008], where the combination of the both fuzzy logic and neural networks may increase the speed and accuracy of the classification [Wang; 0026].
Regarding Claim 4, Kaitha teaches,
The method of claim 3, further comprising: … observing current state of the AI environment by collecting data and converting features from the data into numerical values (servers may be monitored by a monitoring engine (110) [0023], where monitoring engine (110) further provides the results to a result engine (112) [0035] that applies a numerical score to the individual features [0024]);
Kaitha does not appear to disclose and Wang teaches,
initializing the AI environment by setting an initial state of automation processes and creating agents for learning (the FNN may be initialized with initial values [0047]; and the proper FNNs are determined ("creating agents for learning") [0059]);
and setting up the machine learning models and fuzzy rules for the fuzzy logic algorithms based on the numerical values (after determining classification groups ("features") [0060]; where it is demonstrated that the features are numerical from equations R1 and R2 due to the use of fuzzification [0042-0044]; the HFNN is built using the proper FNNs based on the appropriate structure to classify data [0061]).
The same motivation for Claim 3 also applies to Claim 4.
Regarding Claim 5, Kaitha teaches,
The method of claim 4, wherein observing current state of the AI environment by collecting data and converting features from the data into numerical values comprises: selecting features correlated with a test execution time of each test execution (where a desired result of testing may be indicative ("highly correlated") of execution time [0035]; where, as described by example, performance indicators may be selected based on the desired result (as shown by the example, a particular test requiring only three performance requirements) [0024]);
and converting the features into numerical features or applying standardization to the features (a numerical score may be generated for each performance requirement ("feature") [0024 and/or 0035]).
Regarding Claim 6, Kaitha teaches,
The method of claim 5, wherein the test parameters comprise one or more of a type of the given test, a number of test cycles, configurations of the servers, and a test time of the given test (test performance requirements (collected via monitoring) include: server response time ("a test time of each test") [0019]; further, test-defining information also includes the specified configuration for performing the test ("configuration of the servers") [0020])
Regarding Claim 7, Kaitha in view of Gao do not appear to disclose and Wang teaches,
The method of claim 4, wherein setting up the machine learning models and fuzzy rules for the fuzzy logic algorithms based on the numerical values comprises: combining a fuzzy logic system with a multi-layered neural network to set up the machine learning models and the fuzzy rules (where the FNN is fuzzy rules imbedded in a neural network, and where the FNN itself is multi-layered (exemplary shown with 5 layers) [0041]).
The same motivation for Claim 3 also applies to Claim 7.
Claims 10-14 and 17-20 each recite a shift in statutory category and are rejected under 35 U.S.C. 103 as being unpatentable over Kaitha in view of Gao, further in view of Wang, for the same reasons as Claims 3-7 and Claims 3-6, respectively, above.
Response to Arguments
Applicant’s arguments filed 04/27/2026 have been fully considered.
Applicant’s arguments with respect to Claims 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the rejection under 35 U.S.C. 103 in view of the new reference, Gao.
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.
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/A.E.W./Examiner, Art Unit 2113
/MARC DUNCAN/Primary Examiner, Art Unit 2113
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