DETAILED ACTION
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 .
Response to Arguments
Applicant’s arguments with respect to the rejections under 35 USC 101 have been fully considered and are persuasive. Accordingly, the rejections are withdrawn.
Applicant’s arguments with respect to the rejections under 35 USC 103 have been fully considered and are persuasive. Accordingly, the rejections are withdrawn. However, upon further consideration, new grounds of rejection are made.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mohanty (US Pat. No. 11,343,146) in view of Illikkal (US Pub. No. 2024/0015080).
Regarding claim 1, Mohanty shows a system comprising:
a processing device (at least implicitly disclosed as a necessary component of a computer-implemented system); and
a memory device that includes instructions executable by the processing device (at least implicitly disclosed as a necessary component of a computer-implemented system) for causing the processing device to perform operations comprising:
determining a numerical value representative of a [configuration] at an active computing [system] (at least including an encoded value for each of multiple configuration settings: see col. 7, lines 1-48; col. 8, lines 45-67);
computing a set of similarity scores using the numerical value, each similarity score in the set of similarity scores being indicative of a level of similarity of the active computing [system] to each computing [system] of a plurality of computing [systems] with respect to the [configuration] (see col. 10, lines 11-67);
selecting, based on the set of similarity scores and using a machine learning model, a subset of computing [systems] from the plurality of computing [systems] (see Fig. 2, item 234, describing an ML component for similarity matching and which selects “list of devices with similar configuration for proactive resolution”; see col. 7, lines 10-19);
generating a recommended modification to the [configuration] based on the subset of computing [systems] (see col. 7, lines 26-47 and col. 8, lines 1-12); and
adjusting the [configuration] according to the recommended modification (applying or recommending a configuration change: see col. 7, lines 26-47 and col. 8, lines 1-12).
Mohanty does not explicitly show:
that the configuration is an amount of central processing unit (CPU) resources or an amount of memory allocated to a microservice of an active computing cluster;
that the similarity is with respect to the amount of CPU resources or the amount of memory allocated to the microservice;
that the recommended modification is to the amount of CPU resources or the amount of memory allocated to the microservice; and
that the adjusting is the amount of CPI resources or the amount of memory allocated to the microservice.
Illekal shows:
a configuration in the form of an amount of central processing unit (CPU) resources or an amount of memory allocated to a microservice of an active computing cluster (see [0088] and [0091]-[0092]);
determining similarity with respect to the amount of CPU resources or the amount of memory allocated to the microservice (see [0081], [0094], and [0103]-[0104]);
a recommended modification is to an amount of CPU resources or the amount of memory allocated to the microservice (see [0027], [0030]-[0031], and [0105]); and
adjusting the amount of CPI resources or the amount of memory allocated to the microservice (see [0027], [0030]-[0031], and [0105]).
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 Mohanty with the teachings of Illikkal in order to more efficiently allocate resources to the microservices (see Illekal, [0027]).
Regarding claim 2, the combination shows the limitations of claim 1 as applied above and further shows wherein the operations further comprise generating an output comprising the recommended modification and transmitting the output to a user device (see Mohanty, col. 8, lines 1-11).
Regarding claim 3, the combination shows the limitations of claim 1 as applied above and further shows wherein the numerical value is a first numerical value and the set of similarity scores is a first set of similarity scores, and wherein the operations further comprise: determining a second numerical value representative of a configuration setting at the active computing cluster; and computing a second set of similarity scores using the second numerical value, wherein each similarity score in the second set of similarity scores is indicative of a level of similarity of the active computing cluster to each computing cluster of a plurality of computing clusters with respect to the configuration setting (see Mohanty, col. 8, lines 45-67, describing encoding multiple configuration and device attributes to be used in determining similarity; see also Mohanty, col. 10, lines 11-67).
Regarding claim 4, the combination shows the limitations of claim 3 as applied above and further shows wherein the operation of selecting the subset of computing clusters from the plurality of computing clusters further comprises: generating an overall similarity score for each computing cluster of the plurality of computing clusters based on the first set of similarity scores and the second set of similarity scores; inputting the overall similarity score for each computing cluster of the plurality of computing clusters into the machine learning model; and receiving, from the machine learning model, the subset of computing clusters (see Mahonty, col. 10, lines 45-67, describing a similar device recommendation engine that calculates Euclidean distance as a similarity score between systems; note that the engine includes an “ML component,” col. 7, lines 13-19).
Regarding claim 5, the combination shows the limitations of claim 1 as applied above and further shows wherein the operations further comprise: receiving, for each computing cluster of the plurality of computing clusters an additional numerical value; and wherein computing the set of similarity scores using the numerical value further comprises using the additional numerical value for each computing cluster of the plurality of computing clusters (see Mohanty, col. 8, lines 45-67, describing encoding multiple configuration and device attributes for each device; see also Mohanty, col. 10, lines 11-67).
Regarding claim 6, the combination shows the limitations of claim 5 as applied above and further shows wherein the operation of generating the recommended modification is based on the additional numerical value for each computing cluster in the subset of computing clusters (see Mohanty, col. 8, lines 45-67, describing encoding multiple configuration and device attributes for each device; see also Mohanty, col. 9, lines 13-55 and col. 10, lines 11-67).
Regarding claim 7, the combination shows the limitations of claim 1 as applied above and further shows wherein the machine learning model is a first machine learning model, and wherein the operations further comprise, prior to generating the numerical value representative of the amount of CPU resources or the amount of memory allocated to the microservice: inputting a plurality of configuration settings associated with the active computing cluster into a second machine learning model, wherein the plurality of configuration settings include the amount of CPU resources and the amount of memory allocated to the microservice; and outputting, by the second machine learning model the amount of CPU resources or the amount of memory allocated to the microservice (see Mohanty, Fig. 2, step 230 and col. 7, lines 1-10, as combined above).
Claims 8-14 correspond to claims 1-7 and are rejected for the reasons given above, mutatis mutandis.
Claims 15-20 correspond to claims 1-6 and are rejected for the reasons given above, mutatis mutandis.
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 Christopher D. Biagini whose telephone number is (571)272-9743. The examiner can normally be reached weekdays from 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar Louie can be reached at (571) 270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Christopher D. Biagini
Primary Examiner
Art Unit 2445
/Christopher Biagini/Primary Examiner, Art Unit 2445