CTNF 18/137,519 CTNF 71525 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Objection to the Specification 06-11 AIA 2. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Art Rejection 07-20-aia AIA 3. 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 of this title, 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. 07-20-02-aia AIA 4. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA 5. Claim s 1-3, 5-10, 12-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gururaj, U.S. pat. Appl. Pub. No. 2020/0250002, in view of Roytman, U.S. pat. No. 7,234,073 . Per claim 1, Gururaj discloses a computer implemented method comprising: a) receiving an automatic triggering alert for changing a controller proxy in a computer cluster environment based on monitoring the computer cluster environment and policy rules associated with the computer cluster environment, e.g., receiving a configuration change event, wherein the controller proxy comprises a node or a subgroup of nodes acting on behalf of the network cluster to execute application workload (see par 0010, 0038); b) broadcasting/communicating a message based on the triggering alert to a plurality of agents in the computer cluster environment, e.g., communicating the configuration change with other managing agents (see par 0031-0032, 0040); c) determining candidate proxies among the plurality of agents, i.e., determining eligible existing and added nodes or subgroups of nodes, and for each of the candidate proxies, determining a system health status based on a prediction model's forecast and determining a policy compliance score based on the policy rules, i.e., determining a health score for each node or subgroup of nodes represented by a coordinator (see par 0029, 0054); d) based on the system health status and the policy compliance score associated with each of the candidate proxies, selecting a new controller proxy among the candidate proxies for the computer cluster environment, i.e., selecting a node or a subgroup of nodes with a corresponding cluster manager to provide service for the computer cluster environment (see par 0055). Gururaj does not explicitly teach notifying the new controller proxy to perform management of the computer cluster environment. However, Roytman discloses a method of selecting an agent for managing a network in the event of a configuration change including identifying a set of candidate agents, selecting a suitable agent according to a set of rules and informing the selected agent to perform management of the network (see Roytman, col 2, ln 36 – col 3, ln 5). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Gururaj teaching in any network application including Roytman’s network management. This is because it would have enabled selecting the best management agent, e.g., agent with lowest management load (see Roytman, col 3, ln 17-21). Per claim 2-3, Roytman also teaches applying rules to determine candidate agents based on version numbers including determining agents that have the highest version numbers among the plurality of agents (see col 10, ln 26-30). Per claim 5, Gururaj teaches determining a score based on policy rules includes comparing the policy rules against a candidate’s current system attributes, e.g., CPU load, available memory, etc., computing the policy compliant score based in a number of policy rules the candidate is compliant with (see par 0024-0025). Per claim 6, Gururaj teaches computing a combined score using system health status, e.g., load, and the policy compliance score, e.g., latency, and selecting candidate having the highest combined score as new controller (see par 0029). It would have been further obvious to one skilled in the art to recognize that the combined score would be typically a weighted score as it is computed based on different unequaled attributes and/or rules. Per claim 7, Gururaj teaches using a machine-learning model to predict system resource usage and performance metric of an agent using historical information associated with the plurality of agents in the cluster environment (see par 0027). It should be also noted that a machine-learning model is typically built/trained by historical information. Claims 8-10, 12-17 and 19-20 are similar in scope as that of claims 1-3 and 5-7 and hence are rejected for the same rationale set forth for claims 1-3 and 5-7 . 07-21-aia AIA 6. Claim s 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gururaj and Roytman, further in view of Dang, U.S. pat. Appl. Pub. No. 2018/0293488 . Neither Gururaj nor Roytman teach generating a feature map using real time data. However such generation and use of feature map from real time data to build/train a prediction model is well known in the art as disclosed by Dang (see Dang, par 0004). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize feature map in Gururaj because it would have enabled building a machine-learning prediction model more efficiently. Conclusion 7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Viet Vu whose telephone number is 571-272-3977. The examiner can normally be reached on Monday through Thursday from 8:00am to 6:00pm. The Group general information number is 571-272-2400. The Group fax number is 571-273-8300. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Emmanuel Moise, can be reached at 571-272-3865. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /Viet D Vu/ Primary Examiner, Art Unit 2455 6/10/26 Application/Control Number: 18/137,519 Page 2 Art Unit: 2455 Application/Control Number: 18/137,519 Page 3 Art Unit: 2455 Application/Control Number: 18/137,519 Page 4 Art Unit: 2455 Application/Control Number: 18/137,519 Page 5 Art Unit: 2455 Application/Control Number: 18/137,519 Page 6 Art Unit: 2455
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