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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 12, 2026 has been entered.
Response to Amendment and Arguments
Claims 1-20 are pending and are being examined in this application.
In light of Applicant’s amendments to the claims, the 101 rejection is withdrawn.
Applicant’s arguments with respect to the 103 rejections have been considered, but are moot in view of the new ground(s) of rejection provided below.
Allowable Subject Matter
Claims 3-7, 10, 11, and 13-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mermoud et al. (US Pub. 20160352764) in view of Lecue et al. (US Pub. 20190213039).
Referring to claim 1, Mermoud discloses A method performed by a first network node for determining application of at least one machine learning model from a plurality of machine learning models in a multi-vendor communications network [figs. 3 and 4; pars. 17, 51, 55, 56, and 76; a computer network comprises a plurality of provider edges (PEs) and client edges (CEs) that communicate via a network backbone; a server is configured as a supervisory and control agent (SCA), and one or more CEs are configured as distributed learning agents (DLAs); the SCA coordinates deployment of machine learning models to target DLAs], the method comprising:
receiving a request from an actor device operating in a target network to enable running a task for the target network on the multi-vendor communications network by using at least one of the machine learning models from the plurality of machine learning models to perform the task [figs. 3 and 4; pars. 55, 84, and 85; the SCA receives a request from a UI process of a client device (via a CE operating in a local network running a target DLA) to select a machine learning model from a machine learning library to perform anomaly detection for the target DLA];
responsive to the request, determining whether the at least one of the machine learning models from the plurality of machine learning models...to perform the task [pars. 76, 83, and 84; the SCA determines the machine learning model to select based on whether the machine learning model was constructed using similar network and traffic characteristics as the target DLA (i.e., how well the machine learning model will perform the anomaly detection)]; and
responsive to the determination, sending a communication to the actor device, wherein the communication comprises information that a machine learning model from the plurality of machine learning models is ready to perform the task or that no machine learning model was found to perform the task [fig. 3; pars. 55, 69, 84, and 85; the SCA sends a message including only an ID of the (already cached) machine learning model to the target DLA].
Mermoud does not appear to explicitly disclose determining whether the at least one machine learning model can be translated to perform the task using a semantic mapping of data for the target network to the at least one of the machine learning models.
However, Lecue discloses determining whether the at least one machine learning model can be translated to perform the task using a semantic mapping of data for the target network to the at least one of the machine learning models [pars. 10, 11, 18, 21, and 24; a model determination platform determines an AI model associated with a source domain to perform a target task for a target domain; the AI model is identified from a set of AI models based on a set of (semantic) mappings, different clusters, and/or a performance measure based on (semantic) features, (semantic) differentiators, the set of mappings, and/or the different clusters; various semantic correspondences between the target domain and the AI model are used to determine how well the AI model will perform the target task (i.e., how well the AI model can be translated to perform the task)].
It would have been obvious to one ordinary skill in the art to modify the selecting of the machine learning model taught by Mermoud so that the machine learning model is selected based on semantic correspondences as taught by Lecue, with a reasonable expectation of success. The motivation for doing so would have been to facilitate transfer of AI models across different domains [Lecue, par. 10].
Referring to claim 2, Mermoud discloses The method of Claim 1, wherein the task comprises one of: a prediction of a key performance indicator; a proposal for at least one property of the target network; a probability for at least one property of the target network; an action on the target network; an improvement of at least one operating parameter of the target network; a classification of data in the target network [pars. 83 and 84; note the anomaly detection]; and an analysis of data in the target network [pars. 83 and 84; note the anomaly detection].
Referring to claim 8, Mermoud discloses The method of claim 1, further comprising: determining whether the at least one machine learning model comprises an exact match for performing the task using the inputs from each description of a network inventory model that apply to performing the task in the target network [par. 84; note the selecting based on the network and traffic characteristics of the target DLA].
Referring to claim 9, Mermoud and Lecue disclose The method of Claim 8, further comprising: in response to determining that no machine learning model comprises an exact match, determining whether at least one machine learning model from the filtered identification of machine learning models comprises a machine learning model that can be translated to perform the task [Mermoud: pars. 61, 76, 84; note the selecting based on the network and traffic characteristics of the target DLA, where the machine learning model can be updated or combined with other machine learning models to perform the anomaly detection; Lecue: pars. 10, 11, 18, 21, and 24, disclosing using semantic correspondences to identify the AI model (i.e., there may not be an exact match)].
Referring to claim 12, Mermoud discloses The method of claim 1, further comprising: adapting the at least one machine learning model for performing the task [pars. 61 and 76; the machine learning model can be updated or combined with other machine learning models to perform the anomaly detection].
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE PARK whose telephone number is (571)270-7727. The examiner can normally be reached M-F 8AM-5PM.
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/Grace Park/Primary Examiner, Art Unit 2144
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