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 .
Preliminary Amendment
The preliminary amendment filed 4/29/2024 has been entered. Claims 2-5, 7-14 have been amended.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8, 13-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pezeshki et al. (US 2022/0116764 A1), hereinafter “Pezeshki”.
As per claim 1, Pezeshki teaches a machine learning model training method comprising:
“sending, by a distributed node, a first parameter set to a central node, wherein the first parameter set is used to determine a target training method for a machine learning model of the distributed node” at [0045]-[0046], [0084]-[0086], [0096];
(Pezeshki teaches a network 100 includes a base station BS 110 (i.e., “central node”) and a plurality of User Equipment EUs 120(i.e., “distributed node”.) The EU 120 reports a machine learning processing capability (i.e., “first parameter set”) to the base station BS 110. The report indicates machine learning hardware capability, approximate turnaround time for computing the gradient or weight updates in each of the federated learning round, as function of battery status of the UE (i.e., “power consumption”). The BS groups the UEs for different federated learning rounds according to machine learning capability)
“the target training method comprises a first training method or a second training method” at [0082];
(Pezeshki teaches the training method comprises centralized training data in a data center (i.e., “first training method”) or federated training where a group of UEs receives a machine learning model from a base station and work together to )
“receiving, by the distributed node, a first message from the central node, wherein the first message comprises information about the target training method” at [0108]-[0113] and Fig. 7.
(Pezeshki teaches based on the received machine learning capability reports, the base station groups the UEs at time t4 and schedules the UEs 620 in accordance with the grouping. The UA receives a machine learning model from the base station, the machine learning model may be trained in a federated learning process (i.e., “target training method”))
As per claim 2, Pezeshki teaches the method of claim 1, wherein “the first parameter set comprises: energy consumption generated when the distributed node updates a local machine learning model for one time” at [0084].
As per claim 3, Pezeshki teaches the method of claim 2, wherein “the first parameter set further comprises at least one of the following parameters: a quantity of samples in a local dataset of the distributed node, a quantity of times that the distributed node performs model updating in each communication cycle, a transmit power of the distributed node, a transmission rate from the central node to the distributed node, or information about a channel from the central node to the distributed node” at [0084], [0097]-[0105].
As per claim 4, Pezeshki teaches the method of claim 1, wherein “the first training method comprises a centralized learning method and the second training method comprises a federated learning training method” at [0082].
As per claim 5, Pezeshki teaches the method of claim 1, further comprising “sending, by the distributed node, a second message to the central node, wherein the second message is used to feed back that the distributed node supports the first training method and the second training method” at [0088], [0095].
Claims 6-8, 13-19 recite similar limitations as in claims 1-5 and are therefore rejected by the same reasons.
Allowable Subject Matter
Claims 9-12 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.
The following is a statement of reasons for the indication of allowable subject matter: prior art of record does not teach the combination of claimed elements including: before the sending, by the central node, a first message to the distributed node, the machine learning model training method further comprises: determining, by the central node, a first energy consumption index and a second energy consumption index based on the first parameter set, wherein the first energy consumption index indicates an energy consumption level of the first training method for the machine learning model, and the second energy consumption index indicates an energy consumption level of the second training method for the machine learning model; and determining, by the central node, the target training method for the machine learning model of the distributed node based on the first energy consumption index and the second energy consumption index” as recited in dependent claims 9-10.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in 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 case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
July 7, 2026
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