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 .
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 1-5, 15-19 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]-[0106];
(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 base station groups the UEs for different federated learning (i.e., “first training method”)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 train the model (i.e., “second training method”))
“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 15-19 recite similar limitations as in claims 1-5 and are therefore rejected by the same reasons.
Allowable Subject Matter
Claims 6-8, 10-14 are allowed.
The following is an examiner’s statement of reasons for allowance:
Prior art of record does not teach the combination of claimed elements including: “receiving, by a central node, a first parameter set from a distributed node, wherein the first parameter set is used to determine a target training method for a machine learning model of the distributed node, and the target training method comprises a first training method or a second training method; determining, by the central node, a first energy consumption index and a second enemy consumption index based on the first parameter set, wherein the first enemy consumption index indicates an enemy consumption level of the first training method for the machine learning model, and the second enemy consumption index indicates an enemy consumption level of the second training method for the machine learning model; determining, by the central node, the target training method for the machine learning model of the distributed node based on the first enemy consumption index and the second enemy consumption index; and sending, by the central node, a first message to the distributed node, wherein the first message comprises information about the target training method” as recited in independent claim 6.
Thus, prior art of record neither renders obvious nor anticipates the combination of claimed elements in light of the specification. After a further search and a thorough examination of the present application and in light of the prior art made of record, claim 6 is allowed.
Dependent claims 7-8, 10-14 are allowed at least by virtue of their dependency from claim 6.
Response to Arguments
Applicant's arguments filed 9/28/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding claim 1, Applicant argued that Pezeshki does not disclose that a first parameter set is “used to determine a target training method” where “the target training method comprises a first training method or a second training method” as recited by claim 1. On the contrary, Pezeshki teaches at [0045]-[0046], [0084]-[0086], [0096]-[0106] 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 base station groups the UEs for different federated learning (i.e., “first training method”) rounds according to machine learning capability. Pezeshki therefore teaches the machine learning capability (i.e., “first parameter set”) is used by the base station to determine which Use Equipments EUs are used to participated in the federate learning/training (i.e., “first training method”).
Applicant further argued that Pezeshki does not disclose “the target training method comprises a first training method or a second training method”. On the contrary, Pezeshki teaches at [0082] the training method comprises centralized training data in a data center (i.e., “second training method”) or federated training where a group of UEs receives a machine learning model from a base station and work together to train the model (i.e., “first training method”). Pezeshki then teaches at [0096]-[0106] that the base station uses the machine learning capability of each of the UEs to determine whether the US will participate in federated learning or centralized learning. For example, Pezeshki teaches at [0105] that “if the UE is in power savings mode, the UE may decide not to participate in federated learning”
Applicant further argued that Pezeshki does not disclose “receiving… a first message from the central node, wherein the first message comprises information about the target training method” as recited by claim 1. On the contrary, Pezeshki teaches at [0108]-[0113] and Fig. 7 that based on the received machine learning capability reports, the base station (i.e., “central node”) groups the UEs at time t4 and schedules the UEs 620 in accordance with the grouping. The UA receives a machine learning model (i.e., “a first message”) from the base station, the machine learning model may be trained in a federated learning process (i.e., “target training method”).
Conclusion
THIS ACTION IS MADE FINAL. 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 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
October 6, 2026
