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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/30/2025 has been entered.
Claims 1 and 8 have been amended.
Claims 2 and 9 have been canceled.
Claims 1, 3-8, and 10-18 remain pending in the application.
The amendment filed 12/30/2025 is sufficient to overcome the 101 rejections of
claims 1, 3-8, and 10-14. The previous rejections have been withdrawn.
The amendment filed 12/30/2025 is sufficient to overcome the 102 rejections of
claims 1, 3-8, and 10-14. The previous rejections have been withdrawn.
Argument 1, regarding the 101 rejections, applicant argues that the claims integrate the judicial exceptions into the practical application of issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution, wherein clients with greater federated contributions receive greater rewards than clients with lesser federated contributions. Examiner agrees and the 101 rejections have been withdrawn.
Argument 2, regarding the prior art rejections, applicant argues that Toyoda does not teach determining each client’s contribution to the global model but instead teaches calculating the “goodness” of the model, including averaging chosen models.
Examiner notes this argument is moot in view of Baykaner et al (Pub. No.: US 20230066452 A1), hereafter Baykaner.
Baykaner teaches wherein the federated contribution for one of the clients comprises a scalar quantity that represents a deviation or divergence of the prior global machine learning model and the current global machine learning model based on the client’s contribution (the contribution of each worker node is evaluated based on the contribution’s error rate, which is a scalar value. One worker node is determined to contribute significantly more than another worker node if the contribution provides satisfactory training required to reduce the error rate of the computing model, P0085); and issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution, wherein clients with greater federated contributions receive greater rewards than clients with lesser federated contributions (rewards may be provided to worker nodes, with larger rewards being granted to workers who provide a larger contribution of resources towards obtaining a trained computing model, P0081, P0085, P0089).
The full prior art rejections are outlined below.
Claim Rejections - 35 USC § 103
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 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, 3-8, and 10-18 are rejected under 35 U.S.C. 103 as being unpatentable over “Blockchain-Enabled Federated Learning With Mechanism Design”, hereafter Toyoda in view of Baykaner et al (Pub. No.: US 20230066452 A1), hereafter Baykaner.
Regarding claims 1 and 8, Toyoda teaches a method for reward-driven federated learning, comprising: receiving, by a federated contribution computer program executed by a federated node in a distributed ledger network (“our design can be implemented with an existing public blockchain, for example, Ethereum”, P219750, section B1), a plurality of local machine learning model updates from a plurality of clients in the distributed ledger networks (local model is updated by workers, P219747, section 4, left column and P219748, figure 3); retrieving, by the federated contribution computer program, a prior global machine learning model (Figure 6, “Every round K 0 workers are randomly chosen from the participants…The requester sends two symmetric keys, Ke−1 and Ke, a key to decrypt the model updates in the previous round”, P219748-P219749, section 4); calculating, by the federated contribution computer program, a current global machine learning model based on the prior global machine learning model and the plurality of local machine learning model updates (Figure 3, P219748 and figure 6, P219749. “Updated models are encrypted with Ke and stored in data storage”, P219749); determining, by the federated contribution computer program, a federated contribution for each client based on each client's contribution to the current global machine learning model (Votes are counted for models by workers submitted in the current round, figure 7, P219749, reward distribution).
Toyoda does not appear to explicitly teach “wherein the federated contribution for one of the clients comprises a scalar quantity that represents a deviation or divergence of the prior global machine learning model and the current global machine learning model based on the client’s contribution; and issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution, wherein clients with greater federated contributions receive greater rewards than clients with lesser federated contributions”.
Baykaner teaches wherein the federated contribution for one of the clients comprises a scalar quantity that represents a deviation or divergence of the prior global machine learning model and the current global machine learning model based on the client’s contribution (the contribution of each worker node is evaluated based on the contribution’s error rate, which is a scalar value. One worker node is determined to contribute significantly more than another worker node if the contribution provides satisfactory training required to reduce the error rate of the computing model, P0085); and issuing, by the federated contribution computer program, rewards to each client based on the client's federated contribution, wherein clients with greater federated contributions receive greater rewards than clients with lesser federated contributions (rewards may be provided to worker nodes, with larger rewards being granted to workers who provide a larger contribution of resources towards obtaining a trained computing model, P0081, P0085, P0089).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of Toyoda and Baykaner before them, to include Baykaner’s specific teaching of
providing larger rewards to workers who contribute more towards obtaining a trained model in Toyoda’s system of Blockchain-Enabled Federated Learning. One would have been motivated to make such a combination of providing larger rewards to workers who contribute more towards obtaining a trained model (see Baykaner P0081, P0085, P0089) and motivating workers with rewards during federated learning (see Toyoda page 219745, section 2A “Federated Learning”) for improved training of a computing model (see Baykaner P0005).
Claim 8 is similar in scope to Claim 1 and is rejected under a similar rationale. Claim 8 further recites the limitation of “calculating, by the federated contribution computer program, a relative federated contribution for each of the clients, and the rewards are issued based on the client's relative federated contribution”. Baykaner further teaches calculating, by the federated contribution computer program, a relative federated contribution for each of the clients, and the rewards are issued based on the client's relative federated contribution (rewards may be provided to worker nodes, with larger rewards being granted to workers who provide a larger contribution of resources towards obtaining a trained computing model, P0081, P0085, P0089).
Regarding claims 3 and 10, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Toyoda further teaches wherein the rewards comprise a payment (Rewards given to workers may include payments in the form of cryptocurrencies, P219747, section 2).
Regarding claims 4 and 11, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Toyoda further teaches wherein the rewards comprise a fee (If the workers are dishonest they may receive a negative reward, or fee, P219750, section 6).
Regarding claims 5 and 12, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Toyoda further teaches refusing, by the federated contribution computer program, a local machine learning model update from a client with a low federated contribution (Models provided by different workers are evaluated based on their mean absolute errors. Models with the highest mean absolute errors are interpreted as having a low federated contribution, and they are not included in the model update. P219749, left column, algorithm 1).
Regarding claims 6 and 13, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Toyoda further teaches wherein each of the plurality of local machine learning model updates comprise a plurality of weights for the local machine learning models (Models sent in by workers include a set of weights, P219749, section A, right column).
Regarding claims 7 and 14, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Toyoda further teaches wherein each of the plurality of local machine learning model updates comprise the local machine learning models (Workers send in their updated models, P219749, section A, right column).
Regarding claims 15 and 17, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Baykaner further teaches disconnecting, by the federated contribution computer program, one of the clients from the distributed ledger network in response to the client having a federated contribution below a threshold (worker nodes that contribute below a threshold may be excluded from the monitoring arrangement, P0092).
Regarding claims 16 and 18, Toyoda in view of Baykaner teaches the limitations of claims 1 and 8 as outlined above. Baykaner further teaches rejecting, by the federated contribution computer program, a future local machine learning model update from one of the clients in response to the client having a federated contribution below a threshold (worker nodes that have been excluded due to having contributions below a threshold may not have their updates received by the monitoring arrangement, including by other worker nodes participating in the monitoring arrangement, P0092).
Conclusion
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/I.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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