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 . Claims 1-8 are pending, all examined and rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 7 recites a “computer-readable recording medium” to execute the federated learning method. However, the BRI of this term in view of the specification includes transitory media, e.g., signals. The examiner recommends amending to “non-transitory computer-readable recording medium” to overcome this rejection.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Ashour, US PG. Pub #2025/0385774, priority to August 19, 2022 in view of Ortega, US PGPub #2012/0014289, published January 19, 2012 further in view of Akdeniz, U.S. PG Pub #2023/0068386, priority to December 26, 2020
With regard to Independent Claim 1,
Ashour teaches a federated learning method using device clustering performed by at least one processor, the federated learning method comprising… . See e.g., [0006], (“Federated learning (FL) refers to a machine learning technique in which multiple decentralized nodes holding local data samples may train a global machine learning (ML) model…”)
obtaining an arbitrary client group including some clients as a result of performing clustering on a plurality of clients, determining one of the some clients as a leader client… wherein the leader client receives data associated with at least one parameter of a pre-trained model from each of the some clients See e.g., [0034], (“In one example, a FL parameter server (e.g., a base station) may group network nodes (e.g., UEs) together into clusters, led by designated cluster leaders.) See further [0034] and [0102] discusses message passing including local model weights from the pre-trained models on the local nodes between learning nodes and the cluster leader.
Ashour further teaches determining at least one client among the some clients as a target client… See e.g., [0130], (discussing that some of the iterations can include less clients than the entire set, thus those are “target clients.”)
Ashour further teaches receiving some data associated with at least one parameter of the model of the target client from the leader client, wherein the some data is included in the data. See e.g., [0102], (“For instance, individual cluster leaders of respective clusters may pass messages including aggregated local updates to ML model weights to the FL parameter server to aggregate…”)
Ashour does not explicitly disclose that the leaders are selected based on a centroid associated with the clustering. Ashour further does not explicitly disclose that the target clients are selected based on an amount of computing resources of the pre-trained model and a training loss of the pre-trained model.
In an analogous art of clustering, Ortega teaches determining one of the some clients as a leader client based on a centroid associated with the clustering. See [0128], (Discussing cluster algorithm based on k-means, specifically “For each cluster, we choose the cluster-head as the node closest to the centroid of the cluster…”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date having Ashour and Ortega before them to modify the cluster-head selection of Ashour to pick cluster-heads by distance as disclosed by Ortega. One would be motivated to do so as this is a simple method that minimizes the total cost.
Ashour-Ortega does not explicitly disclose that the target clients are selected based on an amount of computing resources of the pre-trained model and a training loss of the pre-trained model.
In an analogous art, Akdeniz teaches target clients selected based on an amount of computing resources of the pre-trained model and a training loss of the pre-trained model. See e.g., [0351-0364], (discussing “Approach 1 – Loss-Based Client Selection” wherein both loss and computing resources are utilized to determine client selection for federated learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date having Ashour-Ortega and Akdeniz before them to perform client selection based on loss and resources as disclosed by Akdeniz. One would be motivated to do so in order to balance accuracy and convergence time.
With regard to Dependent Claim 2,
As discussed with regard to Claim 1, Ashour-Ortega-Akdeniz teaches all of the limitations. Ashour-Ortega-Akdeniz further discloses a first training loss associated with the target client is greater than a second training loss associated with an arbitrary client that is not the target client among some of the clients. Akdeniz, [0364], (discussing that clients with larger losses can be selected for next round of training.)
With regard to Dependent Claim 3,
As discussed with regard to Claim 1, Ashour-Ortega-Akdeniz teaches all of the limitations. Ashour-Ortega-Akdeniz further discloses wherein the clustering is performed based on each communication distance between the plurality of clients. See e.g., Ortega, [0080, 0128], (discussing k-means clustering, e.g., that distance between nodes in each cluster area will be calculated and compared to find the shortest using the k-means algorithm.)
With regard to Dependent Claim 4,
As discussed with regard to Claim 1, Ashour-Ortega-Akdeniz teaches all of the limitations. Ashour-Ortega-Akdeniz further discloses wherein the determining of the one client as the leader client includes determining one client with a shortest distance to the centroid among the some clients as the leader client based on the centroid associated with the clustering. See e.g., Ortega, [0128], (Discussing cluster algorithm based on k-means, specifically “For each cluster, we choose the cluster-head as the node closest to the centroid of the cluster…”)
With regard to Dependent Claim 5,
As discussed with regard to Claim 1, Ashour-Ortega-Akdeniz teaches all of the limitations. Ashour-Ortega-Akdeniz further discloses wherein the clustering includes K-means clustering. See e.g., Ortega, [0080, 0128], (discussing k-means clustering, e.g., that distance between nodes in each cluster area will be calculated and compared to find the shortest using the k-means algorithm.)
With regard to Dependent Claim 6,
As discussed with regard to Claim 1, Ashour-Ortega-Akdeniz teaches all of the limitations. Ashour-Ortega-Akdeniz further discloses calculating a weight based on an amount of computing resources of the target client by using the some data. See e.g., Akdeniz, [0205], (discussing the data at each node is weighed based on various computing resource metrics.)
Ashour-Ortega-Akdeniz further discloses generating a global model by using the weight and the at least one parameter of the model of the target client. See e.g., Ashour, [0006], [0032] (discussing FL in general, generating a global model using para.) Akdeniz, [0122], (discussing FL in general, including the global model is generated/updated using the local node weights and updates.)
With regard to Independent Claims 7 and 8,
These claims are similar in scope to Claim 1 and are rejected under a similar rationale.
Conclusion
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/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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