DETAILED ACTION
1. The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 09/27/2024 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
3. 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.
4. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1, 4-6, 8-9, 11, 14-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Augenstein et al. (Article entitled “Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift”, dated 23 November 2021).
6. Regarding claims 1 and 11, Augenstein teaches a computer-implemented method and system comprising:
A) training, at the client device, a client machine learning (ML) model on client training data (Page 2, Section 2, Page 10); and
B) while training the client ML model: obtaining, from a server, server model weights of a server ML model trained on server training data (Page 2, Section 2, Page 10);
C) the server training data different that the client training data (Page 2, Section 2, Page 10); and
D) transmitting, to the server, client model weights of the client ML model (Page 2, Section 2, Page 10);
E) updating the client ML model using the server model weights (Page 2, Section 2, Page 10);
F) obtaining, from the server, updated server model weights of the server ML model, the updated server model weights updated based on the transmitted client model weights (Page 2, Section 2, Page 10); and
G) further updating the client ML model using the updated server model weights (Page 2, Section 2, Page 10).
The examiner notes that Augenstein teaches “training, at the client device, a client machine learning (ML) model on client training data” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “for client…in parallel do…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes training a client model with client data. The examiner further notes that Augenstein teaches “while training the client ML model: obtaining, from a server, server model weights of a server ML model trained on server training data” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes the obtaining of server model weights. The examiner further notes that Augenstein teaches “the server training data different that the client training data” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes server model weights that are distinct from client model weights. The examiner further notes that Augenstein teaches “transmitting, to the server, client model weights of the client ML model” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes client model weights being transmitted to the global model for subsequent updating of the global model. The examiner further notes that Augenstein teaches “updating the client ML model using the server model weights” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes client model being updated during every iteration using the server model weights. The examiner further notes that Augenstein teaches “obtaining, from the server, updated server model weights of the server ML model, the updated server model weights updated based on the transmitted client model weights” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes client model being updated after every subsequent iteration using the updated server model weights that are updated from transmitted client weights during a previous iteration. The examiner further notes that Augenstein teaches “further updating the client ML model using the updated server model weights” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes client model being updated after every subsequent iteration using the updated server model weights.
Regarding claims 4 and 14, Augenstein teaches a computer-implemented method and system comprising:
A) wherein the client ML model is trained locally on the client device using the client training data that is exclusively stored on the client device (Page 1, Section 1, Page 2, Section 2, Page 3, Section 3, Page 10).
The examiner notes that Augenstein teaches “wherein the client ML model is trained locally on the client device using the client training data that is exclusively stored on the client device” as “Federated learning (FL) [McMahan et al., 2017] is a machine learning setting where multiple ‘clients’ (typically, edge computing devices like mobile phones) collaborate in solving a machine learning problem, under the coordination of a central server. Each client caches raw training data locally, and the data are never exchanged or transferred” (Page 1, Section 1), “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2), “We use aversion of the CelebA dataset [Liu et al., 2015], made into a federated dataset1 by partitioning images up by portrait subject [Caldas et al., 2018]. I.e., we treat each celebrity as having a mobile phone with a cache of photos of themselves, which participates as a client in FL” (Page 3, Section 3), and “for client…in parallel do…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes training a client model with client data (i.e. data stored exclusively at the client).
Regarding claims 5 and 15, Augenstein teaches a computer-implemented method and system comprising:
A) wherein the client training data comprises sensitive data corresponding to the client device (Page 1, Section 1, Page 2, Section 2, Page 3, Section 3, Page 10).
The examiner notes that Augenstein teaches “wherein the client training data comprises sensitive data corresponding to the client device” as “Federated learning (FL) [McMahan et al., 2017] is a machine learning setting where multiple ‘clients’ (typically, edge computing devices like mobile phones) collaborate in solving a machine learning problem, under the coordination of a central server. Each client caches raw training data locally, and the data are never exchanged or transferred” (Page 1, Section 1), “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2), “We use aversion of the CelebA dataset [Liu et al., 2015], made into a federated dataset1 by partitioning images up by portrait subject [Caldas et al., 2018]. I.e., we treat each celebrity as having a mobile phone with a cache of photos of themselves, which participates as a client in FL” (Page 3, Section 3), and “for client…in parallel do…end” (Page 10). The examiner further notes that the algorithm 2 (See Page 10) depicts the synchronous parallel training that includes training a client model with client data (i.e. data stored exclusively at the client). Such client data includes examples of “sensitive” data (See example of celebrity photos stored on their device).
Regarding claims 6 and 16, Augenstein teaches a computer-implemented method and system comprising:
A) wherein the operations further comprise obtaining, from the server, the server model weights at a predetermined interval (Page 2, Section 2, Page 10).
The examiner notes that Augenstein teaches “wherein the operations further comprise obtaining, from the server, the server model weights at a predetermined interval” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that synchronous parallel training depicted in Algorithm 2 is performed at predetermined intervals.
Regarding claims 8 and 18, Augenstein teaches a computer-implemented method and system comprising:
A) wherein the predetermined interval comprises a number of training steps (Page 2, Section 2, Page 10).
The examiner notes that Augenstein teaches “wherein the predetermined interval comprises a number of training steps” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for t…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that synchronous parallel training depicted in Algorithm 2 is performed at predetermined intervals of a number of training steps.
Regarding claims 9 and 19, Augenstein teaches a computer-implemented method and system comprising:
A) wherein the operations further comprise causing the server to update the server ML model by transmitting, to the server, client ML model weights of the client ML model (Page 2, Section 2, Page 10).
The examiner notes that Augenstein teaches “wherein the operations further comprise causing the server to update the server ML model by transmitting, to the server, client ML model weights of the client ML model” as “In synchronous parallel training, we start with a global model, and then separately in parallel perform a round of FEDAVG (with the decentralized data) and steps of training (with the centralized data). This yields two updated versions of the model, one via FL and one via central training. After every round, we take these two versions of the model, merge them together (e.g., average the weights) to form a new global model, and repeat. See Algorithm 2 for details” (Page 2, Section 2) and “Input: initial model…for client…in parallel do…end…Update Global Model…end” (Page 10). The examiner further notes that the global model is updated from the client model weights that are transmitted as depicted in Algorithm 2.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
10. Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Augenstein et al. (Article entitled “Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift”, dated 23 November 2021) as applied to claims 1, 4-6, 8-9, 11, 14-16, and 18-19 above, and further in view of Anwar et al. (U.S. PGPUB 2022/0156633).
11. Regarding claims 2 and 12, Augenstein does not explicitly teach a computer-implemented method and system comprising:
A) wherein the client ML model is randomly initialized.
Anwar, however, teaches “wherein the client ML model is randomly initialized” as “The local model 22 can be randomly initialised” (Paragraph 39).
The examiner further notes that Anwar teaches the concept of randomly initializing a local model in a federated environment. The combination would result in the local models of Augenstein to be randomly initialized.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Anwar’s would have allowed Augenstein’s to provide a method for improving federated learning efficiency, as noted by Anwar (Paragraph 14).
12. Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Augenstein et al. (Article entitled “Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift”, dated 23 November 2021) as applied to claims 1, 4-6, 8-9, 11, 14-16, and 18-19 above, and further in view of Niu et al. (Article entitled “Billion-Scale Federated Learning on Mobile Clients: A Submodel Design with Tunable Privacy”, dated 25 September 2020).
13. Regarding claims 3 and 13, Augenstein does not explicitly teach a computer-implemented method and system comprising:
A) wherein the server ML model is randomly initialized.
Niu, however, teaches “wherein the server ML model is randomly initialized” as “At the initial stage, the cloud server randomly initializes the global model (Line 1)” (Page 410, Section 4.2.1).
The examiner further notes that Niu teaches the concept of randomly initializing a global model in a federated environment. The combination would result in the global model of Augenstein to be randomly initialized.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Niu’s would have allowed Augenstein’s to provide a method for improving federated learning efficiency, as noted by Niu (Page 416, Section 7).
14. Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Augenstein et al. (Article entitled “Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift”, dated 23 November 2021) as applied to claims 1, 4-6, 8-9, 11, 14-16, and 18-19 above, and further in view of Balakrishnan et al. (U.S. PGPUB 2021/0119901).
15. Regarding claims 7 and 17, Augenstein does not explicitly teach a computer-implemented method and system comprising:
A) wherein the predetermined interval comprises a time period.
Balakrishnan, however, teaches “wherein the predetermined interval comprises a time period” as “The federated learning may use a multi-access edge computing (MEC) server that learns a global machine learning model from multiple clients; each of which learn a local machine learning model from the dataset available to them. Instead of the clients sending all the data to a central MEC server to train a machine learning algorithm-such as a deep neural network, recurrent neural network (RNN) etc.—federated learning enables the clients to train a model on their data locally and only share the model weights to the MEC server. The MEC server receives model updates from several clients and calculates the average (or other statistical combination) of the model weights to form the global model. For the next round of client training, the MEC server propagates the global model weights to clients. A federated learning epoch is the time between the beginning of the round and the end of the round. Any data received after the epoch is not part of the round, though the data may be used in a subsequent round” (Paragraph 33).
The examiner further notes that Balakrishnan teaches the concept of performing federated learning for a predetermined epoch (i.e. the claimed time period). The combination would result in the federated learning of Augenstein to be performed for a predetermined interval of a period of time.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Balakrishnan’s would have allowed Augenstein’s to provide a method for avoiding overhead in federated learning, as noted by Balakrishnan (Paragraph 18).
16. Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Augenstein et al. (Article entitled “Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift”, dated 23 November 2021) as applied to claims 1, 4-6, 8-9, 11, 14-16, and 18-19 above, and further in view of Beaufays et al. (WO 2022/019885).
17. Regarding claims 10 and 20, Augenstein does not explicitly teach a computer-implemented method and system comprising:
A) wherein the client ML model comprises a local hotword detection model.
Beaufays, however, teaches “wherein the client ML model comprises a local hotword detection model” as “As some non-limiting example, the hotword detection engine 222 can utilize a combined hotword detection model 222A” (Paragraph 52).
The examiner further notes that Beaufays teaches the concept pf a local hotword detection model in a federated environment. The combination would result in the local models of Augenstein to be hotword detection models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Beaufays’s would have allowed Augenstein’s to provide a method for training models for specific uses, as noted by Beasufays (Paragraph 67).
Conclusion
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2022/0398500 issued to Singhal et al. on 15 December 2022. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to perform federated learning).
U.S. PGPUB 2022/0293093 issued to Beaufays et al. on 15 September 2022. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to perform federated learning).
U.S. PGPUB 2023/0359907 issued to Augenstein et al. on 09 November 2023. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to perform federated learning).
Contact Information
19. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see 20. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Mahesh Dwivedi
Primary Examiner
Art Unit 2168
July 07, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168
Prosecution Insights