DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 4/28/2026. Claims 1-10 and 20-29 are pending in this Office 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 .
Response to Arguments
Applicant's arguments filed 4/28/26 with respect to 103 rejection have been fully considered and are moot in view of new ground of rejection.
Applicant cancelled claims 11-19. Accordingly, the 101 rejection of claims 11-19 has been withdrawn.
Claim Objections
Claim 21 is objected to because of the following informalities: Claim 21 recites in part, “the system of claim 1” . However, claim 1 is a method claim, NOT a system claim. It appears applicant intends to recite “the system of claim 20” since claim 20 is a system claim. Appropriate correction is required
Claim Rejections - 35 USC § 103
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 1-10 and 20-29 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al.(US Patent Application Publication 2023/0038310 A1, hereinafter “Yang”) and further in view of Geraci et al.(US Patent Application Publication 2020/0327433 A1, hereinafter “Geraci”) and further in view of Dong Yang et al.(US Patent Application Publication 2022/0076133 A1, hereinafter “Dong”
As to claim 1, Yang teaches a method implemented by one or more processors of a client device, the method comprising: receiving, from a user of the client device, client data, the client data being generated locally at the client device; (Yang par [0072] teaches local dataset 211)
processing, using an on-device machine learning (ML) model stored locally in on-device memory of the client device, the client data to generate predicted output (Yang par [0072] teaches client device 210 obtains model 100 from the server. It may then train the received model using local dataset 211), wherein the on- device machine learning model includes a plurality of on-device ML layers , and wherein the plurality of on-device ML layers include at least one or more first on-device ML layers and one or more second on-device ML layers (Yang par [0071] teaches the model 100 comprises set of common layers 120 and the set of client specific layers 140) ;
[receive a scheduling signal from a remote system instructing the client device to generate a first update for the one or more first on-device ML layers; generate, using unsupervised learning, a gradient based on the predicted output];
generating, based on the gradient, a first update for the one or more first on-device ML layers of the on-device ML model stored locally in the on-device memory of the client device; and transmitting the first update to a remote system,(Yang par [0075] teaches after the training of the model 100, the client device 210 sends the updated set of common layers 120 to the server computing device 220. Alternatively, the client device may only send parameters of the updated set of common layers 120 that have changed to the server 220)
wherein transmitting the first update to the remote system causes the remote system to update a global ML model stored remotely in remote memory of the remote system, (Yang par [0096] teaches the server 220 may receive an updated set of common layers 120 from each of the client devices 210 and 210’)
wherein the global ML model includes at least one or more first global ML layers and one or more second global ML layers (Yang Fig.2 and par [0071] teaches the model 100 comprises set of common layers 120 and the set of client specific layers 140), and wherein causing the remote system to update the global ML model includes causing the one or more first global ML layers to be updated based on the first update while one or more of the second global ML layers are fixed. (Yang par [0096] teaches the server 220 may receive an updated set of common layers 120 from each of the client devices 210 and 210’)
Yang fails to expressly teach generating, using unsupervised learning, a gradient based on the predicted output.
However, Geraci teaches generating, using unsupervised learning, a gradient based on the predicted output;(Geraci par [0035] teaches learning algorithm may include supervised ,unsupervised, semi-supervised and reinforcement learning. Geraci par [0163] teaches a gradient may be generated based on local model 701 finally refined after a predetermined number of steps is repeatedly performed)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yang and Geraci to achieve the claimed invention. One would have been motivated to make such combination to reduce time consumed to refine the local model.(Geraci par [0065])
Yang and Geraci fail to expressly teach receive a scheduling signal from a remote system instructing the client device to generate a first update for the one or more first on-device ML layers; generate, using unsupervised learning, a gradient based on the predicted output.
However, Dong teaches receive a scheduling signal from a remote system instructing the client device to generate a first update for the one or more first on-device ML layers; generate, using unsupervised learning, a gradient based on the predicted output.(Dong par [0086] teaches a client update 306 is data values and software instructions that, when executed, transmit a global update 314 to clients 324, as described above in conjunction with FIGS. 1 and 2. In at least one embodiment, a client update 306, when first performed, transmits initial data values associated with a global model, such as neural network weights, to each client 324. In at least one embodiment, a client update 306, when first performed, transmits initial data values associated with a global model, such as neural network weights, to a subset of available clients 324. In at least one embodiment, a client update 306 comprises weight values associated with one or more layers of a client neural network)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Yang , Geraci and Dong to achieve the claimed invention. One would have been motivated to make such combination to improve the effectiveness and generalizability of neural network models.(Dong par [0072])
As to claim 2, Yang , Geraci and Dong teach the method of claim 1, wherein the first update transmitted to the remote system comprises the gradient and an indication of the one or more first global ML layers to be updated based on the first update, and wherein the one or more first global ML layers of the global ML model stored remotely at the remote system correspond to the one or more first on- device ML layers of the on-device ML model stored locally at the client device. (Yang Fig.2 and Yang par [0071] teaches the model 100 comprises set of common layers 120 and the set of client specific layers 140. Yang Fig.2 shows server 220 includes layers 120 and 140)
As to claim 3, Yang , Geraci and Dong teach the method of claim 2, wherein causing the one or more first global ML layers to be updated based on the first update while one or more of the second global ML layers are fixed ( Yang par [0096] teaches the server 220 may receive an updated set of common layers 120 from each of the client devices 210 and 210’) comprises: causing, based on the gradient and based on the indication of the one or more first global ML layers to be updated based on the first update, the one or more first global ML layers to be updated based on the gradient to generate one or more updated first global ML layers without updating the one or more second global ML layers , the one or more updated first global ML layers including one or more updated first global weights for the one or more updated first global ML layers. (Yang par [0096] teaches the server 220 may receive an updated set of common layers 120 from each of the client devices 210 and 210’. Yang par [0075] teaches the client device may only send parameters of the updated set of common layers 120 that have changed to the server 220)
As to claim 4, Yang , Geraci and Dong teach the method of claim 1, wherein generating the first update for the one or more first on- device ML layers comprises: causing the one or more first on-device ML layers to be updated based on the gradient to generate one or more updated first on-device ML layers without updating the one or more second on-device ML layers, the one or more updated first on-device ML layers including one or more updated first on-device weights for the one or more updated first on-device ML layers.( Yang par [0075] teaches the client device may only send parameters of the updated set of common layers 120 that have changed to the server 220)
As to claim 5, Yang , Geraci and Dong teach the method of claim 4, wherein the first update transmitted to the remote system comprises the one or more updated first on-device ML layers and an indication of the one or more first global ML layers to be updated based on the first update, and wherein the one or more first global ML layers of the global ML model stored remotely at the remote system correspond to the one or more first on-device ML layers of the on-device ML model stored locally at the client device. (Yang par [0075] teaches the client device may only send parameters of the updated set of common layers 120 that have changed to the server 220.)
As to claim 6, Yang and Geraci teach the method of claim 5, wherein causing the one or more first global ML layers to be updated based on the first update while one or more of the second global ML layers are fixed comprises: causing, based on the one or more updated first on-device ML layers and based on the indication of the one or more first global ML layers to be updated based on the first update, the one or more first global ML layers to be replaced in the remote memory with the one or more updated first on-device ML layers without replacing the one or more second global ML layers.( Yang par [0075] teaches the client device may only send parameters (weights and / or biases) of the updated set of common layers 120 that have changed to the server 220.Yang par [0071] teaches the model 100 comprises set of common layers 120 and the set of client specific layers 140)
As to claim 7, Yang , Geraci and Dong teach the method of claim 4, wherein the first update transmitted to the remote system comprises the one or more updated first on-device weights for the one or more updated first on-device ML layers and an indication of the one or more first global ML layers to be updated based on the first update (Yang par [0075] teaches the client device may only send parameters (weights and / or biases) of the updated set of common layers 120 that have changed to the server 220), and wherein the one or more first global ML layers of the global ML model stored remotely at the remote system correspond to the one or more first on-device ML layers of the on-device ML model stored locally at the client device. (Yang par [0071] teaches the model 100 comprises set of common layers 120 and the set of client specific layers 140)
As to claim 8, Yang , Geraci and Dong teach the method of claim 7, wherein causing the one or more first global ML layers to be updated based on the first update while one or more of the second global ML layers are fixed comprises:
causing, based on the one or more updated first on-device weights for the one or more updated first on-device ML layers and based on the indication of the one or more first global ML layers to be updated based on the first update, one or more first global weights for the one or more first global ML layers to be replaced in the remote memory of the remote system with the one or more updated first on-device weights for the one or more updated first on-device ML layers without replacing one or more second global weights for the one or more second global ML layers. (Yang par [0075] teaches the client device may only send parameters (weights and / or biases) of the updated set of common layers 120 that have changed to the server 220. Yang par [0096] teaches the server 220 may receive an updated set of common layers 120 from each of the client devices 210 and 210’)
As to claim 9, Yang , Geraci and Dong teach the method of claim 1, wherein causing the remote system to update the global ML model further comprises causing the one or more second global ML layers to be updated based on a second update while one or more of the first global ML layers are fixed, and wherein the second update is transmitted to the remote system from an additional client device that is in addition to the client device utilized to generate the first update. (Yang par [0098] teaches the server may aggregate the received updated sets of common layers 120)
As to claim 10, Yang , Geraci and Dong teach the method of claim 9, further comprising: receiving, at the client device and from the remote system, an updated global ML model, the updated global ML model including at least the one or more updated first global ML layers and the one or more updated second global ML layers; and replacing, in the on-device memory of the client device, the on-device ML model with the updated global ML model. ( Yang par [0098] teaches the server may aggregate the received updated sets of common layers 120 to obtain one aggregated set of common layers 120 . Then the server 220 may send the aggregated set of common layers 120 to each of the plurality of client devices 210, 210’)
Claim 20-29 merely recites a system to perform the method of claims 1-10. Accordingly, Yang , Geraci and Dong teach every limitation of claims 20-29 as indicates in the above rejection of claims 1-10 respectively.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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