DETAILED ACTION
This action is in response to the continuation application filed 05 July 2022 and the preliminary amendment filed 02 February 2024.
Claims 1–20 are pending. Claims 1, 10, and 18 are independent.
Claims 1–18 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Rejections—35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1–17 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 10 recite the limitation "the first loss function". There is insufficient antecedent basis for this limitation in the claim.
Claims 2–9 and 11–17 depend from claim 1 and claim 10 (respectively) and therefore are rejected for the same reason.
Claim Rejections—35 U.S.C. § 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.
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 C.F.R. § 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.
Claims 1–4, 10–12, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Holt et al. (US 2018/0174050 A1) [hereinafter Holt] in view of Putman et al. (US 2021/0256116 A1) [hereinafter Putman].
Regarding independent claim 1, Holt teaches [a] model training method, applied to a communication system comprising a first communication apparatus and a second communication apparatus, wherein there is at least one first communication apparatus, a first machine learning model is deployed in the first communication apparatus, and the method comprises: A machine learning model of a communication channel (Holt, ¶ 3). The model may be used for data of a communication device (Holt, ¶ 41). sending, by the first communication apparatus, first data to the second communication apparatus through a channel, wherein the first data is an output result obtained by inputting first training data into the first machine learning model, the first machine learning model comprises a control layer, and the control layer is at least one layer of the first machine learning model; The model includes an encoder model [first model] that receives first inputs and generates second outputs (Holt, ¶ 5). receiving, by the first communication apparatus, a second loss function through a feedback channel, wherein the feedback channel is determined based on an observation error, and the second loss function is obtained by transmitting, through the feedback channel, the first loss function sent by the second communication apparatus; and A loss function is used to determine the difference between a first set of inputs and a third set of outputs from a decoder model (Holt, ¶ 29). updating, by the first communication apparatus, a parameter of the control layer […] and according to the second loss function, to obtain an updated parameter of the control layer, wherein the updated parameter of the control layer is used to update a parameter of the first machine learning model. The loss function is backpropagated to the encoder model, and used to train the encoder model by modifying a weight of the encoder model (Holt, ¶ 29). The encoder model may be a multi-layer neural network (Holt, ¶ 25).
Holt teaches retraining an encoder model but does not expressly teach using Kalman filtering. However, Putman teaches: [updating, by the first communication apparatus, a parameter of the control layer] based on Kalman filtering A Kalman filter is used to determine damage or errors at a given processing node (Putman, ¶ 35).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Holt with those of Putman. Doing so would have been a matter of applying a known technique [a Kalman filter] to a known method ready for improvement [the model of Holt] to yield predictable results [using Kalman filtering to determine data loss over a communication channel].
Regarding dependent claim 2, the rejection of claim 1 is incorporated and Holt/Putman further teaches: wherein updating the parameter of the control layer based on Kalman filtering, to obtain the updated parameter of the control layer further comprises: obtaining, by the first communication apparatus, a Kalman gain based on a prior parameter of the control layer, the second loss function, and an error covariance of the second loss function; and A Kalman gain based on the covariance of input noise (Putman, ¶ 39). updating, by the first communication apparatus, the parameter of the control layer based on the Kalman gain, to obtain the updated parameter of the control layer. The encoder model is trained based on the loss function (Holt, ¶ 29).
Regarding dependent claim 3, the rejection of claim 2 is incorporated and Holt/Putman further teaches: further comprising: updating, by the first communication apparatus, a parameter of a first network layer in the first machine learning model based on the updated parameter of the control layer and the Kalman gain, to obtain an updated parameter of the first network layer, wherein the first network layer comprises a network layer before the control layer; and The encoder model is a multi-layer model (Holt, ¶ 25). obtaining, by the first communication apparatus, the updated first machine learning model based on the updated parameter of the control layer and the updated parameter of the first network layer. The weights of the encoder model are modified by the training [i.e., the weights associated with layers of the model] (Holt, ¶ 29).
Regarding dependent claim 4, the rejection of claim 3 is incorporated and Holt/Putman further teaches: wherein after the obtaining the updated first machine learning model, the method further comprises: sending, by the first communication apparatus, fourth data to the second communication apparatus through the channel, wherein the fourth data is an output result obtained by inputting second training data into the first machine learning model; The encoder and decoder models may be retrained with further data based on the channel conditions changing (Holt, ¶ 24). receiving, by the first communication apparatus, indication information from the second communication apparatus, wherein the indication information indicates the first communication apparatus to stop training of the first machine learning model; and A change in the conditions/behavior of the channel are detected (Holt, ¶ 24). stopping, by the first communication apparatus, the training of the first machine learning model based on the indication information. The training is in response to the behavior or conditions of the channel changing over time [i.e., the training stops when the conditions stop changing] (Holt, ¶ 24).
Regarding independent claim 10, this claim recites limitations similar to those of claim 1, and is rejected for the same reasons.
Regarding dependent claim 11, this claim recites limitations similar to those of claims 2 and 3, and is rejected for the same reasons.
Regarding dependent claim 12, this claim recites limitations similar to those of claim 4, and is rejected for the same reasons.
Regarding independent claim 18, Holt teaches [a]n apparatus, comprising: at least one processor, and A processor (Holt, ¶ 5). one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform operations comprising: The computing device includes processor(s) and memory (Holt, ¶ 45). receiving second data through a channel, wherein the second data is obtained by transmitting, through the channel, first data sent by a first communication apparatus, the first data is an output result obtained by inputting first training data into a first machine learning model, the first machine learning model comprises a control layer, and the control layer is at least one layer of the first machine learning model; The model includes an encoder model [first model] that receives first inputs and generates second outputs (Holt, ¶ 5). inputting the second data into a second machine learning model, to obtain third data; and The data is passed through a channel or channel model and into a decoder model [second ML model] (Holt, ¶ 5). determine a first loss function based on the third data and the first training data; and The loss function determines a difference between the first input data [to the encoder] and the second set of outputs [from the decoder] (Holt, ¶ 5). sending the first loss function to the first communication apparatus through a feedback channel, wherein the feedback channel is determined based on an observation error, and the first loss function is used to update a parameter of the control layer of the first machine learning model. The loss function is backpropagated through the decoder model to the encoder model (Holt, ¶ 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
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/Tyler Schallhorn/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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