DETAILED 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 .
The following action is in response to the original filing of 04/14/2023 and the preliminary amendment of 04/14/2023.
By the amendment, claims 1-20 are canceled. Claims 21-40 are newly added.
Claims 21-40 are pending and have been considered below.
Claim Rejections - 35 USC § 102
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 21-25, 28-33, 35-36 and 38-40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by LEE et al., US 2021/0110261 A1 published 04/15/2021 effective filing of 10/10/2019 [“LEE”].
Regarding claim 21, LEE discloses a method performed by a first node in a wireless network (¶11, Fig. 8, ¶109-119), the method comprising:
monitoring a trigger condition based on a reconstruction loss value of a first artificial intelligence (Al) component of the first node (¶114: UE continuously updating the connection weights of the autoencoder NN through shadow training, ¶117: by the UE, performance of NN during shadow training, defined using MSE value between input/output, satisfies predetermined condition threshold, then perform an aperiodic weight reporting);
transmitting, over the wireless network to a second node having a second Al component, information indicating a plurality of training pairs each comprising encoded channel state information and reference CSI (¶114: UE transmits reference signal to the BS, ¶106, ¶90-96: CSI information each including at least RI, MPI and CQI), wherein the transmitting is based on detection of the trigger condition (¶114);
receiving, from the second node, an indication of tensor state information generated by the second Al component corresponding to the training pairs (¶114: receive from the BS an estimated channel matrix based on the sent signal); and
updating parameters of the first Al component of the first node based on the indication of tensor state information, whereby the reconstruction loss value of the first Al component is reduced (¶114-115: update connection weight to below predetermined condition threshold of autoencoder NN comprising entire Tx NN and Rx NN).
Regarding claim 22, LEE discloses method of claim 21, wherein monitoring a trigger condition comprises monitoring the reconstruction loss value exceeding a threshold value, the reconstruction loss value being calculated by the first Al component (¶117).
Regarding claim 23, LEE discloses method of claim 22, wherein the first Al component comprises an encoder function of the first node (Fig. 8, ¶113: UE).
Regarding claim 24, LEE discloses the method of claim 21, wherein transmitting information indicating a plurality of training pairs further comprises transmitting a plurality of ordered training pairs and an address of the second Al component of the second node (¶90-96, ¶106, ¶114: transmitted CSI information to BS).
Regarding claim 25, LEE discloses the method of claim 21, wherein the second Al component comprises a decoder function of the second node (Fig. 8, ¶113: BS).
Regarding claim 28, LEE discloses the method of claim 21, wherein receiving, from the second node, an indication of tensor state information further comprises receiving an address of the first Al component (¶114: receiving from the BS to the UE).
Regarding claim 29, LEE discloses the method of claim 21, wherein updating parameters further comprises updating one or more of weights and biases of the first Al component (¶114, 117).
Regarding claims 30-31, claims 30-31 recite limitations similar to claims 21-22, respectively, and are similarly rejected.
Regarding claim 32, LEE discloses the WTRU of claim 30, wherein the first Al component comprises an encoder function of the first node and the second Al component comprises a decoder function of the second node (¶113).
Regarding claims 33 and 35-36, claims, 33 and 35-36 recite limitations similar to claims 24 and 28-29, respectively, and are similarly rejected.
Regarding claims 38-39 and 40, claims 38-39 and 40 recite limitations similar to claims 21-22 and 29, respectively, and are similarly rejected.
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.
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.
Claims 26-27, 34 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over LEE in view of TIMO et al., US 2022/0149904 A1 effective filing 03/06/2019 [“TIMO”].
Regarding claim 26, LEE discloses the method of claim 21 wherein an indication of tensor state information generated by the second Al component is received from the second node.
LEE fails to further disclose wherein the indication comprises receiving an indication of gradients of the second Al component.
TIMO discloses methods for updating parameters of autoencoder networked devices using channel state information (TIMO ¶72, ¶74, ¶268). In particular, TIMO discloses providing indication of gradients when training/updating weights of the network autoencoder framework (TIMO ¶289-293) of a base station/user equipment pair (TIMO ¶272-275, ¶287-293). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of LEE and TIMO before them before the effective filing of the claimed invention to combine the indication of gradients used in the updating of weighting parameters of the autoencoding framework of TIMO with the indication of tensor state information used in the updating of weighting parameters in the AI component framework of LEE. One would have been motivated to make this combination in order to support the use of existing machine-learning training methods, as suggested by TIMO (TIMO ¶292-293).
Regarding claim 27, LEE and TIMO disclose the method of claim 26, wherein the indication of gradients of the second Al component is determined as a difference between an output of the second Al component and a reference CSI, wherein a corresponding encoded CSI is an input to the second Al component (TIMO ¶289-293: normalizing CSI-RS measurements used in the gradient-decent backpropagation).
Regarding claim 34, claim 34 recites limitations similar to claim 26 and is similarly rejected.
Regarding claim 37, LEE discloses the WTRU of claim 30, wherein the processor is configured to update parameters of the first Al component.
LEE fails to explicitly disclose wherein the update is after a back propagation over the first Al component is performed.
TIMO discloses methods for updating parameters of autoencoder networked devices using channel state information (TIMO ¶72, ¶74, ¶268). In particular, TIMO discloses updating weights of the network autoencoder framework using gradient-decent backpropagation (TIMO ¶289-293). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of LEE and TIMO before them before the effective filing of the claimed invention to combine the updating of weighting parameters using backpropagation of TIMO with the updating of weighting parameters in the AI component framework of LEE yielding the predictable result of updating the parameters of the first AI component after a back propagation over the first AI component is performed. One would have been motivated to make this combination in order to support the use of existing machine-learning training methods, as suggested by TIMO (TIMO ¶292-293).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rahman; Md. Saifur et al.
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Yoo; Taesang et al.
US 20210264254 A1
MANAGING INFORMATION TRANSMISSION FOR WIRELESS COMMUNICATION
Yoo; Taesang et al.
US 20210266763 A1
CHANNEL STATE INFORMATION (CSI) LEARNING
Yoo; Taesang et al.
US 20210266787 A1
COMPRESSED MEASUREMENT FEEDBACK USING AN ENCODER NEURAL NETWORK
Kim; Sunam et al.
US 20210297178 A1
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Gunduz; Deniz
US 20210319286 A1
JOINT SOURCE CHANNEL CODING FOR NOISY CHANNELS USING NEURAL NETWORKS
Saber; Hamid et al.
US 20230131694 A1
SYSTEMS, METHODS, AND APPARATUS FOR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR A PHYSICAL LAYER OF COMMUNICATION SYSTEM
Maamari Diana et al.
WO 2020192790 A1
SYSTEM AND METHOD FOR REDUCED CSI FEEDBACK AND REPORTING USING TENSORS AND TENSOR DECOMPOSITION
Ramireddy Venkatesh et al.
EP 3576361 A1
EXPLICIT CHANNEL INFORMATION FEEDBACK BASED ON HIGH-ORDER PCA DECOMPOSITION OR PCA COMPOSITION
Wang, Tianqi, et al. "Deep learning for wireless physical layer: Opportunities and challenges." China Communications 14.11 (2017): 92-111.
Wen, Chao-Kai, Wan-Ting Shih, and Shi Jin. "Deep learning for massive MIMO CSI feedback." IEEE Wireless Communications Letters 7.5 (2018): 748-751.
Lin, Chuan, Qing Chang, and Xianxu Li. "A deep learning approach for MIMO-NOMA downlink signal detection." Sensors 19.11 (2019): 2526.
Liao, Yong, et al. "CSI feedback based on deep learning for massive MIMO systems." IEEE Access 7 (2019): 86810-86820.
Liu, Zhenyu, Mason del Rosario, and Zhi Ding. "A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback." arXiv preprint arXiv:2009.09468 (2020).
Sangdeh, Pedram Kheirkhah, et al. "LB-SciFi: Online learning-based channel feedback for MU-MIMO in wireless LANs." 2020 IEEE 28th International Conference on Network Protocols (ICNP). IEEE, 16 Oct 2020.
Wu, Dehao, Maziar Nekovee, and Yue Wang. "Deep learning-based autoencoder for m-user wireless interference channel physical layer design." IEEE Access 8 (2020): 174679-174691.
Ye, Hao, et al. "Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels." IEEE Transactions on Wireless Communications 19.5 (2020): 3133-3143.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141