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 .
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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/12/26 has been entered.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ottersten et al. (Ottersten) (US 2021/0345134) (of record) in view of Luo et al. (Luo) (US 2021/0320678) (of record) and Tan et al. (Tan) (US 2019/0014488).
As to claim 1, while Ottersten discloses a networking device (UE 120, Fig. 1; paragraph 63-68), comprising:
a wireless transceiver configured to detect radio frequency (RF) spectrum conditions local to the networking device and generate a representation of the RF spectrum conditions (paragraph 57-58, 102-103, 105, 119, 238);
an operative neural network (ONN) (neural network machine learning model; paragraph 58, 143, 233) configured to determine transceiver parameters based on the representation of the RF spectrum conditions (paragraph 101-109); and
a controller configured to:
cause the representation of the RF spectrum conditions to be transmitted to a network node (measurement information sent from UE to intermediate node; Fig. 6, paragraph 126, 239); and
reconfigure the ONN based on neural network (NN) weight parameters (the neural network model comprising weighted coefficients; paragraph 114-117, 143) generated by a training neural network (TNN) remote from the networked device, the NN weight parameters being a function of the representation of the RF spectrum conditions (updated model information from intermediate node sent to UE; Fig. 6, paragraph 121-126),
Ottersten fails to specifically disclose wherein the neural network is hardware implemented and constructing a Deep reinforcement Learning (DRL) tuple.
In an analogous art, Luo discloses a networking device for transmitting/receiving communications over a network (see Fig. 1-2b, 6a-b; paragraph 35-45) which includes a hardware implemented (FPGA; paragraph 57, 86, 129, 136, 190) neural network (Fig. 2a-b, 5a, 6a-b, 40; paragraph 48-52, 57, 82) configured to determine transceiver parameters (paragraph 50-52, 55-56) based on a representation of RF spectrum conditions (paragraph 48-49, 59) so as to reduce latency (paragraph 85) than can be achieved via software on a CPU.
Additionally, in an analogous art, Tan discloses a method for wireless network optimization by adjusting cell parameters using Deep reinforcement Learning (DRL) (Fig. 5-6, 10; paragraph 33, 40, 47, 50-51, 61) by constructing a Deep reinforcement Learning (DRL) tuples (paragraph 33, 51-53, 72-76) constructed from cell parameters (paragraph 62-68) so as to take advantage of a Deep reinforcement Learning process for adjusting one or more cell parameters of cells associated with base stations in the wireless network (paragraph 7-8, 39-40) and improve its intelligence for effectively selecting an action (paragraph 40).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ottersten’s system to include wherein the neural network is hardware implemented, as taught in combination with Luo, for the typical benefit of reducing system latency (paragraph 85).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ottersten and Luo’s system to include constructing a Deep reinforcement Learning (DRL) tuple, as taught in combination with Tan, for the typical benefit of optimizing one or more cell parameters of cells associated with base stations in the wireless network (paragraph 7-8, 39-40) and improving intelligence for effectively selecting an action (paragraph 40).
As to claim 2, Ottersten, Luo and Tan disclose wherein the representation of the RF spectrum conditions includes I/Q samples (see Ottersten at paragraph 106, 155).
As to claim 3, Ottersten, Luo and Tan disclose wherein the controller is further configured to generate an ONN input state based on the representation of the RF spectrum conditions see Ottersten at paragraph 102-105 and Luo at paragraph 48-49, 59) and wherein the ONN is further configured to process the ONN input state to determine the transceiver parameters (see Ottersten at paragraph 106-109 and Luo at paragraph 50-52, 55-56).
As to claim 4, Ottersten, Luo and Tan disclose wherein the wireless transceiver is further configured to reconfigure at least one internal transmission or reception protocol based on the transceiver parameters (see Ottersten at paragraph 106-109 and Luo at paragraph 50-52, 55-56).
As to claim 5, Ottersten, Luo and Tan disclose wherein, following the reconfiguration of the ONN based on the NN weight parameters, the ONN is further configured to determine subsequent transceiver parameters based on a subsequent representation of the RF spectrum conditions generated by the wireless transceiver (repeatedly reconfigured over time with new inputs; see Ottersten at Fig. 6, paragraph 58-59, 88-90, 126 and Luo at Fig. 5A-E; paragraph 82-115).
As to claim 6, Ottersten, Luo and Tan disclose wherein the networking device is a battery-powered Internet of things (IoT) device (see Ottersten at paragraph 57, 68 and Luo at paragraph 40).
As to claim 7, Ottersten, Luo and Tan disclose wherein the ONN is further configured to determine the transceiver parameters within 1 millisecond of the wireless transceiver generating a representation of the RF spectrum conditions (see Luo at paragraph 85).
As to claim 8, Ottersten, Luo and Tan disclose wherein the ONN is configured in a first processing pipeline, and further comprising a second processing pipeline configured to 1) buffer the representation of the RF spectrum conditions concurrently with the ONN determining the transceiver parameters, and 2) provide the representation of the RF spectrum conditions to the wireless transceiver in synchronization with the transceiver parameters (delay buffering and providing to the transceivers to adjust while also transmitting to the training unit; Fig. 5A-E, 6A-B; see Luo at paragraph 52, 82-115, 117-122).
As to claim 9, while Ottersten discloses a method of configuring a wireless transceiver (within UE 120, Fig. 1; paragraph 63-68), comprising:
detecting radio frequency (RF) spectrum conditions local to the networking device and generating a representation of the RF spectrum conditions (paragraph 57-58, 102-103, 105, 119, 238);
determining, at an operative neural network (ONN) (paragraph 58, 143, 233), transceiver parameters based on the representation of the RF spectrum conditions (paragraph 101-109);
reconfiguring at least one internal transmission or reception protocol of the wireless transceiver based on the transceiver parameters (paragraph 106-109);
transmitting the representation of the RF spectrum conditions to a network node remote from the wireless transceiver (measurement information sent from UE to intermediate node; Fig. 6, paragraph 126, 239); and
reconfiguring the ONN based on neural network (NN) weight parameters (the neural network model comprising weighted coefficients; paragraph 114-117, 143) generated by a training neural network (TNN), the NN weight parameters being a function of the representation of the RF spectrum conditions (updated model information from intermediate node sent to UE; Fig. 6, paragraph 121-126),
Ottersten fails to specifically disclose wherein the neural network is hardware implemented and constructing a Deep reinforcement Learning (DRL) tuple.
In an analogous art, Luo discloses a networking device for transmitting/receiving communications over a network (see Fig. 1-2b, 6a-b; paragraph 35-45) which includes a hardware implemented (FPGA; paragraph 57, 86, 129, 136, 190) neural network (Fig. 2a-b, 5a, 6a-b, 40; paragraph 48-52, 57, 82) configured to determine transceiver parameters (paragraph 50-52, 55-56) based on a representation of RF spectrum conditions (paragraph 48-49, 59) so as to reduce latency (paragraph 85) than can be achieved via software on a CPU.
Additionally, in an analogous art, Tan discloses a method for wireless network optimization by adjusting cell parameters using Deep reinforcement Learning (DRL) (Fig. 5-6, 10; paragraph 33, 40, 47, 50-51, 61) by constructing a Deep reinforcement Learning (DRL) tuples (paragraph 33, 51-53, 72-76) constructed from cell parameters (paragraph 62-68) so as to take advantage of a Deep reinforcement Learning process for adjusting one or more cell parameters of cells associated with base stations in the wireless network (paragraph 7-8, 39-40) and improve its intelligence for effectively selecting an action (paragraph 40).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ottersten’s system to include wherein the neural network is hardware implemented, as taught in combination with Luo, for the typical benefit of reducing system latency (paragraph 85).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ottersten and Luo’s system to include constructing a Deep reinforcement Learning (DRL) tuple, as taught in combination with Tan, for the typical benefit of optimizing one or more cell parameters of cells associated with base stations in the wireless network (paragraph 7-8, 39-40) and improving intelligence for effectively selecting an action (paragraph 40).
As to claim 10, Ottersten, Luo and Tan disclose training the TNN based on the representation of the RF spectrum conditions (see Ottersten at Fig. 6, paragraph 126, 142-147); and
generating, via the TNN, the NN parameters as a result of the training (see Ottersten at Fig. 3, 4A, 6, paragraph 126, 142-147);
As to claim 11, Ottersten, Luo and Tan disclose training the TNN in a manner that is asynchronous to operation of the ONN (see Ottersten at Fig. 3, 4A, 6, paragraph 58, 126, 142-147, 231-233).
As to claim 12, Ottersten, Luo and Tan disclose training the TNN based on at least one state/action/reward generated from the representation of the RF spectrum (see Ottersten at Fig. 3, 4A, 6, paragraph 58-59, 118-122, 142-147 and Tan at paragraph 68-75).
As to claim 13, Ottersten, Luo and Tan disclose updating a TNN experience buffer to include the at least one state/action/reward tuple (see Ottersten at Fig. 3, 4A, 6, paragraph 58-59, 118-122, 142-147 and Tan at paragraph 68-75).
As to claim 14, Ottersten, Luo and Tan disclose transmitting the NN weight parameters from the network node to the wireless transceiver (updated model information from intermediate node sent to UE; see Ottersten at Fig. 6, paragraph 121-126, 145-147).
As to claim 15, Ottersten, Luo and Tan disclose training a software-defined NN to classify among different state conditions of a RF spectrum;
translating the state of the software-defined NN to ONN parameters (see Ottersten at paragraph 11, 56-58, 74, 88, 101, 121, 227-233);
comparing the ONN parameters against at least one of a size constraint and a latency constraint (different models with different data rates and latency requirements for different purposes; see Ottersten at paragraph 11, 56-58, 74, 88, 101, 121, 217, 227-233); and
causing the ONN to be configured based on the ONN parameters (see Ottersten at paragraph 11, 56-58, 74, 88, 101, 121, 217, 227-233).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James R Sheleheda whose telephone number is (571)272-7357. The examiner can normally be reached M-F 8 am-5 pm CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached at (571) 272-3982. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James R Sheleheda/ Primary Examiner, Art Unit 2424
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