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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/20/2023, 10/27/2023 and 10/17/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Claims 1-10 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention and specie (Group A/Specie I of claim 1 and Group B/Specie II of claims 2-10), there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 01/20/2026.
Specification
The disclosure is objected to because of the following informalities:
The original specification in Par. 0091 and 0094 recites “DRMS-based channel estimation 1304”. Applicant is advised to revise this recitation into “DMRS-based channel estimation 1304”.
Appropriate correction is required.
Claim Objections
Claim 11 is objected to because of the following informalities:
Claim 11 shows the claim limitation “communication signal processing control information” in line 10. Applicant is advised to revise this recitation into “the communication signal processing control information” as this claim limitation has already been recited beforehand in lines 5-6.
Appropriate correction is required.
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.
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.
Claim(s) 11-15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chandrasekhar et al. (US 2019/0277957; hereinafter Chandrasekhar) in view of Bai et al. (US 2020/0259545; hereinafter Bai).
Regarding claim 11, Chandrasekhar shows a communication device (Figure 3B shows a base station (BS) performing in part the disclosed method.) comprising:
a receiver (Figure 3B; BS includes an RF transceiver.) configured to receive a superposition of sounding reference signals sent by a plurality of other communication devices; and
a processor (Figure 3B; BS includes a processor.) configured to
control a neural network to determine communication signal processing control information from receive signals (Figures 7 and 11C; Par. 0079-0084, 0139; training a neural network based on UL SRS frequency domain measurements to determine/predict UE speed classes.);
supply an input according to the received sounding reference signals to the neural network (Figures 7 and 11C; Par. 0079-0080, 0084 0118-0119, 0123; the base station buffers a number of uplink (UL) SRS frequency domain measurements derived from different UL SRS transmissions of the mobile client device in step 1122. During training, frequency domain channel measurements are input to an AI classifier in step 1126. AI classifier estimates the speed class (among M>1 speed classes) for one or more UE based on uplink signal measurements associated with each UE.) and
perform radio communication signal processing in accordance with communication signal processing control information output by the neural network in response to the input (Figures 7C and 11; Par. 0079-0084, 0139; After training, the neural network will have the capability to perform radio communication signal processing by predicting the UE speed classes based on the channel measurements.).
Chandrasekhar shows all of the elements including the received sounding reference signals, as discussed above. Chandrasekhar does not specifically show a superposition of sounding reference signals.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Bai. Specifically, Bai shows a superposition of sounding reference signals (Figure 2; Par. 0081, 0106; the UE 115-a may also transmit beams 210-a through 210-d for reception by the base station. The transmit beams 210 are transmitted according to a same or different beam sweep pattern. Signals transmitted by the UE (and received by the base station) includes at least sounding reference signals.).
In view of the above, having the system of Chandrasekhar, then given the well-established teaching of Bai, it would have been obvious before the effective filing date of the claimed invention to modify the system of Chandrasekhar as taught by Bai, in order to provide motivation to support an improved beam management procedure, such as a beam switch procedure, which may improve wireless communications and reduce overhead and latency in the wireless communications system (Par. 0103 of Bai).
Regarding claim 12, modified Chandrasekhar shows wherein the communication signal processing control information comprises at least one of a channel frequency response (Examiner elects this claim limitation for prosecution. Chandrasekhar: Figure 9; Par. 0091-0098; the category output by the ML classifier includes at least one an estimate of either a Doppler frequency or a range of Doppler frequency of a dominant Radio Frequency (RF) propagation path. The Doppler frequency or a range of Doppler frequency of a dominant Radio Frequency (RF) propagation path is based at least in part on the Doppler PSD of the SRS signals.),
a channel frequency response averaged over multiple subcarriers, channel frequency responses for the plurality of other communication devices, compressed receive spatial compression weights for the plurality of other communication devices, transmit beamforming weights for the plurality of other communication devices, a channel quality and compressed channel frequency responses.
Regarding claim 13, modified Chandrasekhar shows wherein the neural network is a recurrent neural network (Chandrasekhar: Par. 0123; recurrent neural network utilized.).
Regarding claim 14, modified Chandrasekhar shows wherein the communication device is a base station (Chandrasekhar: Figure 3B shows a base station (BS) performing in part the disclosed method.).
Regarding claim 15, modified Chandrasekhar shows wherein the receiver is configured to receive the superposition of sounding reference signals via each of a plurality of receive antennas resulting in a superposition of sounding reference signals for each receive antenna (Bai: Figure 2; Par. 0081; the base station is equipped with multiple antennas, which may be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. MIMO communications may employ multipath signal propagation to increase the spectral efficiency by transmitting or receiving multiple signals via different spatial layers, which may be referred to as spatial multiplexing. The multiple signals may, for example, be transmitted by the transmitting device via different antennas or different combinations of antennas. Likewise, the multiple signals may be received by the receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream, and may carry bits associated with the same data stream (e.g., the same codeword) or different data streams. Different spatial layers may be associated with different antenna ports used for channel measurement and reporting.) and wherein the processor is configured to generate the input to the neural network from the superpositions of sounding reference signals received for the receive antennas (Chandrasekhar: Figures 7 and 11C; Par. 0079-0080, 0084 0118-0119, 0123; the base station buffers a number of uplink (UL) SRS frequency domain measurements derived from different UL SRS transmissions of the mobile client device in step 1122. During training, frequency domain channel measurements are input to an AI classifier in step 1126. AI classifier estimates the speed class (among M>1 speed classes) for one or more UE based on uplink signal measurements associated with each UE.).
Regarding claim 17, modified Chandrasekhar shows wherein the superposition of sounding reference signals comprises a signal component for each of a plurality of subcarriers (Chandrasekhar: Par. 0115; the extracted features are outputs of a linear or non-linear function of real and imaginary portions of channel measurements derived from UL SRS measurements per transmit and receive antenna pair during each UL SRS transmission occasion, wherein the linear or non-linear function comprises a pre-processing process of applying a fusion function of the real and imaginary portions of the channel measurements, wherein the fusion function is configured to output the real and imaginary portions of the channel measurements in a frequency domain, or a subcarrier domain.).
Regarding claim 18, modified Chandrasekhar shows wherein the processor is configured to divide, for each of the other communication devices, the superposition of sounding reference signals by the sounding reference signal sent by the other communication device, wherein the input comprises the results of the division for each of the other communication devices (Chandrasekhar: Figure 7; Par. 0080-0083; the input features to the AI classifier are derived from the SRS measurements spaced P ms apart where P is the spacing between consecutive SRS transmissions. The dataset comprising the input features are divided into a training dataset for a training phase and a test dataset for a test phase. The flowchart is divided into a training stage (steps 740 and 750) and a test stage (steps 760 and 770).).
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Chandrasekhar in view of Bai and Wang et al. (US 2023/0082795; hereinafter Wang).
Regarding claim 16, modified Chandrasekhar shows all of the elements including the superpositions of sounding reference signals received, as discussed above. Modified Chandrasekhar does not specifically show wherein the processor is configured to compress the sounding reference signals received for the receive antennas to sounding reference signals received for a set of virtual antennas with a lower number than the number of receive antennas and to generate the input to the neural network from the sounding reference signals received for the set of virtual antennas.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Wang. Specifically, Wang shows wherein the processor is configured to compress the sounding reference signals received for the receive antennas to sounding reference signals received for a set of virtual antennas with a lower number than the number of receive antennas and to generate the input to the neural network from the sounding reference signals received for the set of virtual antennas (Figure 10; Par. 0116, 0134-0137, 0145; The gNB 102 can check one or more power statistics of each antenna and determine whether this antenna will be selected. Given the power statistics of antennas, the gNB 102 can either pick Y.sub.1 antennas with the largest power statistics or pick antennas with power statistics satisfying certain criterion. Given the received SRS signals 605, the TdXcorr function, the TdACF function, and the power fluctuation on different antennas can be used for the channel classification operation 640. Additionally or alternatively, the features 635 derived from TdXcorr, TdACF and power fluctuation on different antennas can be used for the channel classification operation 640. By using the features 635 instead of raw TdXcorr, TdACF and power fluctuation on different antennas, the channel classification operation 640 can be performed using simpler machine learning (ML) tools.).
In view of the above, having the system of Chandrasekhar, then given the well-established teaching of Wang, it would have been obvious before the effective filing date of the claimed invention to modify the system of Chandrasekhar as taught by Wang, in order to provide motivation to reduce the computational complexity for ML applications (Par. 0103 of Wang).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240275519 A1 - relates to wireless networks, and more specifically to techniques for training network-based decoders of user equipment (UE)-encoded feedback about a downlink (DL) channel from the wireless network to the UE, such as when the network decoder and/or UE encoder use artificial intelligence (AI) and/or machine learning (ML) techniques.
US 20240106508 A1 - Embodiments are disclosed in which a first device channel state information characterizing a wireless communication channel between the first device and a second device, and trains a machine learning (ML) module of the first device using the CSI as an ML module input and one or more modulation and coding scheme (MCS) parameters as an ML module output to satisfy a training target.
US 20220271802 A1 - A method for operating a user equipment (UE) comprises receiving configuration information about L uplink (UL) reference signals (RSs), M downlink (DL) RSs, and a calibration report, where the L UL RSs are associated with the M DL RSs; transmitting the UL RSs according to the configuration information; measuring the DL RSs; determining, based on the measured DL RSs and a reference DL RS, calibration information; and transmitting the calibration report including the calibration information.
US 20210264254 A1 - A base station may apply an encoder neural network to assistance information that may aid a wireless device in communicating with the base station to generate encoded assistance information.
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/REDENTOR PASIA/Primary Examiner, Art Unit 2413