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 12/01/2023 and 02/05/2025 are considered. The submission is in compliance with the provisions of 37 CFR 1.97.
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 person shall be entitled to a patent unless –
(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.
Claim(s) 1-4, 7-9, 11, 14-19, 21-22 and 25-27 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tang et al. (US 2024/0106508; hereinafter Tang).
Regarding claim 1, Tang shows an apparatus (Figure 6 shows network device/TRP performing in-part the method of Figure 15.) configured for wireless communications, comprising:
one or more memories (Figure 6 shows memory 456); and
one or more processors coupled to the one or more memories (Figure 6 shows processor 454 coupled to memory 456.), the one or more processors being configured to cause the apparatus to:
obtain, from a user equipment (UE), one or more parameters of a UE receiver (Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452.);
determine one or more channel characteristics of a communication channel between the apparatus and the UE based on a measurement of a signal received from the UE (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.);
estimate a response of the UE receiver communicating on the communication channel having the one or more channel characteristics (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.) based on a digital representation of the UE receiver, wherein the digital representation of the UE receiver is based on the one or more parameters of the UE receiver (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.);
determine, based on the estimated response, at least one parameter for communication on the communication channel with the UE (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.);
send, to the UE, an indication of the at least one parameter (Figure 15; Par. 0224; scheduling information, for the scheduled downlink transmission, sent to the UE wherein the scheduling information includes one or more MCS parameters.); and
communicate with the UE in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.).
Regarding claim 2, Tang wherein the digital representation comprises a machine learning model (Figure 15; Par. 0223; the trained ML modules of UE 402 and TRP 452 are used to predict optimal MCS parameters for downlink communication between TRP 452 and UE 402 in the normal operations phase 1404.) configured to estimate the response of the UE receiver (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.), and wherein the one or more parameters comprise one or more coefficients for the machine learning model (Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452. Training data is also provided to ML module of UE 402.).
Regarding claim 3, Tang shows wherein, to estimate the response of the UE receiver, the one or more processors are configured to further cause the apparatus to:
provide, to the machine learning model, input comprising the one or more channel characteristics (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and
obtain, from the machine learning model, output comprising the estimated response (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain as output, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402.
Regarding claim 4, Tang shows wherein the estimated response comprises an indication of mutual information decoded via the digital representation of the UE receiver (Figure 14; Par. 0232; in the normal operation phase 1404, compressed DL channel state information z received by TRP 452 at 1439 is successfully decoded to obtain reconstructed DL channel state information H′, which is then used as an input of the ML module of TRP 452 at 1436.).
Regarding claim 7, Tang shows wherein the one or more parameters comprise a receiver index indicating a receiver architecture of the UE receiver (Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.).
Regarding claim 8, Tang shows wherein the receiver architecture comprises one or more of: a channel estimation type or a demodulator type (Examiner elects this claim limitation for prosecution: Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.).
Regarding claim 9, Tang shows wherein the channel estimation type comprises one or more of: frequency domain minimum mean square error (MMSE)-based channel estimation, or time domain MMSE-based channel estimation (Examiner submits that the claimed subject matter presented in this claim refers back to an alternative claim limitation that was not elected for prosecution. Therefore, this claim is also rejected based on the same reasoning as presented in the rejection of claim 8.).
Regarding claim 11, Tang shows wherein the one or more parameters further comprise a number of receive antennas of the UE (Par. 0134; rank indicator is also determined as part of the channel state information.).
Regarding claim 14, Tang shows wherein the at least one parameter comprises: a modulation and coding scheme (MCS) (Par. 0134; noted MCS.), a precoding matrix indicator (PMI) (Par. 0134; noted PMI), a rank indicator (RI) (Par. 0134; noted RI.), or a combination thereof.
Regarding claim 15, Tang shows an apparatus (Figure 6 shows a UE performing in-part the method of Figure 15.) configured for wireless communications, comprising:
one or more memories (Figure 6 shows memory 208); and
one or more processors coupled to the one or more memories (Figure 6 shows processor 210 coupled to memory 208.), the one or more processors being configured to cause the apparatus to:
send one or more parameters of a user equipment (UE) receiver (Figure 15; Par. 0153, 0224; UL reference signal transmitted from UE 402 at 1430 is used to obtain uplink channel state information. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452.), the one or more parameters indicating a digital representation of the UE receiver used to estimate a response of the UE receiver communicating on a communication channel having one or more channel characteristics (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.);
receive at least one parameter for communicating on the communication channel (Figure 15; Par. 0224; UE receives scheduling information for the scheduled downlink transmission, wherein the scheduling information includes one or more MCS parameters.), the at least one parameter based at least in part on the one or parameters and the one or more channel characteristics of the communication channel (Figure 15; Par. 0224; uplink channel state information is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and
communicate on the communication channel in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.).
Regarding claims 16, 17, 18, 19, 21 and 25, these claims are rejected based on the same reasoning as presented in the rejection of claims 2, 7, 8, 9, 11 and 14, respectively.
Regarding claim 22, Tang shows wherein the one or more processors are further configured to cause the apparatus to send a reference signal on the communication channel (Figure 15 shows UE 402 transmitting an uplink reference signal, i.e. SRS, to the TRP 452.).
Regarding claim 26, Tang shows a method (Figure 6 shows network device/TRP performing in-part the method of Figure 15.) for wireless communications by an apparatus, comprising:
obtaining, from a user equipment (UE), one or more parameters of a UE receiver (Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452.);
determining one or more channel characteristics of a communication channel between the apparatus and the UE based on a measurement of a signal received from the UE (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.);
estimating a response of the UE receiver communicating on the communication channel having the one or more channel characteristics (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.) based on a digital representation of the UE receiver, wherein the digital representation of the UE receiver is based on the one or more parameters of the UE receiver (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.);
determining, based on the estimated response, at least one parameter for communication on the communication channel with the UE (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.);
sending, to the UE, an indication of the at least one parameter (Figure 15; Par. 0224; scheduling information, for the scheduled downlink transmission, sent to the UE wherein the scheduling information includes one or more MCS parameters.); and
communicating with the UE in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.).
Regarding claim 27, Tang shows a method for wireless communications by an apparatus comprising:
sending one or more parameters of a user equipment (UE) receiver (Figure 15; Par. 0153, 0224; UL reference signal transmitted from UE 402 at 1430 is used to obtain uplink channel state information. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452.), the one or more parameters indicating a digital representation of the UE receiver used to estimate a response of the UE receiver communicating on a communication channel having one or more channel characteristics (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.);
receiving at least one parameter for communicating on the communication channel (Figure 15; Par. 0224; UE receives scheduling information for the scheduled downlink transmission, wherein the scheduling information includes one or more MCS parameters.), the at least one parameter based at least in part on the one or parameters and the one or more channel characteristics of the communication channel (Figure 15; Par. 0224; uplink channel state information is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and
communicating on the communication channel in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.).
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.
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) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Li et al. (US 2025/0220471; hereinafter Li).
Regarding claim 5, Tang shows wherein, to estimate the response of the UE receiver, the one or more processors are configured to further cause the apparatus to:
provide, to the machine learning model, input comprising one or more combinations of inputs, each of the one or more combinations of inputs comprising the one or more channel characteristics and one or more of a respective modulation and coding scheme (MCS) (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).), a respective precoding matrix indicator (PMI), or a respective rank indicator (RI); and
for each of the one or more combinations of inputs, obtain, from the machine learning model, output comprising an indication (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).).
Tang shows all of the elements as discussed above. Tang does not specifically show obtaining an output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Li. Specifically, Li shows obtaining an output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response (Figure 13; Par. 0172; the network node determines degradation information based on the at least one ML-model output and the communication information associated with communication performance of the UE. The degradation information is determined by performing analyzing an uplink control information (UCI) false detection probability, based on a cyclic redundancy check (CRC) length for a first report payload.).
In view of the above, having the system of Tang, then given the well-established teaching of Li, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Li, in order to provide motivation for improving machine learning (ML) model performance by detecting ML model performance degradation and analyzing causes for the degradation (Par. 0002 of Li).
for each of the one or more combinations of inputs, obtain, from the machine learning model, output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response (Li: Figure 13; Par. 0172; the network node determines degradation information based on the at least one ML-model output and the communication information associated with communication performance of the UE. The degradation information is determined by performing analyzing an uplink control information (UCI) false detection probability, based on a cyclic redundancy check (CRC) length for a first report payload.).
Regarding claim 6, modified Tang shows wherein the at least one parameter comprises the one or more of the respective MCS (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).), the respective PMI, or the respective RI of one of the one or more combinations of inputs that would pass the CRC.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of O’Shea et al. (US 2020/0343985; hereinafter O’Shea).
Regarding claim 10, Tang shows all of the elements except wherein the demodulator type comprises one or more of: a minimum mean square error (MMSE)-based demodulator, or a maximum likelihood-based demodulator.
However, the above-mentioned claim limitations are well-established in the art as evidenced by O’Shea. Specifically, O’Shea shows wherein the demodulator type comprises one or more of: a minimum mean square error (MMSE)-based demodulator, or a maximum likelihood-based demodulator (Par. 0083; training may include using other estimation or equalization approaches. For example, linear MMSE, max likelihood, successive interference cancellation (SIC), or other suitable approaches can be used to produce estimates of the channel response.)
In view of the above, having the system of Tang, then given the well-established teaching of O’Shea, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by O’Shea, in order to provide motivation for an optimization approach with different free parameters, lower bit error rate performance, improved error vector magnitude, frame error rate, enhance bitrates, among other improvements, can be attained over a given communications channel (Par. 0004 of O’Shea).
Regarding claim 20, this claim is rejected based on the same reasoning as presented in the rejection of claim 10.
Claim(s) 12 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Wei et al. (US 2025/0374155; hereinafter Wei).
Regarding claim 12, Tang shows all of the elements including obtaining the one or more parameters, as discussed above. Tang does not specifically show obtaining the one or more parameters prior to radio resource control (RRC) connection establishment between the apparatus and the UE.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Wei. Specifically, Wei shows obtaining the one or more parameters prior to radio resource control (RRC) connection establishment between the apparatus and the UE (Par. 0183; an evaluation or estimation of the loss function value corresponding to the outcome selected by the model at step S808 may be done at step S814, and the model used in the determination at step S808 may be updated at step S816, based on the input parameter values determined at step S804 and the determined (e.g. estimated) loss function. Step S816 is performed prior to establishing an RRC connection in the repeat step of S802.).
In view of the above, having the system of Tang, then given the well-established teaching of Wei, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Wei, in order to provide motivation to maintain (or minimize any degradation of) the ability to transmit and receive data (Par. 0011 of Wei).
Regarding claim 23, this claim is rejected based on the same reasoning as presented in the rejection of claim 12.
Claim(s) 13 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Wang et al. (US 2020/0351697; hereinafter Wang).
Regarding claim 13, Tang shows all of the elements, as discussed above. Tang does not specifically show sending an indication to the UE to reduce a rate of communicating channel state feedback (CSF) in response to obtaining the one or more parameters.
However, the above-mentioned claim limitations are well-established in the art as evidenced by Wang. Specifically, Wang shows sending an indication to the UE to reduce a rate of communicating channel state feedback (CSF) in response to obtaining the one or more parameters (Figure 6; Par. 0051; while in battery saving mode, CSI reference signals can continue to be sent, but the number of CSI reference signals can be reduced, for example, by not transmitting any aperiodic CSI reference signals, and correspondingly, by not providing an aperiodic CSI report.).
In view of the above, having the system of Tang, 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 Tang as taught by Wang, in order to provide motivation to use of antenna schemes that reduce the number of antennas being used and also reduce the number of transmissions, which can reduce power consumption when the battery is already low on charge. (Par. 0011 of Wang).
Regarding claim 24, this claim is rejected based on the same reasoning as presented in the rejection of claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250038811 A1 - relates generally to wireless communications, and more particularly, to techniques of monitoring the performance and applicability of Artificial Intelligence (AI) or Machine Learning (ML) models used for downlink (DL) Channel State Information (CSI) compression in wireless communication systems.
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 20240088968 A1 - relates generally to wireless communication systems and, more specifically, to a method and apparatus for support of machine learning (ML) or artificial intelligence (AI)-assisted channel state information (CSI) feedback.
US 20200228179 A1 - relates generally to the field of mobile communication and, more specifically, to channel state information acquisition frameworks in wireless communication systems for advanced networks (e.g., 4G, 5G, and beyond).
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/REDENTOR PASIA/Primary Examiner, Art Unit 2413