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 .
Claim Objections
Claim(s) 1-2 and 7 is/are objected to because of the following informalities:
Claim 1, the Examiner suggests changing to “… wherein, for the one or more spatial related parameters, [[the]] at least one parameter related to the learning algorithm ...”
Claim 2, the Examiner suggests changing to “… wherein, for the one TCI states, the at least one parameter related to the learning algorithm ...”
Claim 7, the Examiner suggests changing to “... wherein [[the]] a specific parameter for the learning algorithm ...”
Appropriate correction(s) is/are required.
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.
Claim(s) 1, 11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAVVA (US 20210351885) in view of Li (US 20230421223 A1).
With respect to independent claims:
Regarding claim(s) 1/11/13, CHAVVA teaches A method for performing channel state information (CSI) reporting by a user equipment (UE) in a wireless communication system ([Fig.8], a UE generates a “CSI report.”), the method comprising:
receiving, from a network device, at least one reference signal (RS) ([0151], “receiving CSI-RS and/or SSB, by the UE 601.”) based on one or more spatial related parameters ([0150 and Fig.8, step 1], the UE receives “CSI-ResourceConfig IE” which includes information regarding “ports through which the CSI-RS can be received.”);
performing channel estimation based on the at least one RS ([0153], “At step 803, the method includes computing, by the UE 601, feedback parameters based on the information included in the CSI-RS and/or SSB ... Examples of feedback parameters include, but not limited to, PMI, CQI.”) and a learning algorithm ([0154], “computing the feedback parameters using a ML based learning model.”); and
transmitting, to the network device, CSI based on the channel estimation ([0159], “At step 805, the method includes generating, by the UE 601, at least one CSI report comprising the computed feedback parameters.” And [0160], “the method includes sending, by the UE 601, the at least one CSI report to the gNB 607.”).
However, CHAVVA does not specifically disclose wherein, for the one or more spatial related parameters, the at least one parameter related to the learning algorithm is configured in units of spatial related parameters or in units of spatial related parameter groups including at least one spatial related parameter.
In an analogous art, Li discloses performing channel estimation based on the at least one RS and a learning algorithm ([0123], “UE 115-b may determine the CSI using the one or more neural networks and using measurements made by UE 115-b on the second number of antenna port.”); and
transmitting, to the network device, CSI based on the channel estimation ([0125], “At 420, UE 115-b may transmit a report including the CSI.”),
wherein ([0123], “using measurements made by UE 115-b on the second number of antenna ports as inputs to the one or more neural networks.”), for the one or more spatial related parameters ([0122], “UE 115-b may receive a CSI-RS over each antenna port included in the second number of antenna ports, and UE 115-b may perform the measurements for each received CSI-RS.”), the at least one parameter related to the learning algorithm is configured in units of spatial related parameters ([0123], “second number of antenna ports.”) or in units of spatial related parameter groups including at least one spatial related parameter.
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of CHAVVA to specify number of antenna port as taught by Li. The motivation/suggestion would have been because there is a need to input to a neural network for computing CSI.
Claim(s) 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAVVA in view of Li, and further in view of Shen (US 20220342713).
Regarding claim(s) 3, Shen teaches further comprising: reporting, to the network device, information on whether to support the at least one parameter related to the learning algorithm ([0079], “The AI/ML capability information indicates the resource information used by the terminal to process a certain AI/ML service. For example, the AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service.”). .
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of Chavva to specify reporting capability as taught by Shen. The motivation/suggestion would have been because there is a need to indicate a UE is capable of perform machine learning.
Regarding claim(s) 4, Shen teaches wherein the information on whether to support is reported using at least one of a UE capability reporting procedure or a CSI reporting procedure ([0079], “The AI/ML capability information indicates the resource information used by the terminal to process a certain AI/ML service. For example, the AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service.”). .
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of Chavva to specify reporting capability as taught by Shen. The motivation/suggestion would have been because there is a need to indicate a UE is capable of perform machine learning.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAVVA in view of Li, and further in view of Ling (US 20170103298).
Regarding claim(s) 5, Ling teaches wherein, based on the learning algorithm corresponding to a convolution neural network (CNN) ([0027], “parameters of the CNN accelerator are identified.”), the at least one parameter related to the learning algorithm includes a CNN related parameter, and wherein the CNN related parameter includes at least one of a size of an input value, a size of a kernel ([0027], “The parameters of a CNN algorithm may include a number of kernels to instantiate for each layer identified.”), a size of padding, or a size of a stride.
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of Chavva to specify number of kernels as taught by Ling. The motivation/suggestion would have been because there is a need to perform CNN algorithm.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAVVA in view of Li, and further in view of Wang (US 20220407745).
Regarding claim(s) 6, Wang teaches wherein the at least one parameter related to the learning algorithm further includes an RS related parameter for the at least one RS ([0198], “the first device uses a part of the received third CSI as input data of training the neural network, and the first device may determine, based on the density of the low-density reference signal configured in the RRC message, a size of the CSI used as the input data.”), and
wherein the RS related parameter includes at least one of a density of RSs ([0198], “density of the low-density reference signal configured in the RRC message.”), a pattern of RSs, or a number of RSs.
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of Chavva to specify reference signal density as taught by Wang. The motivation/suggestion would have been because there is a need to determine size of CSI as input data for neural network.
Regarding claim(s) 7, Wang teaches wherein the specific parameter for the learning algorithm and the RS related parameter are configured in association with each other ([0198], “the first device uses a part of the received third CSI as input data of training the neural network, and the first device may determine, based on the density of the low-density reference signal configured in the RRC message, a size of the CSI used as the input data.”).
Therefore, it would have been obvious to one with ordinary skill in the art at the time before the effective filing date of the claim invention to have modified the method of Chavva to specify reference signal density as taught by Wang. The motivation/suggestion would have been because there is a need to determine size of CSI as input data for neural network.
Allowable Subject Matter
Claims 2 and 8-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIREN QIN whose telephone number is (571)272-5444. The examiner can normally be reached on M-F 9-6 PM.
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/ZHIREN QIN/Examiner, Art Unit 2411