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 response filed on 07/18/2026 has been entered and made of record.
Claims 1 and 15 have been amended.
Claims 1-20 are currently pending.
Response to Arguments
Applicant's arguments filed 07/18/2026 have been fully considered but they are not persuasive. Claim 15, the applicant argued that WANG fails to disclose feature of "extracting a feature correlation over time of the plurality of channel matrices from the feature vector through one or more recurrent neural networks (RNNs)" in the claim 15.
In response to applicant’s argument, the examiner respectfully disagrees with the above argument. As shown in Fig.1a-b, Wang clearly teaches that the UE is extracting spatial features and interframe correlation i.e., feature correlation over time correlation of the channel matrices from the channel vectors through one or more recurrent neural networks (RNNs) since frequency domain channel vectors i.e., column vectors of Ht and spatial domain channel vectors (i.e., row vectors of Ht) e.g., feature vector are performed if the number of transmit antennas, Nt --> +∞, is very large based on the compressed channel matrices and the extracted feature time correlation, Noted: each CsiNet outputs two matrices with size N’c x Nt as extracted features from the angular-delay domain and motivated by the RCNN that excels in extracting spatial-temporal features for video representation (see Wang, page 1 right column lines 19-40, and page 2 left column Observation 1 lines 1-21, Fig.1a-b page 2 right column lines 11-33).
Claim 1, the applicant argued that WANG still fails to disclose that the M-dimensional real-valued codeword vector is determined based on the compressed channel matrices and the extracted feature correlation over time.
In response to applicant’s argument, the examiner respectfully disagrees with the above argument. As shown in Fig.1a-b, Wang clearly teaches that the frequency domain channel vectors i.e., column vectors of Ht and spatial domain channel vectors (i.e., row vectors of Ht) e.g., feature vector is determined if the number of transmit antennas, Nt --> +∞, is very large based on the compressed channel matrices and the extracted feature time correlation since a feature vector e.g., an M-dimensional real-valued codeword vector st (M<N) is determined based on the compressed channel matrices and the extracted time correlation, Noted: each CsiNet ouputs two matrices with size N’c x Nt as extracted features from the angular-delay domain and motivated by the RCNN that excels in extracting spatial-temporal features for video representation (see Wang, page 2 left column Observation 1 lines 1-21, page 2 left column Observation 2 lines 1-21 and Fig.1a-b page 2 right column lines 11-33).
Claim 8, Applicant make arguments the same argument as in claim 1. Please see the above for examiner’s response.
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 of this title, 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 1-2, 7, 8-9, 14 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. [hereinafter as Li], US 2012/0045003 A1 in view of Liu et al. [hereinafter as Liu], WO 2021108940 A1 further in view of WANG, T., et al.; "Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels;" April 2019; pps. 1-4.
Regarding claim 1, Li discloses wherein a method for channel state information (CSI) compression at a user equipment (UE) (Fig.1 [0009], a method for channel state information (CSI) quantization/compression at a communication station/user equipment), the method comprising:
obtaining a plurality of first channel matrices that each indicates the CSI of a communication channel between the UE and a base station (BS) at a different time during a time period (Fig.1-2 [0019]-[0020], receiving station 204 is obtaining channel matrices or beamforming matrices i.e., a plurality of first channel matrices transmitted by transmitting station 202 that each indicates the channel state information (CSI) of a communication channel between the receiving station 204/UE and a transmitting station 202/base station (BS) at a different time during a time period e.g., over time samples taken at different times).
However, Li does not explicitly disclose wherein compressing each of the plurality of first channel matrices into a respective compressed channel matrix through one or more convolutional neural networks (CNNs).
In the same field of endeavor, Liu teaches wherein compressing each of the plurality of first channel matrices into a respective compressed channel matrix through one or more convolutional neural networks (CNNs) (Fig.1-2 [0057], the channel matrix i.e., each of the plurality of first channel matrices is compressed to fewer channel elements of coefficients i.e., respective compressed channel matrix through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and Fig.1 [0032], a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN)).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li to incorporate the teaching of Liu in order to provide for improving the training efficiency. It would have been beneficial to compress the channel matrix i.e., each of the plurality of first channel matrices to fewer channel elements of coefficients i.e., respective compressed channel matrix through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and, a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN) as taught by Liu to have incorporated in the system of Li to achieve satisfactory CSI feedback and recovery capability. (Liu, Fig.1 [0032], Fig.1-2 [0035] and Fig.1-2 [0057])
However, Li and Liu do not explicitly disclose wherein extracting a feature correlation over time from the plurality of first channel matrices through one or more recurrent neural networks (RNNs); and determining a feature vector based on the plurality of compressed channel matrices and the extracted feature correlation over time.
In the same field of endeavor, Wang teaches wherein extracting a feature correlation over time from the plurality of first channel matrices through one or more recurrent neural networks (RNNs) (page 1 right column lines 19-40, extracting spatial features and interframe correlation i.e., feature correlation over time correlation from the channel matrices through one or more recurrent neural networks (RNNs) and page 2 left column Observation 2 lines 15-25, extract time correlation from the previously recovered channel matrices); and
determining a feature vector based on the plurality of compressed channel matrices and
the extracted feature correlation over time (page 2 left column Observation 1 lines 1-21, determining frequency domain channel vectors i.e., column vectors of Ht and spatial domain channel vectors (i.e., row vectors of Ht) e.g., feature vector if the number of transmit antennas, Nt --> +∞, is very large based on the compressed channel matrices and the extracted feature time correlation and page 2 left column Observation 2 lines 1-21, determining a feature vector e.g., an M-dimensional real-valued codeword vector st (M<N) based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li and Liu to incorporate the teaching of Wang in order to provide for improvement in resolution. It would have been beneficial to extract spatial features and interframe correlation i.e., feature correlation over time correlation from the channel matrices through one or more recurrent neural networks (RNNs) and, determine an M-dimensional real-valued codeword vector st (M<N) based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices as taught by Wang to have incorporated in the system of Li and Liu to provide for improving trade-off between compression radio (CR) and recovery quality. (Wang, page 1 right column lines 19-40, page 2 left column Observation 1 lines 1-21, page 2 left column Observation 2 lines 1-21 and page 2 right column lines 6-17)
Regarding claim 2, Li, Liu and Wang disclosed all the elements of claim 1 as stated above wherein Li further discloses receiving, from the BS, a plurality of reference signals during the time period (Fig.1-2 [0019]-[0020], receiving signals/a plurality of reference signals of a channel estimate or channel state information from the transmitting station 202/base station during the time period and Fig.2-3 [0022][0044], reference signals for quantizing);
determining a plurality of second channel matrices based on the plurality of reference
signals (Fig.1-2 [0020][0022], determining the channel matrices or beamforming matrices/a plurality of second channel matrices based on the signals/plurality of reference signals); and
transforming each of the plurality of second channel matrices into a respective one of the plurality of first channel matrices (Fig.1-2 [0020][0027], changing/transforming each of the channel matrices or beamforming matrices/plurality of second channel matrices into each of the channel matrices/a respective one of the plurality of first channel matrices).
Regarding claim 7, Li, Liu and Wang disclosed all the elements of claim 1 as stated above wherein Liu further discloses sending to the BS the feature vector for CSI feedback (Fig.1 [0032]-[0033], sending to the second device 220/BS the feature vector for CSI feedback).
Regarding claim 8, Li discloses wherein a user equipment (UE) (Fig.1 [0009], a communication station/user equipment), comprising:
processing circuitry configured to obtain a plurality of first channel matrices that each indicates channel state information (CSI) of a communication channel between the UE and a base station (BS) at a different time during a time period (Fig.1 [0017], processing circuitry of the communication station/user equipment and Fig.1-2 [0019]-[0020], receiving station 204 is obtaining channel matrices or beamforming matrices i.e., a plurality of first channel matrices transmitted by transmitting station 202 that each indicates the channel state information (CSI) of a communication channel between the receiving station 204/UE and a transmitting station 202/base station (BS) at a different time during a time period e.g., over time samples taken at different times).
However, Li does not explicitly disclose wherein compress each of the plurality of first channel matrices into a respective compressed channel matrix through one or more convolutional neural networks (CNNs).
In the same field of endeavor, Liu teaches wherein compress each of the plurality of first channel matrices into a respective compressed channel matrix through one or more convolutional neural networks (CNNs) (Fig.1-2 [0057], the channel matrix i.e., each of the plurality of first channel matrices is compressed to fewer channel elements of coefficients i.e., respective compressed channel matrix through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and Fig.1 [0032], a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN)).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li to incorporate the teaching of Liu in order to provide for improving the training efficiency. It would have been beneficial to compress the channel matrix i.e., each of the plurality of first channel matrices to fewer channel elements of coefficients i.e., respective compressed channel matrix through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and, a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN) as taught by Liu to have incorporated in the system of Li to achieve satisfactory CSI feedback and recovery capability. (Liu, Fig.1 [0032], Fig.1-2 [0035] and Fig.1-2 [0057])
However, Li and Liu do not explicitly disclose wherein extract a feature correlation over time from the plurality of first channel matrices through one or more recurrent neural networks (RNNs); and determine a feature vector based on the plurality of compressed channel matrices and the extracted feature correlation over time.
In the same field of endeavor, Wang teaches wherein extract a feature correlation over time from the plurality of first channel matrices through one or more recurrent neural networks (RNNs) (page 1 right column lines 19-40, extracting spatial features and interframe correlation i.e., feature correlation over time correlation from the channel matrices through one or more recurrent neural networks (RNNs) and page 2 left column Observation 2 lines 15-25, extract time correlation from the previously recovered channel matrices); and
determine a feature vector based on the plurality of compressed channel matrices
and the extracted feature correlation over time (page 2 left column Observation 1 lines 1-21, determining frequency domain channel vectors i.e., column vectors of Ht and spatial domain channel vectors (i.e., row vectors of Ht) e.g., feature vector if the number of transmit antennas, Nt --> +∞, is very large based on the compressed channel matrices and the extracted feature time correlation and page 2 left column Observation 2 lines 1-21, determining a feature vector e.g., an M-dimensional real-valued codeword vector st (M<N) based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li and Liu to incorporate the teaching of Wang in order to provide for improvement in resolution. It would have been beneficial to extract spatial features and interframe correlation i.e., feature correlation over time correlation from the channel matrices through one or more recurrent neural networks (RNNs) and, determine an M-dimensional real-valued codeword vector st (M<N) based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices as taught by Wang to have incorporated in the system of Li and Liu to provide for improving trade-off between compression radio (CR) and recovery quality. (Wang, page 1 right column lines 19-40, page 2 left column Observation 1 lines 1-21, page 2 left column Observation 2 lines 1-21 and page 2 right column lines 6-17)
Regarding claim 9, Li, Liu and Wang disclosed all the elements of claim 8 as stated above wherein Li further discloses receiving circuitry configured to receive a plurality of reference signals from the BS during the time period (Fig.1 [0017], radio transceiver/ receiving circuitry and Fig.1-2 [0019]-[0020], receiving signals/a plurality of reference signals of a channel estimate or channel state information from the transmitting station 202/base station during the time period and Fig.2-3 [0022][0044], reference signals for quantizing),
wherein the processing circuitry is further configured to determine a plurality of second channel matrices based on the plurality of reference signals (Fig.1-2 [0020][0022], determining the channel matrices or beamforming matrices/a plurality of second channel matrices based on the signals/plurality of reference signals), and
transform the plurality of second channel matrices into the plurality of first channel matrices (Fig.1-2 [0020][0027], changing/transforming each of the channel matrices or beamforming matrices/plurality of second channel matrices into each of the channel matrices/a respective one of the plurality of first channel matrices).
Regarding claim 14, Li, Liu and Wang disclosed all the elements of claim 8 as stated above wherein Liu further discloses transmitting circuitry configured to send to the BS the feature vector for CSI feedback (Fig.1 [0017], radio transceiver/ transmitting circuitry and Fig.1 [0032]-[0033], sending to the second device 220/BS the feature vector for CSI feedback).
Regarding claim 15, Li discloses wherein a method for channel state information (CSI) decompression at a base station (BS) (Fig.1 [0009], a method for channel state information (CSI) quantization/decompression at an access point/base station), the method comprising:
each of the plurality of channel matrices indicating the CSI of a communication channel between the UE and the BS at a different time during a time period (Fig.1-2 [0019]-[0020], channel matrices or beamforming matrices i.e., a plurality of channel matrices that each indicates the channel state information (CSI) of a communication channel between a receiving station 204/UE and a transmitting station 202/base station (BS) at a different time during a time period e.g., over time samples taken at different times).
However, Li does not explicitly disclose wherein receiving a feature vector from a user equipment (UE); decompressing the feature vector into a plurality of channel matrices through one or more convolutional neural networks (CNNs).
In the same field of endeavor, Liu teaches wherein receiving a feature vector from a user equipment (UE) (Fig.1-2 [0033][0038], receiving a feature vector s as CSI from a first device 210/user equipment (UE));
decompressing the feature vector into a plurality of channel matrices through one or more convolutional neural networks (CNNs) (Fig.1-2 [0057][0064], the second device 220/base station is decompressing the quantized coefficients/feature vector into the channel matrix i.e., plurality of channel matrices through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and Fig.1 [0032], a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN)).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li to incorporate the teaching of Liu in order to provide for improving the training efficiency. It would have been beneficial to decompress the quantized coefficients/feature vector into the channel matrix i.e., plurality of channel matrices through NN encode 105 i.e., one or more convolutional neural networks (CNNs) and, a CSI encoder 105 and a CSI decoder 110 are designed jointly using a convolutional NN (CNN) as taught by Liu to have incorporated in the system of Li to achieve satisfactory CSI feedback and recovery capability. (Liu, Fig.1 [0032], Fig.1-2 [0035] and Fig.1-2 [0057][0064])
However, Li and Liu do not explicitly disclose wherein extracting a feature correlation over time of the plurality of channel matrices from the feature vector through one or more recurrent neural networks (RNNs); and determining the CSI of the communication channel based on the plurality of channel matrices and the extracted feature correlation over time.
In the same field of endeavor, Wang teaches wherein extracting a feature correlation over time of the plurality of channel matrices from the feature vector through one or more recurrent neural networks (RNNs) (page 1 right column lines 19-40, extracting spatial features and interframe correlation i.e., feature correlation over time correlation of the channel matrices from the channel vectors through one or more recurrent neural networks (RNNs) and page 2 left column Observation 2 lines 15-25, extract time correlation from the previously recovered channel matrices); and
determining the CSI of the communication channel based on the plurality of channel
matrices and the extracted feature correlation over time (page 2 left column Observation 1 lines 1-21, determining the CSI of frequency domain channel vectors i.e., column vectors of Ht and spatial domain channel vectors (i.e., row vectors of Ht) if the number of transmit antennas, Nt --> +∞, is very large based on the channel matrices and the extracted feature time correlation and page 2 left column Observation 2 lines 1-21, determining the CSI of the communication channel based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li and Liu to incorporate the teaching of Wang in order to provide for improvement in resolution. It would have been beneficial to extract spatial features and interframe correlation i.e., feature correlation over time correlation of the channel matrices from the channel vectors through one or more recurrent neural networks (RNNs) and, determine the CSI of the communication channel based on the compressed channel matrices and the extracted time correlation from the previously removed channel matrices as taught by Wang to have incorporated in the system of Li and Liu to provide for improving trade-off between compression radio (CR) and recovery quality. (Wang, page 1 right column lines 19-40, page 2 left column Observation 1 lines 1-21, page 2 left column Observation 2 lines 1-21 and page 2 right column lines 6-17)
Regarding claim 16, Li, Liu and Wang disclosed all the elements of claim 15 as stated above wherein Li further discloses sending a plurality of reference signals to the UE during the time period (Fig.1-2 [0019]-[0020], transmitting signals/a plurality of reference signals of a channel estimate or channel state information to the receiving station 204/UE during the time period and Fig.2-3 [0022][0044], reference signals for quantizing), wherein the feature vector is determined by the UE based on the plurality of reference signals (Fig.1-2 [0020][0022], determining the channel matrices or beamforming matrices/ feature vector based on the signals/plurality of reference
signals).
Additionally, Liu discloses the feature vector is determined by the UE based on the plurality of reference signals (Fig.1-2 [0033][0038], determining the feature vector based on the signals/plurality of reference signals).
Claims 3-5, 10-12 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. [hereinafter as Li], US 2012/0045003 A1 in view of Liu et al. [hereinafter as Liu], WO 2021108940 A1 in view of WANG, T., et al.; "Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels;" April 2019; pps. 1-4 further in view of Hindy et al. (provisional application No. 63/224293 filed on 07/21/2021) [hereinafter as Hindy], US 2024/0364485 A1.
Regarding claim 3, Li, Liu and Wang disclosed all the elements of claim 2 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of second channel matrices is in a three dimensional domain that is represented by a transmit antenna index of the BS, a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of second channel matrices is in a three dimensional domain that is represented by a transmit antenna index of the BS (Fig.1&4-6 [0080]-[0081], each of the plurality of matrices/second channel matrices is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS), a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081][0137], δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the base station/BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS, δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the base station/BS and delay-Doppler information for a delay component index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], Fig.1&4-6 [0137] and Fig.1&4-6 [0098]-[0099][0146])
Regarding claim 4, Li, Liu, Wang and Hindy disclosed all the elements of claim 3 as stated above wherein Liu further discloses each of the one or more CNNs is a three dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions of 3x3/three dimensional CNN).
Regarding claim 5, Li, Liu and Wang disclosed all the elements of claim 2 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of second channel matrices is in a four dimensional domain that is represented by a receive antenna index of the UE, a transmit antenna index of the BS, a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE, a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of second channel matrices is in a four dimensional domain that is represented by a receive antenna index of the UE, a transmit antenna index of the BS (Fig.1&4-6 [0080]-[0081][0088], each of the plurality of matrices/second channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, transmit K antenna index of the base station/BS), a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE, a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081][0137], δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, a transmit beam index of the BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit K antenna index of the BS, δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS and delay-Doppler information for a delay component index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], Fig.1&4-6 [0137] and Fig.1&4-6 [0098]-[0099][0146])
Regarding claim 10, Li, Liu and Wang disclosed all the elements of claim 9 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of second channel matrices is in a three dimensional domain that is represented by a transmit antenna index of the BS, a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of second channel matrices is in a three dimensional domain that is represented by a transmit antenna index of the BS (Fig.1&4-6 [0080]-[0081], each of the plurality of matrices/second channel matrices is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS), a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081][0137], δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the base station/BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS, δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the base station/BS and delay-Doppler information for a delay component index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], Fig.1&4-6 [0137] and Fig.1&4-6 [0098]-[0099][0146])
Regarding claim 11, Li, Liu, Wang and Hindy disclosed all the elements of claim 10 as stated above wherein Liu further discloses each of the one or more CNNs is a three dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions of 3x3/three dimensional CNN).
Regarding claim 12, Li, Liu and Wang disclosed all the elements of claim 9 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of second channel matrices is in a four dimensional domain that is represented by a receive antenna index of the UE, a transmit antenna index of the BS, a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE, a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of second channel matrices is in a four dimensional domain that is represented by a receive antenna index of the UE, a transmit antenna index of the BS (Fig.1&4-6 [0080]-[0081][0088], each of the plurality of matrices/second channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, transmit K antenna index of the base station/BS), a time domain index, and a frequency domain index, and each of the plurality of first channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE, a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081][0137], δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, a transmit beam index of the BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit K antenna index of the BS, δ time domain index, and a spatial and frequency domain index, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS and delay-Doppler information for a delay component index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], Fig.1&4-6 [0137] and Fig.1&4-6 [0098]-[0099][0146])
Regarding claim 17, Li, Liu and Wang disclosed all the elements of claim 15 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081], each of the plurality of matrices/second channel matrices is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit K antenna index of the base station/BS, δ time domain index, and a spatial and frequency domain index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], and Fig.1&4-6 [0098]-[0099][0146])
Regarding claim 18, Li, Liu, Wang and Hindy disclosed all the elements of claim 17 as stated above wherein Liu further discloses each of the one or more CNNs is a three dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions of 3x3/three dimensional CNN).
Regarding claim 19, Li, Liu and Wang disclosed all the elements of claim 15 as stated above.
However, Li, Liu and Wang do not explicitly disclose wherein each of the plurality of channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE, a transmit beam index of the BS, a delay component index, and a Doppler component index.
In the same field of endeavor, Hindy teaches wherein each of the plurality of channel matrices is in a four dimensional domain that is represented by a receive beam index of the UE (Fig.1&4-6 [0080]-[0081][0088], each of the plurality of matrices/ channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, transmit beam index of the base station/BS), a transmit beam index of the BS (Fig.1&4-6 [0080]-[0081][0137], each of the plurality of matrices/first channel matrices is in multiple dimensions/a four dimensional domain that is represented by a receive K antenna index of the UE, a transmit beam index of the BS), a delay component index, and a Doppler component index (Fig.1&4-6 [0080][0089], delay-Doppler information for a delay component index, and a Doppler component index, [0098]-[0099], [0146]).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu and Wang to incorporate the teaching of Hindy in order to provide for CSI-RS configuration enhancements. It would have been beneficial to use each of the plurality of matrices/second channel matrices which is in a three dimensional domain that is represented by a transmit beam index of the base station/BS, and each of the plurality of matrices/first channel matrices is in a three dimensional domain that is represented by a transmit beam index of the BS and delay-Doppler information for a delay component index, and a Doppler component index as taught by Hindy to have incorporated in the system of Li, Liu and Wang to provide for successively improving initial CSI estimation and generating new CSI samples. (Hindy, Fig.1&4-6 [0080]-[0081], Fig.1&4-6 [0089], Fig.1&4-6 [0094], Fig.1&4-6 [0137] and Fig.1&4-6 [0098]-[0099][0146])
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. [hereinafter as Li], US 2012/0045003 A1 in view of Liu et al. [hereinafter as Liu], WO 2021108940 A1 in view of WANG, T., et al.; "Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels;" April 2019; pps. 1-4 in view of Hindy et al. (provisional application No. 63/224293 filed on 07/21/2021) [hereinafter as Hindy], US 2024/0364485 A1 further in view of Trusov et al. [hereinafter as Trusov], US 20220309320 A1.
Regarding claim 6, Li, Liu, Wang and Hindy disclosed all the elements of claim 5 as stated above wherein Liu further discloses each of the one or more CNNs is a four dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions/four dimensional CNN).
Even though Li, Liu, Wang and Hindy disclose each of the one or more CNNs is a four dimensional CNN, in the same field of endeavor, Trusov teaches wherein each of the one or more CNNs is a four dimensional CNN (Fig.1&2A-B [0050], each of the one or more CNNs is a four dimensional CNN).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu, Wang and Hindy to incorporate the teaching of Trusov in order to provide for resource-efficiency AI application. It would have been beneficial to use each of the one or more CNNs which is a four dimensional CNN as taught by Trusov to have incorporated in the system of Li, Liu, Wang and Hindy to provide for higher computational efficiency and flexibility of the GeMM micro-kernel. (Trusov, Fig.1&2A-B [0050] and Fig.1&6 [0066])
Regarding claim 13, Li, Liu, Wang and Hindy disclosed all the elements of claim 12 as stated above wherein Liu further discloses each of the one or more CNNs is a four dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions/four dimensional CNN).
Even though Li, Liu, Wang and Hindy disclose each of the one or more CNNs is a four dimensional CNN, in the same field of endeavor, Trusov teaches wherein each of the one or more CNNs is a four dimensional CNN (Fig.1&2A-B [0050], each of the one or more CNNs is a four dimensional CNN).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu, Wang and Hindy to incorporate the teaching of Trusov in order to provide for resource-efficiency AI application. It would have been beneficial to use each of the one or more CNNs which is a four dimensional CNN as taught by Trusov to have incorporated in the system of Li, Liu, Wang and Hindy to provide for higher computational efficiency and flexibility of the GeMM micro-kernel. (Trusov, Fig.1&2A-B [0050] and Fig.1&6 [0066])
Regarding claim 20, Li, Liu, Wang and Hindy disclosed all the elements of claim 12 as stated above wherein Liu further discloses each of the one or more CNNs is a four dimensional CNN (Fig.1 [0033], each of the one or more CNNs is a M-dimensional vector with dimensions/four dimensional CNN).
Even though Li, Liu, Wang and Hindy disclose each of the one or more CNNs is a four dimensional CNN, in the same field of endeavor, Trusov teaches wherein each of the one or more CNNs is a four dimensional CNN (Fig.1&2A-B [0050], each of the one or more CNNs is a four dimensional CNN).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention was made to provide to have modified Li, Liu, Wang and Hindy to incorporate the teaching of Trusov in order to provide for resource-efficiency AI application. It would have been beneficial to use each of the one or more CNNs which is a four dimensional CNN as taught by Trusov to have incorporated in the system of Li, Liu, Wang and Hindy to provide for higher computational efficiency and flexibility of the GeMM micro-kernel. (Trusov, Fig.1&2A-B [0050] and Fig.1&6 [0066])
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/V.L/Examiner, Art Unit 2414
/EDAN ORGAD/Supervisory Patent Examiner, Art Unit 2414
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