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 .
DETAILED ACTION
This communication is in response to applicant’s 04/21/2026 amendment or response in the application of VITTHALADEVUNI et al. for “CONFIGURABLE METRICS FOR CHANNEL STATE COMPRESSION AND FEEDBACK” filed 01/04/2023. The amendment or response to the claims have been entered. No claims have been canceled. No claims have been added. Claims 1-3, 5-6, 10-12, 14-17, 25, 33, 37-56 are now pending.
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) 1-2, 4, 10, 12, 14-16, 25, 33, 37, 39, 45, 47-51, 53-54, 56 is/are rejected under 35 U.S.C. 103 as being unpatentable over CHAVVA (US 2021/0351885 A1) in view of WANG et al. (US 2021/0182658 A1), hereinafter WANG.
Regarding claim 1, CHAVVA discloses a method for wireless communication at a user equipment (UE) (UE equipment 601 comprises of processor 602, see figures 6A-6B), comprising:
receiving, from a base station, an indication of a level of accuracy for reporting channel state feedback to the base station for training a neural network (at step 801, …receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig., see ¶ 0149; the processor 602 of the UE 601 comprises a computing unit 602a, a database 602b, and a neural network 602c. The UE 601 can generate at least one CSI report and send the at least one CSI report to the gNB 607, see ¶ 0115; the ML based learning model is a Neural Network 602c, which is included in the processor 602. The processor 602 can compute and predict the values of the feedback parameters using the neural network 602c, see ¶ 0116),
the neural network comprising a first neural network at an encoder for encoding the channel state feedback and a second neural network at a decoder for decoding the channel state feedback (the UE comprising ML for autoencoding and ML model for auto decoding, see figure 15A-15B, ¶ 0185-0186);
receiving downlink data or reference signals from the base station (at step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig. The embodiments herein utilize the CSI-RS to estimate channel metrics and basement metrics, see ¶ 0151); and
reporting the channel state feedback to the base station corresponding to the level of accuracy based at least in part on the downlink data or the reference signals (at step 806, the method includes sending, by the UE 601, the at least one CSI report to the gNB 607, if the current slot is a reporting time slot. In an embodiment, the plurality of CSI reports is sent to the gNB 607 at the same time, see ¶ 0160).
CHAVVA fails to disclose that the UE receiving from the base station an loss function for training a neural network pair comprising a first network neural network to minimize a different between channel state feedback input and channel state feedback output.
In the same field of endeavor, KIM discloses neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error to update the weight of each node in the neural network (see ¶ 0171), where the neural network encoder is included in a user equipment (see claim 12).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG’s teaching in the network taught by CHAVVA for the use of neural networks for encoding and decoding at the User Equipment (UE)—such as smartphones and IoT devices—is primarily focused on optimizing physical layer communications and enabling efficient distributed machine learning.
Regarding claim 2, CHAVVA discloses training the neural network pair using the loss function (training the ML model, to improve the accuracy of the prediction, by minimizing a cost function, such that the entire CSI can be fitted between the CSI reporting occasions (slots). The embodiments include training the ML model to ensure that the predicted and computed values of the feedback parameters closely match the actual values at the time of scheduling, see ¶ 0171).
Regarding claim 4, CHAVVA discloses encoding the channel state feedback using the first neural network at the encoder based at least in part on the training; and reporting the encoded channel state feedback (generating the CSI report comprises encoding, by the neural network (602c), the compiled at least one of the computed values of the CSI feedback parameters and the predicted values the CSI feedback parameters, see ¶ 0043).
Regarding claim 10, CHAVVA discloses the level of accuracy is based at least in part on one or more of a subband (timeslot), spatial layer, or channel tap to which the channel state feedback corresponds, the method further comprising: receiving data from the base station on the subband or spatial layer or in accordance with the channel tap based at least in part on reporting the channel state feedback corresponding to the level of accuracy (the CSI-ReportConfig IE can include CSI reporting periodicity, time slots in which the CSI report is to be sent by the UE 601 to the gNB 607, feedback parameters that are to be included in the CSI report by the UE 601, and so on, see ¶ 0119).
Regarding claim 14, CHAVVA discloses receiving the indication of the level of accuracy comprises: receiving the indication of the level of accuracy in radio resource control (RRC) signaling or in a media access control (MAC) control element (MAC-CE) (the embodiments include receiving a Radio Resource Configuration (RRC) message, which comprises of feedback configuration for CSI-Reference Signals (CSI-RS). The feedback configuration for CSI-RS, received from the gNB, may include information such as CSI reporting periodicity; port, time (slot or symbol) and frequency, information pertaining to reception of the CSI-RS, type of measurement, parameters to be included in the CSI report, feedback configuration type, and so on, see ¶ 0091).
Regarding claim 15, CHAVVA discloses a method for wireless communication at a base station, comprising:
transmitting, to a user equipment (UE), an indication of a level of accuracy for reporting channel state feedback to the base station (at step 801, …receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig., see ¶ 0149; the processor 602 of the UE 601 comprises a computing unit 602a, a database 602b, and a neural network 602c. The UE 601 can generate at least one CSI report and send the at least one CSI report to the gNB 607, see ¶ 0115; the ML based learning model is a Neural Network 602c, which is included in the processor 602. The processor 602 can compute and predict the values of the feedback parameters using the neural network 602c, see ¶ 0116),
the neural network comprising a first neural network at an encoder for encoding the channel state feedback and a second neural network at a decoder for decoding the channel state feedback (the UE comprising ML for autoencoding and ML model for auto decoding, see figure 15A-15B, ¶ 0185-0186);
transmitting downlink data or reference signals to the UE (at step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig. The embodiments herein utilize the CSI-RS to estimate channel metrics and basement metrics, see ¶ 0151); and
receiving channel state feedback from the UE corresponding to the level of accuracy based at least in part on a transmission of the downlink data or reference signals to the UE (at step 806, the method includes sending, by the UE 601, the at least one CSI report to the gNB 607, if the current slot is a reporting time slot. In an embodiment, the plurality of CSI reports is sent to the gNB 607 at the same time, see ¶ 0160).
CHAVVA fails to disclose that the UE receiving from the base station an loss function for training a neural network pair comprising a first network neural network to minimize a different between channel state feedback input and channel state feedback output.
In the same field of endeavor, KIM discloses neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error to update the weight of each node in the neural network (see ¶ 0171), where the neural network encoder is included in a user equipment (see claim 12).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG’s teaching in the network taught by CHAVVA for the use of neural networks for encoding and decoding at the User Equipment (UE)—such as smartphones and IoT devices—is primarily focused on optimizing physical layer communications and enabling efficient distributed machine learning.
Regarding claim 16, CHAVVA discloses training the neural network pair using the loss function (training the ML model, to improve the accuracy of the prediction, by minimizing a cost function, such that the entire CSI can be fitted between the CSI reporting occasions (slots). The embodiments include training the ML model to ensure that the predicted and computed values of the feedback parameters closely match the actual values at the time of scheduling, see ¶ 0171).
Regarding claim 25, CHAVVA discloses an apparatus for wireless communication at a user equipment (UE), comprising:
means for receiving, from a base station, an indication of a level of accuracy for reporting channel state feedback to the base station for training a neural network pair (at step 801…receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig., see ¶ 0149; the processor 602 of the UE 601 comprises a computing unit 602a, a database 602b, and a neural network 602c. The UE 601 can generate at least one CSI report and send the at least one CSI report to the gNB 607, see ¶ 0115; the ML based learning model is a Neural Network 602c, which is included in the processor 602. The processor 602 can compute and predict the values of the feedback parameters using the neural network 602c, see ¶ 0116), the neural network comprising a first neural network at an encoder for encoding the channel state feedback and a second neural network at a decoder for decoding the channel state feedback (the UE comprising ML for autoencoding and ML model for auto decoding, see figure 15A, ¶ 0185);
means for receiving downlink data or reference signals from the base station (at step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig. The embodiments herein utilize the CSI-RS to estimate channel metrics and basement metrics, see ¶ 0151); and
means for reporting the channel state feedback to the base station corresponding to the level of accuracy based at least in part on the downlink data or the reference signals (At step 806, the method includes sending, by the UE 601, the at least one CSI report to the gNB 607, if the current slot is a reporting time slot. In an embodiment, the plurality of CSI reports is sent to the gNB 607 at the same time, see ¶ 0160).
CHAVVA fails to disclose that the UE receiving from the base station an loss function for training a neural network pair comprising a first network neural network to minimize a different between channel state feedback input and channel state feedback output.
In the same field of endeavor, KIM discloses neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error to update the weight of each node in the neural network (see ¶ 0171), where the neural network encoder is included in a user equipment (see claim 12).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG’s teaching in the network taught by CHAVVA for the use of neural networks for encoding and decoding at the User Equipment (UE)—such as smartphones and IoT devices—is primarily focused on optimizing physical layer communications and enabling efficient distributed machine learning.
Regarding claim 33, CHAVVA discloses an apparatus for wireless communication at a user equipment (UE), comprising: a processor, memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to:
receiving, from a base station, an indication of a level of accuracy for reporting channel state feedback to the base station for training a neural network pair (at step 801… receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig., see ¶ 0149; the processor 602 of the UE 601 comprises a computing unit 602a, a database 602b, and a neural network 602c. The UE 601 can generate at least one CSI report and send the at least one CSI report to the gNB 607, see ¶ 0115; the ML based learning model is a Neural Network 602c, which is included in the processor 602. The processor 602 can compute and predict the values of the feedback parameters using the neural network 602c, see ¶ 0116), the neural network comprising a first neural network at an encoder for encoding the channel state feedback and a second neural network at a decoder for decoding the channel state feedback (the UE comprising ML for autoencoding and ML model for auto decoding, see figure 15A, ¶ 0185);
receiving downlink data or reference signals from the base station (at step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig. The embodiments herein utilize the CSI-RS to estimate channel metrics and basement metrics, see ¶ 0151); and
reporting the channel state feedback to the base station corresponding to the level of accuracy based at least in part on the downlink data or the reference signals (at step 806, the method includes sending, by the UE 601, the at least one CSI report to the gNB 607, if the current slot is a reporting time slot. In an embodiment, the plurality of CSI reports is sent to the gNB 607 at the same time, see ¶ 0160).
CHAVVA fails to disclose that the UE receiving from the base station an loss function for training a neural network pair comprising a first network neural network to minimize a different between channel state feedback input and channel state feedback output.
In the same field of endeavor, KIM discloses neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error to update the weight of each node in the neural network (see ¶ 0171), where the neural network encoder is included in a user equipment (see claim 12).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG’s teaching in the network taught by CHAVVA for the use of neural networks for encoding and decoding at the User Equipment (UE)—such as smartphones and IoT devices—is primarily focused on optimizing physical layer communications and enabling efficient distributed machine learning.
Regarding claim 37, CHAVVA discloses training the neural network pair using the loss function (training the ML model, to improve the accuracy of the prediction, by minimizing a cost function, such that the entire CSI can be fitted between the CSI reporting occasions (slots). The embodiments include training the ML model to ensure that the predicted and computed values of the feedback parameters closely match the actual values at the time of scheduling, see ¶ 0171).
Regarding claim 39, CHAVVA discloses encoding the channel state feedback using the first neural network at the encoder based at least in part on the training; and reporting the encoded channel state feedback (generating the CSI report comprises encoding, by the neural network (602c), the compiled at least one of the computed values of the CSI feedback parameters and the predicted values the CSI feedback parameters, see ¶ 0043).
Regarding claim 45, CHAVVA discloses the level of accuracy is based at least in part on one or more of a subband (timeslot), spatial layer, or channel tap to which the channel state feedback corresponds, the method further comprising: receiving data from the base station on the subband or spatial layer or in accordance with the channel tap based at least in part on reporting the channel state feedback corresponding to the level of accuracy (the CSI-ReportConfig IE can include CSI reporting periodicity, time slots in which the CSI report is to be sent by the UE 601 to the gNB 607, feedback parameters that are to be included in the CSI report by the UE 601, and so on, see ¶ 0119).
Regarding claim 47, CHAVVA discloses identify a number of bits for reporting the channel state feedback based at least in part on the level of accuracy, wherein the number of bits is directly related to the level of accuracy; and report, the channel state feedback corresponding to the level of accuracy with the identified number of bits (the network device 110 obtains the CSI from a plurality of terminal devices 120 and an indication of a plurality of CSI accuracies corresponding to the CSI. The CSI accuracy may be indicated by an index of 1-3 bits. In this way, the network device 110 may learn the accuracy level of each received CSI, see ¶ 0044).
Regarding claim 49, CHAVVA discloses receiving the indication of the level of accuracy comprises: receiving the indication of the level of accuracy in radio resource control (RRC) signaling or in a media access control (MAC) control element (MAC-CE) (the embodiments include receiving a Radio Resource Configuration (RRC) message, which comprises of feedback configuration for CSI-Reference Signals (CSI-RS). The feedback configuration for CSI-RS, received from the gNB, may include information such as CSI reporting periodicity; port, time (slot or symbol) and frequency, information pertaining to reception of the CSI-RS, type of measurement, parameters to be included in the CSI report, feedback configuration type, and so on, see ¶ 0091).
Regarding claim 50, CHAVVA discloses an apparatus for wireless communication at a base station, comprising: a processor, memory coupled with the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to:
transmit, to a user equipment (UE), an indication of a level of accuracy for reporting channel state feedback to the base station (at step 801, …receiving a feedback configuration, by the UE 601, from the gNB 607. The feedback configuration is relevant to reception of CSI-RS and/or SSB. The feedback configuration can be used by the UE 601 to send the CSI as a feedback report. The UE 601 can receive the feedback configuration in a RRC message. The RRC message includes CSI-MeasConfig, CSI-ResourceConfig, CSI-ReportConfig, and CodebookConfig., see ¶ 0149; the processor 602 of the UE 601 comprises a computing unit 602a, a database 602b, and a neural network 602c. The UE 601 can generate at least one CSI report and send the at least one CSI report to the gNB 607, see ¶ 0115; the ML based learning model is a Neural Network 602c, which is included in the processor 602. The processor 602 can compute and predict the values of the feedback parameters using the neural network 602c, see ¶ 0116),
the neural network comprising a first neural network at an encoder for encoding the channel state feedback and a second neural network at a decoder for decoding the channel state feedback (the UE comprising ML for autoencoding and ML model for auto decoding, see figure 15A-15B, ¶ 0185-0186);
transmit downlink data or reference signals to the UE (at step 802, the method includes receiving CSI-RS and/or SSB, by the UE 601, if a current slot includes the CSI-RS and/or SSB. The UE 601 determines whether a current slot includes the CSI-RS, and which future slot is likely to include the CSI-RS; based on the information included in the CSI-ResourceConfig. The embodiments herein utilize the CSI-RS to estimate channel metrics and basement metrics, see ¶ 0151); and
receive channel state feedback from the UE corresponding to the level of accuracy based at least in part on a transmission of the downlink data or reference signals to the UE (at step 806, the method includes sending, by the UE 601, the at least one CSI report to the gNB 607, if the current slot is a reporting time slot. In an embodiment, the plurality of CSI reports is sent to the gNB 607 at the same time, see ¶ 0160).
CHAVVA fails to disclose that the UE receiving from the base station an loss function for training a neural network pair comprising a first network neural network to minimize a different between channel state feedback input and channel state feedback output.
In the same field of endeavor, KIM discloses neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error to update the weight of each node in the neural network (see ¶ 0171), where the neural network encoder is included in a user equipment (see claim 12).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG’s teaching in the network taught by CHAVVA for the use of neural networks for encoding and decoding at the User Equipment (UE)—such as smartphones and IoT devices—is primarily focused on optimizing physical layer communications and enabling efficient distributed machine learning.
Regarding claim 51, CHAVVA discloses training the neural network pair using the loss function (training the ML model, to improve the accuracy of the prediction, by minimizing a cost function, such that the entire CSI can be fitted between the CSI reporting occasions (slots). The embodiments include training the ML model to ensure that the predicted and computed values of the feedback parameters closely match the actual values at the time of scheduling, see ¶ 0171).
Regarding claim 53, CHAVVA discloses receiving, from the base station, an indication to train a plurality of neural network pairs based at least in part on a plurality of levels of accuracy, the plurality of neural network pairs comprising the neural network pair; and training each of the plurality of neural network pairs based at least in part on a respective level of accuracy of the plurality of levels of accuracy (the neural network 602c can be trained to accurately predict the feedback parameters. In an embodiment, the prediction accuracy can be improved by minimizing a cost function, such that the entire CSI can be fitted between the CSI reporting slots. The neural network 602c training ensures that the predicted values of the feedback parameters match the actual counterpart values at the time of scheduling the PDSCH, see ¶ 0110; the hierarchical neural network model includes three neural networks for predicting each of the feedback parameters, RI, PMI, and CQI. The individual computation and prediction of the feedback parameters, using individual neural networks, can simplify the training of the neural networks and reduce the computational complexity of the computation and prediction, see ¶ 0165).
Regarding claim 54, CHAVVA discloses the level of accuracy is based at least in part on one or more of a subband (timeslot), spatial layer, or channel tap to which the channel state feedback corresponds, the method further comprising: receiving data from the base station on the subband or spatial layer or in accordance with the channel tap based at least in part on reporting the channel state feedback corresponding to the level of accuracy (the CSI-ReportConfig IE can include CSI reporting periodicity, time slots in which the CSI report is to be sent by the UE 601 to the gNB 607, feedback parameters that are to be included in the CSI report by the UE 601, and so on, see ¶ 0119).
Claim(s) 5, 17, 40 and 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of CHAVVA-KIM in view of WANG et al. (US 2021/0342687 A1), hereinafter WANG ‘687.
Regarding claims 5, 17, 40 and 52, the combination of CHAVVA-KIM fails to explicitly disclose receiving, from the UE, coefficients for a neural network at a decoder for decoding the channel state feedback from the UE; and decoding the channel state feedback from the UE using the neural network at the decoder.
In the same field of endeavor, WANG ‘687 discloses receiving, from the UE, metric 825 (coefficients) for a neural network at a decoder for decoding the channel state feedback from the UE; and decoding the channel state feedback from the UE using the neural network at the decoder (see ¶ figure 8 and ¶ 0115).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement WANG ‘687’s teaching in the network taught by the combination of CHAVVA-KIM for adapting to changing environment of automation to provide ability to process information rapidly, simultaneously while reducing potential human errors.
Claim(s) 11, 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of CHAVVA-KIM in view of KIM et al. (US 2022/0158773 A1), hereinafter KIM ‘773.
Regarding claims 11 and 46, the combination of CHAVVA-KIM fails to disclose receiving a retransmission of the same data that the UE failed to decode based at least in part on reporting the channel state feedback corresponding to the level of accuracy.
In the same field of endeavor, KIM ‘773 discloses the receiving STA failed to decoding the received data, receiving the data retransmitted from the transmitting STA within the time limit after transmitting NACK to transmitting STA (see ¶ 0288).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement KIM’s teaching in the network taught by the combination of the combination of CHAVVA-KIM for providing network’s reliability.
Claim(s) 12, 47-48, and 56 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of CHAVVA-KIM in view of HAO et al. (US 2020/0358492 A1), hereinafter HAO.
Regarding claims 12, 47, 56, the combination of CHAVVA-KIM fails to disclose the apparatus transmit an indication of a number of bits for the UE to use to report the channel state feedback based at least in part on the level of accuracy, wherein the number of bits is directly related to the level of accuracy; and receive the channel state feedback corresponding to the level of accuracy with the number of bits
In the same filed of endeavor, HAO discloses the network device 110 obtains the CSI from a plurality of terminal devices 120 and an indication of a plurality of CSI accuracies corresponding to the CSI. The CSI accuracy may be indicated by an index of 1-3 bits. In this way, the network device 110 may learn the accuracy level of each received CSI, see ¶ 0044).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement HAO’s teaching in the network taught by the combination of CHAVVA-KIM more bits per user allow for higher CSI precision, while fewer bits require heavy quantization, increasing feedback error in 4G or 5G systems.
Regarding claim 48, HAO discloses receive an indication of the number of bits for reporting the channel state feedback based at least in part on the level of accuracy (the CSI accuracy may be indicated by an index of 1-3 bits. In this way, the network device 110 may learn the accuracy level of each received CSI …the network device 110 may transmit the indication of a required CSI accuracy to each terminal device 120. For example, the network device 110 may transmit the indication to each terminal device 120 in the physical downlink control channel (PDCCH), see ¶ 0044).
Allowable Subject Matter
Claims 3, 6, 38, 41-44, 55 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.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2, 5, 10-12, 14-17, 25, 33, 37-40, 45-54, 56 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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