DETAILED ACTION
Applicant’s response filed on 08/12/2026 has been entered and made of record.
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 Status
Claims 1, 5-6, 11, 13, 15 and 18 are amended.
Claims 4, 14 and 20 are canceled.
New claims 21-23 are added.
Claims 1-3, 5-13, 15-19 and 21-23 are pending for examination.
Applicant Argument
Applicant’s arguments (remark pages 10-12), filed on 08/12/2026, with respect to claims 1-3, 5-13, 15-19 and 21-23 have been considered but are moot in view of the new ground of rejection below which better address the claimed invention as amended.
This Office Action is made Final.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-3, 5-6, 8, 11-13, 15, 18-19 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Hajri et al. (US 20220386292 A1), hereinafter “Hajri”, in view of Guo et al. (“Overview of Deep Learning-Based CSI Feedback in Massive MIMO Systems”), hereinafter “Guo”, in view of Zirwas et al. (US 20120250557 A1), hereinafter “Zirwas”.
Per claim 1, 11 and 18:
Regarding claim 1, Hajri teaches ‘A terminal’ (Hajri: [FIG.1]: “UE”); ‘comprising: a receiver configured to’ (Hajri: [FIG.1]: “RX”; [0016]: “Each of the one or more transceivers 130 includes a receiver, Rx”);
‘receive a pilot signal from a base station at a plurality of times’ (Hajri: [FIG.3]: step 306: “UE receives downlink reference signals for channel and/or interference measurements”; [FIG.4]: three times CSI-RS “406” (pilot signal));
‘the plurality of times having a first time interval’ (Hajri: [TABLE 1]: “CSI-RS period” : “2 ms”, having time interval of 2 ms);
‘a processor configured to’ (Hajri: [FIG.1]: “Processor(s)”; [0016]: “The module 140 may be implemented in hardware as module 140-1, such as being implemented as part of the one or more processors”);
‘predict a channel corresponding to a second time interval based on the pilot signal by using a neural network’ (Hajri: [FIG.6]: “Predicted CQI”; [0012]: “UE prediction based on a gradient boost regressor”; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0043]: “The coefficients of a CSI prediction model (e.g. the coefficients of a neural network”; [0079]: “CQI prediction model feedback enables finer time resolution of CQI feedback”; [0082]: “obtain a higher time granularity for the CQI without increasing its reporting periodicity”; predict CSI for each next 4 subframe (time interval of 1 ms) from input of 3 previous CQI with 2 ms period (time interval of 2 ms), where 1 ms < 2 ms);
‘the second time interval being shorter than the first time interval’ (discussed in element above);
‘obtain channel state information based on the predicted channel’ (Hajri: [0069]: “the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements, the CSI reporting configuration and configured prediction windows”);
‘a transmitter configured to’ (Hajri: [FIG.1]: “TX”; [0016]: “Each of the one or more transceivers 130 includes a receiver, Rx, 132 and a transmitter, Tx”);
‘provide the channel state information to the base station’ (Hajri: [0069]: “the UE transmits triggered CSI report(s) in uplink control information”, report CSI to the base station);
‘wherein the neural network comprises a plurality of layers’ (Hajri: [0043]: “a neural network”; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates”, a gradient boost regressor would have input layer and prediction layer). However, Hajri fails to expressly teach ‘neural network comprises a plurality of layers’;
‘the plurality of layers including: a coarse transformer layer configured to learn one or more channel parameters of the pilot signal received at the first time interval’ (Hajri: [FIG.6]: “Actual CQI”; [FIG.4]; [0042]: “AI generated model parameters used in the prediction procedures”; [0035]: “machine learning algorithms that can achieve good prediction performance for CSI in future time”; [0061]: “the UE may reset its model to learn from scratch based on new samples”; [TABLE 1]: “CSI-RS period” : “2 ms”; [0076]: “The UE computes a prediction model based on multiple CSI-RS measurements”). However, Hajri fails to expressly teach ‘a coarse transformer layer’;
‘predict a first channel parameter, among the one or more channel parameters, to be changed’ (Hajri: [FIG.6]: “predicted CQI”; [FIG.4]; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0043]: “for a CSI quantity for which prediction is enabled/configured … The coefficients of a CSI prediction model (e.g. the coefficients of a neural network”; [0030]: “CSI quantities which may include, CQI, L1-RSRP, PMI, L1-SINR, RI, LI, CRI, SSBRI”). However, Hajri fails to expressly teach ‘to be changed’;
‘a fine transformer layer configured to interpolate the first channel parameter in the time domain at the second time interval’ (Hajri: [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0079]: “CQI prediction model feedback enables finer time resolution of CQI feedback”; [0082]: “obtain a higher time granularity for the CQI without increasing its reporting periodicity”; predict CSI for each next 4 subframe (time interval of 1 ms) from input of 3 previous CQI with 2 ms period (time interval of 2 ms); [0029]: “CSI prediction may be performed at the UE side using … interpolation operations”). However, Harji fails to expressly teach ‘a fine transformer layer configured to interpolate the first channel parameter in the time domain’.
Guo in the same field of endeavor teaches ‘neural network comprises a plurality of layers’ (Guo: [FIG.11]: “Encoder architecture in ConvCsiNet” with a plurality of layers), a TransNet with transformer architecture (Guo: [Page 8026, Col 1]: “each NN layer passes its feature maps through all subsequent NN layers … The transformer architecture is applied to CSI feedback”; [Page 8026, Col 2]: “a more powerful two-layer transformer architecture, namely, TransNet, is proposed. On the basis of fully excavating the power of the transformer, feedback performance is greatly improved”; [FIG.10], [FIG.11]: “Encoder” : NN layers) and refine CSI accuracy by interpolation (Guo: [Page 2025, Col 2]: “ConvCsiNet is also based on convolutional layers and the dimension increase is realized by bilinear interpolation. ConvCsiNet [43] can greatly improve CSI feedback accuracy”, refine (improve) by interpolation; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”; [FIG.8]: “Input feature”, “Refined feature”; [Abstract]: “improve CSI feedback accuracy”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching with that of Hajri for neural network to include a coarse transformer layer configured to learn one or more channel parameters of the pilot signal received at the first time interval and predict a first channel parameter, among the one or more channel parameters, to be changed; and a fine transformer layer configured to interpolate the first channel parameter at the second time interval, in order to improve CSI feedback accuracy (see reference quotes in element above).
Combination of Hajri and Guo does not expressly teach, but Zirwas in the same field of endeavor teaches ‘interpolate the first channel parameter in the time domain’ (Zirwas: [FIG.4]: “InV”, “PreT”; [0091]: “the time evolution of the value of a channel matrix element h.sub.xy(t) as a function of time t and definitions of the prediction horizon PreH, the measurement time MeT and the prediction time PreT with interpolation Inv and extrapolation ExV of CSI prediction”; [0102]: “a prediction time PreT. In this way, problems due to outdating of the channel state related information may be overcome and the feedback overhead may be reduced”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zirwas’s teaching with that of combination of Hajri and Guo in order to overcome CSI outdating problem and reduce feedback overhead (see reference quotes in element above).
Regarding claim 18, Hajri teaches ‘A terminal’ (Hajri: [FIG.1]: “UE”); ‘comprising: a receiver configured to’ (Hajri: [FIG.1]: “RX”; [0016]: “Each of the one or more transceivers 130 includes a receiver, Rx”);
‘receive a pilot signal from a base station at a plurality of times’ (Hajri: [FIG.3]: step 306: “UE receives downlink reference signals for channel and/or interference measurements”; [FIG.4]: three times CSI-RS “406” (pilot signal));
‘the plurality of times having a first time interval’ (Hajri: [TABLE 1]: “CSI-RS period” : “2 ms”, having time interval of 2 ms);
‘a processor configured to’ (Hajri: [FIG.1]: “Processor(s)”; [0016]: “The module 140 may be implemented in hardware as module 140-1, such as being implemented as part of the one or more processors”);
‘output channel state information based on the pilot signal being input into a neural network’ (Hajri: [FIG.6]: “Predicted CQI”; [0012]: “UE prediction based on a gradient boost regressor”; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0043]: “The coefficients of a CSI prediction model (e.g. the coefficients of a neural network”; [0069]: “the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements”);
‘a transmitter configured to’ (Hajri: [FIG.1]: “TX”; [0016]: “Each of the one or more transceivers 130 includes a receiver, Rx, 132 and a transmitter, Tx”);
‘provide the output channel state information to the base station’ (Hajri: [0069]: “the UE transmits triggered CSI report(s) in uplink control information”, report CSI to the base station);
‘wherein the neural network comprises a plurality of layers’ (Hajri: [0043]: “a neural network”; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates”, a gradient boost regressor would have input layer and prediction layer). However, Hajri fails to expressly teach ‘neural network comprises a plurality of layers’;
‘the plurality of layers including: a coarse transformer layer configured to learn one or more channel parameters of the pilot signal received at the first time interval’ (Hajri: [FIG.6]: “Actual CQI”; [FIG.4]; [0042]: “AI generated model parameters used in the prediction procedures”; [0035]: “machine learning algorithms that can achieve good prediction performance for CSI in future time”; [0061]: “the UE may reset its model to learn from scratch based on new samples”; [TABLE 1]: “CSI-RS period” : “2 ms”; [0076]: “The UE computes a prediction model based on multiple CSI-RS measurements”). However, Hajri fails to expressly teach ‘a coarse transformer layer’;
‘predict a first channel parameter, among the one or more channel parameters, to be changed’ (Hajri: [FIG.6]: “predicted CQI”; [FIG.4]; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0043]: “for a CSI quantity for which prediction is enabled/configured … The coefficients of a CSI prediction model (e.g. the coefficients of a neural network”; [0030]: “CSI quantities which may include, CQI, L1-RSRP, PMI, L1-SINR, RI, LI, CRI, SSBRI”). However, Hajri fails to expressly teach ‘to be changed’;
‘a fine transformer layer configured to interpolate the first channel parameter in the time domain at a second time interval shorter than the first time interval’ (Hajri: [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”; [0079]: “CQI prediction model feedback enables finer time resolution of CQI feedback”; [0082]: “obtain a higher time granularity for the CQI without increasing its reporting periodicity”; predict CSI for each next 4 subframe (time interval of 1 ms) from input of 3 previous CQI with 2 ms period (time interval of 2 ms); [0029]: “CSI prediction may be performed at the UE side using … interpolation operations”). However, Harji fails to expressly teach ‘a fine transformer layer configured to interpolate the first channel parameter in the time domain’.
Guo in the same field of endeavor teaches ‘neural network comprises a plurality of layers’ (Guo: [FIG.11]: “Encoder architecture in ConvCsiNet” with a plurality of layers), a TransNet with transformer architecture (Guo: [Page 8026, Col 1]: “each NN layer passes its feature maps through all subsequent NN layers … The transformer architecture is applied to CSI feedback”; [Page 8026, Col 2]: “a more powerful two-layer transformer architecture, namely, TransNet, is proposed. On the basis of fully excavating the power of the transformer, feedback performance is greatly improved”; [FIG.10], [FIG.11]: “Encoder” : NN layers) and refine CSI accuracy by interpolation (Guo: [Page 2025, Col 2]: “ConvCsiNet is also based on convolutional layers and the dimension increase is realized by bilinear interpolation. ConvCsiNet [43] can greatly improve CSI feedback accuracy”, refine (improve) by interpolation; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”; [FIG.8]: “Input feature”, “Refined feature”; [Abstract]: “improve CSI feedback accuracy”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching with that of Hajri for neural network to include a coarse transformer layer configured to learn one or more channel parameters of the pilot signal received at the first time interval and predict a first channel parameter, among the one or more channel parameters, to be changed; and a fine transformer layer configured to interpolate the first channel parameter at the second time interval, in order to improve CSI feedback accuracy (see reference quotes in element above).
Combination of Hajri and Guo does not expressly teach, but Zirwas in the same field of endeavor teaches ‘interpolate the first channel parameter in the time domain’ (Zirwas: [FIG.4]: “InV”, “PreT”; [0091]: “the time evolution of the value of a channel matrix element h.sub.xy(t) as a function of time t and definitions of the prediction horizon PreH, the measurement time MeT and the prediction time PreT with interpolation Inv and extrapolation ExV of CSI prediction”; [0102]: “a prediction time PreT. In this way, problems due to outdating of the channel state related information may be overcome and the feedback overhead may be reduced”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zirwas’s teaching with that of combination of Hajri and Guo in order to overcome CSI outdating problem and reduce feedback overhead (see reference quotes in element above).
Regarding claim 11, claim 11 recites the method implemented by the terminal of claim 1 (see rejection of claim 1 above).
Per claim 2, 12 and 19:
Regarding claim 2, combination Hajri, Guo and Zirwas teaches the terminal of claim 1 (discussed above).
Hajri teaches ‘wherein the pilot signal is a channel state information reference signal (CSI-RS)’ (Hajri: [0133]: “CSI-RS channel state information reference signal”).
Regarding claim 19, combination Hajri, Guo and Zirwas teaches the terminal of claim 18 (discussed above).
Hajri teaches ‘wherein the pilot signal is a channel state information reference signal (CSI-RS)’ (Hajri: [0133]: “CSI-RS channel state information reference signal”).
Regarding claim 12, claim 12 recites the method implemented by the terminal of claim 2 (see rejection of claim 2 above).
Per claim 3 and 13:
Regarding claim 3, combination Hajri, Guo and Zirwas teaches the terminal of claim 2 (discussed above).
Hajri teaches ‘wherein the neural network is configured to: receive the CSI-RS as an input, and output the predicted channel based on the CSI-RS’ (Hajri: [0012]: “UE prediction based on a gradient boost regressor”; [0043]: “a neural network”; [0076]: “The UE computes a prediction model based on multiple CSI-RS measurements”; [0077]: ““the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates”; [0040]: “the predicted CSI quantity … the output of a CSI prediction model”).
Regarding claim 13, claim 13 recites the method implemented by the terminal of claim 3 (see rejection of claim 3 above).
Per claim 5 and 15:
Regarding claim 5, combination Hajri, Guo and Zirwas teaches the terminal of claim 2 (discussed above).
Hajri teaches ‘wherein the one or more channel parameters comprise at least one of a direction of a receive path of electromagnetic waves with respect to time in the CSI-RS, an angle of the receive path of the electromagnetic waves, a time of receiving the electromagnetic waves’ (these are optional);
‘an intensity of the electromagnetic waves’ (Hajri: [0030]: “CSI quantities which may include, CQI, L1-RSRP”, L1-RSRP (an intensity of the electromagnetic waves)).
Regarding claim 15, claim 15 recites the method implemented by the terminal of claim 5 (see rejection of claim 5 above).
Regarding claim 6, combination Hajri, Guo and Zirwas teaches the terminal of claim 3 (discussed above).
Combination of Hajri and Guo teaches ‘an input layer configured to receive the CSI-RS’ (Hajri: [0043]: “a CSI quantity for which prediction is enabled/configured … The amount of time sample instances used as input in the prediction model”. Guo: [Page 8024, Col 1]: “the features extracted from the input CSI”; FIG.8]: “Input feature”; [Page 8022, Col 2]: “the input and output sequences”; [Page 8027, Col 2]: “the input to NNs”; [Page 8026, Col 1]]: “each NN layer passes its feature maps through all subsequent NN layers”);
‘a pre-processing layer configured to convert the CSI-RS into data for the coarse transformer layer’ (Guo: [FIG.15]: “CSI preprocessing workflow”; [Page 8026, Col 2]: “a more powerful two-layer transformer architecture, namely, TransNet … Preprocessing is essential in data science. Preprocessing of CSI samples affects the performance of DL-based CSI feedback”);
‘a determination layer configured to determine the predicted channel corresponding to the first channel parameter interpolated by the fine transformer layer at the second time interval’ (Hajri: [0069]: “the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements”; [0003]: “determine the at least one channel state information quantity or at least one channel state information prediction model, based on at least one of a downlink reference measurement”; [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”. Guo: [FIG.9]: “UE”; [FIG.10]: “Encoder”; [FIG.11]: “ECN”; [Page 8025, Col 2]: “the encoded convolution network (ECN) block … can greatly improve CSI feedback accuracy”; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”; [Page 8022, Col 2]: “Channel attention focuses on determining which feature map is meaningful … Spatial attention focuses on determining where the features are informative”);
‘an output layer configured to output the predicted channel’ (Hajri: [0040]: “the predicted CSI quantity … the output of a CSI prediction model”. Guo: [Page 8022, Col 1]: “The output of the encoder”; [Page 8020, Col 2]: “Fully Connected Layer”; [FIG.9]: “UE”: “FC Mx1” -> “feedback”, output predicted CSI feedback).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching of transformer with that of Hajri in order to improve CSI accuracy (see reference quotes in element above).
Regarding claim 8, combination Hajri, Guo and Zirwas teaches the terminal of claim 1 (discussed above).
Hajri teaches ‘wherein the channel state information comprises at least one of precoding matrix indicator (PMI), rank indicator (RI)’ (these are optional); ‘channel quality indicator (CQI)’ (Hajri: [0030]: “CSI quantities which may include, CQI”).
Per claim 21, 22 and 23:
Regarding claim 21, combination Hajri, Guo and Zirwas teaches the terminal of claim 1 (discussed above).
Combination of Hajri, Guo and Zirwas teaches ‘wherein the fine transformer layer interpolates the first channel parameter to a plurality of time points spaced apart by the second time interval, and the neural network outputs a plurality of predicted channels respectively corresponding to the plurality of time points’ (Hajri: [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”, predict CSI for each next 4 subframe (time interval of 1ms => spaced apart by 1ms) from input of 3 previous CQI with 2 ms period (time interval of 2 ms). Guo: [Page 2025, Col 2]: “ConvCsiNet is also based on convolutional layers and the dimension increase is realized by bilinear interpolation. ConvCsiNet [43] can greatly improve CSI feedback accuracy”, refine (improve) by interpolation; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”; [Abstract]: “improve CSI feedback accuracy”. Zirwas: [FIG.4]: “InV”, “PreT”; [0091]: “the time evolution of the value of a channel matrix element h.sub.xy(t) as a function of time t and definitions of the prediction horizon PreH, the measurement time MeT and the prediction time PreT with interpolation Inv and extrapolation ExV of CSI prediction”; [0102]: “a prediction time PreT. In this way, problems due to outdating of the channel state related information may be overcome and the feedback overhead may be reduced”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching of refine CSI accuracy by interpolation and Zirwas’s teaching of interpolate channel parameter in the time domain with that of Hajri in order to improve CSI accuracy and overcome CSI outdating problem (see reference quotes in element above).
Regarding claim 23, combination Hajri, Guo and Zirwas teaches the terminal of claim 18 (discussed above).
Combination of Hajri, Guo and Zirwas teaches ‘wherein the fine transformer layer interpolates the first channel parameter to a plurality of time points spaced apart by the second time interval, and the channel state information is output based on the first channel parameter interpolated to the plurality of time points’ (Hajri: [0077]: “the UE is using a gradient boost regressor which gives a prediction of CQI for a given sub-band, for the next 4 subframes given an input of 3 previous CQI estimates (2 subframes between each estimate) for the same subband”, predict CSI for each next 4 subframe (time interval of 1ms => spaced apart by 1ms) from input of 3 previous CQI with 2 ms period (time interval of 2 ms). Guo: [Page 2025, Col 2]: “ConvCsiNet is also based on convolutional layers and the dimension increase is realized by bilinear interpolation. ConvCsiNet [43] can greatly improve CSI feedback accuracy”, refine (improve) by interpolation; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”; [Abstract]: “improve CSI feedback accuracy”. Zirwas: [FIG.4]: “InV”, “PreT”; [0091]: “the time evolution of the value of a channel matrix element h.sub.xy(t) as a function of time t and definitions of the prediction horizon PreH, the measurement time MeT and the prediction time PreT with interpolation Inv and extrapolation ExV of CSI prediction”; [0102]: “a prediction time PreT. In this way, problems due to outdating of the channel state related information may be overcome and the feedback overhead may be reduced”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching of refine CSI accuracy by interpolation and Zirwas’s teaching of interpolate channel parameter in the time domain with that of Hajri in order to improve CSI accuracy and overcome CSI outdating problem (see reference quotes in element above).
Regarding claim 22, claim 22 recites the method implemented by the terminal of claim 21 (see rejection of claim 21 above).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over combination Hajri, Guo and Zirwas as applied to claim 6 above, further in view of Garrett et al. (US 20030161428 A1), hereinafter “Garrett”.
Regarding claim 7, combination Hajri, Guo and Zirwas teaches the terminal of claim 6 (discussed above).
Combination of Hajri and Guo teaches ‘predict an interference pattern of the electromagnetic waves according to the receive path of the electromagnetic waves output by the fine transformer layer’ (Hajri: [0067]: “The UE has a better knowledge of the measured channel and interference conditions and is performing computations to derive CSI prediction models”; [0069]: “CSI quantity for which prediction is enabled/configured. At 306, the UE receives downlink reference signals for a channel and/or interference measurements. At 308, the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements”. Guo: [Page 8030, Col 2]: “the signal propagation environment”; [Page 8036, Col 1]: “the feedback link suffers from various interference”; [Page 8032, Col 2]: “multipath signal”; [Page 8032, Col 2]: “UE estimates the CSI from the received pilot signals”; [Page 8033, Col 2]: “the UE needs to feedback the desired and the interfering CSI”; [Page 8025, Col 1]: “extracting the background or pattern information”; [Page 2025]: “ConvCsiNet is also based on convolutional layers and the dimension increase is realized by bilinear interpolation. ConvCsiNet [43] can greatly improve CSI feedback accuracy”; [Page 8031, Col 2]: “the reconstructed CSI is interpolated with 0 to recover its original dimension, and an extra NN is adopted to refine the interpolated CSI”). However, combination Hajri, Guo and Zirwas fails to expressly teach an interference pattern according to the receive path;
‘determine the predicted channel by using the interference pattern’ (Hajri: [0067]: “The UE has a better knowledge of the measured channel and interference conditions and is performing computations to derive CSI prediction models”. Guo: [Page 8033, Col 2]: “the UE needs to feedback the desired and the interfering CSI”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Guo’s teaching of interpolation with that of Hajri in order to improve CSI feedback accuracy (see reference quotes in element above).
Garrett in the same field of endeavor teaches interference pattern caused by multipath signal to receiver (Garrett: [0003]-[0004]: “signal traverses multiple paths to the receiver resulting in an interference pattern … It is desired to find a thresholding technique that enables relatively high performance levels regardless of environment, multipath and AGC discrepancies”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Garrett’s teaching with that of combination Hajri, Guo and Zirwas in order to achieve high performance (see reference quotes in element above).
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over combination Hajri, Guo and Zirwas, in view of Jwa et al. (US 20100075672 A1), hereinafter “Jwa”.
Per claim 10 and 17:
Regarding claim 10, combination Hajri, Guo and Zirwas teaches the terminal of claim 1 (discussed above).
Hajri teaches ‘obtain precoding matrix indicator (PMI) information, which maximizes data transfer rates at each prediction time of each of a plurality of predicted channels, as the channel state information’ (Hajri: [0057]: “the UE receives downlink reference signals for channels and/or interference measurements … the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements”; [0077]: “a prediction of CQI for a given sub-band, for the next 4 subframes”, 4 prediction time). However, combination Hajri, Guo and Zirwas fails to expressly teach ‘maximizes data transfer rate’;
‘wherein the transmitter is configured to’ (Hajri: [FIG.1]: “TX”); ‘provide at least one piece of the PMI information to the base station’ (Hajri: [0030]: “CSI quantities which may include, CQI, L1-RSRP, PMI”; [0069]: “the UE computes CSI quantity values … the UE transmits triggered CSI report(s) in uplink control information”).
Jwa in the same field of endeavor teaches terminal determine PMI which maximize the data rate (Jwa: [0064]: “the terminal may determine an index of a preceding matrix corresponding to the maximum data rate with respect to the determined rank indicator, as the PMI”; [0049]: “The feedback information generator 440 may generate feedback information based on the calculated data rate … to enable a sum data rate to be maximized based on a determined type of feedback information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jwa’s teaching with that of combination Hajri, Guo and Zirwas to obtain precoding matrix indicator (PMI) information, which maximizes data transfer rates at each prediction time of each of a plurality of predicted channels, as the channel state information in order to maximize a sum data rate (see reference quote in element above).
Regarding claim 17, claim 17 recites the method implemented by the terminal of claim 10 (see rejection of claim 10 above).
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over combination Hajri, Guo and Zirwas, in view of Jwa, and in view of Cho et al. (US 20070245203 A1), hereinafter “Cho”.
Per claim 9 and 16:
Regarding claim 9, combination Hajri, Guo and Zirwas teaches the terminal of claim 1 (discussed above).
Hajri teaches ‘obtain a plurality of data transfer rates at each prediction time of each of a plurality of predicted channels’ (Hajri: [0057]: “the UE receives downlink reference signals for channels and/or interference measurements … the UE computes CSI quantity values and/or CSI quantity prediction models, based on downlink reference measurements”; [0077]: “a prediction of CQI for a given sub-band, for the next 4 subframes”, 4 prediction time). However, combination Hajri, Guo and Zirwas fails to expressly teach ‘obtain a plurality of data transfer rates’;
‘obtain precoding matrix indicator (PMI) information, which maximizes an average value of the obtained plurality of data transfer rates, as the channel state information’ (Hajri: [TABLE 1]: “Parameter” : “PMI codebook”; [0177]: “PMI precoding matrix indicator”). However, combination Hajri, Guo and Zirwas fails to expressly teach ‘maximizes an average value of the obtained plurality of data transfer rates’;
‘wherein the transmitter is configured to’ (Hajri: [FIG.1]: “TX”); ‘provide the PMI information to the base station’ (Hajri: [0030]: “CSI quantities which may include, CQI, L1-RSRP, PMI”; [0069]: “the UE computes CSI quantity values … the UE transmits triggered CSI report(s) in uplink control information”).
Jwa in the same field of endeavor teaches generate feedback based on calculated data rate to maximize sum data rate (Jwa: [0049]: “The feedback information generator 440 may generate feedback information based on the calculated data rate … to enable a sum data rate to be maximized based on a determined type of feedback information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jwa’s teaching with that of combination Hajri, Guo and Zirwas to obtain a plurality of data transfer rates at each prediction time of each of a plurality of predicted channels in order to maximize a sum data rate (see reference quote in element above).
Combination Hajri, Guo, Zirwas and Jwa does not expressly teach ‘maximizes an average value of the obtained plurality of data transfer rates’.
First, maximize an average data rate would be an obvious alternate to maximum the sum data rate.
Moreover, Cho in the same field of endeavor teaches channel state estimator to determine data rate at which an average date rate is maximized (Cho: [0051]: “the channel state estimator 201 may use the SNR data of the previous cycle … determines an instantaneous data rate, at which an average data rate is maximized”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cho’s teaching with that of combination of Hajri, Guo, Zirwas and Jwa to obtain precoding matrix indicator (PMI) information, which maximizes an average value of the obtained plurality of data transfer rates, as the channel state information in order to achieve an average data rate is maximized (see reference quotes in element above).
Regarding claim 16, claim 16 recites the method implemented by the terminal of claim 9 (see rejection of claim 9 above).
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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/G.F./Examiner, Art Unit 2462
/YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462
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