DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
This action is in response to communications filed on 4/5/2024.
Claims 1-20 have been examined and are rejected.
Priority
This application claims priority to provisional application 63/494,965 filed 4/7/2023.
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.
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 1-9, 15, 17, & 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1).
With regard to Claim 1, Lee teaches:
An apparatus for a user equipment (UE), the apparatus comprising: processing circuitry to:
configure the UE to perform channel measurements based on Channel State Information (CSI) Reference Signals (CSI-RS) received from a 5th generation NodeB (gNB) at different time instances; (a UE may receive configuration information for CSI-RS resources which are transmitted periodically (including a first transmitted CSI-RS), and may then periodically measure the quality of periodically transmitted CSI-RS resources and periodically transmit a CSI report [Lee: 0054; Fig. 5]);
calculate, from the CSI-RS received at the different time instances, a predicted CSI for a later time instance after the different time instances; (CSI predictor may be configured based on AI and trained to predict a channel state for a time point in the near future, using a channel state history and/or an environmental factor that may affect a channel by deriving predicted CSI for a second time point (i.e., a future time point) later than a first time point (i.e., a past time point) [Lee: 0064; 0006]);
perform a channel measurement based on a CSI-RS received in the later time instance; (a UE may receive configuration information for CSI-RS resources which are transmitted periodically (including a second transmitted CSI-RS), and may then periodically measure the quality of periodically transmitted CSI-RS resources and periodically transmit a CSI report [Lee: 0054; Fig. 5]);
calculate an actual CSI based on the CSI-RS received in the later time instance; determine metrics based on a comparison between the predicted CSI and the actual CSI; (identify the accuracy of predicted CSI by comparing actual CSI measured by a UE with CSI predicted by a CSI predictor [Lee: 0101]);
a memory configured to store the metrics; (processor(s) 102 may receive radio signals including second information/signals through the transceiver and then store information obtained by processing the second information/signals in the memory(s) 104 [Lee: 0144]).
However, Lee does not explicitly teach:
and encode the metrics for transmission in a CSI report to the gNB.
In a similar field of endeavor involving comparing an estimated channel property with a measured channel property, Irfan discloses:
and encode the metrics for transmission in a CSI report to the gNB; (the WTRU may determine a confidence level for the one or more estimated TDCPs based on one or more of channel conditions, and send a CSI report to the network that indicates the one or more estimated TDCPs and the confidence level for the one or more estimated TDCPs [Irfan: 0004-7]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Irfan in order to encode the metrics for transmission in a CSI report to the gNB in the system of Lee.
One of ordinary skill in the art would have been motivated to combine Lee with Irfan as doing so would allow the network to use the prediction quality measures to determine the reliability of the WTRU's prediction and allow the gNB to adjust the WTRU's precoding selection with its own determination depending on the WTRU's capability [Irfan: 0265-66].
With regard to Claim 2, Lee-Irfan teaches:
The apparatus of claim 1, wherein the processing circuitry is configured to calculate the predicted CSI using a one-sided Artificial Intelligence/Machine Learning model (AI/ML) model at the UE; (the CSI predictor may be implemented by referring to an AI model and may be implemented in the UE [Lee: 0064-65]).
With regard to Claim 3, Lee-Irfan teaches:
The apparatus of claim 1, wherein the metrics include at least one of mean square error (MSE), normalized mean square error (NMSE), generalized cosine similarity (GCS), or square generalized cosine similarity (SGCS); (the measure quality of prediction may consist of one or more of a channel measurement (e.g., the mean squared error (MSE) of the channel estimates, measure of confidence output from estimation algorithm, and/or type of estimation algorithm) [Irfan: 0265]).
With regard to Claim 4, Lee-Irfan teaches:
The apparatus of claim 1, wherein: a subset of bits of the CSI report is determined based on CSI prediction performance derived based on different matrices, a first matrix of the matrices is based on CSI-RS measurements, and a second matrix of the matrices is based on a CSI prediction algorithm implemented at the UE; (the CSI predictor may predict H [t+D] using H [t] as an input, where H [t] represents a channel matrix at time t, and the predicted H [t+D] represents a predicted channel matrix for time t+D [Lee: 0064]. Irfan teaches a WTRU may include a number of bits in a CSI report to indicate the number of matrices [Irfan: 0084]).
With regard to Claim 5, Lee-Irfan teaches:
The apparatus of claim 4, wherein: the first matrix is one of a channel matrix, eigenvectors of the channel matrix, or a precoding matrix, and the second matrix corresponds to a predicted channel matrix, eigenvectors of the predicted channel matrix, or the precoding matrix; (the CSI predictor may predict H [t+D] using H [t] as an input, where H [t] represents a channel matrix at time t, and the predicted H [t+D] represents a predicted channel matrix for time t+D [Lee: 0064]).
With regard to Claim 6, Lee-Irfan teaches:
The apparatus of claim 4, wherein the CSI prediction algorithm corresponds to Y = p(X1, X2, …, XI, P), where X1, X2, …, XI are matrixes that are at least one of measured or calculated based on CSI-RS in multiple time instances, P is a vector of prediction parameters, and p() is a prediction function; (the CSI predictor may predict H [t+D] using H [t] as an input, where H [t] represents a channel matrix at time t, and the predicted H [t+D] represents a predicted channel matrix for time t+D [Lee: 0064], wherein the CSI predictor utilizes a set of weights for the AI/ML model [Lee: 0071-72]).
With regard to Claim 7, Lee-Irfan teaches:
The apparatus of claim 4, wherein the processing circuitry determines CSI prediction performance M from at least one of M = g(X, Y) or M = g(X, Y)/g(X, Xn), where Xn is a matrix at least one of measured or calculated based on CSI-RS, and g() is a prediction performance matrix; (the CSI predictor may predict H [t+D] using H [t] as an input, where H [t] represents a channel matrix at time t, and the predicted H [t+D] represents a predicted channel matrix for time t+D [Lee: 0064]. Irfan teaches a WTRU may determine a confidence level (CL) indicative of a prediction accuracy of the CSI based on comparing the predicted CSI and actual CSI measurement in the future slot associated with the predicted CSI [Irfan: 0204-205]).
With regard to Claim 8, Lee-Irfan teaches:
The apparatus of claim 7, wherein the processing circuitry quantizes M and encodes the CSI prediction performance M in at least one of the CSI report using the subset of bits or in a report separate from the CSI report; (the WTRU may send a report that indicates the one or more estimated TDCPs and the confidence level, wherein the report indicates a null value for the one or more estimated TDCPs when the confidence level is below a pre-configured threshold, and wherein the WTRU may quantize the TDCPs [Irfan: 0138; 0007; Claims 21-22]. Examiner notes that use of the threshold quantizes the prediction performance data CL).
With regard to Claim 9, Lee-Irfan teaches:
The apparatus of claim 7, wherein the subset of bits corresponds to a comparison of M and T, where T is a threshold that is at least one of configured via higher layers or derived at the UE; (the report indicates a null value for the one or more estimated TDCPs when the confidence level is below the pre-configured threshold, wherein the threshold may be fixed and/or configured by the gNB. [Irfan: 0138; 0007; Claims 21-22]).
With regard to Claim 15, Lee-Irfan teaches:
The apparatus of claim 1, wherein the processing circuitry calculates the metrics separately at least one of per UE receive antenna or per layer; (a CL may be reported per measurement resource and a WTRU may report beam/polarization and DFT vector indices of NZCs per layer [Irfan: 0102; 0091; 0209]).
With regard to Claims 17 & 19-20, they appear substantially similar to the limitations recited by claims 1-3 and consequently do not appear to teach or further define over the citations provided for said claims. Accordingly, claims 17 & 19-20 are rejected for the same reasons as set forth in claims 1-3.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1) as applied to Claim 1 above, and further in view of Niu et al. (US 2025/0330285 A1).
With regard to Claim 10, Lee-Irfan teaches:
The apparatus of claim 1, wherein the processing circuitry determines CSI prediction performance; (identify the accuracy of predicted CSI by comparing actual CSI measured by a UE with CSI predicted by a CSI predictor [Lee: 0101]).
However, Lee-Irfan does not teach:
and determines whether to use CSI prediction based on whether the CSI prediction performance at least meets a minimum threshold CSI prediction performance.
In a similar field of endeavor involving AI based CSI prediction, Niu discloses:
wherein the processing circuitry determines CSI prediction performance and determines whether to use CSI prediction based on whether the CSI prediction performance at least meets a minimum threshold CSI prediction performance; (the UE may monitor performance of the predicted channel information, and in response to determining that the monitored performance is below a threshold, the UE may transmit, to the base station, a request to deactivate prediction based feedback [Niu: 0147]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee-Irfan in view of Niu in order to determine whether to use CSI prediction based on whether the CSI prediction performance at least meets a minimum threshold CSI prediction performance in the system of Lee-Irfan.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan with Niu as doing so would conserve resources in situations in which CSI prediction performance is lacking.
Claim 11 & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1) as applied to Claim 1 above, and further in view of Katla et al. (US 2025/0253998 A1).
With regard to Claim 11, Lee-Irfan teaches the apparatus of claim 1, but does not teach:
wherein the processing circuitry encodes, for transmission to the gNB, UE capabilities that include at least one of a number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances.
In a similar field of endeavor involving CSI prediction, Katla discloses:
wherein the processing circuitry encodes, for transmission to the gNB, UE capabilities that include at least one of a number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances; (the AI/ML-based CSI prediction capability of the WTRU may be reported in a radio resource control (RRC) message and may comprise parameters including: AI/ML model type, e.g., RNN, LSTM or the like; AI/ML model ID, which may indicate the model size; the length of the CSI history buffer (“N”) used (e.g., needed) for prediction; the sampling period for the CSI historical information/predicted CSI values; the maximum length of the CnSI prediction window (maximum look-ahead window, LAmax); and training dataset information, which may include information on the characteristics of the channel model that was used to train the AI/ML CSI predictor (for example delay spread, coherence bandwidth, coherence time, Doppler, etc.) [Katla: 0168-74]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee-Irfan in view of Katla in order to encode, for transmission to the gNB, UE capabilities that include at least one of a number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances in the system of Lee-Irfan.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan with Katla as doing so would enable gNB configurations to be specified based on the reported UE capabilities.
With regard to Claim 13, Lee-Irfan teaches the apparatus of claim 1, but does not teach:
wherein the processing circuitry encodes, for transmission to the gNB in at least one of the CSI report, a different CSI report, or Medium Access Control (MAC) Control Element (CE), that at least one of a received number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances is insufficient to achieve a minimum level of CSI prediction performance.
In a similar field of endeavor involving CSI prediction, Katla discloses:
wherein the processing circuitry encodes, for transmission to the gNB in at least one of the CSI report, a different CSI report, or Medium Access Control (MAC) Control Element (CE), that at least one of a received number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances is insufficient to achieve a minimum level of CSI prediction performance; (the WTRU may be configured for CSI prediction (for example with the CSI prediction look-ahead window size L, as well as the number of CSI-RS temporal samples, N, for example, for (e.g., required) for building the CSI history buffer), wherein the WTRU may receive periodic CSI-RS samples and determine if the CSI history buffer is complete such that if the CSI history buffer is not complete the WTRU may transmit a legacy CSI report [Katla: 0204; Fig. 7]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee-Irfan in view of Katla in order to transmit at least one of a received number of CSI-RS time instances used for CSI prediction or a time gap between adjacent CSI-RS time instances is insufficient to achieve a minimum level of CSI prediction performance in the system of Lee-Irfan.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan with Katla as doing so would allow a legacy CSI to be reported while building the historical CSI data [Katla: 0190].
Claims 12 & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1) in view of Katla et al. (US 2025/0253998 A1) as applied to Claim 11 above, and further in view of Li et al. (US 2026/0095798 A1).
With regard to Claim 12, Lee-Irfan-Katla teaches the apparatus of claim 11, but dos not teach:
wherein the processing circuitry encodes, for transmission to the gNB, the UE capabilities in at least one of a CSI report or Medium Access Control (MAC) Control Element (CE).
In a similar field of endeavor involving transmitting reports of predicted measurement results, Li discloses:
wherein the processing circuitry encodes, for transmission to the gNB, the UE capabilities in at least one of a CSI report or Medium Access Control (MAC) Control Element (CE); (the UE may transmit or otherwise provide a UE capability message indicating support for indicating the differences (e.g., in a second CSI report conveyed in a dynamically triggered CSI report and/or in a MAC-CE report) [Li: 0126]).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed invention to modify Lee-Irfan-Katla in view of Li in order to encode, for transmission to the gNB, the UE capabilities in at least one of a CSI report or Medium Access Control (MAC) Control Element (CE) in the system of Lee-Irfan-Katla.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan-Katla with Li as doing so would allow for more rapid communication with the gNB.
With regard to Claim 18, it appears substantially similar to the limitations recited by claims 11-12 and consequently does not appear to teach or further define over the citations provided for said claims. Accordingly, claim 18 is rejected for the same reasons as set forth in claims 11-12.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1) as applied to Claim 1 above, and further in view of Li et al. (US 2025/0386231 A1).
With regard to Claim 14, Lee-Irfan teaches the apparatus of claim 1, but does not teach:
wherein the processing circuitry uses all CSI-RS time instances for training of prediction parameters, in response a determination that a higher layer parameter timeRestrictionForChannelMeasurements is at least one of enabled or not configured.
In a similar field of endeavor involving AI/ML prediction based on CSI-RSs, Li discloses:
wherein the processing circuitry uses all CSI-RS time instances for training of prediction parameters, in response a determination that a higher layer parameter timeRestrictionForChannelMeasurements is at least one of enabled or not configured; (the CSI report configuration may indicate a time restriction for channel measurements parameter (e.g., timeRestrictionForChannelMeasurements) and may be set with a value of configured or not configured [Li: 0099]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee-Irfan in view of Li in order to use all CSI-RS time instances for training of prediction parameters, in response a determination that a higher layer parameter timeRestrictionForChannelMeasurements is at least one of enabled or not configuredin the system of Lee-Irfan.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan with Li as doing so would allow the UE to determine which CSI-RS is to be measured for a specific report [Li: 0099].
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2025/0141515 A1) in view of Irfan et al. (US 2026/0074758 A1) as applied to Claim 1 above, and further in view of Studer et al. (US 2020/0382228 A1).
With regard to Claim 16, Lee-Irfan teaches the apparatus of claim 1, but does not teach:
wherein the processing circuitry uses a real value or an absolute value of the metrics for complex-valued metrics.
In a similar field of endeavor involving estimating CSI between a given wireless device and one or more base station, Studer discloses:
wherein the processing circuitry uses a real value or an absolute value of the metrics for complex-valued metrics; (utilizing the entry-wise real, imaginary part, angle, or absolute value of the scaled CSI moments [Studer: 0136]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee-Irfan in view of Studer in order to use a real value or an absolute value of the metrics for complex-valued metrics in the system of Lee-Irfan.
One of ordinary skill in the art would have been motivated to combine Lee-Irfan with Studer as doing so would improve network efficiency by reducing the amount of reported data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Echigo et al. (US 2024/0187186 A1) which teaches using a machine learning model trained based on first configuration of the CSI-RS to control performing prediction based on second configuration of the CSI-RS.
In the case of amendments, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and support, for ascertaining the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUSTIN J MOREAU whose telephone number is (571) 272-5179. The examiner can normally be reached Monday-Friday 9:00 - 6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Gillis can be reached on 571-272-7952. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AUSTIN J MOREAU/Primary Examiner, Art Unit 2446
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