DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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, 5, 7-9, 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (hereinafter Guan)(US 2025/0030612) in view of Tong et al. (hereinafter Tong)(US 2024/0022927).
Regarding claim 1, Guan teaches an apparatus for wireless communication, comprising: a processor; and a memory coupled with the processor, the processor configured to: receive one or more parameters associated with a non-terrestrial network(Fig. 7, item 710; P[0225], receives one configuration associated with ML model; configuration indicates the duration to obtain measurement result; P[0038], NTN networks);
select an artificial intelligence/machine learning (AI/ML) prediction model, based on one or more parameters, for channel state information (CSI) aging (P[0177], initial validation procedure may be performed to select a ML model from the plurality of ML models; validation duration; P[0178], selects one ML model from the plurality of ML models); and
apply the selected AI/ML prediction model to predict one or more CSI quantities to for CSI aging phenomenon in the non-terrestrial networks(Fig. 7, item 720; P[0226-0227], apply the configuration; validation duration; P[0067], CSI prediction).
Guan did not teach specifically aging compensation. However, Tong teaches in an analogous art aging compensation(P[0175], channel aging; prediction to reduce the impact of channel information becoming obsolete, which is also referred to as channel aging). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus having channel aging compensation in order to have improved accuracy.
Regarding claim 2, Guan teaches the apparatus of claim 1, wherein the apparatus receives the one or more parameters associated with the non-terrestrial network via radio resource control (RRC) signaling(P[0101; 0120; 0126; 0138; 0211], RRC signaling).
Regarding claim 5, Tong teaches the apparatus of claim 1, wherein the one or more parameters associated with the non-terrestrial network include a cell layout configuration for one or more cells of the non-terrestrial network, a weather condition within the non-terrestrial network, or an atmospheric condition within the non-terrestrial network, or any combination thereof(P[0193], atmospheric absorption).
Regarding claim 7, Guan teaches the apparatus of claim 1, wherein the processor transmits one or more prediction model capabilities of the apparatus to the non-terrestrial network(P[0104-0108], capability information about ML model).
Regarding claim 8, Tong teaches the apparatus of claim 1, wherein the processor transmits one or more prediction model capabilities and associated model training deficiencies of the apparatus to the non-terrestrial network(P[0141], compensating for deficiencies in models; training for machine learning) .
Regarding claim 9, Tong teaches the apparatus of claim 1, wherein the processor selects the AI/ML prediction model based on the one or more parameters associated with the non-terrestrial network and without additional information from the non-terrestrial network(P[0218], without needing to reconfigure UEs).
Regarding claim 11, Tong teaches the apparatus of claim 1, wherein the selected AI/ML prediction model includes a deep neural network model, a linear regression model, a support vector machines model, a learning vector quantization model, or a decision tree model(P[0327], deep neural network).
Claims 12-13 are rejected for the same reason as set forth in claims 1-2 respectively.
Claim(s) 3-4, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (hereinafter Guan)(US 2025/0030612) in view of Tong et al. (hereinafter Tong)(US 2024/0022927) and Jeon et al. (hereinafter Jeon)(US 2022/0287104).
Regarding claim 3, the combination of Guan and Tong teaches all the particulars of the claim except, wherein the apparatus receives, from a network entity, a system information block (SIB) including the one or more parameters associated with the non-terrestrial network. However, Jeon teaches in an analogous art wherein the apparatus receives, from a network entity, a system information block (SIB) including the one or more parameters associated with the non-terrestrial network(P[0017], SIB ; P[0103-0104]). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus wherein the apparatus receives, from a network entity, a system information block (SIB) including the one or more parameters associated with the non-terrestrial network in order to have improved accuracy.
Regarding claim 4, Jeon teaches the apparatus of claim 1, wherein the one or more parameters associated with the non-terrestrial network include a current orbit of a satellite of the non-terrestrial network, a current position of the satellite of the non-terrestrial network, or a speed vector defining a current speed of the satellite of the non-terrestrial network, or any combination thereof(P[0104], satellite trajectory; satellite location).
Claim 14 is rejected for the same reason as set forth in claim 3.
Claim(s) 6, 10, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (hereinafter Guan)(US 2025/0030612) in view of Tong et al. (hereinafter Tong)(US 2024/0022927) and Jeon et al. (hereinafter Jeon-1)(US 2022/0294666).
Regarding claim 6, the combination of Guan and Tong teaches all the particulars of the claim except, wherein the apparatus comprises a user equipment (UE) associated with a group of UEs for receiving the one or more parameters associated with the non-terrestrial network; and wherein the group of UEs are grouped based on a respective location associated with each respective UE of the group of UEs, a channel aging type associated with each respective UE of the group of UEs, or a respective prediction modeling capability of each respective UE of the group of UEs, or any combination thereof. However, Jeon-1 teaches in an analogous art wherein the apparatus comprises a user equipment (UE) associated with a group of UEs for receiving the one or more parameters associated with the non-terrestrial network; and wherein the group of UEs are grouped based on a respective location associated with each respective UE of the group of UEs, a channel aging type associated with each respective UE of the group of UEs, or a respective prediction modeling capability of each respective UE of the group of UEs, or any combination thereof(P[0116], group of UEs; precise channel state information ; delay due to relative velocity between BS and UE; delay profile can be grouped and assigned under common RNTI; configuration parameters for a AI/ML model can be done under group common signaling). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus wherein the apparatus comprises a user equipment (UE) associated with a group of UEs for receiving the one or more parameters associated with the non-terrestrial network; and wherein the group of UEs are grouped based on a respective location associated with each respective UE of the group of UEs, a channel aging type associated with each respective UE of the group of UEs, or a respective prediction modeling capability of each respective UE of the group of UEs, or any combination thereof in order to have improved accuracy.
Regarding claim 10, the combination of Guan and Tong teaches all the particulars of the claim except, wherein the processor: transmits prediction model capabilities and associated model training deficiencies of the apparatus to the non-terrestrial network; receives, from the non-terrestrial network, prediction scenario information and channel state information quantity information; and updates prediction models of the apparatus using the prediction scenario information and channel state information quantity information received from the non-terrestrial network. However, Jeon-1 teaches in an analogous art wherein the processor: transmits prediction model capabilities and associated model training deficiencies of the apparatus to the non-terrestrial network; receives, from the non-terrestrial network, prediction scenario information and channel state information quantity information; and updates prediction models of the apparatus using the prediction scenario information and channel state information quantity information received from the non-terrestrial network(Fig. 4; items 401, capability information including support if ML approach for channel prediction; 402, model parameters based on model training, ML model to be used; 403, parameter updates). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus wherein the processor: transmits prediction model capabilities and associated model training deficiencies of the apparatus to the non-terrestrial network; receives, from the non-terrestrial network, prediction scenario information and channel state information quantity information; and updates prediction models of the apparatus using the prediction scenario information and channel state information quantity information received from the non-terrestrial network in order to have improved accuracy.
Claim 15 is rejected for the same reason as set forth in claim 10.
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Jeon et al. (hereinafter Jeon-1)(US 2022/0287104) in view of Tong et al. (hereinafter Tong)(US 2024/0022927).
Regarding claim 16, Jeon-1 teaches a network entity of a non-terrestrial network that provides wireless communication services to user equipment, the network entity comprising: a processor; and a memory coupled with the processor, the processor configured to: receive information from at least one other user equipment that indicates prediction success rates for the other user equipment for predictions of one or more channel state information (CSI) quantities for CSI (item 401 in Fig. 4); transmit one or more parameters associated with the non-terrestrial network(item 402 in Fig. 4); and configure an artificial intelligence/machine learning (AI/ML) prediction model based on the parameters associated with the non-terrestrial networks or based on the information received from the at least one other user equipment that that indicates the prediction success rates for the other user equipment(item 403 in Fig. 4; also P[0116-0117], group of UEs).
Jeon-1 did not teach specifically aging compensation. However, Tong teaches in an analogous art aging compensation(P[0175], channel aging; prediction to reduce the impact of channel information becoming obsolete, which is also referred to as channel aging). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus having channel aging compensation in order to have improved accuracy.
Regarding claim 17, Jeon-1 in view of Tong teaches the network entity of claim 16, wherein the information received from the at least one other user equipment includes information that identifies the AI/ML prediction models utilized by the at least one other user equipment when predicting CSI quantities for the non-terrestrial network(P[0130], UE can report model parameter updates).
Regarding claim 18, Jeon-1 in view of Tong teaches the network entity of claim 16, wherein the at least one other user equipment (UE) includes multiple UEs, and where each of the multiple UEs employs a different machine-learning prediction model for predicting CSI quantities for the non-terrestrial network, and wherein the information received from the multiple UEs includes information that identifies success rates for the different prediction models utilized by the multiple UEs when predicting the CSI quantities(Tong: P[0930], UE with higher capability and UE with lower capability reports differently; Jeon-1: P[0116-0117]).
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Jeon et al. (hereinafter Jeon-1)(US 2022/0287104) in view of Tong et al. (hereinafter Tong)(US 2024/0022927) and Yang et al. (hereinafter Yang)(US 2023/0053572)/
Regarding claim 19, Jeon-1 in view of Tong teaches the network entity of claim 16, wherein the network entity configures the prediction model employed by the user equipment parameter to a report that identifies from the received information a prediction model having a highest success rate for predicting the one or more CSI quantities for CSI aging compensation of the non-terrestrial network(Tong: P[0141], compensate for the deficiencies of the model; P[0930], UE with higher capability and UE with lower capability reports differently; Jeon-1: P[0116-0117]).
Jeon-1 in view of Tong did not teach specifically by adding a prediction model accuracy. However, Yang teaches in an analogous art by adding a prediction model accuracy(P[0083], AL/ML model with accuracy/confidence level; UE side predictions based on plurality of devices). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus having by adding a prediction model accuracy in order to have improved accuracy.
Regarding claim 20, Jeon-1 in view of Tong teaches the network entity of claim 16, wherein the network entity configures the prediction model employed by the user equipment to a report that identifies from the received information a prediction model having a highest success rate for predicting the one or more CSI quantities for CSI aging compensation of the non-terrestrial network(Tong: P[0141], compensate for the deficiencies of the model; P[0930], UE with higher capability and UE with lower capability reports differently; Jeon-1: P[0116-0117]).
Jeon-1 in view of Tong did not teach specifically by adding a prediction model accuracy. However, Yang teaches in an analogous art by adding a prediction model accuracy(P[0083], AL/ML model with accuracy/confidence level; UE side predictions based on plurality of devices). Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to have the apparatus having by adding a prediction model accuracy in order to have improved accuracy.
Conclusion
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/MUTHUSWAMY G MANOHARAN/ Primary Examiner, Art Unit 2647
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