DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 1/13/2026. Claims 1-30 are pending in this Office 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 .
Response to Arguments
Applicant's arguments filed on 1/13/26 with respect to rejection of claims under 35 USC 103 have been considered but are moot in view of the new ground of rejection.
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.
Claims 1-8,12-19 and 23-30 are rejected under 35 U.S.C. 103 as being unpatentable over Isaksson et al.( US Patent Application Publication 2020/0413316 A1, hereinafter “Isaksson”) and further in view of Kaya et al .( US Patent Application Publication 2022/0190883 A1, hereinafter “Kaya”)
As to claim 1, Isaksson teaches an apparatus, comprising: a processor; memory coupled with the processor; and instructions stored in the memory and operable, when executed by the processor, to cause the apparatus to:
receive a message instructing the apparatus to switch from a current machine learning (ML)-based feature data extraction model to one of a plurality of ML-based feature data extraction models of the apparatus based on a state of the apparatus,(Isaksson par [0057] teaches the radio network node provides to the wireless communication device , the indicator indicating the model and the one or more trained model parameters for the model)
the plurality of ML-based feature data extraction models each providing ML-based feature data for predicting a beam blockage between the apparatus and a network entity (Isaksson par [0058] teaches the wireless communication device thus selects the model based on the indicator from a list with indexed models already preconfigured at the wireless device) , the message expressly indicating the one of the plurality of ML-based feature data extraction models(Isaksson par [0057] teaches the radio network node provides to the wireless communication device , the indicator indicating the model and the one or more trained model parameters for the model);
switch from the current ML-based feature data extraction model to the one of the ML-based feature data extraction models based at least in part on the message, the current ML-based feature data extraction model being a first neural network, and the one of the ML-based feature data extraction models being a second neural network;(Isaksson par [0058] teaches the wireless communication device thus selects the model based on the indicator)
transmit, in response to the switch, the ML-based feature data for predicting the beam blockage based on the one of the ML-based feature data extraction models;( Isaksson par [0059] teaches the wireless communication device may trigger sending measurement reports to the radio network node) and [ change a receive beam from a first receive direction to a second receive direction in response to a beam blockage prediction based on the transmitted ML-based feature data responsive to the switch]
Isaksson does not teach change a receive beam from a first receive direction to a second receive direction in response to a beam blockage prediction based on the transmitted ML-based feature data responsive to the switch.
However, Kaya teaches change a receive beam from a first receive direction to a second receive direction in response to a beam blockage prediction based on the transmitted ML-based feature data responsive to the switch.(Kaya par [0048] teaches the base station may change its receive beam and/or transmit beam with respect to the UE to a beam that is indicated or listed on the predicted future beam sequence for the UE)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Isaksson and Kaya to achieve the claimed invention. One would have been motivated to make such combination to improve base station performance in terms of beam selection and/or reduce beam tracking overhead. (Kaya par [0068])
As to claim 2, Isaksson and Kaya teach the apparatus of claim 1, wherein the plurality of ML-based feature data extraction models include different computation speeds and different detection accuracies.(Isaksson par [0086] teaches as the performance of the model increases with size of the model , the wireless device may need specialized hardware)
As to claim 3, Isaksson and Kaya teach the apparatus of claim 1, wherein the message is indicative of a performance of at least one of a plurality of ML models for beam management. ( Isaksson par [0089] teaches the wireless communication device obtains the indictor indicating the model and one or more trained parameters)
As to claim 4, Isaksson and Kaya teach the apparatus of claim 1, wherein the message further comprises instructions for the apparatus to transmit different ML-based feature data for different ML models for beam management. (Isaksson par [0089] teaches the wireless communication device obtains the indictor indicating the model and one or more trained parameters. The model is related to events such as a beam reselection procedure or cell reselection procedure)
As to claim 5, Isaksson and Kaya teach the apparatus of claim 1, wherein the state of the apparatus comprises at least one of:
a mobility status of the apparatus, a number of user equipments (UEs) in an area of the apparatus, a data processing capability of the apparatus, an amount of uplink traffic sharing a bandwidth of the apparatus, or an uplink traffic load of a network including the apparatus. (Isaksson par [0067] teaches the movement of the wireless communication device may help in predicting future position of the wireless device and therefore be taken as a parameter in predicting the best beam)
As to claim 6, Isaksson and Kaya teach the apparatus of claim 1, wherein the instructions, when executed by the processor, further cause the apparatus to: receive an aggregated performance characteristic of an ML model for beam blockage prediction, the ML model for beam blockage prediction including an aggregate of input ML-based feature data from a plurality of UEs including the apparatus, wherein the switch to the one of the ML-based feature data extraction models is further based on the aggregated performance characteristic. (Isaksson par [0054] teaches to train a more accurate mode, the inputs of many wireless communication devices are used)
As to claim 7, Isaksson and Kaya teach the apparatus of claim 1, wherein the instructions, when executed by the processor, further cause the apparatus to: transmit a confidence level associated with the ML-based feature data. ( Isaksson par [0094] teaches the radio network node receives from one more wireless communication devices the data associated with the measurements performed by the one or more wireless communication devices. Isaksson par [0095] teaches the received data may comprise measured signal strength or quality)
As to claim 8, Isaksson and Kaya teach the apparatus of claim 7, wherein the switch to the one of the ML-based feature data extraction models is based on a capability of the one of the ML-based feature data extraction models to derive the confidence level. (Isaksson par [0088] teaches the wireless communication device may send a capability indication to the radio network node that indicates a capability of supporting one or more models)
Claims 12-19 merely recite a method performed by the apparatus of claims 1-8 respectively. Accordingly, Isaksson and Kaya teach every limitation of claims 12-19 as indicates in the above rejection of claims 1-8 respectively.
As to claim 23, Isaksson teaches a network node, comprising: a processor; memory coupled with the processor; and instructions stored in the memory and operable, when executed by the processor, to cause the network node to:
receive first machine learning (ML)-based feature data from a user equipment (UE) based on a first ML-based feature data extraction model of the UE; transmit a message instructing the UE to switch from the first ML-based feature data extraction model to a second ML-based feature data extraction model of the UE based on a state of the UE, (Isaksson par [0057] teaches the radio network node provides to the wireless communication device , the indicator indicating the model and the one or more trained model parameters for the model)
the message expressly indicating the second ML-based feature data extraction model , the first ML-based feature data extraction model being a first neural network, and the second ML-based feature data extraction model being a second neural network; (Isaksson par [0057] teaches the radio network node provides to the wireless communication device , the indicator indicating the model and the one or more trained model parameters for the model)
receive, in response to the switch, second ML-based feature data from the UE based on the second ML-based feature data extraction model of the UE (Isaksson par [0059] teaches the wireless communication device may trigger sending measurement reports to the radio network node);
[and adjust a beam operation based on a determined beam blockage prediction in response to the second ML-based feature data.]
Isaksson does not teach adjust a beam operation based on a determined beam blockage prediction in response to the second ML-based feature data.
However, Kaya teaches adjust a beam operation based on a determined beam blockage prediction in response to the second ML-based feature data.(Kaya par [0048] teaches the base station may change its receive beam and/or transmit beam with respect to the UE to a beam that is indicated or listed on the predicted future beam sequence for the UE)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Isaksson and Kaya to achieve the claimed invention. One would have been motivated to make such combination to improve base station performance in terms of beam selection and/or reduce beam tracking overhead. (Kaya par [0068])
As to claim 24, Isaksson and Kaya teach the network node of claim 23, wherein the first ML-based feature data extraction model and the second ML-based feature data extraction model include different computation speeds and different detection accuracies. (Isaksson par [0086] teaches as the performance of the model increases with size of the model , the wireless device may need specialized hardware)
As to claim 25, Isaksson and Kaya teach the network node of claim 23, wherein the network node further includes a plurality of ML models for beam management, and the message is based on a performance of at least one of the ML models for beam management. ( Isaksson par [0089] teaches the wireless communication device obtains the indictor indicating the model and one or more trained parameters)
As to claim 26, Isaksson and Kaya teach the network node of claim 23, wherein the message further comprises instructions for the UE to transmit different ML-based feature data for different ML models for beam management of the network node. (Isaksson par [0089] teaches the wireless communication device obtains the indictor indicating the model and one or more trained parameters. The model is related to events such as a beam reselection procedure or cell reselection procedure)
Claims 27-30 merely recite a method performed by the network node of claims 23-26 respectively. Accordingly, Isaksson and Kaya teach every limitation of claims 27-30 as indicates in the above rejection of claims 23-26 respectively.
Claims 9-11 and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Isaksson , Kaya and further in view of Batts et al .( US Patent Application Publication 2020/0339151 A1, hereinafter “Batts”)
As to claim 9, Isaksson and Kaya teach the apparatus of claim 1, [wherein the message further comprises instructions for the apparatus to reconfigure a sensor of the apparatus], and the ML-based feature data is further based on the sensor. (Isaksson par [0059] teaches input to the model may be provided from the wireless communication devices)
Isaksson and Kaya fail to expressly teach wherein the message further comprises instructions for the apparatus to reconfigure a sensor of the apparatus.
However, Batt teaches instructions for the apparatus to reconfigure a sensor of the apparatus.(Batts par [0200] teaches upon determining that a first subset of two or more sensors is determined to have a level of confidence that is less than a confidence level threshold or has failed, the autonomous vehicle can reconfigure one or more of the remainder of the plurality of sensors to adjust their respective coverage areas)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Isaksson , Kaya and Batts to achieve the claimed invention. One would have been motivated to make such combination to improve data availability for predicting beam blockage)
As to claim 10, Isaksson, Kaya and Batts teach the apparatus of claim 9, wherein the message is received in response to a satisfied criteria for sensor reconfiguration.( Batts par [0200] teaches upon determining that a first subset of two or more sensors is determined to have a level of confidence that is less than a confidence level threshold or has failed)
As to claim 11, Isaksson, Kaya and Batts teach the apparatus of claim 9, wherein the instructions, when executed by the processor, further cause the apparatus to: determine that a criteria for sensor reconfiguration is satisfied; and reconfigure at least one of a field of view (FoV), a range, a measurement update rate, a resolution, or a frame rate of the sensor in response to the criteria being satisfied.( Batts par [0200] teaches if some sensors 1310 fail or the level of confidence is less than the confidence level threshold of each particular sensor, the AV 1304, via the sensor controller 1328, can change a viewing angle of one or more the remaining sensors to increase the coverage area of those respective sensors)
As to claims 20-22 , see the above rejection of claims 9-11.
Conclusion
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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