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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-42 have been considered but are moot due to the introduction of new references necessitated by amendments to the claims.
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.
Claim(s) 1-4, 6, 12-13, 15, 18, 22-25, 27, 33-34, 36, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2026/0051937 A1; “Liu”) in view of Karapantelakis et al. (US 2024/0291548 A1; “Karapantelakis”).
Regarding claim 1, Liu teaches a first wireless communication device, comprising:
at least one processor; and at least one memory coupled with the at least one processor, the at least one memory storing instructions executable by the at least one processor to cause the first wireless communication device to [Liu ¶ 0108, Fig. 7: UE comprising processor and a memory; ¶ 0126: implementation through hardware and software (i.e. stored instructions)]:
determine a first set of parameters comprising parameters used for previous communications with a second wireless communication device [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS, within the measurement beam set, wherein a measurement instance is a time instance at which the quality of each measurement beam is measured (or obtained)] within each time interval of a first set of time intervals [Liu ¶ 0059, Fig. 3: the CSI reference resource set configuration should include multiple DL slots for the UE to obtain multiple measurement instances (here, the UE determines CSI-RS set and associated measurement instances)], the first set of parameters associated with a beamforming procedure between the first wireless communication device and the second wireless communication device [Liu ¶¶ 0096 & 0115, Fig. 5: UE (i.e. first wireless device) receiving a configuration for CSI report setting for temporal beam prediction from gNB (i.e. second wireless device)];
predict, at a first time after the first set of time intervals and in accordance with inputting the first set of parameters to a machine learning model, a second set of parameters corresponding to a second set of time intervals after the first time [Liu ¶ 0046, Fig. 1: output from the AI/ML model is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained];
However, Liu does not explicitly disclose select, in accordance with the second set of parameters, a beamforming configuration to be used for future communications between the first wireless communication device and the second wireless communication device that occur within the second set of time intervals; and communicate with the second wireless communication device within the second set of time intervals in accordance with the beamforming configuration.
However, in a similar field of endeavor, Karapantelakis teaches select, in accordance with the second set of parameters, a beamforming configuration to be used for future communications between the first wireless communication device and the second wireless communication device that occur within the second set of time intervals [Karapantelakis ¶¶ 0043-0044: ML model is used to process the property measurements to suggest one or more beam options from among the plurality of beam options for use in exchanging data with the one or more UEs (here, the suggestion beams are for transmissions at a later point in time, i.e., a second set of time intervals) the ML model has suggests one or more beam options from the plurality of beam options, the method then comprises selecting at least one of the suggested beam options]; and
communicate with the second wireless communication device within the second set of time intervals in accordance with the beamforming configuration [ Karapantelakis ¶ 0046: exchanging data with the one or more UEs, using the selected beam options, as shown in step S312].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of selecting and utilizing a beam from a set of suggested beam options as determined from a machine learning module to perform beamformed communications as taught by Karapantelakis. The motivation to combine these references would be to support fast and efficient beam management in a wireless communication system [Karapantelakis ¶ 0007].
Regarding claim 2, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein determining the first set of parameters associated with the first set of time intervals comprises communicating with the second wireless communication device via a first beam in accordance with the first set of parameters device [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS, within the measurement beam set], wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to:
predict, using the machine learning model, an indication of whether the first beam is usable for communications with the second wireless communication device over the second set of time intervals, a validity time associated with a time duration that the first beam is usable for communications, or both, wherein the second set of parameters comprise the indication, the validity time, or both [Liu ¶ 0046, Fig. 1: output from the AI/ML mode is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained (here, the best beams indicated beams that may be used for communication); Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Regarding claim 3, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to receive, from the second wireless communication device, a message indicating the first set of parameters, wherein predicting the second set of parameters is associated with receiving the first set of parameters via the message [Liu ¶ 0096, Fig. 5: UE receives configuration for CSI report setting form temporal beam prediction; ¶ 0098: the CSI reporting setting for temporal beam prediction includes: K.sub.M, indicating the number of measurement instances for the measurement beams for AI/ML input, F.sub.P, indicating the number of future instances for beam prediction, N.sub.F, indicating the number of reported beams for each future instance, and T, indicating the number of slots or symbols between two adjacent measurement instances and between two adjacent prediction instances (i.e. parameters used for beam detection for AI/ML beam prediction input)].
Regarding claim 4, Liu in view of Karapantelakis teaches the first wireless communication device of claim 3, wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to transmit, to the second wireless communication device, a second message indicating the second set of parameters, wherein selecting the beamforming configuration, communicating within the second set of time intervals, or both, is associated with transmitting the second message [Liu ¶ 0100: UE reports, e.g., L1-RSRP, of the best predicted beam (i.e. UE transmits a beam configuration selection implicitly indicated by reporting L1-RSRP of a best predicted beam)].
Regarding claim 6, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to communicate, with the second wireless communication device, a capability message indicating one or more machine learning models supported by the first wireless communication device, supported by the second wireless communication device, or both [Liu ¶ 0096, Fig. 5: UE reports a set of parameters to define each AI/ML model that can be used for temporal beam prediction], the one or more machine learning models including the machine learning model, wherein predicting the second set of parameters is associated with the capability message [Liu ¶ 0046, Fig. 1: output from the AI/ML model is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained; ¶ 0047: the AI/ML model may be selected form multiple models (i.e. based on the reported AI/ML models)].
Regarding claim 12, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein the first set of parameters, the second set of parameters, or both, comprise one or more of
an angle of arrival of communications between the first wireless communication device and the second wireless communication device,
a steering angle of communications between the first wireless communication device and the second wireless communication device,
a transmit sector identifier or a receive sector identifier associated with the first wireless communication device, the second wireless communication device, or both,
a width of a beam used for communication by the first wireless communication device, the second wireless communication device, or both,
a validity time associated with the beam used for communication by the first wireless communication device, the second wireless communication device, or both,
or
channel quality metrics associated with communications exchanged between the first wireless communication device and the second wireless communication device [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS; Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Regarding claim 13, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, however, Liu does not explicitly disclose wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to: perform a set of measurements on communications performed within the second set of time intervals; and train the machine learning model by inputting the second set of parameters, the set of measurements, or both, into the machine learning model.
However, Karapantelakis wherein the instructions are further executable by the processor to cause the apparatus to: perform a set of measurements on communications performed within the second set of time intervals; and train the machine learning model by inputting the second set of parameters, the set of measurements, or both, into the machine learning model [Karapantelakis ¶ 0047: further reference signals, which may be sent subsequent to the step of exchanging data with the one or more UEs may be sent using some or all of the plurality of beam options, wherein the reference signals are sent alternatively to the reference signals that may be sent to at least one of the one or more UEs in the step of selecting at least one of the one or more suggested beam options; ¶ 0038: training the ML model using ongoing observations of beam selections (i.e. second parameters) and the environment (i.e. RS measurements)].
The motivation to combine these references is illustrated in the rejection of claim 1 above.
Regarding claim 15, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein the first wireless communication device comprises a station (STA), a first multi-link device, or both, and wherein the second wireless communication device is a network side device [Liu ¶ 0048: UE performing beam prediction based on configuration from network side, e.g., gNB].
However, Liu does not explicitly disclose the network side device comprises an access point (AP), a second multi-link device, or both.
However, Karapantelakis teaches the network side device comprises an access point (AP), a second multi-link device, or both [Karapantelakis ¶ 0005: gNB (i.e. access point/multi-link device)].
The motivation to combine these references is illustrated in the rejection of claim 1 above.
Regarding claim 18, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, wherein the machine learning model comprises one or more of a time series-based prediction model, a machine learning classifier, or a reinforcement learning model [Liu ¶ 0045: AI/ML Model may be implemented by an RNN (Recurrent Neural Network) (i.e. a series-based model implemented for temporal prediction); Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Regarding claim 22, Liu teaches a method for wireless communication at a first wireless communication device, comprising:
determining a first set of parameters comprising parameters used within each time interval of a first set of time intervals for previous communications with a second wireless communication device [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS, within the measurement beam set, wherein a measurement instance is a time instance at which the quality of each measurement beam is measured (or obtained); ¶ 0059, Fig. 3: the CSI reference resource set configuration should include multiple DL slots for the UE to obtain multiple measurement instances (here, the UE determines CSI-RS set and associated measurement instances)], the first set of parameters associated with a beamforming procedure between the first wireless communication device and the second wireless communication device [Liu ¶¶ 0096 & 0115, Fig. 5: UE (i.e. first wireless device) receiving a configuration for CSI report setting for temporal beam prediction from gNB (i.e. second wireless device)];
predicting, at a first time after the first set of time intervals and in accordance with inputting the first set of parameters to a machine learning model, a second set of parameters corresponding to a second set of time intervals after the first time [Liu ¶ 0046, Fig. 1: output from the AI/ML model is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained].
However, Liu does not explicitly disclose selecting, in accordance with the second set of parameters, a beamforming configuration to be used for future communications between the first wireless communication device and the second wireless communication device that occur within the second set of time intervals; and communicating with the second wireless communication device within the second set of time intervals in accordance with the beamforming configuration.
However, in a similar field of endeavor, Karapantelakis teaches selecting, in accordance with the second set of parameters, a beamforming configuration to be used for future communications between the first wireless communication device and the second wireless communication device that occur within the second set of time intervals [Karapantelakis ¶¶ 0043-0044: ML model is used to process the property measurements to suggest one or more beam options from among the plurality of beam options for use in exchanging data with the one or more UEs (here, the suggestion beams are for transmissions at a later point in time, i.e., a second set of time intervals) the ML model has suggests one or more beam options from the plurality of beam options, the method then comprises selecting at least one of the suggested beam options]; and
communicating with the second wireless communication device within the second set of time intervals in accordance with the beamforming configuration [Karapantelakis ¶ 0044: selecting at least one of the suggested beam options, as shown in step S310; ¶ 0046: exchanging data with the one or more UEs, using the selected beam options, as shown in step S312].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Oppo, with the method of selecting and utilizing a beam from a set of suggested beam options as determined from a machine learning module to perform beamformed communications as taught by Karapantelakis. The motivation to combine these references would be to support fast and efficient beam management in a wireless communication system [Karapantelakis ¶ 0007].
Regarding claim 23, Liu in view of Karapantelakis teaches the method of claim 22, wherein determining the first set of parameters associated with the first set of time intervals comprises communicating with the second wireless communication device via a first beam in accordance with the first set of parameters [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS, within the measurement beam set], wherein predicting the second set of parameters comprises predicting, using the machine learning model, an indication of whether the first beam is usable for communications with the second wireless communication device over the second set of time intervals, a validity time associated with a time duration that the first beam is usable for communications, or both, wherein the second set of parameters comprise the indication, the validity time, or both [Liu ¶ 0046, Fig. 1: output from the AI/ML mode is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained (here, the best beams indicated beams that may be used for communication); Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Regarding claim 24, Liu in view of Karapantelakis teaches the method of claim 22, further comprising receiving, from the second wireless communication device, a message indicating the first set of parameters, wherein predicting the second set of parameters is associated with receiving the first set of parameters via the message [Liu ¶ 0096, Fig. 5: UE receives configuration for CSI report setting form temporal beam prediction; ¶ 0098: the CSI reporting setting for temporal beam prediction includes: K.sub.M, indicating the number of measurement instances for the measurement beams for AI/ML input, F.sub.P, indicating the number of future instances for beam prediction, N.sub.F, indicating the number of reported beams for each future instance, and T, indicating the number of slots or symbols between two adjacent measurement instances and between two adjacent prediction instances (i.e. parameters used for beam detection for AI/ML beam prediction input)].
Regarding claim 25, Liu in view of Karapantelakis teaches the method of claim 24, further comprising transmitting, to the second wireless communication device, a second message indicating the second set of parameters, wherein selecting the beamforming configuration, communicating within the second set of time intervals, or both, is associated with transmitting the second message [Liu ¶ 0100: UE reports, e.g., L1-RSRP, of the best predicted beam (i.e. UE transmits a beam configuration selection implicitly indicated by reporting L1-RSRP of a best predicted beam)].
Regarding claim 27, Liu in view of Karapantelakis teaches the method of claim 22, further comprising communicating, with the second wireless communication device, a capability message indicating one or more machine learning models supported by the first wireless communication device, supported by the second wireless communication device, or both [Liu ¶ 0096, Fig. 5: UE reports a set of parameters to define each AI/ML model that can be used for temporal beam prediction], the one or more machine learning models including the machine learning model, wherein predicting the second set of parameters is associated with the capability message [Liu ¶ 0046, Fig. 1: output from the AI/ML model is the predicted best NF (NF>=1) beams among the beams within the prediction beam set on FP (FP>=1) future instances, e.g., a time instance that is after each of the measurement instances at which the beam measurement results are obtained; ¶ 0047: the AI/ML model may be selected form multiple models (i.e. based on the reported AI/ML models)].
Regarding claim 33, Liu in view of Karapantelakis teaches the method of claim 22, wherein the first set of parameters, the second set of parameters, or both, comprise one or more of
an angle of arrival of communications between the first wireless communication device and the second wireless communication device,
a steering angle of communications between the first wireless communication device and the second wireless communication device,
a transmit sector identifier or a receive sector identifier associated with the first wireless communication device, the second wireless communication device, or both,
a width of a beam used for communication by the first wireless communication device, the second wireless communication device, or both,
a validity time associated with the beam used for communication by the first wireless communication device, the second wireless communication device, or both, or
channel quality metrics associated with communications exchanged between the first wireless communication device and the second wireless communication device [Liu ¶ 0046, Fig. 1: input to the AI/ML Model is the historical, e.g. on measurement instances, beam measurement results, e.g. L1-RSRP, of the measurement beams, e.g., CSI-RS; Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Regarding claim 34, Liu in view of Karapantelakis teaches the method of claim 22, however, Liu does not explicitly disclose further comprising: performing a set of measurements on communications performed within the second set of time intervals; and training the machine learning model by inputting the second set of parameters, the set of measurements, or both, into the machine learning model.
However, Karapantelakis teaches performing a set of measurements on communications performed within the second set of time intervals; and training the machine learning model by inputting the second set of parameters, the set of measurements, or both, into the machine learning model [Karapantelakis ¶ 0047: further reference signals, which may be sent subsequent to the step of exchanging data with the one or more UEs may be sent using some or all of the plurality of beam options, wherein the reference signals are sent alternatively to the reference signals that may be sent to at least one of the one or more UEs in the step of selecting at least one of the one or more suggested beam options; ¶ 0038: training the ML model using ongoing observations of beam selections (i.e. second parameters) and the environment (i.e. RS measurements)].
The motivation to combine these references is illustrated in the rejection of claim 22 above.
Regarding claim 36, Liu in view of Karapantelakis teaches the method of claim 22, wherein the first wireless communication device comprises a station (STA), a first multi-link device, or both, and wherein the second wireless communication device is a network side device [Liu ¶ 0048: UE performing beam prediction based on configuration from network side, e.g., gNB].
However, Liu does not explicitly disclose the network side device comprises an access point (AP), a second multi-link device, or both.
However, Karapantelakis teaches the network side device comprises an access point (AP), a second multi-link device, or both [Karapantelakis ¶ 0005: gNB (i.e. access point/multi-link device)].
The motivation to combine these references is illustrated in the rejection of claim 1 above.
Regarding claim 39, Liu in view of Karapantelakis teaches the method of claim 22, wherein the machine learning model comprises one or more of a time series-based prediction model, a machine learning classifier, or a reinforcement learning model [Liu ¶ 0045: AI/ML Model may be implemented by an RNN (Recurrent Neural Network) (i.e. a series-based model implemented for temporal prediction); Examiner’s Note: the limitations are written in the alternative, therefore, it is only necessary that one of the alternative limitations be taught by the applied references].
Claim(s) 8, 11, 16-17, 20, 29, 32, 37-38, and 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over OPPO, “Other aspects of AI/ML for beam management”, 3GPP TSG RAN WG1 Meeting #111, R1-2211481, Toulouse, France, November 15-18, 2022 (“OPPO”).
Regarding claim 8, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, however, does not explicitly disclose wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to perform a sector-level sweep procedure, a beam refinement procedure, or both, with the second wireless communication device in accordance with the beamforming configuration, wherein communicating with the second wireless communication device in accordance with the beamforming configuration is associated with performing the sector-level sweep procedure, the beam refinement procedure, or both.
However, in a similar field of endeavor, OPPO teaches wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to perform a sector-level sweep procedure, a beam refinement procedure, or both, with the second wireless communication device in accordance with the beamforming configuration, wherein communicating with the second wireless communication device in accordance with the beamforming configuration is associated with performing the sector-level sweep procedure, the beam refinement procedure, or both [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 11, Liu in view of Karapantelakis in view of OPPO teaches the first wireless communication device of claim 8, however, Liu does not explicitly disclose wherein the first wireless communication device is associated with a plurality of beam sectors, a plurality of beams, or both, wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to predict, using the machine learning model, a subset of beam sectors, a subset of the plurality of beams, or both, that are to be used for wireless communications with the second wireless communication device, wherein the second set of parameters comprise indications of the subset of the plurality of beam sectors, the subset of the plurality of beams, or both, wherein the sector-level sweep procedure, the beam refinement procedure, or both, are performed across the subset of the plurality of beam sectors, the subset of the plurality of beams, or both.
However, OPPO teaches wherein the first wireless communication device is associated with a plurality of beam sectors, a plurality of beams, or both, wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to predict, using the machine learning model, a subset of beam sectors, a subset of the plurality of beams, or both, that are to be used for wireless communications with the second wireless communication device, wherein the second set of parameters comprise indications of the subset of the plurality of beam sectors, the subset of the plurality of beams, or both, wherein the sector-level sweep procedure, the beam refinement procedure, or both, are performed across the subset of the plurality of beam sectors, the subset of the plurality of beams, or both [OPPO p. 8, sec. 2.5: AI/ML model utilizes historical results of set B beams to predict a set A of beams; p. 9, sec. 2.6.2.1: Top-K beams (i.e. subset) are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 8 above.
Regarding claim 16, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, however, Liu does not explicitly disclose the network side device comprises an access point (AP), a second multi-link device, or both.
However, Karapantelakis teaches the network side device comprises an access point (AP), a second multi-link device, or both [Karapantelakis ¶ 0005: gNB (i.e. access point/multi-link device)].
The motivation to combine these references is illustrated in the rejection of claim 1 above.
However, Liu in view of Karapantelakis does not explicitly disclose wherein the first wireless communication device comprises a network side device, and wherein the second wireless communication device comprises a station (STA), a second multi-link device, or both.
However, in a similar field of endeavor, OPPO teaches wherein the first wireless communication device comprises a network side device, and wherein the second wireless communication device comprises a station (STA), a second multi-link device, or both [OPPO p. 1, sec. 2: UE (i.e. terminal) in communication with NW (i.e. network side device); p. 2, sec. 2.1.1: AI/ML model can be applied at UE or NW].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement at either link direction based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 17, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, however, does not explicitly disclose
However, in a similar field of endeavor, OPPO teaches wherein the first wireless communication device comprises a first station (STA), and wherein the second wireless communication device comprises a second STA [OPPO p. 1, sec. 2: UE (i.e. terminal) in communication with NW (i.e. network side device is analogous to a station based on BRI); p. 2, sec. 2.1.1: AI/ML model can be applied at UE or NW].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement at either link direction based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 20, Liu in view of Karapantelakis teaches the first wireless communication device of claim 1, however, does not explicitly disclose
However, in a similar field of endeavor, OPPO teaches wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to predict, using the machine learning model, one or more mobility metrics associated with a relative level of mobility for the first wireless communication device, the second wireless communication device, or both, wherein the second set of parameters comprise the one or more mobility metrics [OPPO p. 6, sec. 2.3: Assistance information used as AI/ML input, e.g., positioning, UE direction, positioning related measurements; p. 7, sec. 2.4: AI/ML model outputs (i.e. based on mobility inputs), e.g., predicted beams, dwell time, confidence, etc. (i.e. mobility metrics)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to predict mobility metrics based on AI/ML modeling as taught by OPPO. The motivation to combine these references would be to improve positioning accuracy in the context of beam management [OPPO p. 1, sec. 1].
Regarding claim 29, Liu in view of Karapantelakis teaches the method of claim 22, however, does not explicitly disclose further comprising performing a sector-level sweep procedure, a beam refinement procedure, or both, with the second wireless communication device in accordance with the beamforming configuration, wherein communicating with the second wireless communication device in accordance with the beamforming configuration is associated with performing the sector-level sweep procedure, the beam refinement procedure, or both.
However, in a similar field of endeavor, OPPO teaches performing a sector-level sweep procedure, a beam refinement procedure, or both, with the second wireless communication device in accordance with the beamforming configuration, wherein communicating with the second wireless communication device in accordance with the beamforming configuration is associated with performing the sector-level sweep procedure, the beam refinement procedure, or both [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 32, Liu in view of Karapantelakis in view of OPPO teaches the method of claim 29, however, Liu does not explicitly disclose wherein the first wireless communication device is associated with a plurality of beam sectors, a plurality of beams, or both, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a subset of the plurality of beam sectors, a subset of the plurality of beams, or both, that are to be used for wireless communications with the second wireless communication device, wherein the second set of parameters comprise indications of the subset of the plurality of beam sectors, the subset of the plurality of beams, or both, wherein the sector-level sweep procedure, the beam refinement procedure, or both, are performed across the subset of the plurality of beam sectors, the subset of the plurality of beams, or both.
However, OPPO teaches wherein the first wireless communication device is associated with a plurality of beam sectors, a plurality of beams, or both, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a subset of the plurality of beam sectors, a subset of the plurality of beams, or both, that are to be used for wireless communications with the second wireless communication device, wherein the second set of parameters comprise indications of the subset of the plurality of beam sectors, the subset of the plurality of beams, or both, wherein the sector-level sweep procedure, the beam refinement procedure, or both, are performed across the subset of the plurality of beam sectors, the subset of the plurality of beams, or both [OPPO p. 8, sec. 2.5: AI/ML model utilizes historical results of set B beams to predict a set A of beams; p. 9, sec. 2.6.2.1: Top-K beams (i.e. subset) are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 29 above.
Regarding claim 37, Liu in view of Karapantelakis teaches the method of claim 22, however, Liu does not explicitly disclose the network side device comprises an access point (AP), a second multi-link device, or both.
However, Karapantelakis teaches the network side device comprises an access point (AP), a second multi-link device, or both [Karapantelakis ¶ 0005: gNB (i.e. access point/multi-link device)].
The motivation to combine these references is illustrated in the rejection of claim 1 above.
However, Liu in view of Karapantelakis does not explicitly disclose wherein the first wireless communication device comprises a network side device, and wherein the second wireless communication device comprises a station (STA), a second multi-link device, or both.
However, in a similar field of endeavor, OPPO teaches wherein the first wireless communication device comprises a network side device, and wherein the second wireless communication device comprises a station (STA), a second multi-link device, or both [OPPO p. 1, sec. 2: UE (i.e. terminal) in communication with NW (i.e. network side device); p. 2, sec. 2.1.1: AI/ML model can be applied at UE or NW].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement at either link direction based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 38, Liu in view of Karapantelakis teaches the method of claim 22, however, does not explicitly disclose wherein the first wireless communication device comprises a first station (STA), and wherein the second wireless communication device comprises a second STA.
However, in a similar field of endeavor, OPPO teaches wherein the first wireless communication device comprises a first station (STA), and wherein the second wireless communication device comprises a second STA [OPPO p. 1, sec. 2: UE (i.e. terminal) in communication with NW (i.e. network side device is analogous to a station based on BRI); p. 2, sec. 2.1.1: AI/ML model can be applied at UE or NW].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to perform beam sweeping or beam refinement at either link direction based on predicted beams as taught by OPPO. The motivation to combine these references would be to improve beam management accuracy [OPPO p. 1, sec. 1].
Regarding claim 41, Liu in view of Karapantelakis teaches the method of claim 22, however, does not explicitly disclose wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to predict, using the machine learning model, one or more mobility metrics associated with a relative level of mobility for the first wireless communication device, the second wireless communication device, or both, wherein the second set of parameters comprise the one or more mobility metrics.
However, in a similar field of endeavor, OPPO teaches wherein the instructions to predict the second set of parameters are executable by the at least one processor to cause the first wireless communication device to predict, using the machine learning model, one or more mobility metrics associated with a relative level of mobility for the first wireless communication device, the second wireless communication device, or both, wherein the second set of parameters comprise the one or more mobility metrics [OPPO p. 6, sec. 2.3: Assistance information used as AI/ML input, e.g., positioning, UE direction, positioning related measurements; p. 7, sec. 2.4: AI/ML model outputs (i.e. based on mobility inputs), e.g., predicted beams, dwell time, confidence, etc. (i.e. mobility metrics)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of to predict mobility metrics based on AI/ML modeling as taught by OPPO. The motivation to combine these references would be to improve positioning accuracy in the context of beam management [OPPO p. 1, sec. 1].
Claim(s) 7 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Karapantelakis in view of Shao et al., (US 2024/0284200 A1; “Shao”).
Regarding claim 7, Liu in view of Karapantelakis teaches the first wireless communication device of claim 6, however, does not explicitly disclose wherein the instructions are further executable by the at least one processor to cause the first wireless communication device to communicate, with the second wireless communication device in accordance with the capability message, an additional message indicating one or more inputs to the machine learning model, wherein inputting the first set of parameters to the machine learning model is associated with the additional message.
However, in a similar field of endeavor, Shao teaches communicating, with the second wireless communication device in accordance with the capability message, an additional message indicating one or more inputs to the machine learning model, wherein inputting the first set of parameters to the machine learning model is associated with the additional message [Shao ¶¶ 0027-0029, Fig. 1: first communication node receives beam detection configuration information (i.e. additional message), e.g., pattern for beam detection, sent by a second communication node, and subsequently the first communication node performs AI prediction beam quality detection according to the beam detection configuration information to obtain a beam quality value (here, a beam pattern would indicate which beam measurements are input into an ML model)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of performing beam measurement on configured beams, wherein the measurements are used with an AI model to select a new beam as taught by Shao. The motivation to combine these references would be to improve beam prediction in consideration of UE environment and movement [Shao ¶ 0002].
Regarding claim 28, Liu in view of Karapantelakis teaches the method of claim 27, however, does not explicitly disclose further comprising communicating, with the second wireless communication device in accordance with the capability message, an additional message indicating one or more inputs to the machine learning model, wherein inputting the first set of parameters to the machine learning model is associated with the additional message.
However, in a similar field of endeavor, Shao teaches communicating, with the second wireless communication device in accordance with the capability message, an additional message indicating one or more inputs to the machine learning model, wherein inputting the first set of parameters to the machine learning model is associated with the additional message[Shao ¶¶ 0027-0029, Fig. 1: first communication node receives beam detection configuration information (i.e. additional message), e.g., pattern for beam detection, sent by a second communication node, and subsequently the first communication node performs AI prediction beam quality detection according to the beam detection configuration information to obtain a beam quality value (here, a beam pattern would indicate which beam measurements are input into an ML model)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of performing beam measurement on configured beams, wherein the measurements are used with an AI model to select a new beam as taught by Shao. The motivation to combine these references would be to improve beam prediction in consideration of UE environment and movement [Shao ¶ 0002].
Claim(s) 9-10 and 30-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Karapantelakis in view of OPPO in view of Google, “On Enhancements of AI/ML based Beam Management”, 3GPP TSG RAN WG1 #111, R1-2211127, Toulouse, France, November 14-18, 2022 (cited in Applicant’s IDS submitted 03/08/2024; “Google”).
Regarding claim 9, Liu in view of Karapantelakis in view of OPPO teaches the apparatus of claim 8, however, Liu does not explicitly disclose wherein the instructions to perform the sector-level sweep procedure, the beam refinement procedure, or both are executable by the processor to cause the apparatus to perform both the sector-level sweep procedure and the beam refinement procedure.
However, OPPO teaches wherein the instructions to perform the sector-level sweep procedure, the beam refinement procedure, or both are executable by the processor to cause the apparatus to perform both the sector-level sweep procedure and the beam refinement procedure [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 8 above.
However, Liu in view of Karapantelakis in view of OPPO does not explicitly disclose performing both the sector-level sweep procedure and the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being greater than a threshold difference.
However, in a similar field of endeavor, Google teaches performing both the sector-level sweep procedure and the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being greater than a threshold difference [Google p. 6, sec. 3.3: if L1-RSRP for predicted beam (i.e. second parameters) is greater than L1-RSRP for the current beam (i.e. first parameters), UE switches to predicted beam; p. 4, sec. 2.2: AI/ML model used for beam refinement, wherein the procedure is used to perform Rx beam sweeping at the UE side (here, e.g., if predicted L1-RSRP – current L1-RSRP is greater than a threshold of 0, then the predicted beam is used for beam sweeping)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for beam sweeping and beam refinement based on a comparison of RSRP of a current beam and a predicted beam as taught by Google. The motivation to combine these references would be to provide performance validation in a beam sweeping or beam refinement procedure [Google p. 6, sec. 3.3].
Regarding claim 10, Liu in view of Karapantelakis in view of OPPO teaches the apparatus of claim 8, however, Liu does not explicitly disclose wherein the instructions to perform the sector-level sweep procedure, the beam refinement procedure, or both are executable by the processor to cause the apparatus to perform the beam refinement procedure.
However, OPPO teaches wherein the instructions to perform the sector-level sweep procedure, the beam refinement procedure, or both are executable by the processor to cause the apparatus to perform the beam refinement procedure [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 8 above.
However, Liu in view of Karapantelakis in view of OPPO does not explicitly disclose performing the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being less than a threshold difference.
However, in a similar field of endeavor, Google teaches performing the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being less than a threshold difference [Google p. 6, sec. 3.3: if L1-RSRP for predicted beam (i.e. second parameters) is greater than L1-RSRP for the current beam (i.e. first parameters), UE switches to predicted beam; p. 4, sec. 2.2: AI/ML model used for beam refinement, wherein the procedure is used to perform Rx beam sweeping at the UE side (here, e.g., if predicted L1-RSRP – current L1-RSRP is less than a threshold of 0, then the current beam is used for beam sweeping)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for beam sweeping and beam refinement based on a comparison of RSRP of a current beam and a predicted beam as taught by Google. The motivation to combine these references would be to provide performance validation in a beam sweeping or beam refinement procedure [Google p. 6, sec. 3.3].
Regarding claim 30, Liu in view of Karapantelakis in view of OPPO teaches the method of claim 29, however, Liu does not explicitly disclose wherein performing the sector-level sweep procedure, the beam refinement procedure, or both comprises performing both the sector-level sweep procedure and the beam refinement procedure.
However, OPPO teaches performing both the sector-level sweep procedure and the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being greater than a threshold difference [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 29 above.
However, Liu in view of Karapantelakis in view of OPPO does not explicitly disclose performing both the sector-level sweep procedure and the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being greater than a threshold difference.
However, in a similar field of endeavor, Google teaches performing both the sector-level sweep procedure and the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being greater than a threshold difference [Google p. 6, sec. 3.3: if L1-RSRP for predicted beam (i.e. second parameters) is greater than L1-RSRP for the current beam (i.e. first parameters), UE switches to predicted beam; p. 4, sec. 2.2: AI/ML model used for beam refinement, wherein the procedure is used to perform Rx beam sweeping at the UE side (here, e.g., if predicted L1-RSRP – current L1-RSRP is greater than a threshold of 0, then the predicted beam is used for beam sweeping)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for beam sweeping and beam refinement based on a comparison of RSRP of a current beam and a predicted beam as taught by Google. The motivation to combine these references would be to provide performance validation in a beam sweeping or beam refinement procedure [Google p. 6, sec. 3.3].
Regarding claim 31, Liu in view of Karapantelakis in view of OPPO teaches the method of claim 29, however, Liu does not explicitly disclose wherein performing the sector-level sweep procedure, the beam refinement procedure, or both comprises performing the beam refinement.
However, OPPO teaches wherein performing the sector-level sweep procedure, the beam refinement procedure, or both comprises performing the beam refinement [OPPO p. 9, sec. 2.6.2.1: Top-K beams are reported to NW (i.e. beamforming configuration) to perform P2 beam sweeping, and may perform follow-up beam sweeping to determine Rx beam to receive predicted Tx beam (i.e. beam refinement procedure)].
The motivation to combine these references is illustrated in the rejection of claim 29 above.
However, Liu in view of Karapantelakis in view of OPPO does not explicitly disclose performing the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being less than a threshold difference.
However, in a similar field of endeavor, Google teaches performing the beam refinement procedure in accordance with the beamforming configuration and a difference between the second set of parameters and the first set of parameters being less than a threshold difference [Google p. 6, sec. 3.3: if L1-RSRP for predicted beam (i.e. second parameters) is greater than L1-RSRP for the current beam (i.e. first parameters), UE switches to predicted beam; p. 4, sec. 2.2: AI/ML model used for beam refinement, wherein the procedure is used to perform Rx beam sweeping at the UE side (here, e.g., if predicted L1-RSRP – current L1-RSRP is less than a threshold of 0, then the current beam is used for beam sweeping)].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for beam sweeping and beam refinement based on a comparison of RSRP of a current beam and a predicted beam as taught by Google. The motivation to combine these references would be to provide performance validation in a beam sweeping or beam refinement procedure [Google p. 6, sec. 3.3].
Claim(s) 14 and 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Karapantelakis in view of Zhang et al. (US 2025/0293752 A1; “Zhang”).
Regarding claim 14, Liu in view of Karapantelakis teaches the apparatus of claim 1, however, does not explicitly disclose wherein the instructions are further executable by the processor to cause the apparatus to communicate within the first set of time intervals within a first frequency band, wherein determining the first set of parameters is associated with communicating within the first frequency band, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a second frequency band for communications between the first wireless communication device and the second wireless communication device, wherein the second set of parameters comprise the second frequency band, and wherein communicating within the second set of time intervals is performed within the second frequency band.
However, in a similar field of endeavor, Zhang teaches wherein the instructions are further executable by the processor to cause the apparatus to communicate within the first set of time intervals within a first frequency band, wherein determining the first set of parameters is associated with communicating within the first frequency band, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a second frequency band for communications between the first wireless communication device and the second wireless communication device, wherein the second set of parameters comprise the second frequency band, and wherein communicating within the second set of time intervals is performed within the second frequency band [Zhang ¶¶ 0030-0031: best base station beam may therefore be selected for DL transmission using an AI based algorithm so that the UE can identify the best UE beam to accommodate the best base station beam, wherein a FR1 channel may be used to predict a beam for a FR2 channel].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for a channel operating in FR2 based on measured channel conditions on FR1 as taught by Zhang. The motivation to combine these references would be to reduce reference signal measurement overhead in a beam selection operation [Zhang ¶ 0030].
Regarding claim 35, Liu in view of Karapantelakis teaches the method of claim 22, however, does not explicitly disclose further comprising communicating within the first set of time intervals within a first frequency band, wherein determining the first set of parameters is associated with communicating within the first frequency band, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a second frequency band for communications between the first wireless communication device and the second wireless communication device, wherein the second set of parameters comprise the second frequency band, and wherein communicating within the second set of time intervals is performed within the second frequency band.
However, in a similar field of endeavor, Zhang teaches communicating within the first set of time intervals within a first frequency band, wherein determining the first set of parameters is associated with communicating within the first frequency band, wherein predicting the second set of parameters comprises predicting, using the machine learning model, a second frequency band for communications between the first wireless communication device and the second wireless communication device, wherein the second set of parameters comprise the second frequency band, and wherein communicating within the second set of time intervals is performed within the second frequency band [Zhang ¶¶ 0030-0031: best base station beam may therefore be selected for DL transmission using an AI based algorithm so that the UE can identify the best UE beam to accommodate the best base station beam, wherein a FR1 channel may be used to predict a beam for a FR2 channel].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of determining beam configuration for a channel operating in FR2 based on measured channel conditions on FR1 as taught by Zhang. The motivation to combine these references would be to reduce reference signal measurement overhead in a beam selection operation [Zhang ¶ 0030].
Claim(s) 19 and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Karapantelakis in view of Li (US 2025/0300707 A1; “Li”).
Regarding claim 19, Liu in view of Karapantelakis teaches the apparatus of claim 1, however, does not explicitly disclose wherein the instructions are further executable by the processor to cause the apparatus to receive, from the second wireless communication device, a message indicating the machine learning model, wherein determining the first set of parameters, predicting the second set of parameters, or both, is based at least in part on receiving the message indicating the machine learning model.
However, in a similar field of endeavor, Li teaches wherein the instructions are further executable by the processor to cause the apparatus to receive, from the second wireless communication device, a message indicating the machine learning model, wherein determining the first set of parameters, predicting the second set of parameters, or both, is based at least in part on receiving the message indicating the machine learning model [Li ¶ 0071: terminal may receive the AI beam model sent by the network side device, and determine the first receive beam characteristic corresponding to the AI beam model].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of receiving a beam prediction AI model from the network to perform beam prediction as taught by Li. The motivation to combine these references would be to improve the accuracy of beam prediction [Li ¶ 0033].
Regarding claim 40, Liu in view of Karapantelakis teaches the method of claim 22, however, does not explicitly disclose further comprising receiving, from the second wireless communication device, a message indicating the machine learning model, wherein determining the first set of parameters, predicting the second set of parameters, or both, is based at least in part on receiving the message indicating the machine learning model.
However, in a similar field of endeavor, Li teaches receiving, from the second wireless communication device, a message indicating the machine learning model, wherein determining the first set of parameters, predicting the second set of parameters, or both, is based at least in part on receiving the message indicating the machine learning model [Li ¶ 0071: terminal may receive the AI beam model sent by the network side device, and determine the first receive beam characteristic corresponding to the AI beam model].
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model that inputs beam information of past time instances and outputs beam information for future time instances as taught by Liu, with the method of receiving a beam prediction AI model from the network to perform beam prediction as taught by Li. The motivation to combine these references would be to improve the accuracy of beam prediction [Li ¶ 0033].
Allowable Subject Matter
Claims 5, 21, 26, and 42 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.
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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/BRIAN P COX/Primary Examiner, Art Unit 2474
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