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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-7, 9-11, and 13-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. US (2026/0012829).
Regarding Claim 1, Wang discloses a method performed by a terminal in a wireless communication system (see Figures 1-2 & Para’s [0044-0046] i.e., UE 102 & [0055]), the method comprising: receiving, from a base station (see Figure 1 i.e., base station 104, Figure 3 i.e., network equipment 300 & Para’s [0047-0048] & [0062]), a first message comprising configuration information on measurement associated with artificial intelligence (AL) or machine learning (ML) model, (see Fig. 4B i.e., AI model 420 & Para’s [0066] i.e., the gNB may configure an RRC_CONNECTED UE to perform measurements. The network may configure the UE to report the measurement results in accordance with the measurement configuration…the measurement configuration is provided by means of dedicated signaling (i.e., “first message comprising configuration information”), [0067] i.e., the network may configure the UE to report the measurement based on CSI-RS resources, [0068-0070] i.e., the measurement configuration includes the following parameters: (e.g., reference signal (RS) frequency/time location) on which the UE shall perform the measurements (i.e., “configuration information”), [0079-0080] i.e., Fig. 4B is a schematic diagram illustrating an example of using AI/ML approach for beam measurement, [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported & [0082] i.e., In this case, if the AI model 420 is trained and deployed at the UE side, the gNB may potentially configure less CSI-RS resources (i.e., configured CSI-RS resources may be configuration information on measurement associated with AI model), & [0146] i.e., The indication on the reference signal to be measured on (i.e., “configuration information”) of the model needs, such as the CSI-RS/SSB for the AI model for beam management)
identifying output of the AL or ML model; (see Fig. 4B i.e., Best Tx beam & corresponding L1-RSRP 424 output from the AI model 420 & Para’s [0079-0080] i.e., A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection & [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported, [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input, & [0157] measurement result of output of a monitored AI model)
and transmitting, to the base station, a second message comprising information on the output of the AL or ML model, (see Para’s [0081] Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “information on the output”) and reported (i.e., the best Tx beam indices & L1-RSRP 424 will be reported (i.e., “second message”) to the network (i.e., “the base station”)), [0148-0151] i.e., the UE reports measurement results such as the output of the AI model, & [0157-0158] i.e., the monitoring report may include measurement result of output of a monitored AI model)
Regarding Claim 2, Wang discloses the method of claim 1, wherein the output of the AL or ML model (see Fig. 4B, output 424 of AI model 420) is based on performance metrics (see Fig. 4B i.e., L1-RSRP measurement results 422) associated with at least one of beam prediction accuracy, information on link quality, or a difference of channel information, (see Para [0081] i.e., Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 (i.e., “information on link quality”) may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 3, Wang discloses the method of claim 1, wherein the output of the AL or ML model is at least one of information on a predicted beam, information on confidence, or information on probability, (see Fig. 4B i.e., AI model 420 output is the Best Tx Beams & Para’s [0080] i.e., beam prediction…A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection, [0081] i.e., With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported, & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 5, Wang discloses a method performed by a base station in a wireless communication system (see Figure 1 i.e., base station 104, Figure 3 i.e., network equipment 300 & Para’s [0047-0048] & [0062]), the method comprising: transmitting, to a terminal (see Figures 1-2 & Para’s [0044-0046] i.e., UE 102), a first message comprising configuration information on measurement associated with artificial intelligence (AL) or machine learning (ML) model; (see Fig. 4B i.e., AI model 420 & Para’s [0066] i.e., the gNB may configure an RRC_CONNECTED UE to perform measurements. The network may configure the UE to report the measurement results in accordance with the measurement configuration…the measurement configuration is provided by means of dedicated signaling (i.e., “first message comprising configuration information”), [0067] i.e., the network may configure the UE to report the measurement based on CSI-RS resources, [0068-0070] i.e., the measurement configuration includes the following parameters: (e.g., reference signal (RS) frequency/time location) on which the UE shall perform the measurements (i.e., “configuration information”), [0079-0080] i.e., Fig. 4B is a schematic diagram illustrating an example of using AI/ML approach for beam measurement, [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported & [0082] i.e., In this case, if the AI model 420 is trained and deployed at the UE side, the gNB may potentially configure less CSI-RS resources (i.e., configured CSI-RS resources may be configuration information on measurement associated with AI model), & [0146] i.e., The indication on the reference signal to be measured on (i.e., “configuration information”) of the model needs, such as the CSI-RS/SSB for the AI model for beam management)
and receiving, from the terminal, a second message comprising information on output of the AL or ML model, (see Fig. 4B i.e., Best Tx beam & corresponding L1-RSRP 424 output from the AI model 420 & Para’s [0081] Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “information on the output”) and reported (i.e., the best Tx beam indices & L1-RSRP 424 will be reported (i.e., “second message”) to the network (i.e., “the base station”)), [0148-0151] i.e., the UE reports measurement results such as the output of the AI model, & [0157-0158] i.e., the monitoring report may include measurement result of output of a monitored AI model)
Regarding Claim 6, Wang discloses the method of claim 5, wherein the output of the AL or ML model (see Fig. 4B, output 424 of AI model 420) is based on performance metrics (see Fig. 4B i.e., L1-RSRP measurement results 422) associated with at least one of beam prediction accuracy, information on link quality, or a difference of channel information. (see Para [0081] i.e., Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 (i.e., “information on link quality”) may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 7, Wang discloses the method of claim 5, wherein the output of the AL or ML model is at least one of information on a predicted beam, information on confidence, or information on probability. (see Fig. 4B i.e., AI model 420 output is the Best Tx Beams & Para’s [0080] i.e., beam prediction…A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection, [0081] i.e., With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported, & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 9, Wang discloses a terminal (see Fig. 2 i.e., user equipment 200 & Para’s [0044-0046] & [0055]) in a wireless communication system (see Fig. 1 & Para’s [0044-0046]), the terminal comprising: a transceiver (see Fig. 2 i.e., transceiver 210 & Para’s [0055] & [0060-0061]); and at least one processor coupled with the transceiver (see Fig. 2 i.e., processor 202 & Para [0056]) and configured to: receive, from a base station (see Figure 1 i.e., base station 104, Figure 3 i.e., network equipment 300 & Para’s [0047-0048] & [0062]), a first message comprising configuration information on measurement associated with artificial intelligence (AL) or machine learning (ML) model, (see Fig. 4B i.e., AI model 420 & Para’s [0066] i.e., the gNB may configure an RRC_CONNECTED UE to perform measurements. The network may configure the UE to report the measurement results in accordance with the measurement configuration…the measurement configuration is provided by means of dedicated signaling (i.e., “first message comprising configuration information”), [0067] i.e., the network may configure the UE to report the measurement based on CSI-RS resources, [0068-0070] i.e., the measurement configuration includes the following parameters: (e.g., reference signal (RS) frequency/time location) on which the UE shall perform the measurements (i.e., “configuration information”), [0079-0080] i.e., Fig. 4B is a schematic diagram illustrating an example of using AI/ML approach for beam measurement, [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported & [0082] i.e., In this case, if the AI model 420 is trained and deployed at the UE side, the gNB may potentially configure less CSI-RS resources (i.e., configured CSI-RS resources may be configuration information on measurement associated with AI model), & [0146] i.e., The indication on the reference signal to be measured on (i.e., “configuration information”) of the model needs, such as the CSI-RS/SSB for the AI model for beam management)
identify output of the AL or ML model; (see Fig. 4B i.e., Best Tx beam & corresponding L1-RSRP 424 output from the AI model 420 & Para’s [0079-0080] i.e., A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection & [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported, [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input, & [0157] measurement result of output of a monitored AI model)
and transmit, to the base station, a second message comprising information on the output of the AL or ML model, (see Para’s [0081] Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “information on the output”) and reported (i.e., the best Tx beam indices & L1-RSRP 424 will be reported (i.e., “second message”) to the network (i.e., “the base station”)), [0148-0151] i.e., the UE reports measurement results such as the output of the AI model, & [0157-0158] i.e., the monitoring report may include measurement result of output of a monitored AI model)
Regarding Claim 10, Wang discloses the terminal of claim 9, wherein the output of the AL or ML model (see Fig. 4B, output 424 of AI model 420) is based on performance metrics (see Fig. 4B i.e., L1-RSRP measurement results 422) associated with at least one of beam prediction accuracy, information on link quality, or a difference of channel information. (see Para [0081] i.e., Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 (i.e., “information on link quality”) may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 11, Wang discloses the terminal of claim 9, wherein the output of the AL or ML model is at least one of information on a predicted beam, information on confidence, or information on probability. (see Fig. 4B i.e., AI model 420 output is the Best Tx Beams & Para’s [0080] i.e., beam prediction…A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection, [0081] i.e., With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported, & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 13, Wang discloses a base station (see Figure 1 i.e., base station 104, Figure 3 i.e., network equipment 300 & Para’s [0047-0048] & [0062]) in a wireless communication system (see Fig. 1 & Para [0044]), the base station comprising: a transceiver (see Fig. 3 i.e., transceiver 310 & Para’s [0062-0065]); and at least one processor coupled with the transceiver (see Fig. 3 i.e., processor 302 & Para [0063]) and configured to: transmit, to a terminal (see Figures 1-2 & Para’s [0044-0046] i.e., UE 102), a first message comprising configuration information on measurement associated with artificial intelligence (AL) or machine learning (ML) model; (see Fig. 4B i.e., AI model 420 & Para’s [0066] i.e., the gNB may configure an RRC_CONNECTED UE to perform measurements. The network may configure the UE to report the measurement results in accordance with the measurement configuration…the measurement configuration is provided by means of dedicated signaling (i.e., “first message comprising configuration information”), [0067] i.e., the network may configure the UE to report the measurement based on CSI-RS resources, [0068-0070] i.e., the measurement configuration includes the following parameters: (e.g., reference signal (RS) frequency/time location) on which the UE shall perform the measurements (i.e., “configuration information”), [0079-0080] i.e., Fig. 4B is a schematic diagram illustrating an example of using AI/ML approach for beam measurement, [0081] i.e., Each Tx beam and each Rx beam form a beam pair represented by a circle in Fig. 4B. Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported & [0082] i.e., In this case, if the AI model 420 is trained and deployed at the UE side, the gNB may potentially configure less CSI-RS resources (i.e., configured CSI-RS resources may be configuration information on measurement associated with AI model), & [0146] i.e., The indication on the reference signal to be measured on (i.e., “configuration information”) of the model needs, such as the CSI-RS/SSB for the AI model for beam management)
and receive, from the terminal, a second message comprising information on output of the AL or ML model, (see Fig. 4B i.e., Best Tx beam & corresponding L1-RSRP 424 output from the AI model 420 & Para’s [0081] Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16, as illustrated in the example, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “information on the output”) and reported (i.e., the best Tx beam indices & L1-RSRP 424 will be reported (i.e., “second message”) to the network (i.e., “the base station”)), [0148-0151] i.e., the UE reports measurement results such as the output of the AI model, & [0157-0158] i.e., the monitoring report may include measurement result of output of a monitored AI model)
Regarding Claim 14, Wang discloses the base station of claim 13, wherein the output of the AL or ML model (see Fig. 4B, output 424 of AI model 420) is based on performance metrics (see Fig. 4B i.e., L1-RSRP measurement results 422) associated with at least one of beam prediction accuracy, information on link quality, or a difference of channel information. (see Para [0081] i.e., Four of the beam pairs that are shaded may be measured, and the L1-RSRP measurement results 422 (i.e., “information on link quality”) may be inputted into an AI model 420. With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained (i.e., “output”) and reported & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
Regarding Claim 15, Wang discloses the base station of claim 13, wherein the output of the AL or ML model is at least one of information on a predicted beam, information on confidence, or information on probability, (see Fig. 4B i.e., AI model 420 output is the Best Tx Beams & Para’s [0080] i.e., beam prediction…A typical deployment with an AI/ML approach is to apply an AI model to assist the best beams selection, [0081] i.e., With the trained AI model 420, using the measurement results from some resources, e.g., 4 from 16 as illustrated, the best Tx beam indices and the corresponding L1-RSRP 424 may be obtained and reported, & [0114] i.e., AI models used for beam prediction (i.e., “output”) requiring measured beams as input)
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 4, 8, 12, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US (2026/0012829) in view of Ryden et al. US (2024/0049003).
Regarding Claims 4 and 12, Wang discloses the method and terminal of claims 1 and 9, but does not disclose the claim feature of further comprising: receiving, from the base station, a third message comprising information on model management indication. However the claim feature would be rendered obvious in view of Ryden et al. US (2024/0049003).
Ryden discloses receiving, form the base station, a third message comprising information on model management indication (In light of the applicants specification in Para’s [00249-00250] i.e., the model management indication information may include at least one of the following: information indicating model updating (Ryden, see Fig. 14 & Para’s [0055-0056] & [0142] i.e., a user device requests a model update in step 1402, and receives a model update (i.e., “third message”) in step 1403).
(Ryden suggests the UE can request a new model update when the UE is not in the area where the model is valid or when the model has expired for obtaining a valid model and an updated model may be configured by the network to reflect evolving network conditions such as changes to the communication network environment, (see Para’s [0141-0142])).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date for the terminal which communicates with the base station as disclosed in Wang to include receiving, from the base station, a third message comprising information on model management indication as disclosed in the teachings of Ryden, because the motivation lies in Ryden that the UE can request a new model update when the UE is not in the area where the model is valid or when the model has expired for obtaining a valid model and an updated model may be configured by the network to reflect evolving network conditions such as changes to the communication network environment.
Regarding Claims 8 and 16, the claims are directed towards a method performed by a base station and a base station which perform the same claim feature as claim 4 with respect to transmitting the third message by the base station. Therefore claims 8 and 16 are rejected as obvious over the combination of Wang in view of Ryden as in claim 4.
Conclusion
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/ADNAN BAIG/Primary Examiner, Art Unit 2461