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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claim 10 is objected to because of the following informalities: Claim 10 line 3, states “non-serving cell”, the examiner believes this should be amended to -- serving cell --. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because referring to the specification page 14 lines 12 to 22, refers to running program code will be distributed, also, page 15 lines 21 to 22, discloses “and so on.”, also, the specification page 36, lines 9 to 35, refers removable memory, which are deemed non-statutory. “A computer readable medium” needs to be amended to -- A non-transitory computer readable medium --, in order to overcome the 101 rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-9, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US. 2025/0260466 A1) hereinafter Li in view of Cirik et al. (US 2019/0215888 A1) hereinafter Cirik.
Regarding claim 1 – Li does not explicitly disclose determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell.
Cirik discloses determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell, refer to paragraph [0414] - The wireless device may determine if a difference between a first received signal strength (RSS) of the first beam and a second RSS of the second beam exceeds the first value. The difference exceeding the first value may indicate that the second beam is not sufficiently close in quality to the first beam.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li with Cirik to provide Li with determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell.
The suggestion/motivation is that Li discloses refer to paragraph [0065] - a UE 115 may receive one or more of the signals transmitted by the base station 105 in different directions and may report to the base station 105 an indication of the signal that the UE 115 received with a highest signal quality or an otherwise acceptable signal quality compared to other measured beams
The benefit being the Base Station will receive information about the signal quality of the transmitted beams of the Base station.
Li discloses providing the determined difference as an input for a machine learning model, wherein the output of the machine learning model is numerical data or categorical data, determining, based on the output of the machine learning model, that a measurement report should be provided to the network; and providing the measurement report to the network, refer to paragraph [0039] - For example, the second communication device may use the machine learning model (for example, a machine learning model determined at the second communication device or configured for the second communication device by the first communication device) to estimate a probability of the beam change occurring at the future time or over the future duration, and in some examples, the second communication device may compare the estimated probability to a threshold to determine whether the beam change will occur. The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
Regarding claim 2 – Li discloses An apparatus comprising: at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: refer to Figure 8 and paragraph [0137] - the communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be implemented in hardware (for example, in communications management circuitry). The hardware may include a processor, a digital signal processor (DSP), a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some examples, a processor and memory coupled with the processor may be configured to perform one or more of the functions described herein (for example, by executing, by the processor, instructions stored in the memory).
Refer to claim 1 for - determine, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell; provide the determined difference as an input for a machine learning model, wherein the output of the machine learning model is numerical data or categorical data; determine, based on the output of the machine learning model, that a measurement report should be provided to the network; and provide the measurement report to the network.
Regarding claim 4 – Li and Cirik disclose claim 2. Li discloses receive a configuration from the network to train the machine learning model at the user equipment and use at least one of the determined difference and the determined timing advance values to train the machine learning model at the user equipment, refer to paragraph [0040] - The first communication device (for example, the base station) may provide (for example, indicate) a machine learning model, such as via a machine learning model configuration (for example an HMM configuration), to improve procedures for predicting beam changes by increasing the reliability of beam change predictions, such as at the second communication device (for example, the UE). In some implementations, using a machine learning model to predict the probability that the beam change will occur will reduce a miss detection error rate and a false alarm rate associated with beam change predictions and avoid unnecessary changes or operations.
Regarding claim 5 – Li and Cirik disclose claim 2. Li discloses provide an indication of the determined difference and the determined timing advance values to the network for use in training the machine learning model at the network, refer to paragraph [0081] - the network may train one or more machine learning models and configure (for example, transmit an indication for) the UE 115-a to use one or more of the trained models for beam change predictions. In some examples, the network may train, such as through federated learning, a global HMM for beam change predictions or problems associated with beam changes.
Regarding claim 6 – Li and Cirik disclose claim 2. Li discloses provide an indication of the output of the machine learning model in the measurement report, refer to paragraph [0039] - The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
Regarding claim 7 – Li and Cirik disclose claim 2. Li discloses wherein the measurement report comprises an indication of the determined difference, refer to paragraph [0039] - For example, the second communication device may use the machine learning model (for example, a machine learning model determined at the second communication device or configured for the second communication device by the first communication device) to estimate a probability of the beam change occurring at the future time or over the future duration, and in some examples, the second communication device may compare the estimated probability to a threshold to determine whether the beam change will occur. The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
Regarding claim 8 – Li and Cirik disclose claim 2. Li discloses comparing the output of the machine learning model to a threshold value, refer to paragraph [0011] - an indication of a threshold and determining whether the probability satisfies the threshold, where transmitting the status indication may be based on the probability satisfying the threshold, also paragraph [0118] - the UE 115 may recommend (for example indicated) the threshold to the base station 105 prior to being configured or updated with the threshold (for example, or prior to receiving an indication of a machine learning model, or a configuration for a machine learning model, such as an HMM configuration). The threshold may be a value between 0 and 1.
Regarding claim 9 – Li and Cirik disclose claim 2. Li discloses 9. Li discloses wherein the numerical data comprises a probability value, refer to the Abstract - The UE may determine a probability associated with an adjustment of the beam. The probability may indicate whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based on the reference signal. The UE may transmit an indication of the probability associated with the adjustment of the beam to the network node, also, paragraph [0006] - determining a probability associated with an adjustment of the beam, the probability indicating whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based on the reference signal, and transmitting, to the network node, an indication of the probability associated with the adjustment of the beam.
Regarding claim 22 – Li discloses at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: refer to paragraph [0007] - Another innovative aspect of the subject matter described in this disclosure can be implemented in an apparatus for wireless communication at a UE. The apparatus includes a processor, memory coupled with the processor, and instructions stored in the memory. The instructions may be executable by the processor to cause the apparatus to receive, from a network node, a reference signal associated with a beam of a set of beams used for wireless communications at the UE, each beam of the set of beams being associated with a respective beam index, determine a probability associated with an adjustment of the beam
For the below elements please refer to claims 2 and 4 above.
provide a configuration of a machine learning model to a user equipment from a serving cell of a network, wherein the input for the machine learning model is the difference between signal strength for a first beam of the serving cell and signal strength for a second beam of the serving cell determined at the user equipment and the output of the machine learning model is numerical data or categorical data;
and
receive a measurement report from the user equipment, wherein the user equipment determines to provide the measurement report based on the outcome of the machine learning model.
Regarding claim 23 – Li discloses A -- non-transitory -- computer readable medium comprising instructions which, when executed by an apparatus, cause the apparatus to perform at least the following: refer to paragraph [0009] - Another innovative aspect of the subject matter described in this disclosure can be implemented in a non-transitory computer-readable medium storing code for wireless communication at a UE. The code includes instructions executable by a processor to receive, from a network node, a reference signal associated with a beam of a set of beams used for wireless communications at the UE,
For the below elements please refer to claim 2.
determining, at a user equipment, a difference between signal strength for a first beam of a serving cell of a network and signal strength for a second beam of the serving cell; providing the determined difference as an input for a machine learning model, wherein the output of the machine learning model is numerical data or categorical data; determining, based on the output of the machine learning model, that a measurement report should be provided to the network; and providing the measurement report to the network.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Li (same as above) in view of Cirik (same as above) further in view of Prasad et al. (US 2024/0121060 A1) hereinafter Prasad.
Regarding claim 3 – Li and Cirik disclose claim 2. Li and Cirik do not disclose explicitly determine a timing advance value of the serving cell and a timing advance value of at least one non-serving cell and provide the determined timing advance values as an input to the machine learning model.
Prasad discloses determine a timing advance value of the serving cell and a timing advance value of at least one non-serving cell and provide the determined timing advance values as an input to the machine learning model, refer to paragraph [0340] - the wireless device may obtain an index to a lookup table as one output of the one or more outputs of/from the learned model. The lookup table may contain one or more candidate values of the TA value. The lookup table may be predefined/preconfigured within/in/for the wireless device. The lookup table may be indicated in the one or more configuration parameters, also, paragraph [0383] - the wireless device may select (e.g., determine or compute or calculate) the TA value using a learned model. The learned model may be trained with a learning algorithm (e.g., machine learning algorithm, artificial intelligence algorithm, deep learning algorithm). The wireless device may use one or more inputs to the learned model to obtain one or more outputs.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Prasad to provide Li and Cirik with determine a timing advance value of the serving cell and a timing advance value of at least one non-serving cell and provide the determined timing advance values as an input to the machine learning model.
The suggestion/motivation is that Li discloses a Machine Learning model that can predict a beam change, refer to paragraph [0039] - For example, the second communication device may use the machine learning model (for example, a machine learning model determined at the second communication device or configured for the second communication device by the first communication device) to estimate a probability of the beam change occurring at the future time or over the future duration, and in some examples, the second communication device may compare the estimated probability to a threshold to determine whether the beam change will occur. The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
The benefit being the Machine Learning model can predict the TA value based on learned data to aid in CHO.
Claims 10-21 are rejected under 35 U.S.C. 103 as being unpatentable over Li (same as above) in view of Cirik (same as above) further in view of Cui et al. (US 12,096,265 B2) hereinafter Cui.
Regarding claim 10 – Li and Cirik disclose claim 2. Li and Cirik do not explicitly disclose being further configured to determine, at the user equipment, a difference between signal strength for a first beam of at least one serving cell and signal strength for a second beam of the at least one non-serving cell,
Cui discloses being further configured to determine, at the user equipment, a difference between signal strength for a first beam of at least one serving cell and signal strength for a second beam of the at least one non-serving cell, refer to claim 1.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with being further configured to determine, at the user equipment, a difference between signal strength for a first beam of at least one serving cell and signal strength for a second beam of the at least one non-serving cell,
The suggestion/motivation is that Cirik discloses performing measurements on various reference signal strengths, refer to paragraph [0414] - The wireless device may determine if a difference between a first received signal strength (RSS) of the first beam and a second RSS of the second beam exceeds the first value. The difference exceeding the first value may indicate that the second beam is not sufficiently close in quality to the first beam.
The benefit being that the measurements will aid in determining that a handover might be needed before communication between the UE and the serving cell is lost.
Li and Cirik disclose provide the determined difference for the serving cell and the
determined difference for the non-serving cell as an input for a further machine
learning model, wherein the output of the further machine learning model is
numerical data or categorical data, and to determine, based on the output of the
further machine learning model, refer to Li paragraph [0039] - ] - For example, the second communication device may use the machine learning model (for example, a machine learning model determined at the second communication device or configured for the second communication device by the first communication device) to estimate a probability of the beam change occurring at the future time or over the future duration, and in some examples, the second communication device may compare the estimated probability to a threshold to determine whether the beam change will occur. The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
Li and Cirik do not explicitly disclose that a conditional handover procedure should be performed and perform the conditional handover procedure.
Cui discloses that a conditional handover procedure should be performed and perform the conditional handover procedure, refer to claim 1.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with that a conditional handover procedure should be performed and perform the conditional handover procedure.
The suggestion/motivation is that Cirik discloses handover, refer to paragraph [0091] - mobility functions which may comprise at least one of a handover (e.g. intra NR mobility or inter-RAT mobility) and a context transfer; or a wireless device cell selection and reselection and control of cell selection and reselection.
The benefit being that that CHO can be set up ahead of a loss of service between the UE and the serving base station.
Regarding claim 11 - Li and Cirik and Cui disclose claim 10. Li discloses comparing the output of the further machine learning model to a threshold value, refer to paragraph [0011] - an indication of a threshold and determining whether the probability satisfies the threshold, where transmitting the status indication may be based on the probability satisfying the threshold, also paragraph [0118] - the UE 115 may recommend (for example indicated) the threshold to the base station 105 prior to being configured or updated with the threshold (for example, or prior to receiving an indication of a machine learning model, or a configuration for a machine learning model, such as an HMM configuration). The threshold may be a value between 0 and 1.
Regarding claim 12 – Li and Cirik and Cui disclose claim 10. Li and Cirik do not explicitly disclose determine a timing advance value of the serving cell of the network and a timing advance of the at least one non-serving cell and to provide the determined timing advance values as a further input for the further machine learning model.
Cui discloses determine a timing advance value of the serving cell of the network and a timing advance of the at least one non-serving cell and to provide the determined timing advance values as a further input for the further machine learning model, refer to paragraph (103) - the UE requests to synchronize to the T-gNB, the T-gNB allocates uplink transmission resources for the UE and notifies the UE of the time advance (TA), and the UE transmits the RRC connection reconfiguration completing signaling to the T-gNB.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with determine a timing advance value of the serving cell of the network and a timing advance of the at least one non-serving cell and to provide the determined timing advance values as a further input for the further machine learning model.
The suggestion/motivation is that Li discloses a Machine Learning model that can predict a beam change, refer to paragraph [0039] - For example, the second communication device may use the machine learning model (for example, a machine learning model determined at the second communication device or configured for the second communication device by the first communication device) to estimate a probability of the beam change occurring at the future time or over the future duration, and in some examples, the second communication device may compare the estimated probability to a threshold to determine whether the beam change will occur. The second communication device may transmit an indication of the estimated probability (or the beam change prediction) to the first communication device, such that the first communication device may account for or use the beam change prediction indication in signaling to the second communication device or for adjustments for the second communication device.
The benefit being the Machine Learning model can predict the TA value based on learned data to aid in CHO.
Regarding claim 13 – Li and Cirik and Cui disclose claim 10. Li discloses receive a configuration from the network to train the machine learning model at the user equipment and use at least one of the determined difference and the determined timing advance values to train the machine learning model at the user equipment, refer to paragraph [0040] - The first communication device (for example, the base station) may provide (for example, indicate) a machine learning model, such as via a machine learning model configuration (for example an HMM configuration), to improve procedures for predicting beam changes by increasing the reliability of beam change predictions, such as at the second communication device (for example, the UE). In some implementations, using a machine learning model to predict the probability that the beam change will occur will reduce a miss detection error rate and a false alarm rate associated with beam change predictions and avoid unnecessary changes or operations.
Regarding claim 14 – Li and Cirik and Cui disclose claim 12. Li discloses provide an indication of the determined difference and the determined timing advance values to the network for use in training the machine learning model, refer to paragraph [0081] - the network may train one or more machine learning models and configure (for example, transmit an indication for) the UE 115-a to use one or more of the trained models for beam change predictions. In some examples, the network may train, such as through federated learning, a global HMM for beam change predictions or problems associated with beam changes.
Regarding claim 15 – Li and Cirik disclose claim 10. Li and Cirik do not explicitly disclose wherein the first beam comprises a channel state information reference signal and the second beam comprises a synchronization signal or a physical broadcast channel.
Cui discloses wherein the first beam comprises a channel state information reference signal and the second beam comprises a synchronization signal or a physical broadcast channel, refer to Figure 2 and paragraph (5) - The processing circuitry is configured to: measure, based on acquired measurement configuration, new radio synchronized signals (NR-SSs) from a serving cell and one or more target cells, to acquire a first measurement result; measure, based on the measurement configuration, channel state information reference signals (CSI-RSs) from the serving cell and the one or more target cells, to acquire a second measurement result; and generate a measurement report comprising the first measurement result and the second measurement result
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with wherein the first beam comprises a channel state information reference signal and the second beam comprises a synchronization signal or a physical broadcast channel.
The suggestion/motivation is that Cirik discloses refer to paragraph [0363] - The wireless device 3101 may select an RS (e.g., the RS may be associated with a SSB or CSI-RS) as the at least one candidate beam, for example, if the RSRP of the RS is higher than a threshold. The wireless device 3101 may select a new beam identification RS (e.g., SSB only, CSI only, or SSB+CSI-RS) as a new candidate beam, for example, if the RSRP of the new beam identification RS is higher than a threshold.
The benefit being the difference between signal strength will be between the CSI-RS of the serving cell and the SSB of a beam for the target cell.
Regarding claim 16 – Li and Cirik and Cui disclose claim 15. Li and Cirik do not explicitly disclose further configured to receive an indication of a first beam index and a second beam index and to determine the first beam and the second beam based on the indication of the first beam index and the second beam index.
Cui discloses further configured to receive an indication of a first beam index and a second beam index and to determine the first beam and the second beam based on the indication of the first beam index and the second beam index, refer to paragraph (69) - it can be configured that there is a predetermined relationship between the beam indexes of the other beams described above and the beam index of the selected beam, so that the base station of the handover target cell determines the beam selected by the user based on the received access request, also paragraph (118).
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with further configured to receive an indication of a first beam index and a second beam index and to determine the first beam and the second beam based on the indication of the first beam index and the second beam index.
The suggestion/motivation is that Cirik discloses refer to paragraph [0309] - One or more SS blocks, one or more CSI-RS resources, and/or one or more demodulation reference signals (DM-RSs) of a PBCH may be used as a RS for measuring a quality of a beam pair link. Each of the one or more CSI-RS resources may be associated with a CSI-RS resource index (CRI).
The benefit being the indexing will provide faster implementation of the CHO.
Regarding claim 17 – Li and Cirik and Cui disclose claim 16. Li and Cirik do not explicitly disclose wherein the measurement report comprises an indication of the difference between signal strength for a first beam of the serving cell and signal strength for a second beam of the serving cell determined at the user equipment and an indication of the difference to a non-serving cell of the network.
Cui discloses wherein the measurement report comprises an indication of the difference between signal strength for a first beam of the serving cell and signal strength for a second beam of the serving cell determined at the user equipment and an indication of the difference to a non-serving cell of the network, refer to claim 1.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with disclose wherein the measurement report comprises an indication of the difference between signal strength for a first beam of the serving cell and signal strength for a second beam of the serving cell determined at the user equipment and an indication of the difference to a non-serving cell of the network.
The suggestion/motivation is that Cirik discloses refer to Figure 46 and paragraph [0414] - The wireless device may determine if a difference between a first received signal strength (RSS) of the first beam and a second RSS of the second beam exceeds the first value.
The benefit being the target cell will provide faster implementation of the CHO.
Regarding claim 18 – Li and Cirik and Cui disclose claim 17. Li and Cirik do not explicitly disclose the indication to the non-serving cell in a handover request message.
Cui discloses the indication to the non-serving cell in a handover request message, refer to Figure 7 (#4.) and paragraph (91) - The acquiring unit 202 is further configured to acquire an access request from the user, and the handover unit 203 determines the beam selected by the user based on the access request.
At the time of the invention, it would have been obvious to an ordinary person of skill in the art, prior to the effective date of the application, to combine Li and Cirik with Cui to provide Li and Cirik with the indication to the non-serving cell in a handover request message.
The suggestion/motivation is that Cirik discloses paragraph (0095) - At RRC connection establishment/re-establishment/handover procedure, one serving cell may provide NAS mobility information, and at RRC connection re-establishment/handover, one serving cell may provide a security input. This cell may be referred to as the PCell. Depending on the capabilities of the wireless device, SCells may be configured to form together with the PCell a set of serving cells. The configured set of serving cells for the wireless device may comprise one PCell and one or more SCells.
The benefit being the request message will provide faster implementation of the CHO.
Regarding claim 19 – Li and Cirik and Cui disclose claim 17. Li and Cirik disclose to provide a configuration from the network to the user equipment to train the machine learning model at the user equipment, refer to Li - discloses receive a configuration from the network to train the machine learning model at the user equipment and use at least one of the determined difference and the determined timing advance values to train the machine learning model at the user equipment, refer to paragraph [0040] - The first communication device (for example, the base station) may provide (for example, indicate) a machine learning model, such as via a machine learning model configuration (for example an HMM configuration), to improve procedures for predicting beam changes by increasing the reliability of beam change predictions, such as at the second communication device (for example, the UE). In some implementations, using a machine learning model to predict the probability that the beam change will occur will reduce a miss detection error rate and a false alarm rate associated with beam change predictions and avoid unnecessary changes or operations.
Regrading claim 20 – Li and Cirik and Cui disclose claim 17. Li and Cirik disclose receive an indication of the determined difference and timing advance values for the serving cells and non-serving cells for use in training the machine learning model at the network and use the received difference and the received timing advance values to train the machine learning model at the network – refer to Li - paragraph [0081] - the network may train one or more machine learning models and configure (for example, transmit an indication for) the UE 115-a to use one or more of the trained models for beam change predictions. In some examples, the network may train, such as through federated learning, a global HMM for beam change predictions or problems associated with beam changes.
Regarding claim 21 – Li and Cirik and Cui disclose claim 17. Li and Cirik disclose wherein the numerical data comprises a probability value, refer to Li - refer to the Abstract - The UE may determine a probability associated with an adjustment of the beam. The probability may indicate whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based on the reference signal. The UE may transmit an indication of the probability associated with the adjustment of the beam to the network node, also, paragraph [0006] - determining a probability associated with an adjustment of the beam, the probability indicating whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based on the reference signal, and transmitting, to the network node, an indication of the probability associated with the adjustment of the beam.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guan et al. (US 2025/0168682 A1) discloses methods, devices, and computer readable medium for communication.
Wang et al. (US 2025/0365710 A1) discloses resource configuration method for SSB beams, network device, terminal device, and storage medium.
Marupaduga (US 11,659,543 B1) discloses dynamic SSB bean allocation.
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John Pezzlo
30 December 2025
/John Pezzlo/
Primary Examiner, Art Unit 2465B