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 .
DETAILED ACTION
This action is responsive to the Application filed on 4/19/2023. Claims 1-20 are pending in the case. Claims 1, 14, and 18 are independent claims.
Examiner’s Note:
The claims mentioned “training” and “model” and “learning model” but does not specifically go into details as how the data is being trained or learned. Under BRI, the Examiner will interpret these terms as software models that can process certain data.
Claim Rejections - 35 USC § 102
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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al (US 20210235407 A1).
Referring claim 1, Zhang discloses a method, comprising:
broadcasting, by a radio access network node comprising a processor, a learning model configuration information block message comprising a training configuration resource indication that is indicative of a training configuration resource usable to broadcast, by the radio access network node, a learning model training configuration; ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.”) and
broadcasting, by the radio access network node, the learning model training configuration according to the training configuration resource. ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.”)
Referring claim 2, Zhang discloses the method of claim 1, wherein the learning model training configuration comprises a training action indication indicative of a training action performable by an idle user equipment. ([0052] of Zhang, idle mode)
Referring claim 3, Zhang discloses the method of claim 1, wherein the learning model training configuration comprises a training action indication indicative of a training action, wherein the training action corresponds to a radio function learning model, wherein performing the training action is to result in a determined radio function parameter value corresponding to the radio function learning model, (the Specification is not clear on what is “radio function parameter value”, under BRI, it is data related to that radio function model. Here, [0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”) and wherein the learning model configuration information block message further comprises a training result resource indication that is indicative of a training result resource usable to transmit the determined radio function parameter value, ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”) the method further comprising:
transmitting, by the radio access network node to the idle user equipment, the determined radio function parameter value according to the training result resource. ([0090] of Zhang, “serving base station 105-d may configure table 320 such that the table 320 accounts for possible conditions UE 115-b may encounter based on the current trajectory of the UE 115. Sometime after receiving message 315-a, UE 115-a may switch to a non-connected mode such as an idle or inactive mode. Upon receiving the table 320 or some other indicator of the set of TA commands and associated conditions, UE 115-b may determine the current condition of UE 115-b. For example, UE 115-b may determine the RSRP of the serving base station 105-d and one or more neighboring base stations 105, or the relative delay between serving base station 105-d and another base station 105, or the position of UE 115-b, or a combination thereof.”)
Referring claim 4, Zhang discloses the method of claim 2, wherein the training action corresponds to a radio function learning model, wherein performing the training action is to result in a determined radio function parameter value corresponding to the radio function learning model, the method further comprising:
receiving, by the radio access network node from the idle user equipment, a radio resource control signal message comprising the determined radio function parameter value; ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”)and
establishing, by the radio access network node using the determined radio function parameter value received from the idle user equipment in the radio resource control signal message, a connection with the idle user equipment, as a result of which the idle user equipment becomes a connected user equipment. ([0079] of Zhang, “To mitigate power consumption and transmission delay associated with a UE transmitting uplink data transmissions in idle mode, a UE may be configured to more frequently have a valid TA to allow the UE to transmit using PUR. In some cases, a base station may configure a UE with one or more TA commands and one or more conditions associated with each TA command. For example, the base station may transmit one or more TA commands and one or more conditions to the UE, where each TA command may have a corresponding set of conditions. The UE may identify the current condition of the UE and determine whether the current condition of the UE matches one of the sets of conditions indicated by the base station. The UE may select a TA command based on finding a match and apply the valid TA of the TA command to transmit an uplink PUR transmission. Additionally or alternatively, the base station may transmit neural network parameters to the UE that indicate a neural network model. The UE may use the neural network model to determine a TA command associated with a valid TA based on the current condition of the UE.”)
Referring claim 5, Zhang discloses the method of claim 4, wherein the determined radio function parameter value comprises a timing advance value corresponding to a timing advance corresponding to the radio access network node with respect to the idle user equipment. ([00082] of Zhang, “ In some implementations, a UE 115 may receive a TA command and one or more conditions from a base station 105 (e.g., serving base station 105-a) while the UE 115 is in a connected mode. The TA command may indicate a TA value that the UE may use for transmissions in the connected mode and transmissions in a non-connected mode (e.g., an idle mode), where the received conditions may indicate under what conditions the TA is valid.”)
Referring claim 6, Zhang discloses the method of claim 4, wherein the determined radio function parameter value comprises a best serving beam indication corresponding to a beam associated with the radio access network node having a higher signal strength than other signal strengths associated with other beams, other than the beam, corresponding to the radio access network node. ([0078] of Zhang)
Referring claim 7, Zhang discloses the method of claim 2, wherein the learning model training configuration comprises a training resource indication that is indicative to the idle user equipment of a training resource usable to perform, by the idle user equipment, the training action. (The specification does not clearly define what is “training resource indication”, under BRI, it is interpreted as whether the idle user equipment is idle or not. [00082] of Zhang, “ In some implementations, a UE 115 may receive a TA command and one or more conditions from a base station 105 (e.g., serving base station 105-a) while the UE 115 is in a connected mode. The TA command may indicate a TA value that the UE may use for transmissions in the connected mode and transmissions in a non-connected mode (e.g., an idle mode), where the received conditions may indicate under what conditions the TA is valid.”)
Referring claim 8, Zhang discloses the method of claim 7, wherein the radio access network node is a first radio access network node, the method further comprising:
receiving, by the first radio access network node from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, a non-training resource indication that is indicative to the first radio access network node of a non-training resource to be reserved by the second radio access network node and usable by the second radio access network node to conduct non-training operations; ([0016] of Zhang, “In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, determining the TA command further may include operations, features, means, or instructions for comparing the current condition of the UE with one or more of the sets of conditions, where each set of conditions includes a first reference signal received power (RSRP) range associated with a serving base station, a second RSRP range associated with one or more neighboring base stations, or a combination thereof, and selecting the TA command based on the set of conditions that includes the current condition of the UE.” Hence, the second RSRP range is associated with different neighboring base stations which don’t have to be included with the first RSRP ranged stations to the base station for data processing purposes/training purposes)and
scheduling, by the first radio access network node, the training resource to avoid overlap of the training resource corresponding to the first radio access network node with the non-training resource corresponding to the second radio access network node. ([0059] of Zhang, “The UEs 115 may be dispersed throughout a coverage area 110 of the wireless communications system 100, and each UE 115 may be stationary, or mobile, or both at different times. The UEs 115 may be devices in different forms or having different capabilities. Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be able to communicate with various types of devices, such as other UEs 115, the base stations 105, or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment), as shown in FIG. 1.”)
Referring claim 9, Zhang discloses the method of claim 1, wherein the radio access network node is a first radio access network node, wherein the learning model training configuration comprises a training action indication indicative of a training action to be performed by at least one idle user equipment with respect to the first radio access network node to result in a first determined learning model parameter value, the method further comprising:
receiving, by the first radio access network node from the at least one idle user equipment, the first determined learning model parameter value; ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”)
receiving, by the first radio access network node from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, a second determined learning model parameter value, wherein the training action was performed by at least one of the at least one idle user equipment with respect to the second radio access network node to result in the second determined learning model parameter value; ([0087]-[0094] of Zhang, conditions related to serving base stations and neighboring base stations, such as “he condition may be a relative delay between a serving base station 105-d and a neighboring base station 105. For example, a first TA command (e.g., TA command 0) may be associated with a first relative delay range between a serving base station 105-d and a neighboring base station 105 (e.g., [0, T0]). In another example, a second TA command (e.g., TA command 1) may be associated with a second relative delay range between a serving base station 105-d and a neighboring base station 105 (e.g., [T0, T1]). The neighboring base stations 105 associated with the first and second TA commands may be the same or different. Additionally or alternatively, the condition may be a positional range of the UE 115. In some cases, the position of the UE 115 may be expressed based on the position of the UE 115 within a cell, such as positions 325-a, 325-b, or 325-c, or may be expressed based on the position on the UEs position in relation to one or more base stations 105, or a combination thereof. In some cases, a first TA command (e.g., TA command 0) may be associated with a first positional range (e.g., positioning range 0) and a second TA command (e.g., TA command 1) may be associated with a second positional range (e.g., positioning range 1)”)
determining, by the first radio access network node, a composite determined learning model parameter value based on the first determined learning model parameter value and based on the second determined learning model parameter value; ([0087]-[0094] of Zhang)and
broadcasting, by the first radio access network node to the at least one idle user equipment via a composite result information block message, the composite determined learning model parameter value. ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”)
Referring claim 10, Zhang discloses the method of claim 9, wherein the training action corresponds to a radio function learning model, and wherein the composite determined learning model parameter value is usable by the at least one idle user equipment to train the radio function learning model to result in a trained learning model at the at least one idle user equipment. ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”)
Referring claim 11, Zhang discloses the method of claim 10, further comprising:
receiving, by the first radio access network node from the at least one idle user equipment, a connection request message, comprising a performance indicator estimated by the at the at least one idle user equipment using the trained learning model to result in an estimated performance indicator; ([0099] of Zhang, “determining the availability of the TA command may be performed when the UE 115 is in an idle mode with respect to the base station 105. In some cases, the message may be received when the UE 115 is in a connected mode with respect to the base station 105. In some cases, the uplink transmission may be an uplink signal or uplink channel transmitted on PUR based on a PUR configuration. In some cases, the uplink transmission may be a msgA transmission included a small data transmission, where the msgA transmission may be associated with a 2-step random access procedure, and where the msgA transmission is transmitted using PUR when the TA command is available. In some cases, the UE 115 may avoid transmitting the uplink transmission in an absence of the TA command and prepare a msg3 transmission including a small data transmission in connection with a four-step random access procedure, and transmit the msg3 transmission to a target base station 105 based on a timing determined from the four-step random access procedure”) and
based on the estimated performance indicator, establishing a connection with the idle user equipment, as a result of which the idle user equipment becomes a connected user equipment with respect to the first radio access network node. ([0079] of Zhang, “To mitigate power consumption and transmission delay associated with a UE transmitting uplink data transmissions in idle mode, a UE may be configured to more frequently have a valid TA to allow the UE to transmit using PUR. In some cases, a base station may configure a UE with one or more TA commands and one or more conditions associated with each TA command. For example, the base station may transmit one or more TA commands and one or more conditions to the UE, where each TA command may have a corresponding set of conditions. The UE may identify the current condition of the UE and determine whether the current condition of the UE matches one of the sets of conditions indicated by the base station. The UE may select a TA command based on finding a match and apply the valid TA of the TA command to transmit an uplink PUR transmission. Additionally or alternatively, the base station may transmit neural network parameters to the UE that indicate a neural network model. The UE may use the neural network model to determine a TA command associated with a valid TA based on the current condition of the UE.”)
Referring claim 12, Zhang discloses the method of claim 1, wherein the learning model configuration information block message is a system information block message. ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.”)
Referring claim 13, Zhang discloses the method of claim 1, wherein the learning model configuration information block message is a master information block message. ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.” here the service base station is the master base station)
Referring claim 14, Zhang discloses a first radio access network node, comprising: a processor configured to:
receive, from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, a non-training resource indication that is indicative to the first radio access network node of a non-training resource to be used by the second radio access network node to conduct a non-training operation; ([0016] of Zhang, “In some examples of the method, apparatuses, and non-transitory computer-readable medium described herein, determining the TA command further may include operations, features, means, or instructions for comparing the current condition of the UE with one or more of the sets of conditions, where each set of conditions includes a first reference signal received power (RSRP) range associated with a serving base station, a second RSRP range associated with one or more neighboring base stations, or a combination thereof, and selecting the TA command based on the set of conditions that includes the current condition of the UE.” Hence, the second RSRP range is associated with different neighboring base stations which don’t have to be included with the first RSRP ranged stations to the base station for data processing purposes/training purposes)
schedule a training resource, to be used by at least one idle mode user equipment to perform a training action with respect to the first radio access network node, as a result of which the training resource and the non-training resource are non-overlapping; ([0059] of Zhang, “The UEs 115 may be dispersed throughout a coverage area 110 of the wireless communications system 100, and each UE 115 may be stationary, or mobile, or both at different times. The UEs 115 may be devices in different forms or having different capabilities. Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be able to communicate with various types of devices, such as other UEs 115, the base stations 105, or network equipment (e.g., core network nodes, relay devices, integrated access and backhaul (IAB) nodes, or other network equipment), as shown in FIG. 1.”)
broadcast a master information block message comprising a training configuration resource indication that is indicative of a training configuration resource to be used to broadcast, by the first radio access network node, a learning model training configuration; ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.” here the service base station is the master base station) and
broadcast the learning model training configuration according to the training configuration resource. ([0093] of Zhang, “In some implementations, serving base station 105-g may transmit one or more neural network parameters in message 415-a via downlink transmission 405 to UE 115-c. The neural network parameters may indicate a neural network model 420 that UE 115-c may use to determine a valid TA based on the current condition of the UE 115-a.” here the service base station is the master base station)
Referring claim 15, Zhang discloses the first radio access network node of claim 14, wherein the learning model training configuration comprises a training resource indication that is indicative to the at least one idle mode user equipment of the training resource to be used to perform the training action by the at least one idle mode user equipment. ([0079] of Zhang, “To mitigate power consumption and transmission delay associated with a UE transmitting uplink data transmissions in idle mode, a UE may be configured to more frequently have a valid TA to allow the UE to transmit using PUR. In some cases, a base station may configure a UE with one or more TA commands and one or more conditions associated with each TA command. For example, the base station may transmit one or more TA commands and one or more conditions to the UE, where each TA command may have a corresponding set of conditions. The UE may identify the current condition of the UE and determine whether the current condition of the UE matches one of the sets of conditions indicated by the base station. The UE may select a TA command based on finding a match and apply the valid TA of the TA command to transmit an uplink PUR transmission. Additionally or alternatively, the base station may transmit neural network parameters to the UE that indicate a neural network model. The UE may use the neural network model to determine a TA command associated with a valid TA based on the current condition of the UE.”)
Referring claim 16, Zhang discloses the first radio access network node of claim 14, wherein the master information block message further comprises a training result resource indication that is indicative of a training result resource to be used by the at least one idle mode user equipment to receive, from the first radio access network node, a training result that results from performing, by the at least one idle mode user equipment, the training action. ([0079] of Zhang, “To mitigate power consumption and transmission delay associated with a UE transmitting uplink data transmissions in idle mode, a UE may be configured to more frequently have a valid TA to allow the UE to transmit using PUR. In some cases, a base station may configure a UE with one or more TA commands and one or more conditions associated with each TA command. For example, the base station may transmit one or more TA commands and one or more conditions to the UE, where each TA command may have a corresponding set of conditions. The UE may identify the current condition of the UE and determine whether the current condition of the UE matches one of the sets of conditions indicated by the base station. The UE may select a TA command based on finding a match and apply the valid TA of the TA command to transmit an uplink PUR transmission. Additionally or alternatively, the base station may transmit neural network parameters to the UE that indicate a neural network model. The UE may use the neural network model to determine a TA command associated with a valid TA based on the current condition of the UE.”)
Referring claim 17, Zhang discloses the first radio access network node of claim 14, wherein the processor is further configured to: determine a training result that results from performing, by the at least one idle mode user equipment, the training action; and based on the training result, establishing a connection with the at least one idle mode user equipment, as a result of which the at least one idle mode user equipment becomes an at least one connected mode user equipment. ([0079] of Zhang, “To mitigate power consumption and transmission delay associated with a UE transmitting uplink data transmissions in idle mode, a UE may be configured to more frequently have a valid TA to allow the UE to transmit using PUR. In some cases, a base station may configure a UE with one or more TA commands and one or more conditions associated with each TA command. For example, the base station may transmit one or more TA commands and one or more conditions to the UE, where each TA command may have a corresponding set of conditions. The UE may identify the current condition of the UE and determine whether the current condition of the UE matches one of the sets of conditions indicated by the base station. The UE may select a TA command based on finding a match and apply the valid TA of the TA command to transmit an uplink PUR transmission. Additionally or alternatively, the base station may transmit neural network parameters to the UE that indicate a neural network model. The UE may use the neural network model to determine a TA command associated with a valid TA based on the current condition of the UE.”)
Referring claim 18, Zhang discloses a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor of a first radio access network node, facilitate performance of operations, comprising:
broadcasting a first information block message comprising a training configuration resource indication that is indicative of a training configuration resource; broadcasting a learning model training configuration according to the training configuration resource, wherein the learning model training configuration comprises a training action indication indicative of a training action to be performed by a first of at least one idle user equipment with respect to the first radio access network node to result in a first determined learning model parameter value; ([0090] of Zhang, “serving base station 105-d may configure table 320 such that the table 320 accounts for possible conditions UE 115-b may encounter based on the current trajectory of the UE 115. Sometime after receiving message 315-a, UE 115-a may switch to a non-connected mode such as an idle or inactive mode. Upon receiving the table 320 or some other indicator of the set of TA commands and associated conditions, UE 115-b may determine the current condition of UE 115-b. For example, UE 115-b may determine the RSRP of the serving base station 105-d and one or more neighboring base stations 105, or the relative delay between serving base station 105-d and another base station 105, or the position of UE 115-b, or a combination thereof.”)
receiving, from the first of the at least one idle user equipment, the first determined learning model parameter value; ([0093] of Zhang, “The neural network model 420 may determine a valid TA based on the current condition of UE 115-c and output the TA command associated with the appropriate TA at the output 435 of the neural network model 420. UE 115-c may apply the TA value associated with the outputted TA command to uplink transmission 410 to transmit a small data transmission message 415-b using PUR. In some cases, configuring a UE 115 with a neural network model may reduce overhead.”)
receiving, from a second radio access network node that is a neighboring radio access network node with respect to the first radio access network node, a second determined learning model parameter value, wherein the training action was performed by at least a second of the at least one idle user equipment with respect to the second radio access network node to result in the second determined learning model parameter value; ([0087]-[0094] of Zhang, conditions related to serving base stations and neighboring base stations, such as “the condition may be a relative delay between a serving base station 105-d and a neighboring base station 105. For example, a first TA command (e.g., TA command 0) may be associated with a first relative delay range between a serving base station 105-d and a neighboring base station 105 (e.g., [0, T0]). In another example, a second TA command (e.g., TA command 1) may be associated with a second relative delay range between a serving base station 105-d and a neighboring base station 105 (e.g., [T0, T1]). The neighboring base stations 105 associated with the first and second TA commands may be the same or different. Additionally or alternatively, the condition may be a positional range of the UE 115. In some cases, the position of the UE 115 may be expressed based on the position of the UE 115 within a cell, such as positions 325-a, 325-b, or 325-c, or may be expressed based on the position on the UEs position in relation to one or more base stations 105, or a combination thereof. In some cases, a first TA command (e.g., TA command 0) may be associated with a first positional range (e.g., positioning range 0) and a second TA command (e.g., TA command 1) may be associated with a second positional range (e.g., positioning range 1)”)
determining, based on the first determined learning model parameter value and based on the second determined learning model parameter value, an updated learning model; ([0094] of Zhang, “UE 115-c may include a suggested update to the neural network model 420 in small data transmission message 415-b based on the current condition of UE 115-c. Additionally or alternatively, UE 115-c may include a request for updated neural network parameters in small data transmission message 415-b. In some cases, base station 105-g may update the neural network parameters based on a request from UE 115-c, the suggested updated from UE 115-c, or based on the small data transmission received from UE 115-c, or a combination thereof. In some cases, UE 115-c may enter into a connected state with base station 105-g and UE 115-c and base station 105-g may exchange information relating to updating the neural network model.”) and
broadcasting, to the first of the at least one idle user equipment via a second information block message, the updated learning model. ([0094] of Zhang, “UE 115-c may include a suggested update to the neural network model 420 in small data transmission message 415-b based on the current condition of UE 115-c. Additionally or alternatively, UE 115-c may include a request for updated neural network parameters in small data transmission message 415-b. In some cases, base station 105-g may update the neural network parameters based on a request from UE 115-c, the suggested updated from UE 115-c, or based on the small data transmission received from UE 115-c, or a combination thereof. In some cases, UE 115-c may enter into a connected state with base station 105-g and UE 115-c and base station 105-g may exchange information relating to updating the neural network model.”)
Referring claim 19, Zhang discloses the non-transitory machine-readable medium of claim 18, wherein the training action was performed by the first of the at least one idle user equipment with respect to the second radio access network node to result in the second determined learning model parameter value. ([0090] of Zhang, “serving base station 105-d may configure table 320 such that the table 320 accounts for possible conditions UE 115-b may encounter based on the current trajectory of the UE 115. Sometime after receiving message 315-a, UE 115-a may switch to a non-connected mode such as an idle or inactive mode. Upon receiving the table 320 or some other indicator of the set of TA commands and associated conditions, UE 115-b may determine the current condition of UE 115-b. For example, UE 115-b may determine the RSRP of the serving base station 105-d and one or more neighboring base stations 105, or the relative delay between serving base station 105-d and another base station 105, or the position of UE 115-b, or a combination thereof.”)
Referring claim 20, Zhang discloses the non-transitory machine-readable medium of claim 18, the operations further comprising: transmitting, to the second radio access network node via a backhaul link, the updated learning model. ([0060] of Zhang, backhaul links 120).
Conclusion
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142