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 .
This office action is in response to the amendment filed on 07/13/2026. Claims 1-12, 18-25 are pending in this application and have been considered below.
Response to Amendment
Applicant's arguments with respect to claims 1-12, 18-25 have been considered but are moot in view of the new ground(s) of rejection because of the amendment changes the scope of the invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-12, 18-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2023/0100253 A1) (Zhu herein after) in view of Wang et al. (US 2025/0048085 A1) (Wang herein after).
Re Claim 1 and Claim 18, Zhu discloses a user equipment (UE), a processor, and a method for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory (processor and memory [0047]) and configured to cause the UE to:
transmit a first response message comprising the capability information, wherein the capability information indicates one or more AI functionalities supported by the UE (UE 120 transmits, to the base station 110, UE capability information indicating at least one radio capability of the UE and at least one machine learning capability of the UE [0095]);
receive, in response to the first response message and based at least in part on the one or more AI functionalities, a second request message comprising at least one configuration for an AI functionality of the one or more AI functionalities (base station 110 (e.g., via the CU-CP 712) transmits, to the UE 120, machine learning configuration information based on the UE capability information received at time L. In some cases, the base station 110 may transmit the machine learning configuration information in an RRC reconfiguration message [0102]); and
transmit, in response to the second request message, a second response message comprising feedback that indicates whether an AI model at the UE is applicable for the at least one configuration, wherein the AI model is associated with the AI functionality (In response to receiving the RRC reconfiguration message including the machine learning configuration information, the UE 120 transmits an RRC reconfiguration complete message to the base station 110, which may indicate that the UE 120 successfully received the machine learning configuration information [0104]; the UE 120 and/or network entity 802 indicates that the at least one machine learning model is ready to be used [0108]).
Zhu teaches the claimed invention except receive a first request message for capability information associated with artificial intelligence (AI); and transmit, in response to the first request message, a first response message.
However, Wang discloses information transmission method and system wherein terminal equipment receives a capability query request of AI/ML transmitted by a network device; and, the terminal equipment feeds back a capability query response or report to the network device according to the capability query request ([0075]-[0078]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Zhu, by making use of the technique taught by Wang, in order to improve the system efficiency by reducing load and delay.
Both references are within the same field of telecommunication, and in particular of new radio system, the modification does not change a fundamental operating principle of Zhu, nor does Zhu teach away from the modification (Zhu merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the method and system taught by Wang is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of receive a first request message for capability information associated with artificial intelligence (AI); and transmit, in response to the first request message, a first response message.
Re Claims 2 and 19, the combined teachings disclose the UE of claim 1 and the method of claim 18, Wang discloses wherein the first request message comprises a UE AI capability inquiry (capability query request of AI/ML transmitted by a network device [0076]).
Re Claims 3 and 20, the combined teachings disclose the UE of claim 1 and the method of claim 18, Wang discloses wherein the at least one processor is further operable to cause the UE to receive a subset of the at least one configuration for the AI functionality (a terminal equipment with an AI/ML capability initiates an AI/ML capability report request to a network side. After receiving the report request, the network side device transmits AI/ML configuration information to the terminal equipment. The terminal equipment reports model information possessed by the terminal equipment according to the configuration information, the model information including model identification information. The reported model information is a subset of model information transmitted by the network device [0350]).
Re Claim 4, the combined teachings disclose the UE of claim 1, Wang discloses wherein the at least one processor is further operable to cause the UE to determine whether the at least one configuration is applicable for the AI functionality based at least in part on one or more parameters of the at least one configuration (terminal equipment receives configuration information of the network device for a certain signal processing function, the configuration information including an identification of the AI/ML model group and/or the model, and performs the signal processing by using the AI/ML model corresponding to the identification of the AI/ML model group and/or the model [0111]).
Re Claim 5, the combined teachings disclose the UE of claim 1, Wang discloses wherein the feedback comprises an acknowledgment (ACK) or a negative acknowledgment (NACK) (ACK/NACK [0119]).
Re Claim 6, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the feedback indicates that the AI model is applicable for the at least one configuration (The machine learning configuration information may include an indication of the at least one NNF (e.g., the accepted NNF list) and the at least one machine learning model corresponding to the at least one NNF. In some cases, the at least one NNF is indicated by an NNF ID and the at least one machine learning model is indicated by a machine learning model ID. As noted, the at least one machine learning model may be associated with a model structure and one or more sets of parameters (e.g., weights, biases, and/or activation functions) [0102]), and wherein the at least one processor is further operable to cause the UE to use the AI model for AI inference associated with the AI functionality (different sets of parameters may be used with the model structure. This may allow the UE 120 to use one model structure for performing the machine learning-based wireless communications management procedure while adaptively changing the set of parameters used with the model structure depending on the particular geographic area or configuration of the UE [0103]).
Re Claim 7, the combined teachings disclose the UE of claim 1, Wang discloses wherein the at least one processor is further operable to cause the UE to receive the first request message from a network entity, wherein the network entity comprises a base station or a network function of a core network (receives a capability query request of AI/ML transmitted by a network device; and, the terminal equipment feeds back a capability query response or report to the network device according to the capability query request, [0075]-[0078]).
Re Claim 8, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the first request message comprises an AI information element (IE) (information element, [0039]).
Re Claims 9 and 25, Zhu discloses a base station for wireless communication and a method performed by a base station, comprising: at least one memory; and at least one processor coupled with the at least one memory (processor and memory [0008]) and configured to cause the base station to:
receive a first response message comprising the capability information, wherein the capability information indicates one or more AI functionalities supported by a user equipment (UE) (UE 120 transmits, to the base station 110, UE capability information indicating at least one radio capability of the UE and at least one machine learning capability of the UE [0095]);
transmit, in response to the first response message and based at least in part on the one or more AI functionalities, a second request message comprising at least one configuration for an AI functionality of the one or more AI functionalities (base station 110 (e.g., via the CU-CP 712) transmits, to the UE 120, machine learning configuration information based on the UE capability information received at time L. In some cases, the base station 110 may transmit the machine learning configuration information in an RRC reconfiguration message [0102]); and
receive, in response to the second request message, a second response message comprising feedback that indicates whether an AI model at the UE is applicable for the at least one configuration, wherein the AI model is associated with the AI functionality (In response to receiving the RRC reconfiguration message including the machine learning configuration information, the UE 120 transmits an RRC reconfiguration complete message to the base station 110, which may indicate that the UE 120 successfully received the machine learning configuration information [0104]; the UE 120 and/or network entity 802 indicates that the at least one machine learning model is ready to be used [0108]).
Zhu teaches the claimed invention except transmit a first request message for capability information associated with artificial intelligence (AI); receive, in response to the first request message, a first response message.
However, Wang discloses information transmission method and system wherein terminal equipment receives a capability query request of AI/ML transmitted by a network device; and, the terminal equipment feeds back a capability query response or report to the network device according to the capability query request ([0075]-[0078]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Zhu, by making use of the technique taught by Wang, in order to improve the system efficiency by reducing load and delay.
Both references are within the same field of telecommunication, and in particular of new radio system, the modification does not change a fundamental operating principle of Zhu, nor does Zhu teach away from the modification (Zhu merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the method and system taught by Wang is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of transmit a first request message for capability information associated with artificial intelligence (AI); receive, in response to the first request message, a first response message.
Re Claim 10, the combined teachings disclose the base station of claim 9, Wang discloses wherein the first request message comprises a UE AI capability inquiry (capability query request of AI/ML transmitted by a network device [0076]).
Re Claim 11, the combined teachings disclose the base station of claim 9, Wang discloses wherein the at least one processor is further operable to cause the base station to transmit a subset of the at least one configuration for the AI functionality (a terminal equipment with an AI/ML capability initiates an AI/ML capability report request to a network side. After receiving the report request, the network side device transmits AI/ML configuration information to the terminal equipment. The terminal equipment reports model information possessed by the terminal equipment according to the configuration information, the model information including model identification information. The reported model information is a subset of model information transmitted by the network device [0350]).
Re Claim 12, the combined teachings disclose the base station of claim 9, Zhu discloses wherein the first request message comprises an AI information element (IE) (information element, [0039])
Re Claim 21, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the at least one processor is further operable to cause the UE to receive an indication to activate at least one second configuration for the AI functionality (when the UE 120 is in a second cell, the UE 120 may select a second set of parameters for use with the model structure to perform the machine learning-based wireless communications management procedure [0111]).
Re Claim 22, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the feedback indicates that the AI model is not applicable for the at least one configuration, and wherein the at least one processor is further operable to cause the UE to receive, in response to the second response message, at least one additional configuration for the AI functionality (UE may trigger machine learning for a network-based model. In some aspects of the present disclosure, triggers for a UE machine learning (ML) request may be based on a scope of the model. That is, each artificial intelligence or machine learning (AI/ML) model may have an applicable scope. When the UE transitions into or out of the applicable scope of a model, a UE ML request may be triggered for configuration of a network model. [0038]).
Re Claim 23, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the feedback indicates that the AI model is not applicable for the at least one configuration, and wherein the at least one processor is further operable to cause the UE to receive, in response to the second response message, a deconfiguration of the AI functionality, wherein the deconfiguration indicates to the UE to refrain from using the AI model for the AI functionality (UE may trigger machine learning for a network-based model. In some aspects of the present disclosure, triggers for a UE machine learning (ML) request may be based on a scope of the model. That is, each artificial intelligence or machine learning (AI/ML) model may have an applicable scope. When the UE transitions into or out of the applicable scope of a model, a UE ML request may be triggered for configuration of a network model. To avoid the flooding of too many UE ML requests, the network may configure a blacklist, and/or whitelist of UE triggers. The network may also configure a prohibit timer to prevent sending of requests too frequently. The black list, prohibit timer, and/or whitelist may be configured by a radio resource control (RRC) reconfiguration message. For a neural network function (NNF) or model in neither the whitelist nor blacklist, the UE can autonomously request network configuration. In some aspects, the network only allows requesting of a model in the whitelist. After the UE capability exchange and receiving the prohibit timer, whitelist, and black list, the UE may request the network to configure machine learning functions for execution on the network [0038]).
Re Claim 24, the combined teachings disclose the UE of claim 1, Zhu discloses wherein the at least one processor is further operable to cause the UE to: detect, subsequent to transmitting the second response message, a change in a condition associated with the UE, wherein the condition comprises at least one of a battery condition, a memory condition, or a mobility condition; and transmit, based at least in part on the change in the condition, a status report that indicates a change in applicability of the AI model for the AI functionality (machine learning assistance IE may include an event indicating an applicable condition change, in other words, informing the network of what has changed for an already configured model. For example, for a cell-specific model, the UE may inform the network of a cell change, which specifies the network needs a new model and/or parameter set. The event may indicate a model ID, condition (e.g., cell, area), and whether the UE is transitioning into or out of the condition [0122]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH T LAM whose telephone number is (571)270-1862. The examiner can normally be reached M-F 8:30-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hannah S. Wang can be reached at (571) 272-9018. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KENNETH T LAM/Primary Examiner, Art Unit 2631
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