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 .
Information Disclosure Statement
The information disclosure statement(s) was/were submitted on 07/26/2024 and 12/23/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. ( US 2023/0075276 A1), hereinafter referred to as Zhu, and further in view of Wang et al. ( US 2025/0048085 A1), hereinafter referred to as Wang.
Regarding claim 1, Zhu teaches: An apparatus configured for wireless communications (see FIG. 14; ¶[0175], FIG. 14 shows a diagram of a system 1400 including a device 1405 that supports configuring a UE for machine learning in accordance with aspects of the present disclosure. The device 1405 may be an example of or include the components of a device 1105, a device 1205, or a base station 105), comprising:
one or more memories (Memory 1430);
and one or more processors (Processor 1440) coupled to the one or more memories (see FIG. 14), the one or more processors being configured to cause the apparatus to ( see ¶[0179], The processor 1440 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 1430) to cause the device 1405 to perform various functions (e.g., functions or tasks supporting configuring a UE for machine learning) ):
obtain a first request for an indication of at least one configuration for use at one or more first network entities during one or more machine learning operations associated with one or more machine learning functions available for activation at a user equipment (UE) ( see ¶[0006], the UE may send a message to the network requesting to implement machine learning (e.g., based on some trigger). The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set. In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0077], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0188]);
send, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions; and
communicate with the UE while using the at least one configuration (see ¶[0003], … a network may configure a UE for machine learning and the UE may utilize machine learning to perform tasks such as cell reselection, beam failure, beam management, etc.; ¶[0006], …When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0048], the UE may utilize machine learning to perform cell reselection, channel state information (CSI) reporting, etc. To utilize machine learning, the UE may obtain knowledge of a neural network function, a machine learning model, and corresponding parameters; ¶[0077], …When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0193]).
Although Zhu teaches the network may configure the machine learning model and the corresponding parameters at the UE in response to the request message (¶[0006], ¶[0050], ¶[0077]), Zhu, however, fails to explicitly teach information related to send, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions.
However, Wang, in the same or similar field of endeavor teaches: send, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 2, the combination teaches: The apparatus of claim 1, wherein the one or more machine learning operations comprises one or more of:
data collection for training of a machine learning model associated with the one or more machine learning functions ( see Wang, ¶[0223], … the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets ); or
inference operations of the machine learning model ( see Wang, ¶[0324], After receiving the query information and/or the report configuration information, the terminal equipment reports relevant model information. The reported information may include identification information of the model, such as first identification information. The function may be CSI compressed feedback, CSI prediction, beam prediction, positioning and other functions ).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 3, the combination teaches: The apparatus of claim 1, wherein to send the first association, the one or more processors are configured to cause the apparatus to send, to the UE, a machine learning configuration for inference operations of the at least one machine learning function, wherein the machine learning configuration indicates the first association ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]; ¶[0223], … the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets; ¶[0324], After receiving the query information and/or the report configuration information, the terminal equipment reports relevant model information. The reported information may include identification information of the model, such as first identification information. The function may be CSI compressed feedback, CSI prediction, beam prediction, positioning and other functions).
Regarding claim 4, the combination teaches: The apparatus of claim 1, wherein: to obtain the first request, the one or more processors are configured to cause the apparatus to obtain the first request from the UE, wherein the first request is further for one or more data collection configurations associated with the one or more machine learning functions (see Zhu, ¶[0006], the UE may send a message to the network requesting to implement machine learning (e.g., based on some trigger). The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set. In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0077], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0188]; and to send the indication of the first association, the one or more processors are configured to cause the apparatus to send the one or more data collection configurations that include the indication of the first association ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
Regarding claim 5, the combination teaches: The apparatus of claim 4, wherein at least one data collection configuration of the one or more data collection configurations indicates training data for training of a machine learning model associated with a machine learning function of the one or more machine learning functions ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]; ¶[0223], For another example, whether the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets).
Regarding claim 6, the combination teaches: The apparatus of claim 1, wherein to obtain the first request, the one or more processors are configured to cause the apparatus to obtain the first request from a second network entity (see Zhu, ¶[0084], At 325, the UE 115-b may exchange capability information related to machine learning with the network. In some examples, the CU-CP 305 may send a message to the UE 115-b enquiring about the capability information and the UE 115-b may send the capability information to the CU-CP 305 based on receiving the message enquiring about the capability information. Upon receiving the capability information from the UE 115-b, the CU-CP 305 may forward the capability information to the CU-XP 310), wherein the first request indicates the UE is capable of performing the one or more machine learning functions ( see Zhu, ¶[0085], In some examples, the UE 115-b may request to implement machine learning (perform machine learning for a specific task); ¶[0077], … In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks ).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the combination with Zhu's one or more other embodiment’s teachings. The motivation is to reduce signaling overhead and reduce power consumption at the UE ( see Zhu, ¶[0006] ).
Regarding claim 7, the combination teaches: The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to obtain, from the UE, an indication of an identifier associated with the at least one machine learning function, wherein the first association is based on the identifier ( see Zhu, ¶[0077], The UE 115 may transmit capability information to the network (e.g., the base station 105). The capability information may include one or more of a list of potential neural network functions, a list of potential machine learning models, or an indication of whether or not the UE 115 may request machine learning. Based on the capability information, the network may select a set of neural network functions, a set of machine learning models, and sets of corresponding parameters and indicate them to the UE 115 … In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0084]; ¶[0087], Upon receiving the machine learning request from the UE 115-b, the network (e.g., CU-XP 310 or CU-CP 305) may select a neural network function (e.g., from the one or more neural network functions indicated in the machine learning request message received at 335) and a machine learning model (e.g., select a machine learning model corresponding to the model ID indicated in the machine learning request message received at 335) and configure the UE 115-b with the machine learning model as well as a corresponding set of parameters at 340).
Regarding claim 12, Zhu teaches: An apparatus configured for wireless communications (see Fig. 10; ¶[0142], FIG. 10 shows a diagram of a system 1000 including a device 1005 that supports configuring a UE for machine learning in accordance with aspects of the present disclosure. The device 1005 may be an example of or include the components of a device 705, a device 805, or a UE 115 as described herein), comprising:
one or more memories (Memory (1030)); and
one or more processors ( Processor (1040)) coupled to the one or more memories (see Fig. 10), the one or more processors being configured to cause the apparatus to ( see ¶[0146], The processor 1040 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 1030) to cause the device 1005 to perform various functions (e.g., functions or tasks supporting configuring a UE for machine learning) ) :
send a first request for an indication of at least one configuration for use at one or more first network entities during one or more machine learning operations associated with one or more machine learning functions available for activation at the apparatus ( see ¶[0006], the UE may send a message to the network requesting to implement machine learning (e.g., based on some trigger). The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set. In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0077], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0188]);
obtain an indication of a first association between at least one configuration and at least one machine learning function of the one or more machine learning functions; and
communicate, while using a machine learning model associated with the at least one machine learning function, with a network entity of the one or more first network entities (see ¶[0003], … a network may configure a UE for machine learning and the UE may utilize machine learning to perform tasks such as cell reselection, beam failure, beam management, etc.; ¶[0006], …When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0048], the UE may utilize machine learning to perform cell reselection, channel state information (CSI) reporting, etc. To utilize machine learning, the UE may obtain knowledge of a neural network function, a machine learning model, and corresponding parameters; ¶[0077], …When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0193]).
Although Zhu teaches the network may configure the machine learning model and the corresponding parameters at the UE in response to the request message (¶[0006], ¶[0050], ¶[0077]), Zhu, however, fails to explicitly teach information related to obtain an indication of a first association between at least one configuration and at least one machine learning function of the one or more machine learning functions.
However, Wang, in the same or similar field of endeavor teaches: obtain an indication of a first association between at least one configuration and at least one machine learning function of the one or more machine learning functions ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 13, the combination teaches: The apparatus of claim 12, wherein to communicate with the network entity, the one or more processors are configured to cause the apparatus to train the machine learning model based on a data collection configuration that indicates the first association (see Zhu, ¶[0006], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
Regarding claim 14, the combination teaches: The apparatus of claim 12, wherein the one or more processors are configured to cause the apparatus to perform one or more inference operations using the machine learning model based on a machine learning configuration that indicates the first association ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]; ¶[0223], … the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets; ¶[0324], After receiving the query information and/or the report configuration information, the terminal equipment reports relevant model information. The reported information may include identification information of the model, such as first identification information. The function may be CSI compressed feedback, CSI prediction, beam prediction, positioning and other functions).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 15, the combination teaches: The apparatus of claim 12, wherein the one or more machine learning operations comprises one or more of:
data collection for training of a machine learning model associated with the one or more machine learning functions ( see Wang, ¶[0223], … the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets ); or
inference operations of the machine learning model ( see Wang, ¶[0324], After receiving the query information and/or the report configuration information, the terminal equipment reports relevant model information. The reported information may include identification information of the model, such as first identification information. The function may be CSI compressed feedback, CSI prediction, beam prediction, positioning and other functions ).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 16, the combination teaches: The apparatus of claim 12, wherein to obtain the first association, the one or more processors are configured to cause the apparatus to obtain a machine learning configuration for inference operations of the at least one machine learning function, wherein the machine learning configuration indicates the first association (see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]; ¶[0223], … the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets; ¶[0324], After receiving the query information and/or the report configuration information, the terminal equipment reports relevant model information. The reported information may include identification information of the model, such as first identification information. The function may be CSI compressed feedback, CSI prediction, beam prediction, positioning and other functions).
Regarding claim 17, the combination teaches: The apparatus of claim 12, wherein:
to send the first request, the one or more processors are configured to cause the apparatus to send the first request, wherein the first request is further for one or more data collection configurations associated with the one or more machine learning functions (see Zhu, ¶[0006], the UE may send a message to the network requesting to implement machine learning (e.g., based on some trigger). The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set. In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0077], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0188]); and
to obtain the indication of the first association, the one or more processors are configured to cause the apparatus to obtain the one or more data collection configurations that include the indication of the first association (see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
Regarding claim 18, the combination teaches: The apparatus of claim 17, wherein at least one data collection configuration of the one or more data collection configurations indicates training data for training of a machine learning model associated with a machine learning function of the one or more machine learning functions ( see Wang, ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]; ¶[0223], For another example, whether the terminal side may transmit AI/ML model-related data or data set information, if the network side may confirm such information, the network side may command the terminal side to collect data for training and may further accumulate data sets for training, and by transmitting configuration information, the network side asks the terminal side to report collected training data or data sets).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Regarding claim 19, the combination teaches: The apparatus of claim 12, wherein the one or more processors are configured to cause the apparatus to send an indication of an identifier associated with the at least one machine learning function, wherein the first association is based on the identifier ( see Zhu, ¶[0077], The UE 115 may transmit capability information to the network (e.g., the base station 105). The capability information may include one or more of a list of potential neural network functions, a list of potential machine learning models, or an indication of whether or not the UE 115 may request machine learning. Based on the capability information, the network may select a set of neural network functions, a set of machine learning models, and sets of corresponding parameters and indicate them to the UE 115 … In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0084]; ¶[0087], Upon receiving the machine learning request from the UE 115-b, the network (e.g., CU-XP 310 or CU-CP 305) may select a neural network function (e.g., from the one or more neural network functions indicated in the machine learning request message received at 335) and a machine learning model (e.g., select a machine learning model corresponding to the model ID indicated in the machine learning request message received at 335) and configure the UE 115-b with the machine learning model as well as a corresponding set of parameters at 340).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the combination with Zhu's one or more other embodiment’s teachings. The motivation is to reduce signaling overhead and reduce power consumption at the UE ( see Zhu, ¶[0006] ).
Regarding claim 20, Zhu teaches: A method for wireless communications by an apparatus (see Abstract, Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning), comprising:
obtaining a first request for an indication of at least one configuration for use at one or more first network entities during one or more machine learning operations associated with one or more machine learning functions available for activation at a user equipment (UE) ( see ¶[0006], the UE may send a message to the network requesting to implement machine learning (e.g., based on some trigger). The message may include an indication of a neural network function, a neural network model, and a corresponding parameter set. In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE. When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0077], In response to the request message, the network may configure the machine learning model and the corresponding parameters at the UE 115. When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0188]);
sending, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions; and
communicating with the UE while using the at least one configuration (see ¶[0003], … a network may configure a UE for machine learning and the UE may utilize machine learning to perform tasks such as cell reselection, beam failure, beam management, etc.; ¶[0006], …When the UE obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE and the UE utilize machine learning to perform one or more tasks; ¶[0048], the UE may utilize machine learning to perform cell reselection, channel state information (CSI) reporting, etc. To utilize machine learning, the UE may obtain knowledge of a neural network function, a machine learning model, and corresponding parameters; ¶[0077], …When the UE 115 obtains the machine learning model and the corresponding parameter set, the network may activate machine learning at the UE 115 and the UE 115 utilize machine learning to perform one or more tasks; ¶[0193]).
Although Zhu teaches the network may configure the machine learning model and the corresponding parameters at the UE in response to the request message (¶[0006], ¶[0050], ¶[0077]), Zhu, however, fails to explicitly teach information related to sending, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions.
However, Wang, in the same or similar field of endeavor teaches: sending, to the UE, an indication of a first association between the at least one configuration and at least one machine learning function of the one or more machine learning functions ( see ¶[0111], the 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; ¶[0416]; ¶[0506]-¶[0507]).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify Zhu's teachings with Wang's above teaching in order to improve performance and robustness of the AI/ML model ( see ¶[0177] ). Known work in one field of endeavor (Wang prior art) may prompt variations of it for use in either the same field or different one (Zhu prior art) based on design incentives ( improve performance and robustness of the AI/ML model) or other market forces if the variations are predictable to one or ordinary skill in the art.
Allowable Subject Matter
Claims 8-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shen et al. ( US 2022/0342713 A1) discloses information reporting method, apparatus and device, and storage medium
Zhang et al. ( US 2025/0047571 A1) discloses methods and apparatus for ue-side data collection with ran awareness for wireless communication systems
Khirallah et al. ( WO 2025/033758 A1) discloses method and apparatus for ai/ml data collection and reporting in a wireless communication system
Zhang et al. ( US 2025/0126444 A1) discloses centralized machine learning model configurations. A UE may be configured with a machine learning model by a core network to perform analytics, training, or inferences
Mueck et al. ( US 2024/0298194 A1) discloses systems, methods, and devices related to facilitating machine learning-based operations at a User Equipment (UE) connected to a radio access network (RAN)
Heo ( US 2025/0358649 A1) discloses System and method for configuration of ai/ml ue-sided model for beam management
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/MANG BOI THAWNG/Examiner, Art Unit 2476
/AYAZ R SHEIKH/Supervisory Patent Examiner, Art Unit 2476
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