Response to Amendment
This office action is in response to applicants’ response to restriction requirements received on August 24, 2026.
Claims 3-10 are amended.
Claims 1-10 are pending.
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 received July 18, 2024 has been considered.
Response to Remarks
With respect to the restriction to one of the inventions required under 35 U.S.C. §121 of Group I with Claims 1-8 and Group II with claims 9-10 as set forth in the previous Office Action, the claim amendment, and argument (Remarks filed on August 24, 2026, Pages 6-8), have been fully considered.
Applicant elected Group I without traverse and claims 9 and 10 have been amended to shift their scope into Group I so they will be examined with the elected claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation " wherein the UE capability information comprises information used to identify at least one of the intelligent functionalities or models that the UE capable of supporting, and wherein the inference time information comprises information used to identify the performance of the intelligent model that the UE is capable of performing.” in Pg. 2, line 7-10. It is a little bit unclear how these limitations interrelate. Claim 1 recites “the intelligent functionality or models”, but then only recites “the intelligent model”. It is unclear if this is referring to the same models or separate models or a model, but not a functionality. The language usage appears to be inconsistent.
Additionally, both limitations appear to be indicating the UE capability information contains the same information, support for intelligent models, it’s a bit unclear from the context whether there is an actual difference between the capability information required by the features. Since it is unclear how the claim is being impacted by the recited claim language, the metes and bounds of claim 1 is unclear which renders the claims indefinite.
Claim 5 recites the limitation "for each intelligent functionality, each intelligent model, or each use case." in Pg. 3, line 6. Whereas claim 1 recites “at least one of the intelligent functionalities or models” in Pg. 2, lines 7-8. The scope of the claim is not limiting the base claim and hence the metes and bounds of claim 4 is unclear which renders the claims indefinite.
Claims 2-4 dependents from claim 1 are indefinite for the same rationale.
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 6-7 and 9, are rejected under 35 U.S.C. 102(a)(2) as being anticipated by FUJISHIRO, US 20250168663 A1, (hereinafter FUJISHIRO).
Regarding claim 1, FUJISHIRO teaches a method of operating user equipment (UE) for identifying and managing an intelligent (AI/ML) functionality or model in a wireless communication system, the method comprising (see Abs: A communication method for applying a machine learning technology to wireless communication between a user equipment and a network node in a mobile communication system):
receiving UE capability enquiry from a base station (BS) (see Fig. 18, element S701, S702, ¶ [0147], e.g., As illustrated in FIG. 18, in step S701, the gNB 200 transmits, to the UE 100, a capability inquiry message for requesting transmission of the message including the information element indicating the execution capability for the machine learning processing. The capability inquiry message is an example of the transmission request for requesting transmission of the message including the information element indicating the execution capability for the machine learning processing.); and
transmitting UE capability information to the base station (see Fig. 18, element S702, ¶ [0148], e.g., In step S702, the UE 100 transmits, to the gNB 200, the message including the information element indicating the execution capability (an execution environment for the machine learning processing, from another viewpoint) for the machine learning processing. The gNB 200 receives the message. The message may be an RRC message, for example, a “UE Capability” message defined in the RRC technical specifications, or a newly defined message (e.g., a “UE A1 Capability” message or the like).),
wherein the UE capability information includes inference time information (see ¶ [0149], e.g., The information element indicating the execution capability for the machine learning processing is at least one selected from group consisting of the information elements (A1) to (A3) below; see ¶ [0156], The information element (A2) may be an information element indicating the execution time (response time) required to perform the inference processing.),
wherein the UE capability information comprises information used to identify at least one of the intelligent functionalities or models that the UE capable of supporting (see ¶ [0155], e.g., The information element (A2) is an information element indicating the execution capability for the inference processing. The information element (A2) may be an information element indicating a model supported in the inference processing. The information element may be an information element indicating whether a deep neural network model is able to be supported.), and
wherein the inference time information comprises information used to identify the performance of the intelligent model that the UE is capable of performing (see ¶ [0155] - ¶ [0156], e.g., The information element (A2) may be an information element indicating the execution time (response time) required to perform the inference processing. The information element (A2) may be an information element indicating the number of simultaneous executions of the inference processing (e.g., how many pieces of inference processing can be performed in parallel). The information element (A2) may be an information element indicating the processing capacity of the inference processing.).
Regarding claim 2, FUJISHIRO teaches the limitations of Claim 1.
FUJISHIRO further teaches, wherein the method further comprises receiving, from the base station, instructions to activate a specific intelligent functionality or model determined based on performance of an intelligent model that the user equipment can perform (see ¶ [0159], e.g., In step S703, the gNB 200 determines a model to be configured (deployed) for the UE 100 based on the information element included in the message received in step S702.).
Regarding claim 4, FUJISHIRO teaches the limitations of Claim 1.
FUJISHIRO further teaches, wherein the inference time information includes inference time information about a general model that the user equipment can perform see ¶ [0154] - ¶ [0156], e.g., The information element (A1) may be defined as an information element for the inference processing (model inference) … The information element (A2) may be an information element indicating a model supported in the inference processing … The information element (A2) may be an information element indicating the execution time (response time) required to perform the inference processing.).
Regarding claim 6, FUJISHIRO teaches a method of operating a base station (BS) for identifying and managing an intelligent functionality or model in a wireless communication system, the method comprising (see Abs: A communication method for applying a machine learning technology to wireless communication between a user equipment and a network node in a mobile communication system):
transmitting UE capability enquiry to user equipment (UE) (see Fig. 18, element S701, S702, ¶ [0147], e.g., As illustrated in FIG. 18, in step S701, the gNB 200 transmits, to the UE 100, a capability inquiry message for requesting transmission of the message including the information element indicating the execution capability for the machine learning processing. The capability inquiry message is an example of the transmission request for requesting transmission of the message including the information element indicating the execution capability for the machine learning processing.); and
receiving UE capability information from the user equipment (see Fig. 18, element S702, ¶ [0148], e.g., In step S702, the UE 100 transmits, to the gNB 200, the message including the information element indicating the execution capability (an execution environment for the machine learning processing, from another viewpoint) for the machine learning processing. The gNB 200 receives the message. The message may be an RRC message, for example, a “UE Capability” message defined in the RRC technical specifications, or a newly defined message (e.g., a “UE A1 Capability” message or the like).),
wherein the UE capability information includes inference time information (see ¶ [0149], e.g., The information element indicating the execution capability for the machine learning processing is at least one selected from group consisting of the information elements (A1) to (A3) below; see ¶ [0156], The information element (A2) may be an information element indicating the execution time (response time) required to perform the inference processing.),
and identifying intelligent functionality and model that the user equipment can support, on the basis of at least one of the UE capability enquiry, or the UE capability information (see Fig. 18, element S703, S704, ¶ [0159] - ¶ [0160], e.g., In step S703, the gNB 200 determines a model to be configured (deployed) for the UE 100 based on the information element included in the message received in step S702; In step S704, the gNB 200 transmits a message including the model determined in step S703 to the UE 100. The UE 100 receives the message and performs the machine learning processing (learning processing and/or inference processing) using the model included in the message.), and
and identifying performance of the intelligent model that the UE equipment can perform, on the basis of the inference time informatio (see ¶ [0164] - ¶ [0169], e.g., In step S711, the gNB 200 transmits a configuration message including a model and additional information to the UE 100. The UE 100 receives the configuration message. The configuration message includes at least one selected from the group consisting of the information elements (B1) to (B6) … (B4) Model Execution Requirement … The “model execution requirement” is an information element indicating a performance required to apply (execute) the model (required performance), for example, a processing delay (request latency).)
Regarding claim 7, FUJISHIRO teaches the limitations of Claim 6.
FUJISHIRO further teaches, further comprising transmitting, to the user equipment (UE), instructions to activate a specific intelligent functionality or model determined on the basis of performance of an intelligent model that the UE can perform (see ¶ [0159], e.g., In step S703, the gNB 200 determines a model to be configured (deployed) for the UE 100 based on the information element included in the message received in step S702; see ¶ [0164] - ¶ [0169], e.g., In step S711, the gNB 200 transmits a configuration message including a model and additional information to the UE 100.)
Regarding claim 9, FUJISHIRO teaches the limitations of Claim 6.
FUJISHIRO further teaches, wherein the inference time information includes inference time information about a general model that the user equipment can perform (see ¶ [0154] - ¶ [0156], e.g., The information element (A1) may be defined as an information element for the inference processing (model inference) … The information element (A2) may be an information element indicating a model supported in the inference processing … The information element (A2) may be an information element indicating the execution time (response time) required to perform the inference processing.) .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over FUJISHIRO in view of KANG, et al., EP 4654639 A1, (hereinafter KANG), and in further view of MU, WO 2024234258 A1, (hereinafter MU).
Regarding claim 3, FUJISHIRO teaches the limitations of Claim 1.
FUJISHIRO further teaches,
a method of operating user equipment (UE) for identifying and managing an intelligent (AI/ML) functionality or model in a wireless communication system, the method comprising (see Abs: A communication method for applying a machine learning technology to wireless communication between a user equipment and a network node in a mobile communication system):
however, it does not explicitly teach, wherein the inference time information includes a minimum required inference time for operating the specific intelligent model and functionality; wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols, and wherein the number of OFDM symbols is time from a point in time at which a request for an inference operation for operating the intelligent model and function was received to a point in time at which a result of the inference is reported.
KANG teaches wherein the inference time information includes a minimum required inference time for operating the specific intelligent model and functionality (see ¶ [0017] - ¶ [0018], e.g., The value related to the model may include at least one of i) a value related to whether fine-tuning related to the model is supported, ii) a minimum time required for training, update, fine-tuning, switching, or selection of the model, and/or iii) a value related to fallback of a communication function based on the model. The range information may include at least one of i) candidate values of each capability value, ii) a minimum value of each capability value, and/or iii) a maximum value of each capability value; Also see ¶ [0102], e.g., ii) candidate values or min/max value(s) of required minimum time for model (re-)training/update/fine-tuning/switching/selection).
MU teaches wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols, and wherein the number of OFDM symbols is time from a point in time at which a request for an inference operation for operating the intelligent model and function was received to a point in time at which a result of the inference is reported (see ¶ [0127], e.g., Taking the inference of AI models as an example, the first communication device (e.g., a terminal) can report the first processing time corresponding to each of the supported AI models #1 and #2.For example: the first time corresponding to AI model #1 (e.g., 1 OFDM symbol) and the first processing time corresponding to AI model #2 (e.g., 2 slots).).
It would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified transmitting the inference time to a base station of FUJISHIRO to incorporate the teachings of KANG to include minimum required inference time for operating the intelligent model and functionality and incorporate the teachings of MU to include wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols. Doing so would facilitate in achieving optimized performance of the communication function under dynamically varying UE capability as suggested by KANG (see ¶ [0016], e.g., one or more capability values based on range information are reported. Thus, the network can be informed of information about (dynamic) variation situations related to communication functions due to factors such as model change, model re-training, model update, and the like … In addition, the performance of the communication function based on a model may be optimized according to a dynamically varying UE capability; and performing AI communication processing based on processing an AI model as suggested by MU (see ¶ [0006] - ¶ [0007], the first time being used to represent the processing time of processing an AI task based on an AI model; and performing AI communication processing based on the first time … the first time is represented based on at least one of the following: absolute time; communication time unit; AI time unit, wherein the AI time unit is determined based on a preset AI task.)
Regarding claim 8, FUJISHIRO teaches the limitations of Claim 7.
FUJISHIRO further teaches,
a method of operating user equipment (UE) for identifying and managing an intelligent (AI/ML) functionality or model in a wireless communication system, the method comprising (see Abs: A communication method for applying a machine learning technology to wireless communication between a user equipment and a network node in a mobile communication system):
however, it does not explicitly teach, wherein the inference time information includes a minimum required inference time for operating the specific intelligent model and functionality; wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols, and wherein the number of OFDM symbols is time from a point in time at which a request for an inference operation for operating the intelligent model and function was received to a point in time at which a result of the inference is reported.
KANG teaches wherein the inference time information includes a minimum required inference time for operating the specific intelligent model and functionality (see ¶ [0017] - ¶ [0018], e.g., The value related to the model may include at least one of i) a value related to whether fine-tuning related to the model is supported, ii) a minimum time required for training, update, fine-tuning, switching, or selection of the model, and/or iii) a value related to fallback of a communication function based on the model. The range information may include at least one of i) candidate values of each capability value, ii) a minimum value of each capability value, and/or iii) a maximum value of each capability value; Also see ¶ [0102], e.g., ii) candidate values or min/max value(s) of required minimum time for model (re-)training/update/fine-tuning/switching/selection).
MU teaches wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols, and wherein the number of OFDM symbols is time from a point in time at which a request for an inference operation for operating the intelligent model and function was received to a point in time at which a result of the inference is reported (see ¶ [0127], e.g., Taking the inference of AI models as an example, the first communication device (e.g., a terminal) can report the first processing time corresponding to each of the supported AI models #1 and #2.For example: the first time corresponding to AI model #1 (e.g., 1 OFDM symbol) and the first processing time corresponding to AI model #2 (e.g., 2 slots).).
It would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified transmitting the inference time to a base station of FUJISHIRO to incorporate the teachings of KANG to include minimum required inference time for operating the intelligent model and functionality and incorporate the teachings of MU to include wherein the inference time is represented by the number of orthogonal frequency division multiplexing (OFDM) symbols. Doing so would facilitate in achieving optimized performance of the communication function under dynamically varying UE capability as suggested by KANG (see ¶ [0016], e.g., one or more capability values based on range information are reported. Thus, the network can be informed of information about (dynamic) variation situations related to communication functions due to factors such as model change, model re-training, model update, and the like … In addition, the performance of the communication function based on a model may be optimized according to a dynamically varying UE capability; and performing AI communication processing based on processing an AI model as suggested by MU (see ¶ [0006] - ¶ [0007], the first time being used to represent the processing time of processing an AI task based on an AI model; and performing AI communication processing based on the first time … the first time is represented based on at least one of the following: absolute time; communication time unit; AI time unit, wherein the AI time unit is determined based on a preset AI task.)
Claim(s) 5 and 10, are rejected under 35 U.S.C. 103 as being unpatentable over FUJISHIRO in view of LIU et al., EP 4694063 A1, (hereinafter LIU).
Regarding claim 5, FUJISHIRO teaches the limitations of Claim 1.
FUJISHIRO further teaches, further comprising transmitting an intelligent functionality-related network configuration (RRC configuration) to the base station, ; wherein the network configuration includes each intelligent functionality, each intelligent model, or each use case (see ¶ [0189], e.g., In step S752, the UE 100 transmits a message (report message) including the AI/ML processing load status to the gNB 200. The message may be an RRC message, for example, a “UE Assistance Information” message or “Measurement Report” message. The message may be a newly defined message (e.g., an “A1 Assistance Information” message); see ¶ [0190], e.g., The UE 100 may indicate the “processing load status” for each model. For example, the UE 100 may include at least one set of “model index” and “processing load status” in the message. The “memory load status” may indicate a memory capacity, a memory usage amount, or a memory remaining amount; see ¶ [0191], e.g., In step S752, when the UE 100 wants to stop using a particular model, for example, because of a high processing load or inefficiency, the UE 100 may include in the message information (model index) indicating a model of which configuration deletion or deactivation of model is wanted.),
however, it does not explicitly teach, maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case.
LIU teaches, maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case (see ¶ [0300], e.g., For example, the UE reports AI/ML functionality 1 and AI/ML functionality 2. Assume that AI/ML functionality 1 is defined as the UE can predict channel information on a maximum of 10 slots with the interval between adjacent slots being 2 and can be used for inference below 30Km/h; AI/ML functionality 2 is defined as the UE can achieve inference about 60Km/h and below and can predict channel information on a maximum of 20 slots with the interval between adjacent slots being 5. Thus, the base station may determine the AI/ML functionalities supported by the UE based on the supported AI/ML functionalities reported by the UE.)
It would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified transmitting an intelligent functionality-related network configuration (RRC configuration) to the base station of FUJISHIRO to incorporate the teachings of LIU to include maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case. Doing so would facilitate in achieving AI/ML model recognition on the terminal by the network device as suggested by LIU (see ¶ [0016], e.g., present disclosure may include the following beneficial effects: the terminal may send the first information to the network device, so that the network device may determine the AI/ML functionality supported by the terminal and/or the AI/ML model supported by the terminal based on the first information, thereby completing model recognition on the terminal.).
Regarding claim 10, FUJISHIRO teaches the limitations of Claim 6.
FUJISHIRO further teaches, further comprising receiving an intelligent functionality-related network configuration (RRC configuration) from the user equipment,
wherein the network configuration includes each intelligent functionality, each intelligent model, or each use case (see ¶ [0189], e.g., In step S752, the UE 100 transmits a message (report message) including the AI/ML processing load status to the gNB 200. The message may be an RRC message, for example, a “UE Assistance Information” message or “Measurement Report” message. The message may be a newly defined message (e.g., an “A1 Assistance Information” message); see ¶ [0190], e.g., The UE 100 may indicate the “processing load status” for each model. For example, the UE 100 may include at least one set of “model index” and “processing load status” in the message. The “memory load status” may indicate a memory capacity, a memory usage amount, or a memory remaining amount; see ¶ [0191], e.g., In step S752, when the UE 100 wants to stop using a particular model, for example, because of a high processing load or inefficiency, the UE 100 may include in the message information (model index) indicating a model of which configuration deletion or deactivation of model is wanted.),
however, it does not explicitly teach, maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case.
LIU teaches, maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case (see ¶ [0300], e.g., For example, the UE reports AI/ML functionality 1 and AI/ML functionality 2. Assume that AI/ML functionality 1 is defined as the UE can predict channel information on a maximum of 10 slots with the interval between adjacent slots being 2 and can be used for inference below 30Km/h; AI/ML functionality 2 is defined as the UE can achieve inference about 60Km/h and below and can predict channel information on a maximum of 20 slots with the interval between adjacent slots being 5. Thus, the base station may determine the AI/ML functionalities supported by the UE based on the supported AI/ML functionalities reported by the UE.)
It would have been obvious to one of ordinary skill in the art before the effective
filing date of the claimed invention to have modified transmitting an intelligent functionality-related network configuration (RRC configuration) to the base station of FUJISHIRO to incorporate the teachings of LIU to include maximum inference time information that is recommended for each intelligent functionality, each intelligent model, or each use case. Doing so would facilitate in achieving AI/ML model recognition on the terminal by the network device as suggested by LIU (see ¶ [0016], e.g., present disclosure may include the following beneficial effects: the terminal may send the first information to the network device, so that the network device may determine the AI/ML functionality supported by the terminal and/or the AI/ML model supported by the terminal based on the first information, thereby completing model recognition on the terminal.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240334208 A1 issued to ABEBE et al.
US 20250379634 A1 issued to GUAN et al.
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/POONAM SHARMA/Examiner, Art Unit 2472
/KEVIN T BATES/Supervisory Patent Examiner, Art Unit 2472
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