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 .
Election/Restrictions
Applicant’s election without traverse of Specie IV of claims 6-8, 15-17 and 24-36 in the reply filed on 03/20/2026 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/13/2023 is considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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, 6-11, 15-20 and 24-29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shen et al. (US 2022/0342713; hereinafter Shen).
Regarding claim 1, Shen shows a method (Figures 3 and 6 shows a method performed in part by a UE.) for wireless communications at a user equipment (UE), comprising:
transmitting, to a base station, an indication identifying a first UE capability for a backbone block of a multi-block machine learning application and a second UE capability for a task-specific block of the multi-block machine learning application (Figures 3 and 6; Par. 0078-0079, 0085; a terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device; wherein, the AI/ML capability information indicates resource information used by the terminal to process an AI/ML service. The AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service, and may further include a performance index requirement on wireless transmission of a network side by an AI/ML operation of a certain AI/ML service of the terminal, etc.);
receiving, from the base station, control signaling identifying a configuration for the multi-block machine learning application of the UE at least in part in response to the indication identifying the first UE capability and the second UE capability (Figures 3 and 6; Par. 0087-0089, 0092; the terminal receives AI/ML task configuration information sent by the network device; wherein, the AI/ML task configuration information is used to indicate an AI/ML task configuration allocated by the network device to the terminal according to the AI/ML capability information); and
communicating with the base station or a wireless device using the multi-block machine learning application configured in accordance with the control signaling (Figure 6; noted network device and terminal communicating based on the initial AI/ML model being used.).
Regarding claim 2, Shen shows transmitting, to the base station, an indication that the UE supports the multi-block machine learning application (Figures 3 and 6; Par. 0133-0134; In an implementation, the AI/ML capability information indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The terminal can report the computing power, storage space, power headroom, and performance index requirement for wireless transmission between the terminal and the network device, etc., with which the AI/ML model can be run currently, for splitting and re-splitting the AI/ML operation between the terminal and the network device.), wherein the control signaling configuring the multi-block machine learning application of the UE is received in response to the indication that the UE supports the multi-block machine learning application (Figures 3 and 6; Par. 0135-0136; Correspondingly, the AI/ML task configuration information includes the identity of the AI/ML model needed by the terminal to process the AI/ML service, and/or the AI/ML task configuration information includes an identity of an AI/ML act group to be performed by the terminal; wherein the identity of the AI/ML act group is used to indicate at least one AI/ML act to be performed by the terminal.).
Regarding claim 6, Shen shows transmitting an indicator of a machine learning capability of the UE, wherein the machine learning capability is associated with a combination of the backbone block and the task-specific block (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.).
Regarding claim 7, Shen shows wherein the machine learning capability of the UE comprises an indicator that the UE supports the multi-block machine learning application and a set of indicators identifying the machine learning capability associated with the combination of the backbone block and the task-specific block (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.).
Regarding claim 8, Shen shows wherein the machine learning capability of the UE comprises, for the combination of the backbone block and the task-specific block, a supported size (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates at least the available memory space and/or the power headroom/battery capacity of the terminal for processing AI/ML services, The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), a supported operation (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), a machine learning structure type (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), a supported condition (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), a supported task (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), a supported scenario (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.), or a combination thereof.
Regarding claim 9, Shen shows wherein the indication identifying the first UE capability and the second UE capability comprises:
an indication of neural network types for the backbone block supported by the UE (Figure 8; Par. 0148; As shown in FIG. 8, it's assumed that for an AI/ML task, there are three AI/ML models, model 1, model 2 and model 3, in the storage space of the terminal, and remaining available storage space is as shown in FIG. 8. The terminal reports the stored AI/ML model list and/or the available storage space.), machine learning model size levels for the backbone block supported by the UE (Figure 8; Par. 0148; As shown in FIG. 8, it's assumed that for an AI/ML task, there are three AI/ML models, model 1, model 2 and model 3, in the storage space of the terminal, and remaining available storage space is as shown in FIG. 8. The terminal reports the stored AI/ML model list and/or the available storage space.), operation levels for the backbone block supported by the UE, or a combination thereof, and
an indication of tasks (Figure 8; Par. 0148; The terminal also reports the available computing power and the communication performance index requirement and the like for this AI/ML task to the network device.), scenarios (Figure 8; Par. 0148; The terminal also reports the available computing power and the communication performance index requirement and the like for this AI/ML task to the network device.), or a combination thereof, that the UE supports for the task-specific block.
Regarding claim 10, Shen shows a method (Figures 5-6 shows a method performed in part by a network device.) for wireless communications at a network entity, comprising:
receiving, from a user equipment (UE), an indication identifying a first UE capability for a backbone block of a multi-block machine learning application and a second UE capability for a task-specific block of the multi-block machine learning application (Figures 5-6; Par. 0078-0079, 0085; a terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device; wherein, the AI/ML capability information indicates resource information used by the terminal to process an AI/ML service. The AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service, and may further include a performance index requirement on wireless transmission of a network side by an AI/ML operation of a certain AI/ML service of the terminal, etc.);
transmitting, to the UE in response to the first UE capability and the second UE capability, control signaling to configure the multi-block machine learning application of the UE (Figures 5-6; Par. 0087-0089, 0092; the terminal receives AI/ML task configuration information sent by the network device; wherein, the AI/ML task configuration information is used to indicate an AI/ML task configuration allocated by the network device to the terminal according to the AI/ML capability information); and
communicating with the UE based at least in part on the control signaling (Figure 6; noted network device and terminal communicating based on the initial AI/ML model being used.).
Regarding claim 11, Shen shows receiving, from the UE, an indication that the UE supports the multi-block machine learning application (Figures 3 and 6; Par. 0133-0134; In an implementation, the AI/ML capability information indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The terminal can report the computing power, storage space, power headroom, and performance index requirement for wireless transmission between the terminal and the network device, etc., with which the AI/ML model can be run currently, for splitting and re-splitting the AI/ML operation between the terminal and the network device.), wherein the control signaling configuring the multi-block machine learning application of the UE is received in response to the indication that the UE supports the multi-block machine learning application (Figures 3 and 6; Par. 0135-0136; Correspondingly, the AI/ML task configuration information includes the identity of the AI/ML model needed by the terminal to process the AI/ML service, and/or the AI/ML task configuration information includes an identity of an AI/ML act group to be performed by the terminal; wherein the identity of the AI/ML act group is used to indicate at least one AI/ML act to be performed by the terminal.).
Regarding claim 15, Shen shows receiving an indicator of a machine learning capability of the UE, wherein the machine learning capability is associated with a combination of the backbone block and the task-specific block (Figures 3 and 6; Par. 0081, 0138-0139; the AI/ML capability information reported by the terminal indicates the processing capability, the available memory space, the power headroom/battery capacity of the terminal for processing AI/ML services, and the performance index requirement on wireless transmission of a network device by an AI/ML operation of the terminal. The AI/ML capability information sent by the terminal to the network device may include all capability information related to the AI/ML service. Alternatively, the terminal may send different capability information to the network device according to different service scenarios, or may send corresponding AI/ML capability information according to requirements of the network device.); and
determining the first UE capability for the backbone block and the second UE capability for the task-specific block based at least in part on the machine learning capability that is associated with the combination of the backbone block and the task-specific block (Figures 3 and 6; Par. 0135-0136; Correspondingly, the AI/ML task configuration information includes the identity of the AI/ML model needed by the terminal to process the AI/ML service, and/or the AI/ML task configuration information includes an identity of an AI/ML act group to be performed by the terminal; wherein the identity of the AI/ML act group is used to indicate at least one AI/ML act to be performed by the terminal.).
Regarding claims 16, 17 and 18, these claims are rejected based on the same reasoning as presented in the rejection of claims 7, 8 and 9, respectively.
Regarding claim 19, Shen shows an apparatus (Figure 15 shows a UE performing in part the methods of Figures 3 and 6.) for wireless communications at a user equipment (UE), comprising:
a processor (Figure 15; UE includes a processor.);
memory coupled with the processor (Figure 15; UE includes memory coupled with the processor.); and
instructions stored in the memory and executable by the processor to cause the apparatus (Figure 15; UE includes software stored in memory and executed by the processor to perform the disclosed invention.) to:
transmit, to a base station, an indication identifying a first UE capability for a backbone block of a multi-block machine learning application and a second UE capability for a task-specific block of the multi-block machine learning application (Figures 3 and 6; Par. 0078-0079, 0085; a terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device; wherein, the AI/ML capability information indicates resource information used by the terminal to process an AI/ML service. The AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service, and may further include a performance index requirement on wireless transmission of a network side by an AI/ML operation of a certain AI/ML service of the terminal, etc.);
receive, from the base station, control signaling identifying a configuration for the multi-block machine learning application of the UE at least in part in response to the indication identifying the first UE capability and the second UE capability (Figures 3 and 6; Par. 0087-0089, 0092; the terminal receives AI/ML task configuration information sent by the network device; wherein, the AI/ML task configuration information is used to indicate an AI/ML task configuration allocated by the network device to the terminal according to the AI/ML capability information); and
communicate with the base station or a wireless device using the multi-block machine learning application configured in accordance with the control signaling (Figure 6; noted network device and terminal communicating based on the initial AI/ML model being used.).
Regarding claims 20, 24, 25, 26 and 27, these claims are rejected based on the same reasoning as presented in the rejection of claims 2, 6, 7, 8 and 9, respectively.
Regarding claim 28, Shen shows an apparatus (Figure 16 shows a network device performing in part the method of Figures 5-6.) for wireless communications at a network entity, comprising:
a processor (Figure 16; the network device includes a processor.);
memory coupled with the processor (Figure 16; the network device includes memory coupled with the processor.); and
instructions stored in the memory and executable by the processor to cause the apparatus (Figure 16; the network device includes software stored in memory and executed by the processor to perform the disclosed invention.) to:
receive, from a user equipment (UE), an indication identifying a first UE capability for a backbone block of a multi-block machine learning application and a second UE capability for a task-specific block of the multi-block machine learning application (Figures 5-6; Par. 0078-0079, 0085; a terminal sends artificial intelligence (AI)/machine learning (ML) capability information to a network device; wherein, the AI/ML capability information indicates resource information used by the terminal to process an AI/ML service. The AI/ML capability information may directly include the available computing power, the address of the storage space, the power headroom, the battery capacity, etc., of the terminal for a certain AI/ML service, and may further include a performance index requirement on wireless transmission of a network side by an AI/ML operation of a certain AI/ML service of the terminal, etc.);
transmit, to the UE in response to the first UE capability and the second UE capability, control signaling to configure the multi-block machine learning application of the UE (Figures 5-6; Par. 0087-0089, 0092; the terminal receives AI/ML task configuration information sent by the network device; wherein, the AI/ML task configuration information is used to indicate an AI/ML task configuration allocated by the network device to the terminal according to the AI/ML capability information); and
communicate with the UE based at least in part on the control signaling (Figure 6; noted network device and terminal communicating based on the initial AI/ML model being used.).
Regarding claim 29, this claim is rejected based on the same reasoning as presented in the rejection of claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/REDENTOR PASIA/Primary Examiner, Art Unit 2413
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