DETAILED ACTION
In response to communication filed on 3/9/2026.
Claims 1,2,4-21,23-27, and 30-34 are pending.
Claims 1,2,4-21,23-27, and 30-34 are rejected.
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 .
Response to Amendments
This communication is in response to Applicant’s reply filed under 3 CFR 1.111 on 3/9/2026. Claims 1,20,30 and 31 were amended, claims 3 and 22 were canceled, claims 32-34 were added and claims 1,2,4-21,23-27, and 30-34 remain pending.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1,2,4-9,14,20,21,23, and 30-34 are rejected under 35 U.S.C. 103 as unpatentable over Shen et al. (US Pub. 2022/0342713)(S1 hereafter) in view of Li et al. (US Pub. 2024/0349082)(L2 hereafter).
Regarding claims 1,20,30 and 31, S1 teaches an apparatus (i.e. network)[refer Fig. 1] for wireless communications at a user equipment (i.e. terminal)[refer Fig. 12][paragraph 0164], comprising at least one memory comprising computer-executable instructions [paragraph 0048]; and
one or more processors configured to execute the computer-executable instructions (a terminal comprises of memory and processor to implement functions [paragraph 0048], a network device (i.e. entity) comprises of memory and processor to perform functions [paragraph 0049]) and cause the apparatus to:
receive, at a user equipment from a network (i.e. network device or entity), control information (i.e. task configuration information [paragraph 0137] or indication information)[refer Fig. 6; Indication information for AI/ML model1][refer Fig. 6; Indication information for AI/ML model2],
the control information indicates a first configuration for receiving a first type of machine learning model (i.e. AI/ML model 2 for low computing power)[paragraph 0138][refer Fig. 6] and a second configuration for receiving a second type of machine learning model (i.e. AI/ML model 1 for high computing power)[paragraph 0138][refer Fig. 6],
the first type of machine learning model is configured for a first type (i.e. computing power) of user equipment (AI/ML model 2 is designed for low computing power)[paragraph 0138], and
the second type of machine learning model is configured for a second type of user equipment (AI/ML model 1 for high computing power)[paragraph 0138];
determine to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment [paragraph 0138]; and
receive a first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining (the AI/ML model is distributed and allocated from the network device (i.e. entity) to the terminal based upon capabilities reported by the terminal)[paragraph 0089].
However, S1 doesn’t expressly disclose that the first type of user equipment corresponds to a reduced capability category of user equipment, and the second type of user equipment corresponds to a regular capability category of user equipment.
L2 discloses that a network can provide relevant ML models to a UE, the network can inquire the UE capabilities, such as ML capabilities and hardware capabilities, and based upon the capability information, the network can select or generate a ML configuration for the UE device, such as a ML model/configuration that uses more or fewer resources depending on the UE capabilities [paragraph 0067].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations for low computing and high computing devices [refer S1; paragraph 0138] to incorporate the providing of particular ML models to UE devices based upon their hardware capabilities as taught by L2. One would be motivated to do so to optimize UE performance [refer L2; paragraph 0011].
Regarding claims 2 and 21, S1 teaches the first type of machine learning model results in a lower complexity (i.e. computing) machine learning operation than the second type of machine learning model (i.e. AI/ML model 2 is for low computing power)[paragraph 0138][refer Fig. 6].
Regarding claims 4 and 23, S1 teaches the first configuration schedules a first scheduled downlink message (i.e. from network device to terminal)[refer Fig. 6; Indication information for indicating AI/ML model 2 used by terminal], and the second configuration schedules a second scheduled downlink message [refer Fig. 6; Indication information for indicating AI/ML model 1 used by terminal][paragraph 0137].
Regarding claim 5, S1 teaches the user equipment is the first type of user equipment (i.e. the terminal’s available computing power can be low in comparison to what is needed for an AI/ML model)[paragraph 0138], determining to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment comprises determining to apply the first configuration (AI/ML model 2 is designed for low computing power)[paragraph 0138], and
receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model according to the first configuration (when the computing power needed for running AI/Model 2 is low, the network device can allocate the AI/ML model 2 to the terminal)[paragraph 0138].
Regarding claim 6, S1 teaches the user equipment is the second type of user equipment (i.e. the terminal’s available computing power is high)[paragraph 0138], determining to apply at least one of the first configuration or the second configuration based on whether the user equipment is the first type of user equipment or the second type of user equipment comprises determining to apply the second configuration (when the available computer power is high, the terminal uses AI/ML model 1)[paragraph 0138], and
receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model according to the second configuration (when the computing power needed for running AI/Model 1 is great, the network device can allocate the AI/ML model 1 to the terminal)[paragraph 0138].
Regarding claim 7, S1 teaches determine to apply the first configuration (when the terminal’s available computing power can be low, AI/ML model 2 is allocated to the terminal)[paragraph 0138][refer Fig. 6; Indication information for indicating AI/ML model 2 used by terminal]; and receive a second machine learning model from the network according to the first configuration [refer Fig. 6; Indication information for indicating AI/ML model 2 used by terminal].
Regarding claim 8, S1 teaches to determine to apply one of the first machine learning model or the second machine learning model based on at least one condition of the user equipment [paragraph 0137][refer Fig. 6; Available computer power, storage capacity, power headroom/battery capacity and communication performance index requirement of a terminal for an AI/ML task].
Regarding claim 9, S1 teaches the at least one condition of the user equipment comprises one or more of: a battery state of the user equipment; a power state of the user equipment; or an active bandwidth part (i.e. communication performance index) of the user equipment [paragraph 0136][refer Fig. 6; Available computer power, storage capacity, power headroom/battery capacity and communication performance index requirement of a terminal for an AI/ML task].
Regarding claim 14, S1 teaches the control information comprises one or more medium access control (MAC) control elements (CEs) [paragraph 0045].
Regarding claim 32, S1 fails to disclose the first type of user equipment corresponding to the reduced capability category of user equipment comprises at least one of fewer antennas, narrower bandwidth, or longer processing timelines as compared with the second type of user equipment corresponding to the regular capability category of user equipment.
L2 discloses that a network can provide relevant ML models to a UE, the network can inquire the UE capabilities, such as ML capabilities and hardware capabilities, and based upon the capability information, the network can select or generate a ML configuration for the UE device, such as a ML model/configuration that uses more or fewer resources depending on the UE capabilities [paragraph 0067], hardware capabilities can indicate whether the hardware of the UE can/want support machine learning, such a chip type, max battery capacity, data batching, etc. [paragraph 0022].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations for low computing and high computing devices [refer S1; paragraph 0138] to incorporate the providing of particular ML models to UE devices based upon their specific hardware capabilities as taught by L2. In doing so, some UE devices can have differing hardware resources that would be taken into consideration, such as a differing processing due to the chip type. One would be motivated to do so to optimize UE performance [refer L2; paragraph 0011].
Regarding claim 33, S1 teaches the control information is provided in a message that indicates both the first configuration and the second configuration and is receivable by both the first type of user equipment and the second type of user equipment (UE can receive and store multiple AI/ML models)[paragraph 0112].
Regarding claim 34, S1 fails to disclose the first type of machine learning model is configured for the first type of user equipment corresponding to the reduced capability category of user equipment as defined in network interoperability standards, and the second type of machine learning model is configured for the second type of user equipment corresponding to the regular capability category of user equipment as defined in the network interoperability standards.
L2 discloses that a network can provide relevant ML models to a UE, the network can inquire the UE capabilities, such as ML capabilities and hardware capabilities, and based upon the capability information, the network can select or generate a ML configuration for the UE device, such as a ML model/configuration that uses more or fewer resources depending on the UE capabilities [paragraph 0067], hardware capabilities can indicate whether the hardware of the UE can/want support machine learning, such a chip type, max battery capacity, data batching, etc. [paragraph 0022].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations for low computing and high computing devices [refer S1; paragraph 0138] to incorporate the providing of particular ML models to UE devices based upon their specific hardware capabilities as taught by L2. In doing so, some UE devices can have differing hardware capabilities that would be taken into consideration, such as a differing processing due to the chip type. One would be motivated to do so to optimize UE performance [refer L2; paragraph 0011].
Claims 10,18, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over S1 in view of L2, as applied to claims 1 and 20, in further view of Jeon et al. (US Pub. 2022/0287104)(J1 hereafter).
Regarding claims 10 and 24, S1 fails to disclose receiving the first machine learning model from the network according to at least one of the first configuration or the second configuration based on the determining comprises receiving the first machine learning model via one or more system information blocks (SIBs).
J1 discloses for operations that support ML/AI techniques, ML/AI related configuration information can be signaled by a base station using system information blocks (SIB) [paragraph 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the signaling of AI/ML related configurations using SIBs as taught by J1. One would be motivated to do so to provide the use of a known technique, such as signaling configurations using known procedures such as SIBs, in the field of endeavor of AI/ML to yield predictable results [refer J1; paragraph 0082].
Regarding claim 18, S1 fails to disclose the control information comprises one or more system information blocks (SIBs).
J1 discloses for operations that support ML/AI techniques, ML/AI related configuration information can be signaled by a base station using system information blocks (SIB) [paragraph 0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the signaling of AI/ML related configurations using SIBs as taught by J1. One would be motivated to do so to provide the use of a known technique, such as signaling configurations using known procedures such as SIBs, in the field of endeavor of AI/ML to yield predictable results [refer J1; paragraph 0082].
Claims 11-13,15-17, and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over S1 in view of L2, as applied to claims 1 and 20, in further view of Lee et al. (US Pub. 2021/0110261)(L1 hereafter).
Regarding claims 11 and 25, S1 fails to disclose the control information comprises downlink control information (DCI) received via a physical downlink control channel (PDCCH).
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052] is transmitted on PDCCH [paragraph 0060].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information on a PDCCH as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Regarding claims 12 and 26, S1 fails to disclose the DCI comprises a bitmap or a codepoint configured to indicate a scheduled downlink message for receiving the first machine learning model.
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052], comprises of a bitmap for identifying resource allocation [paragraph 0053].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Regarding claims 13 and 27, S1 fails to disclose the DCI includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052], comprises of CRC that are scrambled with a RNTI corresponding to a UE identity [paragraph 0061].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Regarding claim 15, S1 fails to disclose downlink control information (DCI) scheduling the one or more MAC CEs includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052], comprises of CRC that are scrambled with a RNTI corresponding to a UE identity [paragraph 0061].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Regarding claim 16, S1 fails to disclose the control information comprises a radio resource control (RRC) message.
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that configuration can be performed via RRC signaling (i.e. RRC message)[paragraph 0078].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of RRC signaling to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Regarding claim 17, S1 fails to disclose downlink control information (DCI) scheduling the RRC message includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052], comprises of CRC that are scrambled with a RNTI corresponding to a UE identity [paragraph 0061].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over S1 in view of L2 in further view of J1, as applied to claim 18, in further view of L1.
Regarding claim 19, S1 fails to disclose downlink control information (DCI) scheduling the one or more SIBs includes a cyclic redundancy check (CRC) scrambled via a cell-specific or user equipment group-specific radio network temporary identifier (RNTI).
L1, in the field of transmitting or receiving signaling including channel information using deep learning and AI [paragraph 0002], discloses that a DCI, in which downlink and uplink data is transferred from a base station to a UE [paragraph 0052], comprises of CRC that are scrambled with a RNTI corresponding to a UE identity [paragraph 0061].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of S1 for providing configurations of AI/ML model configurations [refer S1; paragraph 0138] to incorporate the use of DCI to transmit information as taught by L1. One would be motivated to do so to provide the use of a known technique in the field of endeavor to yield predictable results.
Response to Arguments
Applicant’s arguments, see pages 10-12, filed 3/9/2026, with respect to the rejection(s) of claims 1-9,14,20-23 and 30-31 under 35 U.S.C. 102(a)(2) have been fully considered and are persuasive in view of the amendments to the claims and prior discussions in the Interview on 2/13/2026. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the teachings of Li et al. (US Pub. 2024/0349082)(L2 hereafter) as noted in the above rejection, notably with regards to the disclosure that a network can provide relevant ML models to a UE, the network inquiring of the UE capabilities, such as ML capabilities and hardware capabilities, and based upon capability information, the network can select or generate a ML configuration for the UE device, such as a ML model/configuration that uses more or fewer resources depending on the UE capabilities [refer L2; paragraph 0067].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C KAVLESKI whose telephone number is (571)270-3619. The examiner can normally be reached M-F 6:30am-3pm.
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Ryan Kavleski
/R.C.K./
Examiner, Art Unit 2412
/CHARLES C JIANG/Supervisory Patent Examiner, Art Unit 2412
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