DETAILED ACTION
This is in response to the Applicant's arguments and amendments filed on 10 October 2024 in which claims 1-14 are currently pending and claim 15 has been cancelled.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The references listed in the Information Disclosure Statement, filed on 19 November 2024, have been considered by the examiner (see attached PTO-1449 form or PTO/SB/08A and 08B forms).
Specification
The abstract of the disclosure is objected to because it is suggested to remove the words “(FIG. 9)” at the end of the abstract. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 8, 9 are objected to because of the following informalities:
Regarding claim 8, it is unclear what is meant by the claimed limitation “the power consumption indication is or is converted” in line 2.
Regarding claim 9, it is unclear what is meant by the claimed limitation “the QoS indication is or is converted” in line 2.
For the examination on the merits, the claims will be interpreted as best understood. Appropriate correction is required.
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.
Claims 1-4, 6-14 are rejected under 35 U.S.C. 103 as being unpatentable over Vannithamby et al. (PG Pub US 2023/0199669 A1) in view of Zheng et al. (PG Pub US 2025/0267754 A1).
Regarding claims 1, 12, Vannithamby discloses a method and an apparatus.
memory storing computer readable instructions; and processing circuitry configured to execute the computer readable instructions to cause the apparatus to (memory 402, processor 401, fig. 4):
perform a Reinforcement Learning, RL, process (“reinforcement learning model” [0098]) to configure at least one Discontinuous Reception, DRX, cycle for a User Equipment, UE (“the BS 351 may configure the radio resources according to the preference of the terminal device. The BS 351 may adjust the DRX parameters” [0061], “The AI/ML 1402 may be trained to provide an output indicating a DRX parameter for the respective UE” [0123]);
performing the RL process comprises: select, by the RL agent, an action in an action space, each action in the action space corresponds to a DRX cycle configuration defined by a set of at least one DRX cycle configuration parameter, wherein each set of at least one DRX cycle configuration parameter corresponding to an action in the action space includes a DRX cycle active period duration (“The MDP may determine an action from an action set based on a previous observation which may be referred to as a state” [0098], “The AI/ML 1402 may be trained to provide an output indicating a DRX parameter for the respective UE .. one of a DRX Cycle, a duration of ON time within one DRX Cycle, a DRX inactivity timer, a DRX retransmission timer, a short DRX Cycle, a DRX short cycle timer” [0123]);
send to the UE indication to use the DRX cycle configuration corresponding to the selected action (“The AI/ML 1402 may be trained to provide an output indicating a DRX parameter for the respective UE” [0123], “The BS 351 may adjust the DRX parameters” [0061]);
receive, by the RL agent from the UE, state information computed over at least one DRX cycle, each of the at least one DRX cycle being configured based on a DRX cycle configuration indicated by the RL agent, the state information including at least one of a power consumption indication and a Quality of Service, QoS, indication (“The device data may include information with respect to the DRX support of the UE .. or user's preference with respect to power-saving or QoS” [0083], “The UE 2301 may measure the received downlink radio communication signals from the BS (e.g. SSBs or CSI-RSs) to obtain one or more measurement results. Furthermore, the UE 2301 may obtain the context information is provided in this disclosure. The processor of the UE 2301 may provide the input including the one or more measurement results” [0181]);
compute, by the RL agent, a reward based on the state information (“In a next state, the MDP may determine a reward based on the next state and the previous state” [0098]);
update a policy for selecting an action in the action space based on the reward (“the RL agent 1001 may obtain the capability to map the states that the communication activity data 1002 indicates to the actions with a goal to maximize the QoS and/or QoE while preserving maximum power, or while sending as maximum number of PPIs indicating the low power mode, or while maximizing the duration of the low power mode for the device” [0108]).
However, Vannithamby does not explicitly disclose includes a DRX cycle active period duration.
Nevertheless, Zheng discloses “dynamically adjusting UE DRX configurations may include DRX predication in AI-based DRX configuration, AI-based short/long DRX cycle switching, AI-based drx-Inactivity Timer prediction, and switching between AI-DRX and legacy DRX mode (e.g., without AI)” [0122], “This DRX configuration may be signaled, such as via RRC signaling 1006, to the UE 1008 when the UE 1008 is in an active time (e.g., DRX on duration)” [0125].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include a DRX cycle active period duration because “The AI-based DRX configuration can dynamically adjust UE DRX configurations based on a prediction generated by an AI system” [0045].
Regarding claim 13, Vannithamby discloses a method.
receiving, by a User Equipment from a Reinforcement Learning, RL, agent, indication to use a Discontinuous Reception, DRX, cycle configuration (“The AI/ML 1402 may be trained to provide an output indicating a DRX parameter for the respective UE” [0123], “The BS 351 may adjust the DRX parameters” [0061]), the DRX cycle configuration is defined by a set of at least one DRX cycle configuration parameter (“The AI/ML 1402 may be trained to provide an output indicating a DRX parameter for the respective UE .. one of a DRX Cycle, a duration of ON time within one DRX Cycle, a DRX inactivity timer, a DRX retransmission timer, a short DRX Cycle, a DRX short cycle timer” [0123], “the AI/ML may be configured to provide the output based on the set of machine model parameters and the group set of machine model parameters for each one or more UEs” [0118]);
configuring at least one DRX cycle based on the set of at least one DRX cycle configuration parameter, wherein the set of at least one DRX cycle configuration parameter includes a DRX cycle active period duration (“DRX parameters provide indications to the UE with respect to when the UE may operate in ON time or OFF time to receive radio communication signals from the BS” [0074]);
sending, by the UE to the RL agent, state information computed over at least one DRX cycle, each of the at least one DRX cycle is configured based on a DRX cycle configuration indicated by the RL agent, the state information including at least one of a power consumption indication and a QoS indication (“The device data may include information with respect to the DRX support of the UE .. or user's preference with respect to power-saving or QoS” [0083]).
However, Vannithamby does not explicitly disclose includes a DRX cycle active period duration.
Nevertheless, Zheng discloses “dynamically adjusting UE DRX configurations may include DRX predication in AI-based DRX configuration, AI-based short/long DRX cycle switching, AI-based drx-Inactivity Timer prediction, and switching between AI-DRX and legacy DRX mode (e.g., without AI)” [0122], “This DRX configuration may be signaled, such as via RRC signaling 1006, to the UE 1008 when the UE 1008 is in an active time (e.g., DRX on duration)” [0125].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include a DRX cycle active period duration because “The AI-based DRX configuration can dynamically adjust UE DRX configurations based on a prediction generated by an AI system” [0045].
Regarding claims 2, 14, Vannithamby, Zheng discloses everything claimed as applied above. Further, Zheng discloses at least one set of at least one DRX cycle configuration parameter corresponding to an action in the action space includes at least one of a start offset for the DRX cycle active period and a DRX cycle length (“new drxStartOffset” [0128], “length of a DRX cycle” [0151]).
Regarding claim 3, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses the power consumption indication represents a power consumption level determined over the at least one DRX cycle (“The battery information may further include one or more constraints with respect to the battery of the UE. The constraints may include the capacity of the battery of the UE, or an estimated time for the battery to run out according to past operations and the remaining electrical supply power of the battery” [0081], “at least one of a DRX Cycle” [0123]).
Regarding claim 4, Vannithamby, Zheng discloses everything claimed as applied above. Further, Zheng discloses the QoS indication is computed based on Extended Reality, XR, frames received over the at least one DRX cycle (“an extended reality (XR) device” [0049], “predict whether and when switch between the short and long DRX cycles” [0131]).
Regarding claim 6, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses the reward is computed as a function of at least one of a QoS satisfaction based on the QoS indication and a power consumption penalty based on the power consumption indication (“the RL agent 1001 may determine a reward based on the determined action in the first instance time and a second state that the communication activity data 1002 represents at a second instance time. The RL agent 1001 may receive the reward based on at least one of an impact of QoS or QoE based latencies” [0108]).
Regarding claim 7, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses the reward is computed as a weighted sum of rewards computed respectively for different types of frames received by the UE (“the new Q value of the corresponding state-action pair t is based on the old Q value for the state-action pair t and the sum of the reward r obtained by taking action at in the state st with a discount rate γ that is between 0 and 1, in which the weight between the old Q value and the reward portion is determined by the learning rate a” [0180]). Further, Zheng discloses XR frames (“an extended reality (XR) device” [0049]).
Regarding claim 8, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses the power consumption indication is or is converted to a power consumption level coded on n bits, where n is equal or greater than 1, the method comprising determining a state in a state space based on the power consumption level (“The battery information may further include one or more constraints with respect to the battery of the UE” [0081], “perform the AI/ML algorithm based on the battery level of the device, such as when the battery level is above a predefined threshold. Furthermore, the controller 603 may provide instructions to the AI/ML module 602 to operate in a low power mode in which the AI/ML module 602 does not perform the AI/ML algorithm when the device is being charged” [0092]).
Regarding claim 9, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses the QoS indication is or is converted to a QoS level coded on n bits, where n is equal or greater than 1, the method comprising determining a state in a state space based on the QoS level (“the training agent may obtain the training data based on communication activities performed in various conditions, such as various distances to the BS, various application conditions and QoS requirements” [0100], “the communication activity data 1201 may include .. their QoS requirements” [0110]).
Regarding claim 10, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses performing signalling with the UE to agree on at least one of a state space for the state information and an action space (“In a next state, the MDP may determine a reward based on the next state and the previous state. The determined action may influence the probability of the MDP to move into the next state. Accordingly, the MDP may obtain a function that maps the current state to an action to be determined with the purpose of maximizing the rewards” [0169], “the BS 351 may configure the radio resources according to the preference of the terminal device. The BS 351 may adjust the DRX parameters” [0061]).
Regarding claim 11, Vannithamby, Zheng discloses everything claimed as applied above. In addition, Vannithamby discloses performing signalling with the UE to agree on at least one threshold to be used for computing the power consumption level or respectively the QoS level (“perform the AI/ML algorithm based on the battery level of the device, such as when the battery level is above a predefined threshold” [0159], “the BS 351 may configure the radio resources according to the preference of the terminal device. The BS 351 may adjust the DRX parameters” [0061]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Vannithamby, Zheng further in view of Chen et al. (PG Pub US 2025/0227506 A1).
Regarding claim 5, Vannithamby, Zheng discloses everything claimed as applied above. However, Vannithamby, Zheng does not explicitly disclose the QoS indication is computed based on a ratio of a number of Packet Data Units received within a packet delay budget.
Nevertheless, Chen discloses “a ratio of a data amount of the data to a remaining delay budget of the data” [0032].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have the QoS indication be computed based on a ratio of a number of Packet Data Units received within a packet delay budget because “the network device can schedule, based on the required rate of the data, the resource for transmission of the data, to reduce a data transmission delay and improve communication performance” [0033].
Conclusion
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CHRISTINE DUONG FUQUA
Primary Examiner
Art Unit 2462
/CHRISTINE T DUONG/ Primary Examiner, Art Unit 2462 09/04/2026
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