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    CONDITIONAL ARTIFICIAL INTELLIGENCE, MACHINE LEARNING MODEL, AND PARAMETER SET CONFIGURATIONS
    101.
    发明公开
    CONDITIONAL ARTIFICIAL INTELLIGENCE, MACHINE LEARNING MODEL, AND PARAMETER SET CONFIGURATIONS 审中-公开

    公开(公告)号：US20230412470A1

    公开(公告)日：2023-12-21

    申请号：US17841339

    申请日：2022-06-15

    Applicant: QUALCOMM Incorporated

    Inventor： Rajeev Kumar ,  Xipeng Zhu ,  Shankar Krishnan

    IPC: H04L41/16 ,  G06N20/20

    CPC classification number: H04L41/16 ,  G06N20/20 ,  H04W88/02

    Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a message from a network entity indicating a set of machine learning models, a set of parameter sets, or both and one or more usage conditions associated with the machine learning models and parameter sets. Based on a usage condition being satisfied, the UE may select a machine learning model, a parameter set, or both for generating a machine learning inference. For example, the UE may select the machine learning model or the parameter set based on a priority, whether sufficient input data is provided, or based on other usage conditions. The UE may generate the machine learning inference using the selected machine learning model or the selected parameter set, and the UE may transmit a report indicating an output of the machine learning inference to the network entity.

    ML model training procedure
    104.
    发明授权
    ML model training procedure 有权

    公开(公告)号：US11818806B2

    公开(公告)日：2023-11-14

    申请号：US17323242

    申请日：2021-05-18

    Applicant: QUALCOMM Incorporated

    Inventor： Rajeev Kumar ,  Eren Balevi ,  Taesang Yoo ,  Xipeng Zhu ,  Gavin Bernard Horn ,  Shankar Krishnan ,  Aziz Gholmieh

    IPC: H04W16/22 ,  H04W88/08 ,  G06N20/00 ,  G06F18/214

    CPC classification number: H04W88/08 ,  G06F18/214 ,  G06N20/00

    Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an ML model training procedure. A network entity may receive a trigger to activate an ML model training procedure based on at least one of an indication from an ML model repository or a protocol of the network entity. The network entity may transmit an ML model training request to activate the ML model training at one or more nodes. The one or more nodes may be associated with a RAN that may transmit, based on receiving the ML model training request, ML model training results indicative of a trained ML model. In aspects, an apparatus, such as a UE, may train the ML model based on an ML model training configuration received from the RAN, and transmit an ML model training report indicative of the trained ML model.

    Data forwarding during inter-system handovers
    106.
    发明授权
    Data forwarding during inter-system handovers 有权

    公开(公告)号：US11812313B2

    公开(公告)日：2023-11-07

    申请号：US17444804

    申请日：2021-08-10

    Applicant: QUALCOMM Incorporated

    Inventor： Punyaslok Purkayastha ,  Xipeng Zhu

    IPC: H04W36/00

    CPC classification number: H04W36/0066 ,  H04W36/0033

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a source master eNodeB (MeNB) may transmit, to a target Next Generation radio access network (NG-RAN) node, a handover required message to initiate an inter-system handover from an evolved-Universal Mobile Telecommunications System (UMTS) terrestrial radio access network (E-UTRAN) New Radio (NR) dual connectivity (EN-DC) system associated with the source MeNB to an NR standalone (SA) system associated with the target NG-RAN node. The source MeNB may have an indirect path to the target NG-RAN node and a source secondary gNodeB (SgNB) may have a direct path to the target NG-RAN node. The source MeNB may transmit, to the target NG-RAN node and based at least in part on the handover required message, forwarded data via the indirect path between the source MeNB and the target NG-RAN node through a core network. Numerous other aspects are described.

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