17806164. USER EQUIPMENT MACHINE LEARNING SERVICE CONTINUITY simplified abstract (QUALCOMM Incorporated)

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USER EQUIPMENT MACHINE LEARNING SERVICE CONTINUITY

Organization Name

QUALCOMM Incorporated

Inventor(s)

Qing Li of Princeton Junction NJ (US)

Hong Cheng of Basking Ridge NJ (US)

Kapil Gulati of Belle Mead NJ (US)

Kyle Chi Guan of New York NY (US)

Himaja Kesavareddigari of Bridgewater NJ (US)

USER EQUIPMENT MACHINE LEARNING SERVICE CONTINUITY - A simplified explanation of the abstract

This abstract first appeared for US patent application 17806164 titled 'USER EQUIPMENT MACHINE LEARNING SERVICE CONTINUITY

Simplified Explanation

Abstract

The present disclosure relates to wireless communication and specifically to a method for performing a handover in a wireless network while maintaining machine learning services. The method involves a user equipment (UE) transmitting machine learning data to a first network node for use by a first inference host associated with the first network node. The UE then receives a handover command communication from the first network node, indicating that the UE is to perform a handover to a second network node. The handover command communication also includes machine learning inference information associated with a second inference host that is associated with the second network node. The UE then transmits second machine learning data to the second network node for use by the second inference host, based on the received handover command communication.

Bullet Points

  • User equipment (UE) transmits machine learning data to a first network node for use by a first inference host.
  • UE receives a handover command communication from the first network node, indicating a handover to a second network node.
  • Handover command communication includes machine learning inference information associated with a second inference host.
  • UE transmits second machine learning data to the second network node for use by the second inference host.

Potential Applications

  • This technology can be applied in wireless communication networks to enable seamless handover of machine learning services between network nodes.
  • It can be used in various industries where machine learning services are utilized, such as autonomous vehicles, smart cities, and industrial automation.
  • The technology can enhance the performance and reliability of machine learning applications in wireless networks.

Problems Solved

  • The technology solves the problem of maintaining machine learning services during a handover in a wireless network.
  • It ensures uninterrupted machine learning inference by seamlessly transferring the machine learning data from one network node to another.
  • The technology addresses the challenge of maintaining real-time machine learning services in dynamic wireless environments.

Benefits

  • Enables uninterrupted machine learning services during handover, leading to improved user experience and system performance.
  • Enhances the reliability and efficiency of machine learning applications in wireless networks.
  • Facilitates the deployment of machine learning services in various industries by providing seamless handover capabilities.


Original Abstract Submitted

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to first network node for use by a first inference host associated with a first network node, first machine learning data associated with a machine learning service. The UE may receive, from the first network node, a handover command communication indicating that the UE is to perform a handover from the first network node to a second network node, wherein the handover command communication indicates machine learning inference information associated with a second inference host that is associated with the second network node. The UE may transmit, to the second network node for use by the second inference host associated with the second network node, second machine learning data for the machine learning service based at least in part on receiving the handover command communication. Numerous other aspects are described.

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