Qualcomm incorporated (20240107594). USER EQUIPMENT PAIRING AND COOPERATIVE MACHINE LEARNING INFERENCE RESULT SHARING simplified abstract

USER EQUIPMENT PAIRING AND COOPERATIVE MACHINE LEARNING INFERENCE RESULT SHARING

Organization Name

qualcomm incorporated

Inventor(s)

Kyle Chi Guan of New York NY (US)

Anantharaman Balasubramanian of San Diego CA (US)

Mahmoud Ashour of San Diego CA (US)

Kapil Gulati of Belle Mead NJ (US)

Himaja Kesavareddigari of Bridgewater NJ (US)

Qing Li of Princeton Junction NJ (US)

Hong Cheng of Basking Ridge NJ (US)

Preeti Kumari of San Diego CA (US)

USER EQUIPMENT PAIRING AND COOPERATIVE MACHINE LEARNING INFERENCE RESULT SHARING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240107594 titled 'USER EQUIPMENT PAIRING AND COOPERATIVE MACHINE LEARNING INFERENCE RESULT SHARING

Simplified Explanation

The patent application discusses a method for a first user equipment (UE) to improve its inference accuracy by utilizing inference results provided by at least a second UE. The first UE establishes an ML inference result sharing session with one or more second UEs for at least one ML inference task. The first UE receives a set of ML inference results from the second UEs during the sharing session and estimates an aggregated ML inference result based on these results.

• First UE collaborates with second UEs for ML inference tasks
• Receives ML inference results from second UEs
• Estimates aggregated ML inference result based on received results

Potential Applications

The technology can be applied in collaborative machine learning tasks where multiple UEs can share and combine their inference results to improve accuracy.

Problems Solved

1. Enhanced accuracy in ML inference tasks through collaboration 2. Efficient utilization of resources by sharing results among UEs

Benefits

1. Improved accuracy in ML inference tasks 2. Enhanced efficiency in resource utilization 3. Collaborative learning among UEs

Potential Commercial Applications

"Collaborative Machine Learning Inference Optimization for Enhanced Accuracy"

Possible Prior Art

There may be prior art related to collaborative machine learning techniques where multiple devices share and combine their inference results to improve accuracy.

How does this technology impact privacy concerns in sharing inference results among UEs?

The article does not address the potential privacy implications of sharing ML inference results among UEs. It would be important to consider how privacy concerns are addressed in this collaborative framework.

What are the scalability limitations of this technology when dealing with a large number of UEs sharing inference results?

The scalability of the technology in handling a large number of UEs sharing and combining inference results is not discussed. Understanding the limitations in scalability is crucial for real-world implementation.

Original Abstract Submitted

aspects presented herein may enable a first ue to utilize inference results provided by at least a second ue to improve its inference accuracy. in one aspect, a first ue establishes an ml inference result sharing session with one or more second ues for at least one ml inference task. the first ue receives a first set of ml inference results associated with the at least one ml inference task, where the first set of ml inference results is received from the one or more second ues during the ml inference result sharing session. the first ue estimates an aggregated ml inference result based on at least one of the first set of ml inference results or a second set of ml inference results, where the second set of ml inference results is configured by the first ue for the at least one ml inference task.