Synchronized Input Using Predictive Models for Low-Latency Metaverse Links
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Solution Overview
Problem
Existing communication systems face challenges in achieving low latency and high-quality data representation for metaverse applications, particularly when users are far apart, and synchronizing data streams from multiple locations.
Innovation Solution
The system employs predictive models to reduce latency by predicting user actions based on sensor input and synchronizes communication flows using shared communication parameters, including distance and latency, to enable low-latency, high-quality data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If direct interaction between distant users is implemented, then communication quality is maintained, but latency increases with distance
Solution Approach 1:
A predictive model acts as an intermediary between distant users. The model receives sensor data from one user and generates predicted actions that are transmitted to the other user's rendering device, eliminating the need for direct real-time communication over long distances and thus reducing latency without sacrificing interaction quality
Solution Approach 2:
The system performs preliminary actions by predicting future user actions based on historical sensor data before the actual interaction occurs. This allows the receiving end to prepare rendering content in advance, reducing the effective latency of the interaction loop
2Measurement precision
If high-quality data representation is used, then interaction quality improves, but data rate requirements increase
Solution Approach 1:
Instead of transmitting raw sensor data at high quality, the system creates a predictive model that generates synthesized representations of user actions. This copying approach allows high-quality data representation to be achieved locally at the receiving end without transmitting the full original data, thus reducing data rate requirements while maintaining interaction quality
Solution Approach 2:
The system extracts only the essential information needed for prediction from the sensor data and transmits this compressed representation. The predictive model then reconstructs the full high-quality representation locally, separating the transmission function from the rendering function to reduce data rate requirements
3Adaptability or versatility
If multiple users at different locations interact simultaneously, then system capability increases, but synchronization complexity increases
Solution Approach 1:
The system segments the synchronization task by handling each user's data stream independently through their own predictive models. Each user's sensor data is processed locally to generate predicted actions, which are then synchronized at the receiving end. This segmentation approach allows multiple users to interact simultaneously without creating complex inter-dependent synchronization relationships
Solution Approach 2:
By performing preliminary prediction of each user's actions before synchronization, the system establishes a common temporal reference frame for all users. This preliminary action simplifies the subsequent synchronization process, as the predicted actions are already aligned with the expected interaction timeline, reducing the complexity of coordinating multiple users at different locations
Data Source
AI summary
This invention relates to an apparatus for providing synchronized input to a third device, the apparatus comprising a. a memory to store communication parameters shared between the third device and a first device, and/or between the third device and a second device, b. a communication unit to receive a first communication flow from the first device, and/or, a second communication flow from the second device, wherein the communication flows are synchronized based on the communication parameters.