Distributed Vector Drawing Pipeline for Collaboration Latency
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Solution Overview
Problem
Collaboration systems face delays and reduced quality due to the inefficient transfer of complex graphical drawings and annotations between client devices during collaboration sessions, as they require significant data transfer, which can be exacerbated by differing display resolutions and zoom levels.
Innovation Solution
A distributed vector drawing pipeline that processes data at a server node by removing erroneous and redundant sample points, generating graphical data at multiple fidelity levels, and sending data based on the recipient client's zoom level, reducing the amount of data transmitted and improving rendering efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graphical drawing and annotation data is transferred from one client device to another via server node, then collaboration session functionality is enabled, but data transfer delays increase and collaboration quality reduces
Solution Approach 1:
The patent extracts and removes redundant sample points from the graphical data before transmission. The server identifies and eliminates duplicate or unnecessary data points that would otherwise be transmitted across the network, reducing data volume and transfer time while preserving the essential visual information needed for collaboration.
Solution Approach 2:
The patent applies partial action by selectively transmitting only the necessary fidelity level of graphical data based on the recipient's display characteristics. Instead of sending complete high-fidelity data to all clients, the system transmits appropriate portions of data tailored to each client's needs, reducing overall network traffic while maintaining adequate visual quality.
2Loss of information
If complex graphical drawings and annotations are transmitted between client devices, then complete visual information is delivered, but data transfer size increases
Solution Approach 1:
The patent applies local quality by generating graphical data at multiple fidelity levels and selectively delivering appropriate levels to different clients based on their specific display capabilities and zoom levels. Each client receives data with the precise quality needed for their local context, avoiding unnecessary transmission of excessive detail to devices that cannot display it.
Solution Approach 2:
The system extracts and removes redundant sample points from the graphical data. By identifying and eliminating duplicate or unnecessary data points before transmission, the system reduces the overall data volume while preserving the essential visual information required for effective collaboration.
3Manufacturing precision
If graphical data is transmitted at high fidelity to all clients, then visual quality is maintained, but network bandwidth consumption increases
Solution Approach 1:
The patent implements partial action by transmitting only the necessary amount of fidelity for each client's specific needs. Based on each client's display resolution, zoom level, and device capabilities, the server delivers appropriately scaled fidelity levels, ensuring adequate visual quality without the excessive bandwidth consumption of universal high-fidelity transmission.
Solution Approach 2:
The system applies local quality by tailoring the fidelity level of transmitted graphical data to match each recipient client's local display characteristics. Each client receives data with the precise quality needed for their specific context, optimizing the balance between visual quality and network resource utilization.
Data Source
AI summary
Systems and methods are provided for processing data received at a server node from a first client node. The method includes generating selected data by removing one or more redundant sample points from data received from the first client node. A redundant sample point represents a sample point, of the data received from the first client node, that can be regenerated from one or more adjacent sample points, of the data received from the first client node, using interpolation. The method includes generating, from the selected data, graphical data at multiple fidelity levels including at least one of low-fidelity graphical data, medium-fidelity graphical data and high-fidelity graphical data. The method includes sending at least one of the low-fidelity graphical data, the medium-fidelity graphical data and the high-fidelity graphical data to a second client node in dependence on a current zoom level of the second client node.