Renderer Selection Scoring Algorithm for Client Matching
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Solution Overview
Problem
In heterogeneous client-server systems, servers face challenges in efficiently selecting the appropriate renderer to communicate with diverse clients using different browsers or browser versions, leading to maintenance complexities and potential functionality issues.
Innovation Solution
A scoring algorithm that compares client identifiers with client templates to select a renderer based on similarity, using scores calculated by matching characters, ensuring efficient renderer selection and reduced maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server maintains specific renderers for every client type, then the renderer selection accuracy is improved, but the device complexity and maintenance burden increase
Solution Approach 1:
The patent creates a universal renderer selection mechanism that can handle multiple client types through a single scoring-based system rather than maintaining separate renderers for each client. The scoring algorithm universally evaluates client identifiers against available renderers using consistent criteria, making the system adaptable to any client without requiring dedicated maintenance for each type.
Solution Approach 2:
The patent changes the selection parameter from exact client-type matching to a scoring system based on similarity metrics. By evaluating multiple parameters (character matching, pattern recognition) and converting them into scores, the system achieves accurate renderer selection without the complexity of maintaining separate configurations for every client variation.
2Productivity
If the server uses a scoring algorithm to select renderers, then the productivity and efficiency are improved, but the measurement precision of client identification may be reduced
Solution Approach 1:
The scoring algorithm performs partial matching rather than requiring complete exact matches. By evaluating client identifiers against renderer patterns and assigning scores based on the degree of match, the system achieves efficient selection without sacrificing accuracy. The algorithm can identify sufficient similarity to make correct selections without examining every possible detail.
Solution Approach 2:
The scoring system provides feedback through numerical scores that indicate the quality of match between client identifier and renderer. This quantitative feedback allows the system to efficiently evaluate multiple options and select the best match, maintaining precision while improving productivity through automated scoring rather than manual or complex rule-based selection.
Data Source
AI summary
Methods and apparatus, including computer program products, are used to select a renderer. A client identifier is received that identifies a client. The client identifier is a string that is compared with each of one or more client templates, where each client template is associated with one of a plurality of renderers. A score is generated for each comparison, the score reflecting the similarity between the client identifier and the client template. Based on the score, a renderer is selected from the plurality of renderers for use in communication with the client.