Self-Learning Localization Service Using Heuristic Translation Buffer
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Solution Overview
Problem
Existing localization solutions require complex, proprietary infrastructures for offline translation and do not provide real-time translation capabilities, making it difficult for client applications to deliver translated text dynamically to users who speak different languages.
Innovation Solution
A self-learning localization service that uses a centralized system with a translation request buffer and heuristic analysis to delay translations until necessary, allowing for on-the-fly localization and reducing unnecessary translation loads, while providing a single API for developers to manage localization across multiple languages and jurisdictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline localization is used to provide translated texts, then translation accuracy is improved, but real-time translation capability deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-translating strings that are likely to be needed based on heuristic analysis of application behavior patterns. Translations are prepared in advance and cached before actual runtime requests, enabling both accurate pre-computed translations and fast retrieval during execution.
Solution Approach 2:
The localization system dynamically adapts between offline pre-computation mode and online real-time retrieval mode. It adjusts its operation based on runtime conditions, caching frequently accessed translations offline while providing instant access to cached translations during execution, thus achieving both accuracy and speed.
2Adaptability or versatility
If a centralized localization service is implemented to provide real-time translation, then translation availability is improved, but system complexity increases
Solution Approach 1:
The localization service operates autonomously by automatically analyzing application string access patterns, identifying translation needs through heuristic rules, and managing the translation cache without manual intervention. The system self-adjusts its behavior based on observed usage patterns, reducing the need for complex configuration and management infrastructure.
3Loss of information
If translations are pre-computed for all possible scenarios, then translation completeness is improved, but resource consumption increases
Solution Approach 1:
Instead of computing all possible translations, the system performs partial pre-computation only for translations that are predicted to be needed based on heuristic analysis of application behavior. It translates only the necessary subset of strings that match observed patterns, avoiding waste of resources on translations that would never be accessed.
Data Source
AI summary
The disclosure generally describes computer-implemented methods, software, and systems for self-learning localization services. A computer-implemented method includes: identifying, at a location remote from a first application, a request for localization of a string value associated with the first application from a source language to a target language, sending the string value to a translation request buffer in response to a determination that the localization of the string value in the target language is unavailable, and triggering, in response to satisfaction of at least one heuristic analysis, a translation process of the string value from the source language into the target language where the string value is retrieved from the translation request buffer. In some instances, the location remove from the first application is a centralized localization service accessible by remote requests from a plurality of applications.