Automated Contextual Software Localization via Screen State Analysis
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Solution Overview
Problem
The localization process of software products is time-consuming and costly due to the need for manual extraction of language-specific elements, inadequate automatic translation services, and labor-intensive linguistic reviews, which often result in errors and delayed delivery of localized versions.
Innovation Solution
Automated contextual-based software localization involves detecting multiple screen states in source code, generating screen shots and reduced properties files, and creating translation packages that include contextual information, allowing human translators to refer to accurate contexts during the translation process, thereby reducing errors and streamlining the localization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of language-specific elements is performed, then translation accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent segments the localization process into distinct phases: automatic extraction of language-specific elements, contextual analysis, translation, and integration. By dividing the workflow, manual intervention is focused only on critical translation tasks while automated handling manages routine extraction and processing, reducing overall time consumption while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary system that automatically extracts language-specific elements and provides contextual information to translators. This intermediary layer handles the time-consuming extraction and organization tasks, allowing translators to focus on accurate translation without performing manual extraction themselves.
2Loss of time
If inadequate automatic translation services are used, then time consumption is reduced, but translation quality deteriorates
Solution Approach 1:
The patent applies partial automation where automatic translation services handle only portions of the translation task, specifically providing draft translations that are then reviewed and refined by human translators. This partial use of automated services reduces time consumption for routine translations while maintaining quality through human oversight on critical elements.
Solution Approach 2:
The system implements feedback loops where translation results are reviewed and validated against contextual information and source material. This feedback mechanism ensures that automated translations are corrected and improved upon, maintaining high translation quality while still benefiting from the speed of automated services.
3Reliability
If labor-intensive linguistic reviews are performed, then translation errors are reduced, but productivity decreases
Solution Approach 1:
The patent performs preliminary linguistic reviews by automatically analyzing translated content against contextual information, style guides, and terminology databases before final delivery. This preliminary automated review catches many errors early, reducing the need for extensive manual proofreading while maintaining high reliability.
Solution Approach 2:
The system implements self-service mechanisms where translation packages include automated quality checks and contextual validation that translators can review themselves before submission. This self-validation reduces the burden on dedicated review teams while maintaining error reduction through multiple layers of checking.
4Measurement precision
If contextual information is provided to translators, then translation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal translation platform that integrates multiple functions: automatic element extraction, contextual analysis, translation management, and quality review into a single system. This multi-functional approach provides comprehensive contextual information to translators without requiring separate complex tools for each function, managing system complexity through integration.
Solution Approach 2:
The system implements a nested structure where translation packages contain contextual information that is itself organized in nested layers: source code context, user interface context, domain-specific terminology, and style guides. This nested organization provides comprehensive context while managing complexity through hierarchical structuring of information.
Data Source
AI summary
Example embodiments relate to automated contextual-based software localization. In example embodiments, at least one stage computing device may automatically detect at least one screen state related to source code for a software product. The at least one stage computing device may automatically generate at least one reduced properties file, for a first language. Each of the reduced properties files may relate to one of the screen states. The at least one stage computing device may automatically create at least one screen shot. Each of the screen shots may relate to one of the screen states. The at least one stage computing device may automatically create at least one translation package, e.g., one for each screen state. Each translation package may include the screen shot and the reduced properties file associated with particular screen state.