AI Translation for Application Execution Screens
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Solution Overview
Problem
Existing applications lack the ability to translate text on their execution screens into different languages without pre-provided translation data, necessitating a method to effectively utilize an artificial intelligence model for text translation.
Innovation Solution
A device and method that receive user input, obtain resource files, translate text using an artificial intelligence model, and generate the execution screen with translated text, ensuring text is translated from a first language to a second language by applying data from the resource file to the trained AI model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an application does not provide translation data for a target language, then the application cannot display text in that language, but adding translation data for all languages increases device complexity and memory requirements
Solution Approach 1:
An artificial intelligence model serves as an intermediary translation engine between the application and the user's target language. Instead of storing translation data within the application, the system uses the AI model to translate text dynamically when the application executes, allowing the application to support multiple languages without embedding translation data for each language
Solution Approach 2:
The artificial intelligence model provides universal translation capability across multiple languages through a single translation engine. This multi-functional approach allows one translation system to handle numerous language pairs, replacing the need for multiple separate translation data sets for different language combinations
2Adaptability or versatility
If translation data is pre-provided for all languages, then text can be displayed in any language, but this increases the size of resource files and memory consumption
Solution Approach 1:
The translation functionality is extracted from the application's resource files and relocated to an external artificial intelligence model. This extraction removes the burden of storing extensive translation data within the application, reducing resource file size while preserving multilingual support capabilities through the external AI translation service
Solution Approach 2:
Instead of embedding actual translation data for all languages, the system uses a lightweight reference implementation that leverages the artificial intelligence model's translation capabilities. This virtual copy approach allows the application to access translation functionality without duplicating large amounts of translation data locally
3Measurement precision
If manual translation methods are used for text on execution screens, then translation accuracy can be maintained, but this increases processing time and reduces productivity
Solution Approach 1:
The manual or rule-based translation system is replaced with an artificial intelligence-based translation engine. This substitution uses machine learning and neural network algorithms to automatically translate text with high accuracy, eliminating the need for manual translation processes while significantly improving translation speed and efficiency
Data Source
AI summary
The disclosure relates to an artificial intelligence (AI) system that simulates functions such as cognition and judgment of the human brain by utilizing machine learning algorithms such as deep learning and its applications.A method in which a device provides translation information of an application includes obtaining a resource file of an application through an operating system (OS) of the device; translating text to be displayed on an execution screen of the application from the obtained resource file by using an artificial intelligence model; generating the execution screen of the application by using the resource file and the translated text; and displaying the generated execution screen, wherein the translating includes translating the text of a first language into a second language by applying at least a part of data in the resource file to the artificial intelligence model trained to translate the text to be displayed on the execution screen of the application.