AI Server for Multilingual Subtitle Worker Recommendation
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Solution Overview
Problem
Creators of content images face limitations in translating into multiple languages, and finding suitable translators is time-consuming and often results in inaccurate translations due to insufficient information.
Innovation Solution
A server system utilizing an artificial intelligence learning model to manage and facilitate subtitle translation services by recommending suitable workers based on their expertise and performance, integrating image recognition, translation, and review processes to ensure accurate and efficient multilingual subtitle creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If creators translate content images themselves into various languages, then translation cost is reduced, but translation accuracy deteriorates and translation quality becomes insufficient
Solution Approach 1:
The patent introduces an AI translation intermediary system that mediates between the content image and the creator. The AI model analyzes the content image and automatically generates translations in multiple languages, eliminating the need for creators to manually translate while maintaining high translation accuracy through advanced natural language processing algorithms.
Solution Approach 2:
The patent replaces the manual mechanical translation process with an automated AI-based system. Instead of creators manually translating content images into various languages, the system uses machine learning models to automatically perform translation, significantly reducing time and cost while improving consistency and accuracy.
2Manufacturing precision
If translators are requested for content image translation, then translation accuracy is improved, but time consumption increases and information on translators is insufficient
Solution Approach 1:
The patent enables the content image translation system to serve itself by using AI models that automatically analyze content images and generate translations without requiring external human translators. The system self-evaluates translation quality through built-in verification mechanisms, eliminating the time-consuming process of finding and coordinating with external translators.
Solution Approach 2:
The patent performs preliminary actions by pre-training AI models on extensive translation data before actual translation tasks. The system prepares translation templates and language models in advance, so when a content image needs translation, the AI can immediately generate accurate translations without requiring time to search for or brief human translators.
3Manufacturing precision
If human translators are used for content image translation, then translation quality is improved, but translation cost increases
Solution Approach 1:
The patent replaces the human translator mechanical system with an automated AI translation system. The AI model processes content images and generates translations in multiple languages automatically, eliminating the need to pay human translators while maintaining high translation quality through advanced natural language processing and machine learning algorithms.
Solution Approach 2:
The patent changes the fundamental parameter of translation delivery from human-based to AI-based processing. By transforming the translation system into an automated digital process, the cost parameter dramatically decreases while quality parameters are maintained or improved through consistent AI performance and the ability to process multiple languages simultaneously.
4Adaptability or versatility
If multiple languages are translated manually, then multilingual coverage is improved, but productivity deteriorates due to time consumption
Solution Approach 1:
The patent creates a universal AI translation system that can handle multiple languages simultaneously through a single platform. The AI model is designed to process content images and generate translations in various languages at the same time, providing multilingual coverage without requiring separate manual translation processes for each language, thereby dramatically improving productivity.
Data Source
AI summary
Provided are a server and a method for providing a multilingual subtitle service using an artificial intelligence learning model, and a method for controlling the server. The server includes: a communication unit configured to perform data communication with either or both of a first user terminal device of a client requesting translation of a content image and a second user terminal device of a worker performing a translation task; a storage configured to store a worker search list based on learned worker information, and an artificial intelligence learning model for performing a worker's task performance evaluation; and a controller configured to input image information on the content image to the artificial intelligence learning model in accordance with a worker recommendation command of the client to acquire a worker list of workers capable of translating the content image, and control the communication unit to transmit the acquired worker list to the first user terminal device. The worker information includes at least one of: profile information on each worker, a subtitle content task-completed by each worker, and task grade information evaluated for each worker.