Dynamic Model for Multi-lingual Text Input Prediction
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Solution Overview
Problem
Existing multi-lingual data input systems struggle with predicting and suggesting intended language-specific words or phrases in mixed-language contexts, often autocorrecting or suggesting incorrect candidates due to static and independent language models that fail to learn the user's linguistic context.
Innovation Solution
A data input system that uses a dynamic model bespoke to the user, incorporating per term weights to predict and suggest candidates, which learns from the user's behavior and linguistic context, combining multiple language models to accurately offer language-specific options based on context and user history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static and independent language models are used for prediction, then the system structure is simple, but the system cannot adapt to user's linguistic context and mixed-language input
Solution Approach 1:
The patent implements a dynamic model that adapts to the user's linguistic context by learning from their input patterns and language preferences. The system transitions from static language models to a dynamic architecture that continuously adjusts its predictions based on user behavior, enabling it to handle mixed-language input effectively while maintaining a manageable system structure through selective adaptation.
2Measurement precision
If multiple language models are used to support multi-lingual input, then the system's language support is improved, but the system cannot accurately predict language-specific words in mixed contexts
Solution Approach 1:
The patent applies local quality by making predictions language-specific rather than using a single unified model for all languages. The dynamic model adjusts its parameters and prediction strategies based on the detected language context, allowing it to accurately predict language-specific words in mixed-language input while managing multiple language models through context-aware selection and weighting.
3Productivity
If predictive technology is used to suggest candidate items, then the input speed is improved, but multi-lingual users experience frustration due to incorrect autocorrection and candidate suggestions
Solution Approach 1:
The patent implements feedback mechanisms where the system learns from user interactions with predicted candidates. When users select or reject suggestions, the dynamic model adjusts its predictions to better match user preferences and linguistic patterns. This continuous learning process improves both input speed and user experience by reducing incorrect autocorrections and providing more relevant language-specific candidates for multi-lingual users.
Data Source
AI summary
A data input system is described which has a user interface which receives one or more context text items in a sequence of text items input by a user. A processor of the data input system uses a plurality of language models to predict, from each language model, a next item in the sequence of text items. The processor uses a dynamic model which is bespoke to the user as a result of learning text items which the user has previously used, to predict a next item in the sequence of text items. The processor weights the predicted next item from the dynamic model using at least per term weights, each per term weight representing a likelihood of an associated term of the dynamic model given one of the language models.