Text Prediction Engine Using Probability Normalization
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Solution Overview
Problem
Existing text prediction systems for electronic devices lack a robust, fully integrated probabilistic model to accurately predict intended written text, relying on ad-hoc scoring methods that cannot reliably compare predictions over time.
Innovation Solution
A text prediction engine comprising multiple models that generate sequences with associated probability estimates, using a probability generator to normalize and weight predictions based on context and evidence sources, allowing for true probability assignment and threshold-based confidence moderation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ad-hoc scoring methods are used for text prediction, then device complexity is reduced, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The system segments the text prediction task into multiple independent models, each handling different aspects of prediction (e.g., n-gram models, language models, context models). Each model generates probability estimates for sequence predictions, and these segmented models work together to provide comprehensive and accurate predictions while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system merges multiple probability estimates from different models into a unified prediction framework. The probability generator combines probability values from various models (n-gram, language, context models) to produce normalized probability estimates, achieving high prediction accuracy through integrated probabilistic reasoning while managing complexity through systematic combination rules.
2Measurement precision
If multiple models with probability estimates are integrated, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The probability generator serves as a universal component that handles multiple functions: it receives probability estimates from various types of models (n-gram, language, context), normalizes these probabilities, and generates final sequence predictions. This multi-functional component reduces overall system complexity by providing a unified interface for integrating diverse probabilistic models.
Solution Approach 2:
The system transforms the output of multiple models by applying parameter changes through probability normalization. The probability generator adjusts and normalizes probability values from different models to ensure they are comparable and can be systematically combined, enabling accurate integration of multiple models while managing computational complexity through standardized probability transformations.
3Reliability
If probability values are normalized over all possible sequence predictions, then prediction reliability improves, but computational time increases
Solution Approach 1:
The system applies partial normalization by focusing computational effort on the most probable sequence predictions rather than normalizing over all possible sequences. The probability generator identifies and processes the top candidate sequences with highest probability estimates, achieving sufficient prediction reliability through partial normalization while significantly reducing computational time compared to exhaustive normalization.
Data Source
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AI summary
A text prediction engine, a system comprising a text prediction engine, and a method for generating sequence predictions. The text prediction engine, system and method generate a final set of sequence predictions, each with an associated probability value.