Next Word Prediction Database for Real-Time Efficiency
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Solution Overview
Problem
Existing next word prediction methods are inefficient in providing real-time suggestions due to the need for evaluating a large number of candidates based on user input, leading to cumbersome and time-consuming processing.
Innovation Solution
A system and method utilizing a pre-generated suggestion database based on an N-gram language model, where the database is created offline using fixed corpus data, allowing for direct retrieval of predictions at runtime, thus avoiding expensive computations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If next word candidates are evaluated in real-time based on user input, then prediction accuracy is improved, but processing time increases and efficiency deteriorates
Solution Approach 1:
The patent pre-generates a suggestion database containing candidate next words and their conditional probabilities before runtime. This preliminary computation stores evaluation results in advance, allowing the system to retrieve pre-computed suggestions during real-time interaction without performing expensive evaluations on-the-fly, thus resolving the contradiction between accuracy and processing time
Solution Approach 2:
The patent extracts only the necessary components (top-k candidate suggestions with highest conditional probabilities) from the complete evaluation space and stores them in the suggestion database. This extraction approach maintains prediction accuracy by keeping the most relevant candidates while reducing the computational burden during real-time operation
2Reliability
If all next word candidates are evaluated to ensure meaningful suggestions, then suggestion quality is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on evaluating and storing only the most relevant next word candidates (those with highest conditional probabilities) in the suggestion database, rather than uniformly evaluating all possible candidates. This localized approach ensures high suggestion quality while reducing overall computational complexity
Solution Approach 2:
The patent performs partial evaluation by computing and storing only the top-k candidates with highest probabilities in the suggestion database during offline preparation, rather than evaluating all possible next word candidates. This partial action maintains suggestion quality for the most likely candidates while significantly reducing computational complexity
Data Source
AI summary
A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for predicting a next word comprises generating a suggestion database including a plurality of suggestions; receiving an input from a user including a first word and at least one character of a second word; obtaining one or more indices associated with the at least one character from the suggestion database; retrieving one or more suggestions corresponding to the one or more indices from the suggestion database; and presenting the one or more suggestions to the user.