Predictive Text Encoding for Very Short Message Compression
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Solution Overview
Problem
Existing compression schemes provide poor compression for very short content encodings due to limited repetition, often requiring more memory and processing resources than the original message, especially in bandwidth-constrained environments.
Innovation Solution
A predictive encoding scheme using n-gram models and word models that predict subsequent bits based on previously processed bytes and characters, utilizing hash look-up tables trained on predefined data, allowing for lossless compression with reduced memory and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If existing compression schemes are used on very short content encodings, then compression ratio is poor, but memory and processing resources are still consumed
Solution Approach 1:
The patent pre-trains n-gram models and word models on a large corpus of text data before deployment. These pre-trained models contain learned patterns of language and text structures that can be directly applied to compress very short messages without requiring retraining. This preliminary action enables the compression system to achieve good compression ratios even on short content where repetition patterns are limited
Solution Approach 2:
The patent uses n-gram models that copy and reuse probability distributions learned from large training corpora. Instead of learning patterns from scratch on each short message, the system copies statistical patterns from the pre-trained models, enabling effective compression of very short content by leveraging patterns observed in larger text samples during training
2Loss of substance
If repetition-based compression is used on short content, then compression effectiveness is low, but overhead increases
Solution Approach 1:
The patent changes the fundamental parameter of how compression is approached - instead of relying on repetition within the short message itself, the system changes to using predictive models that estimate probable next characters based on learned statistical patterns. This parameter change from repetition-based to prediction-based compression enables effectiveness on very short content where traditional repetition methods fail
Solution Approach 2:
The patent substitutes the mechanical repetition-based compression system with a statistical prediction system using n-gram and word models. This replacement transitions from a system that requires actual repeated sequences to one that uses probabilistic predictions based on learned language patterns, enabling compression of short content without requiring repetition
Data Source
AI summary
An output stream can be generated of compressed textual data. A first portion of the output stream includes an indicator that compression is used and a second portion is the compressed textual data. The textual data can be compressed by processing each byte of an input text bit-by-bit in order from most significant bit to least significant bit to generate an n-byte length context and a word context. While processing each byte, a subsequent bit is predicted based on the n-byte length context and a corresponding prediction model, and the subsequent bit is predicted based on the word context and a corresponding prediction model. The predictions can be combined to generate a final prediction. If the final prediction determines the subsequent bit, then the subsequent bit can be skipped in the output stream. The output stream can be provided to another device over a communication interface.