Dynamic Syntax Learning for Cryptic Command Auto-Completion
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Solution Overview
Problem
Cryptic languages used in Global Distribution Systems (GDS) for the travel industry present challenges due to their non-apparent segmentation and varying syntaxes, making auto-correction and auto-completion tasks difficult without prior extensive knowledge of the language syntax.
Innovation Solution
A dynamic method that learns syntax rules through user data using a general knowledge extraction process to create n-gram trees, which enables runtime auto-completion and auto-correction suggestions by segmenting Cryptic commands into symbols and words, and determining word separators through hierarchical majority voting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statically declaring rules for each possible command construction is used, then auto-correction and auto-completion can be provided, but time and computational resources are consumed
Solution Approach 1:
The patent transitions from static rule declaration to dynamic rule learning. The system continuously learns syntax rules from user inputs and adapts to new Cryptic languages and their variations automatically, eliminating the need for manual rule collection and translation while maintaining high accuracy in auto-correction and auto-completion functions
Solution Approach 2:
The system performs self-learning by automatically extracting syntax rules from observed user data without requiring external manual intervention. The rule base updates itself through continuous learning from training data and runtime user inputs, making the system self-sustaining and adaptive to new languages and syntax variations
2Reliability
If statically declaring rules for each possible command construction is used, then auto-correction and auto-completion can be provided, but computational resources are consumed
Solution Approach 1:
The system replaces computationally intensive static rule declaration with dynamic rule learning from data. By observing patterns in user inputs and automatically extracting syntax rules, the system reduces the computational burden of manually maintaining and translating extensive rule sets while achieving comparable or superior accuracy
Solution Approach 2:
The system learns rules autonomously from training data and user inputs without requiring external computational resources for rule collection and translation. The self-learning mechanism processes data efficiently by identifying patterns automatically, reducing overall computational resource consumption compared to manual rule management
3Measurement precision
If extensive knowledge of Cryptic language syntax is required, then word segmentation can be determined, but human-computer interaction is complicated
Solution Approach 1:
The patent introduces an intermediary system (the learning-based segmentation module) that bridges the gap between raw Cryptic commands and meaningful word segmentation. This intermediary automatically learns and applies segmentation rules, eliminating the need for users to possess extensive syntax knowledge while maintaining high segmentation accuracy through pattern recognition from training data
Solution Approach 2:
The system performs self-segmentation by automatically determining word boundaries in Cryptic commands without requiring user intervention or syntax expertise. The learned segmentation rules are applied autonomously to parse commands into meaningful units, making the system easy to use while maintaining precise segmentation through continuous learning from observed data
4Adaptability or versatility
If a dynamic self-learning method is used, then the system can learn rules of all Cryptic languages, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal learning framework that can adapt to multiple Cryptic languages through a single system architecture. The same learning mechanisms and data structures handle different languages and syntax variations, providing multi-functionality without requiring separate specialized systems for each language, thus managing complexity while maintaining versatility
Solution Approach 2:
The system manages complexity by dynamically adjusting learning parameters and data structures based on the specific Cryptic language being analyzed. Rather than maintaining fixed complex structures for each language, the system adapts its parameters to match the characteristics of each language, achieving versatility through flexible parameter configuration rather than structural complexity
Data Source
AI summary
Systems, methods and computer program products for dynamic language learning and solving auto-correction and auto-completion tasks. By means of a computer, knowledge about the syntax of a Cryptic language is extracted from a training data set of Cryptic commands. This knowledge is stored in an n-gram tree forest. The information of the n-gram trees is then used to facilitate human computer interaction and return improved auto-completion and auto-correction suggestions.