Chat Bot Personalization via Script Parsing and Pattern Matching
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Solution Overview
Problem
Current methods for creating advanced chat bots are labor-intensive, requiring significant human effort to develop and modify, even when adapting from existing systems, due to the complexity of AIML chat bots which can reach up to 50,000 lines of code.
Innovation Solution
The method involves using conversational scripts to replace or augment AIML chat bot responses by parsing text scripts from various sources, such as movie and TV transcripts, game logs, or active chat sessions, and applying AIML reductions and pattern matching to create new chat bots with specific personalities, allowing for efficient generation of new chat bots without extensive manual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AIML chat bot responses are manually coded and customized, then the chat bot can achieve specific personality and conversational patterns, but the development time and labor required increase significantly
Solution Approach 1:
The patent applies the copying principle by extracting response patterns from existing chat bot conversations and dialogue data. Instead of manually coding each response, the system copies conversational patterns from source material (such as movie scripts, book dialogues, or existing chat logs) and uses them to generate new chat bot responses automatically, significantly reducing development time while maintaining personality customization
Solution Approach 2:
The patent implements preliminary action by pre-processing and organizing source dialogue data into structured formats before chat bot deployment. The system performs advance work including parsing dialogue data, extracting response patterns, and organizing them into reusable templates, which eliminates the need for manual response coding during the actual chat bot development phase
2Adaptability or versatility
If AIML chat bot code is extended to add new content or modify personality, then the chat bot functionality is enhanced, but the complexity of maintenance and modification increases
Solution Approach 1:
The patent applies segmentation by dividing the chat bot response generation system into separate modular components: pattern extraction modules, response template modules, and data source modules. Each component handles a specific aspect of the conversation, allowing independent modification and maintenance without affecting the entire system. This modular structure reduces the complexity of maintaining large AIML codebases
Solution Approach 2:
The patent extracts response patterns and conversational logic from the main AIML codebase and stores them in external data structures or separate files. This separation allows the core chat bot engine to remain simple while enabling flexible content updates by merely modifying the extracted pattern data, rather than changing the underlying complex AIML code
3Adaptability or versatility
If a chat bot is created from scratch with custom responses, then the chat bot achieves desired personality, but the amount of manual work required reaches up to 50,000 lines of code
Solution Approach 1:
The patent implements self-service by enabling the chat bot system to automatically generate its own response patterns from provided source material. When given dialogue data (such as movie scripts or conversation logs), the system autonomously extracts patterns, organizes them into templates, and configures the chat bot without requiring manual coding of each response, dramatically reducing the effort from 50,000 lines of code to automated processing
Data Source
AI summary
A system and method for automatically building chat bot content from scripts of conversations. A script is parsed into query-response pairs, so that a computerized device may be programmed to give the same responses when given similar queries. Each query is converted into canonical form and then matched against patterns, as in prior art chat bots. The response is then linked to the matched pattern. A new chat bot is constructed out of the canonical form conversions and the set of pattern-response links. The chat bot is further improved by adding interactive human conversations, provided that those conversations are rated highly. The resulting chat bot can run on a computer server that responds to public queries, and the server can also display context-sensitive advertisements.