Vector Space Intent Classification for Conversational Bots
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Solution Overview
Problem
Current systems for conversational interfaces and bots lack the ability to efficiently process and respond to user queries in natural language, often requiring significant human effort and failing to provide accurate answers when users frame questions differently from the available data.
Innovation Solution
The development of a data processing system that indexes information from various sources, uses natural language understanding to identify user intent, and generates responses based on vector space intent classification and similarity searches, enabling bots to provide accurate answers by creating paraphrases and selecting relevant utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search methods are used in conversational bots, then the system complexity remains low, but the precision of intent identification and answer accuracy deteriorate when users frame questions differently from available data
Solution Approach 1:
The patent transforms the search problem from keyword matching to vector space similarity search by changing the representation parameters of text data. User queries and knowledge base articles are converted into vector embeddings, allowing the system to measure semantic similarity rather than exact keyword matches. This resolves the contradiction by improving intent identification accuracy through continuous vector space comparison while managing complexity through efficient similarity search algorithms.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a semantic vector space system. Instead of relying on exact word matches or simple string comparisons, the system uses vector embeddings to represent meanings and computes cosine similarity to find relevant information. This substitution enables the bot to understand paraphrased queries and improve answer accuracy while maintaining computational feasibility through optimized vector search.
2Reliability
If manual curation of utterances and paraphrases is performed to improve bot responses, then answer accuracy improves, but the loss of time and human effort increases significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate paraphrases and identify relevant utterances through vector space similarity search. Instead of requiring manual curation of multiple paraphrases for each knowledge base article, the system autonomously queries the vector index to find semantically similar user expressions. This resolves the contradiction by maintaining high answer accuracy through automated semantic matching while eliminating the time-consuming manual curation process.
Solution Approach 2:
The patent performs preliminary action by pre-computing vector embeddings for all knowledge base articles and storing them in an index structure before runtime. This preprocessing step enables rapid similarity-based retrieval during actual bot operations without requiring real-time manual intervention. The system prepares the semantic search space in advance, allowing fast and accurate response generation when users pose questions.
3Adaptability or versatility
If the bot uses exact matching of user queries with knowledge base articles, then the device complexity remains low, but the adaptability to different user phrasings and natural language variations deteriorates
Solution Approach 1:
The patent changes the matching parameter from exact string equality to vector space cosine similarity. By representing both user queries and knowledge base articles as vectors in a semantic space, the system can measure degrees of similarity rather than requiring exact matches. This enables the bot to adapt to various user phrasings, synonyms, and natural language variations while managing complexity through efficient vector comparison algorithms and pre-computed embeddings.
Data Source
AI summary
Example data processing systems and methods are described. In one implementation, a system accesses a corpus of data and analyzes the data contained in the corpus of data to identify multiple documents. The system generates vector indexes for the multiple documents such that the vector indexes allow a computing system to quickly access the plurality of documents and identify an answer to a question associated with the corpus of data.