Chatbot Dialog Mapping From Free-Form Input
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Solution Overview
Problem
Existing chatbots are limited by pre-defined intent schemas and training corpora, requiring extensive computational resources and memory to handle the nuances of human speech, making them non-scalable and prone to failure.
Innovation Solution
Implementing a system that generates chatbots using unstructured free-form natural language input to implicitly or explicitly define dialog states and transitions, allowing for quick and efficient deployment in various environments, including voice and text-based conversations, with or without fine-tuning the underlying machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive intent schemas and training examples are manually defined to handle nuances of human speech, then chatbot reliability improves, but computational resource consumption and device complexity increase significantly
Solution Approach 1:
The patent replaces the mechanical system of manually defining and storing extensive intent schemas with a neural network-based semantic similarity system. Instead of using predefined categories and rules, the system uses machine learning models to automatically understand and match user intents through semantic analysis, dramatically reducing the need for manual schema definition while improving reliability.
Solution Approach 2:
The patent changes the fundamental parameter of intent representation from discrete categorized schemas to continuous semantic vectors. By transforming intent matching from a categorical lookup problem to a semantic similarity calculation problem, the system achieves better reliability without requiring extensive manual schema definition, thus reducing device complexity.
2Reliability
If large quantities of intent schemas are stored to mitigate failure instances, then chatbot reliability improves, but memory requirements increase
Solution Approach 1:
The patent replaces the memory-intensive mechanical storage of extensive intent schemas with a neural network model that computes semantic similarity on-demand. Instead of storing and searching through large databases of predefined intents, the system uses learned embeddings to efficiently calculate semantic similarity, reducing memory requirements while maintaining reliability.
Solution Approach 2:
The patent uses vector embeddings as compressed representations of intent schemas. Instead of storing full textual descriptions and categorical definitions of numerous intents, the system stores compact vector representations that capture the essential semantic meaning, dramatically reducing memory requirements while preserving the ability to reliably match user intents.
3Adaptability or versatility
If manual refinement of intent schemas is performed to learn nuances of human speech, then chatbot adaptability improves, but productivity decreases due to extensive computational resources required
Solution Approach 1:
The patent implements self-service through automated intent extraction and clustering. Instead of requiring manual refinement of intent schemas, the system automatically learns from user interactions and speech data, autonomously identifying new intents and refining existing ones. This eliminates the need for manual schema refinement while improving adaptability to nuanced human speech.
Solution Approach 2:
The patent replaces the manual mechanical process of intent schema refinement with automated machine learning processes. Neural networks automatically analyze speech patterns, extract intents, and update the intent model without human intervention, dramatically improving productivity while maintaining high adaptability to human speech nuances.
Data Source
AI summary
Implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform engage in corresponding conversations with additional users. In various implementations, the unstructured free-form natural language input implicitly defines a corresponding dialog state map (e.g., defines corresponding dialog states and/or corresponding dialog state transitions) without defining any explicit dialog states and/or explicit dialog state transitions. In other implementations, the unstructured free-form natural language input is assigned to explicit dialog states and/or explicit dialog state transitions. Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or primed a machine learning model that is already capable of being utilized in conducting generalized conversations. As a result, the chatbot can be generated and deployed in a quick and efficient manner.