Intent-Based NLP Conversation Engine for Multi-Device Context
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Solution Overview
Problem
Current intent-based natural language processing systems face challenges in accurately identifying user intents and providing relevant responses, especially in multi-device and multi-channel environments, where context and user-specific experiences are not adequately managed.
Innovation Solution
A system and method for intent-based natural language processing that involves a conversation engine receiving and processing user utterances, selecting priority intents based on context and confidence scores, executing intent logic, and updating conversation states, while utilizing external controls and channel adapters to manage user-specific experiences and device types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a bot-based natural language processing system is used to convert audio to text and identify actions, then the system can enable intelligent conversations across multiple devices, but the system fails to accurately manage user-specific context and experiences across multi-device and multi-channel environments
Solution Approach 1:
The system segments the natural language processing task into distinct components: audio-to-text conversion, intent identification, context management, and response generation. Each component is handled by specialized modules that can be independently optimized and managed across different devices and channels, improving both versatility and accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between the bot-based NLP system and the user interactions that manages context and user-specific experiences. This intermediary maintains session states and user profiles, enabling accurate intent identification across multiple devices by providing a centralized context management mechanism.
2Productivity
If the system processes text to identify actions and return content, then it can provide automated responses, but it cannot adequately manage conversation state and user context across different channels
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user context, session states, and conversation history before actual intent identification occurs. This preliminary context preparation ensures that when automated responses are generated, the system has access to relevant user-specific information, preventing context loss across channels.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously updates and refines its understanding of user context based on ongoing interactions. Conversation state is fed back into the system after each interaction, allowing the automated response mechanism to maintain accuracy and relevance across multiple devices and channels.
Data Source
AI summary
A method may include for intent-based natural language processing may include conversation engine: receiving from a conversation program executed on a user electronic device, a unique identifier for a user; calling an external controls program with the unique identifier and a type of the user electronic device, wherein the external controls program identifies a directive of intent and an alternate action; receiving the directive of intent and the alternate action; receiving text of an utterance in a conversation from the conversation program; selecting one of a plurality a priority of intents based on the text of the utterance; receiving a plurality of potential intents and a confidence score for each potential intent from a natural language understanding computer program; selecting a selected intent; determining that the directive of intent matches the selected intent; and executing the alternate action.