Augmented Conversational Agent Context Identification
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Solution Overview
Problem
Conventional systems fail to accurately interpret user intentions in natural language queries, particularly when context is not clearly defined, leading to ineffective communication of user desires, such as requesting movie showtimes.
Innovation Solution
An augmented conversational understanding agent that identifies context from natural language phrases, using a combination of speech recognition, natural language understanding, and ontology databases to determine user intentions and perform relevant actions, such as searching for local movie theaters showing a specific film.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use natural language processing without context awareness, then the system is simple to operate, but the system cannot accurately interpret user intentions
Solution Approach 1:
The patent introduces an augmented conversational understanding agent as an intermediary layer between the user's natural language input and the conventional system. This agent analyzes context, identifies user intentions, and translates casual language into effective system queries, thereby improving interpretation accuracy without requiring the entire system to become complex.
Solution Approach 2:
The system is segmented into distinct functional components: the augmented conversational understanding agent that handles context analysis and intention recognition, and the conventional system that executes specific tasks. This segmentation allows the complex context-aware functions to be isolated in the agent while the main system remains relatively simple.
2Ease of operation
If the system requires specialized formatting and syntax for queries, then the system can process information accurately, but the ease of operation decreases
Solution Approach 1:
The augmented conversational understanding agent serves as a mediator that accepts naturally formatted user input and translates it into the specialized syntax and formatting required by the conventional system. This allows users to operate the system in natural language while maintaining reliable communication through proper formatting.
Solution Approach 2:
The system performs self-service by automatically analyzing context and generating appropriately formatted queries without requiring user intervention. The augmented agent autonomously handles the translation from natural language to system-specific syntax, making the system both easy to operate and reliable.
3Loss of information
If the system lacks context awareness in conversation, then the device complexity is low, but the loss of information increases
Solution Approach 1:
The augmented conversational understanding agent acts as an intermediary that captures and retains context information from conversations. It analyzes the contextual meaning of user inputs and preserves this information to improve subsequent query interpretation, preventing information loss without requiring the entire system to be complex.
Solution Approach 2:
The system performs preliminary context analysis through the augmented agent before processing the actual query. This preliminary action of understanding the conversational context ensures that subsequent information processing is more accurate and reduces information loss about user intentions.
Data Source
AI summary
An augmented conversational understanding agent may be provided. Upon receiving, by an agent, at least one natural language phrase from a user, a context associated with the at least one natural language phrase may be identified. The natural language phrase may be associated, for example, with a conversation between the user and a second user. An agent action associated with the identified context may be performed according to the at least one natural language phrase and 201 a result associated with performing the action may be displayed.