Reasoning Agent Context Overlay for Conversation Meaningfulness
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Solution Overview
Problem
Current Reasoning Agents/Behavior Engines (RA/BEs) in virtual agents lack effective context management, either relying solely on current exchanges or being completely context-less, which hinders their ability to maintain meaningful conversations and perform user-requested tasks accurately.
Innovation Solution
The implementation of a Reasoning Agents/Behavior Engine with multiple personas that utilize an episodic context technique, allowing access to context information from various frames, including local, service-based, and remote frames, to update and manage context appropriately in response to user utterances, enabling context recall and aggregation based on persona-specific rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RA/BE relies solely on current exchange context or is completely context-less, then system simplicity is maintained, but conversation meaningfulness and task accuracy deteriorate
Solution Approach 1:
The patent segments context management into multiple independent components: current exchange context, user profile context, and conversational history context. Each segment is managed separately through distinct data structures and retrieval mechanisms, allowing the system to maintain conversation meaningfulness without overwhelming complexity.
Solution Approach 2:
The patent implements nested context structures where conversational episodes are nested within user sessions, which are nested within user profiles. This hierarchical nesting allows efficient context retrieval at multiple levels, improving reliability while managing complexity through organized layers of context information.
2Measurement precision
If RA/BE maintains context across episodes and time periods, then task accuracy and natural language handling are improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing conversational episodes in structured formats during and after interactions. Context information is organized and indexed in advance, enabling efficient retrieval when needed for task execution, thereby improving accuracy without proportionally increasing runtime complexity.
Solution Approach 2:
The patent introduces intermediary components such as context managers and retrieval systems that mediate between the complex context data structures and the reasoning processes. These intermediaries simplify access to context information, allowing high task accuracy while shielding the core reasoning engine from full context management complexity.
3Adaptability or versatility
If RA/BE uses multiple personas with episodic context techniques, then context recall capability is enhanced, but device complexity increases
Solution Approach 1:
The patent creates universal context management mechanisms that serve multiple personas simultaneously. The episodic context techniques and frame structures are designed to be persona-agnostic, allowing the same context retrieval system to support diverse personas (e.g., customer service, technical support, sales) without requiring separate systems for each, thus enhancing adaptability while controlling complexity.
Data Source
AI summary
An agent automation system includes a memory configured to store a reasoning agent/behavior engine (RA/BE) including a first persona and a current context and a processor configured to execute instructions of the RA/BE to cause the first persona to perform actions comprising: receiving intents/entities of a first user utterance; recognizing a context overlay cue in the intents/entities of the first user utterance, wherein the context overlay cue defines a time period; updating the current context of the RA/BE by overlaying context information from at least one stored episode associated with the time period; and performing at least one action based on the intents/entities of the first user utterance and the current context of the RA/BE.