Dialog Content Optimizer for Query Constraint Relaxation
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Solution Overview
Problem
Current natural language dialog systems often provide either no responses or too many responses to user queries, leading to user confusion and overwhelm, especially in cognitively demanding situations, and are limited by static rules and data structures, making them inflexible and unable to adapt to user preferences or historical data.
Innovation Solution
A configurable content optimizer module acts as an intermediary between the dialog management and knowledge management modules, using ontological knowledge to refine queries by relaxing or tightening constraints, and employing strategies to return a reasonable number of responses, customizable for different domains and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current natural language dialog systems return all possible responses from the database, then the system provides complete information, but the user receives too many responses which causes confusion and overwhelm
Solution Approach 1:
The patent extracts only the most relevant responses from the database based on confidence scores and relevance metrics. The system filters out less relevant responses and presents only the top N most relevant ones to the user, thereby maintaining information quality while reducing overwhelming quantity.
Solution Approach 2:
The system dynamically adjusts the number of responses presented to the user based on query confidence scores, relevance metrics, and user context. By changing the parameter of response quantity based on these metrics, the system optimizes between providing complete information and avoiding user overwhelm.
2Measurement precision
If the dialog system requires users to recite specific phrasings or conform to system-dictated formats, then the system can process queries more accurately, but the user interaction complexity increases and natural language flexibility is reduced
Solution Approach 1:
The system dynamically adapts its query processing approach based on the confidence score of the interpreted query. When confidence is high, it processes the query as-is, maintaining natural language flexibility. When confidence is low, it employs disambiguation strategies or asks clarifying questions, thereby maintaining accuracy without rigidly enforcing specific phrasings.
Solution Approach 2:
The system uses confidence scores and relevance metrics as feedback to determine the appropriate response strategy. High confidence queries are processed directly, while low confidence queries trigger additional interaction or disambiguation, allowing the system to maintain accuracy while adapting to natural language input flexibility.
3Device complexity
If the dialog system uses static rules and data structures to process queries, then the system is simpler to implement, but the system cannot adapt to user preferences, historical data, or different applications
Solution Approach 1:
The system transitions from static to dynamic rule application by using confidence scores and relevance metrics to determine which processing strategies to apply. The system dynamically adjusts its behavior based on query characteristics, user context, and historical data, enabling adaptability while maintaining a relatively simple underlying architecture.
Solution Approach 2:
The system changes processing parameters such as confidence thresholds, relevance weights, and response selection criteria based on user preferences, historical data, and application context. This allows the same core system to adapt to different users and applications without requiring completely different implementations.
4Reliability
If the dialog system provides zero responses to a query, then the system avoids providing incorrect information, but the user receives no meaningful information and feels confused
Solution Approach 1:
Instead of returning either zero responses or all possible responses, the system returns a partial set of the most relevant responses based on confidence scores and relevance metrics. This partial action approach ensures the user receives meaningful information without risking incorrect or low-quality responses.
Data Source
AI summary
Embodiments of a configurable content optimizer for use in dialog systems are described. In one embodiment, the content optimizer is a configurable component that acts as an intermediary between a dialog management module and a knowledge management module of a dialog system during the query process. The content optimizer module makes extensive use of the system ontology and organizes items returned by the knowledge base and makes adjustments to the query so that a reasonable number of responses are returned. Each query is broken down into a number of constraints, the constraints are characterized by type, and adjustments are made by strategies that include relaxing or tightening constraints in the query. Generic strategies for the potential adjustments are represented in a configurable manner so that the content optimizing module can be easily applied to new domains.