Unstructured Data Storage for Conversational AI Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conversational AI systems are inflexible and slow to update due to their reliance on structured data schemas, making them inefficient in handling new information and user inputs that do not correspond to pre-defined intents and slots.
Innovation Solution
Implementing unstructured storage and retrieval methods for conversational AI systems, using classifiers to analyze user inputs and direct them through appropriate pipelines for analysis and response, allowing for natural language processing and storage of user inputs as unstructured text, enabling flexible updates and responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If structured data schema is used for storing intent/slot data, then data organization and retrieval are systematic, but system flexibility and update speed deteriorate
Solution Approach 1:
The patent implements a dynamic update mechanism where new intents and slots can be added to the structured data schema at runtime without requiring complete retraining. The system allows incremental updates to the intent-slot structure, enabling the model to adapt to new information while maintaining the organized framework of the structured schema.
Solution Approach 2:
The system performs preliminary organization of data into structured schemas for efficient retrieval, while simultaneously preparing for future updates by maintaining an architecture that accepts incremental additions. This preliminary structuring enables both systematic organization and future adaptability.
2Measurement precision
If multiple task-specific models are deployed, then each model is optimized for its specific intent, but system complexity and retraining time increase
Solution Approach 1:
The patent implements a universal model architecture that can handle multiple intents and slots within a single unified structure. Instead of deploying separate models for each task, the system uses one model that recognizes various intents and their associated slots, reducing complexity while maintaining specialized recognition capabilities through the structured intent-slot schema.
Solution Approach 2:
The system merges multiple task-specific recognition capabilities into a single unified model that processes various intents and slots together. By combining what would otherwise require separate models into one architecture with structured data organization, the system reduces management complexity while preserving intent-specific accuracy.
3Reliability
If models are retrained on newly annotated data, then system accuracy improves, but update time and computational resources increase
Solution Approach 1:
The system performs preliminary organization of newly annotated data into the existing structured intent-slot schema before integration. This preliminary structuring prepares the data for efficient incorporation, reducing the time and computational resources needed for updates while maintaining accuracy improvements from new training data.
Solution Approach 2:
The patent implements dynamic update capabilities that allow the system to incorporate new annotated data incrementally rather than requiring complete retraining. The structured schema enables selective updates to specific intents and slots, reducing update time and computational overhead while still improving system accuracy through new data integration.
4Stability of the object's composition
If structured data schema is enforced, then data consistency is maintained, but ease of adding new information deteriorates
Solution Approach 1:
The system implements a dynamic structured schema that maintains data consistency through defined patterns while allowing flexible addition of new intents and slots. The structure provides consistency rules for data organization but enables straightforward extension with new information types without requiring complete schema redesign.
Solution Approach 2:
The patent segments the data schema into modular intent and slot components that can be independently added or modified. This segmentation allows new information to be incorporated by adding discrete intent-slot pairs to the existing structured framework, maintaining overall data consistency while simplifying the process of adding new information.
Data Source
AI summary
Systems and methods determine a classification for an input. For information-based inputs, information stored as unstructured test may be evaluated to determine a response to the input. A reply may be generated that includes at least the response. For declarative inputs, the input may be stored in a natural language format for later use, such as in reply to a subsequent input.