Non-linear Slot Filling Algorithm for Conversational AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for obtaining structured information from human-computer conversations, such as those using chatbots or AI, rely on decision trees, which are complex to implement and maintain, and fail to effectively utilize analytics to determine next steps or leverage previous conversation data.
Innovation Solution
A system employing a non-linear slot filling algorithm using natural language processing to determine and fill slots in conversations, allowing for more efficient and human-like interaction by prompting users for information in a contextually relevant manner, and utilizing predictive analytics based on individual and collective user histories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decision trees are used to obtain structured information from conversations, then the system can navigate conversation paths, but the implementation and maintenance become complicated
Solution Approach 1:
The patent replaces the mechanical decision tree structure with a neural network-based natural language processing system. Instead of navigating predefined paths through coded decisions, the system uses machine learning models to directly interpret user intent and extract structured information, eliminating the complexity of manual decision tree construction and maintenance while maintaining reliable conversation navigation
Solution Approach 2:
The system enables self-service through automated slot filling algorithms that automatically extract and populate information from user inputs without requiring manual intervention to navigate decision paths. The neural network autonomously determines conversation state and required information, reducing the need for complex predefined conversation flows
2Ease of operation
If decision trees are used for conversation management, then conversation paths can be defined, but the conversations become awkward and dissimilar from human conversations
Solution Approach 1:
The patent introduces dynamic adaptability by replacing static decision tree paths with a neural network system that dynamically adjusts to user inputs in real-time. The system adapts its conversation approach based on the specific input received, allowing for more natural and flexible interactions while maintaining operational control through automated slot filling and conversation state tracking
Solution Approach 2:
The system changes the parameters of conversation management from fixed decision paths to probabilistic neural network outputs. By using machine learning models trained on natural language data, the system can interpret a wide variety of user expressions and maintain natural conversation flow, improving adaptability while preserving ease of operation through automated processing
3Adaptability or versatility
If all possible paths and scenarios are implemented in decision trees, then complete coverage is achieved, but the nets of nodes become difficult to implement and maintain
Solution Approach 1:
The patent replaces the manual construction and maintenance of comprehensive decision tree networks with automated neural network training. The system learns to handle diverse conversation scenarios through training data, eliminating the need for developers to manually implement all possible paths while achieving complete coverage through the model's ability to generalize from training examples
Solution Approach 2:
The system performs preliminary action by pre-training the neural network models on extensive conversation data before deployment. This preliminary training enables the system to handle a wide range of scenarios without requiring manual implementation of each path, reducing implementation difficulty while maintaining comprehensive scenario coverage through the trained model's generalization capabilities
Data Source
AI summary
A system and method obtaining structured information from a conversation including receiving a first input from a user, determining a first set of slots filled based on the first input using natural language processing and a non-linear slot filling algorithm, determining first conversation based on the first set of slots filled, determining a first empty slot associated with the first conversation, prompting the user for a second input, the second input associated with the first empty slot, filling the first empty slot using natural language processing and the non-linear slot filling algorithm, determining that the slots associated with the first conversation are filled; and, responsive to determining that the slots associated with the first conversation are filled, initiating an action associated with the conversation.