Declarative Graph Model for Conversational AI Dialog Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conversational AI systems face complexity and maintainability issues due to imperative programming methods, requiring explicit coding for each step, which becomes cumbersome when scaling to handle multiple dialogs.
Innovation Solution
Implementing a graph-based declarative programming model that visually describes operational and conversational requirements, allowing nodes and edges to perform tasks such as slot filling and external service interactions without detailing control flow, enabling easier creation and modification of dialogs without reprogramming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If imperative programming method is used to implement conversational AI dialogs, then the system can execute tasks with explicit control flow, but the complexity and maintainability overhead increases significantly when scaling to handle hundreds of different dialogs
Solution Approach 1:
The patent segments the conversational AI system into distinct components: intent analyzers that determine user intent, graph databases that store dialog logic as separate graph structures, and execution engines that traverse these graphs. Each dialog is represented as an independent graph with nodes and edges, allowing dialogs to be developed, stored, and executed as separate units. This segmentation enables the system to handle hundreds of dialogs without proportional increases in complexity, as each dialog graph is self-contained and can be managed independently.
Solution Approach 2:
The patent introduces graph structures as an intermediary layer between the user input and the execution engine. Instead of the engine directly interpreting imperative code for each dialog, it traverses graph representations that encode the dialog logic. This intermediary graph model abstracts the complexity of control flow, allowing the system to execute diverse dialogs through a unified graph traversal mechanism, thereby improving scalability while maintaining manageable complexity.
2Ease of manufacture
If imperative programming requires explicit statements for each step of a task, then the control flow is precise and deterministic, but the effort and time required to create and modify dialogs increases
Solution Approach 1:
The patent applies preliminary action by pre-structuring dialog logic into graph formats with defined nodes and edges before execution. The graph database stores these structured representations, allowing developers to design dialogs once in a visual or structured format and then reuse them across multiple execution contexts. This preliminary structuring eliminates the need to write and re-deploy imperative code for each dialog modification, significantly reducing creation effort and deployment time while maintaining precise control flow through the pre-defined graph structure.
Solution Approach 2:
The patent enables copying of dialog graphs as reusable templates. Once a dialog graph is created and validated, it can be copied and instantiated multiple times with different parameters or configurations without requiring re-programming. This copying mechanism allows rapid creation of new dialogs by duplicating and adapting existing graph structures, dramatically reducing both the effort required to create new dialogs and the time needed to deploy modifications.
3Ease of operation
If declarative programming model is used to describe desired outcome without control flow details, then the ease of creation and modification of dialogs increases, but the system requires a graph traversal manager to interpret and execute the declarative graphs
Solution Approach 1:
The patent implements a universal graph traversal manager that can execute any dialog graph regardless of its specific structure or purpose. This single traversal engine handles diverse dialog types (information gathering, task execution, conversational responses) by interpreting the unified graph representation. The universality of the traversal manager simplifies the execution mechanism compared to having separate imperative code for each dialog type, as it provides a single, consistent interpretation layer that works across all dialogs, thereby improving ease of modification without proportionally increasing execution complexity.
Data Source
AI summary
A system, method and program product that provides a conversational AI platform using a declarative graph model. A system is included having a natural language (NL) interface the receives NL user inputs from a message queue; an intent analyzer that determines an intent of a received NL user input and loads a graph associated with the intent; and a graph traversal manager having traversal logic to first traverse the graph first along a start path from an intent node to a dialog node, then traverse an ask path to a question node to obtain missing entity data, then traverse a contacts path to a service node to execute an external service and return a fulfillment response based on submitted entity data, then traverse a replies with path to a response node to create a formatted fulfillment response that is forwarded to the message queue.