Relational Tree Dialog State Tracking
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Solution Overview
Problem
Existing AI systems fail to dynamically track the context and informational content of evolving dialogs, leading to inability to accurately process complex user queries and commands that imply logical or semantic relationships between entities across multiple statements.
Innovation Solution
The method involves modeling user statements as relational trees, storing them in a stack data structure to track the dialog context over time, and updating these trees in response to new statements to reflect changing user intent, allowing for accurate resolution of queries and commands within dynamic dialogs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing systems parse input statements to determine named entities, then entity extraction is achieved, but logical or semantic relationships between entities across multiple statements are lost
Solution Approach 1:
The patent implements nesting by placing relational trees within a stack data structure, where each relational tree represents a statement and contains nested nodes for entities and relationships. This nested structure preserves hierarchical information about entities and their logical connections across multiple statements, solving the information loss problem while maintaining organized complexity.
Solution Approach 2:
The patent transitions from flat entity extraction to multi-dimensional relational trees that add semantic and logical dimensions to entity representation. By organizing entities, attributes, and relationships in a tree structure with multiple levels and connection types, the system captures logical relationships that would be lost in simple entity lists, addressing the information loss issue.
2Reliability
If existing systems process each statement in isolation, then processing speed is maintained, but dialog context tracking fails
Solution Approach 1:
The patent applies preliminary action by building and maintaining relational trees for each statement as it is processed, storing them in a stack. This preliminary structuring of information allows subsequent statements to quickly reference and build upon previous context without reprocessing entire dialog histories, improving both accuracy and efficiency.
Solution Approach 2:
The patent ensures continuity by maintaining the stack of relational trees throughout the dialog, continuously updating and referencing previous statements. This continuous preservation of dialog state allows the system to track context across multiple interactions, improving query processing accuracy while avoiding the need to reprocess entire dialog histories.
3Quantity of substance
If existing systems cache or delete previous statements, then memory usage is optimized, but dialog context is lost
Solution Approach 1:
The patent extracts only the essential relational structure from full dialog statements, storing condensed relational trees in the stack rather than complete statement texts. This extraction approach retains the critical logical relationships and entity connections needed for context tracking while significantly reducing memory requirements compared to storing entire dialog histories.
Solution Approach 2:
Instead of storing full statements and extracting relationships when needed, the patent inverts the approach by pre-extracting and storing only the essential relational structures. This inversion allows the system to maintain dialog context with minimal memory usage while preserving the logical relationships necessary for accurate query processing.
4Ease of operation
If existing systems require complete queries in single statements, then query completeness is ensured, but natural conversational flow is disrupted
Solution Approach 1:
The patent introduces relational trees as an intermediary representation between user statements and query processing. These trees serve as a mediator that captures the semantic meaning and logical relationships of fragmentary statements, allowing the system to accurately interpret incomplete queries by referencing the structured context stored in the stack while maintaining natural conversational flow.
Data Source
AI summary
A method is provided for representing and updating the state of a dialog involving a series of queries and commands to an artificial intelligence system. Each statement within the dialogue may be modeled as a relational tree spanning nodes corresponding to named entities within the statement. A data structure may be used to store each of these trees and to modify them as the dialog progresses. A subsequent statement in the dialog may be parsed and its contents used to update an ongoing search initiated within that dialog. Statements may be used for the update process despite being fragmentary or not corresponding to any predetermined grammar. An algorithm is disclosed for updating the trees within the data structure after a new statement is parsed.