Decision Tree Path Summaries Using LLMs for Multidimensional Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multidimensional data analysis is challenging due to the difficulty in understanding relationships between parameters, as direct table viewing does not indicate these relationships, complicating the analysis of multiple relevant relationships.
Innovation Solution
A computer-implemented method involving a decision tree and a large language model is used to generate natural language summaries of decision tree paths, summarizing leaf nodes and their conditions, enabling clearer understanding of parameter relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multidimensional data is stored in tabular format, then data structure is simple and easy to store, but relationships between parameters are not directly indicated and difficult to understand
Solution Approach 1:
The patent introduces decision trees as an intermediary representation between raw tabular data and human understanding. The decision tree structure acts as a mediator that transforms parameter relationships into visual, interpretable paths while preserving the underlying data relationships. This intermediary representation makes implicit relationships explicit without altering the original data storage format.
Solution Approach 2:
The patent transforms two-dimensional tabular data into a tree-based hierarchical structure with multiple dimensions including decision paths, conditions, and outcomes. This dimensional transformation allows relationships between parameters to be visualized through tree depth, branch structure, and path relationships, making hidden patterns visible while maintaining data integrity.
2Loss of information
If decision tree structure is created to show parameter relationships, then relationships become visible and understandable, but data processing complexity increases
Solution Approach 1:
The patent segments the complex task of relationship visualization into distinct components: data preprocessing, decision tree construction, path extraction, and result presentation. Each component handles a specific aspect of the transformation, reducing overall system complexity by breaking down the monolithic process into manageable, modular stages that can be independently optimized and maintained.
Solution Approach 2:
The decision tree construction process automatically discovers and represents parameter relationships without requiring manual intervention. The system self-organizes the data into meaningful structures through algorithmic processing, eliminating the need for expert domain knowledge to manually map relationships while maintaining accuracy and consistency.
3Loss of information
If multiple relationships between parameters are analyzed, then comprehensive understanding is achieved, but analysis difficulty compounds significantly
Solution Approach 1:
The patent addresses multiple parameter relationships by transforming them into a multi-dimensional tree structure where each dimension represents a different aspect of the relationship (decision conditions, path sequences, outcome probabilities). This dimensional transformation allows simultaneous analysis of multiple relationships without linear complexity increase, as the tree structure naturally organizes and separates intersecting relationships into distinct branches and paths.
Solution Approach 2:
The patent segments complex multidimensional relationships into atomic decision units within the tree structure. Each node represents a single parameter comparison or condition, and each path represents a unique combination of conditions leading to an outcome. This segmentation allows systematic analysis of individual relationships while maintaining the ability to synthesize comprehensive insights from all paths collectively.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for accessing a stored data structure representing a decision tree, determining a plurality of rows of text representing leaf nodes of the decision tree and a plurality of conditions that describe paths to the leaf nodes along with a label for the corresponding leaf node, generating a prompt including the plurality of rows of text and a request to generate a result comprising a natural language summary column, executing the prompt against a large language model, receiving a result comprising a natural language summary column, storing a first natural language summary of a first path from the natural language summary column in association with a first leaf node in the stored data structure, and storing a second natural language summary of a second path from the natural language summary column in association with a second leaf node in the stored data structure.