Target Prediction Using Causal Graphs and Mixed Data Signals
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Solution Overview
Problem
Existing future-casting technologies face limitations in accurately predicting targets due to the challenge of integrating structured and unstructured data, particularly in predicting numerical values, and lack the ability to analyze causal relationships effectively.
Innovation Solution
A target prediction method and system that utilizes a language model to detect target influence variables at a semantic level, filtering and integrating structured and unstructured data to generate a causal relationship graph, and performing sentiment analysis to predict short-term to long-term target outlooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for future prediction, then prediction capability is provided, but accuracy is limited due to inability to effectively integrate structured and unstructured data
Solution Approach 1:
The patent combines structured data (numerical values, time series) and unstructured data (text documents, news articles) into a unified prediction framework. The system integrates multiple data types by extracting features from both structured datasets and unstructured text, then feeds them together into the prediction model to improve overall prediction accuracy while maintaining versatility across different data formats.
Solution Approach 2:
The patent introduces an intermediary layer that processes unstructured text data by extracting meaningful features and converting them into a format compatible with structured data. This intermediary text analysis component bridges the gap between different data types, enabling effective integration without losing the unique characteristics of each data source.
2Reliability
If semantic level analysis is performed to detect target influence variables, then prediction reliability is improved, but system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct functional modules: a text analysis module for processing unstructured data, a structured data processing module, a feature extraction module, and a prediction module. Each module performs a specific function, making the overall complex system more manageable and maintainable while achieving reliable predictions through semantic level analysis of target influence variables.
3Adaptability or versatility
If multiple data sources are integrated for comprehensive prediction, then prediction coverage is improved, but data processing complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data sources (structured databases, unstructured text files, news feeds, social media data) through a common architecture. The system uses standardized interfaces and processing pipelines that work across different data types, expanding prediction coverage without proportionally increasing processing complexity.
Data Source
AI summary
A target prediction method for predicting a future outlook of a target performed by a computing device or a processor may collect related structured and unstructured data when a user requests predictive generation, analyze the relationship between the target and a variable affecting the target at a semantic level, and compute a target outlook of a future.