Unstructured Data Translator GUI for Structured Investment Analysis
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Solution Overview
Problem
Existing investment analysis in private-sector companies requires substantial time and resources due to the manual processing of vast amounts of unstructured data, making it difficult to identify and analyze potential investment targets efficiently.
Innovation Solution
An AI-driven system that automatically transforms unstructured information from various sources into structured data, such as Excel or Google Sheets, for easy display and analysis on customizable dashboards, using fine-tuned language models to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of unstructured data is used to identify investment targets, then information accuracy can be maintained through human judgment, but time consumption and resource requirements increase substantially
Solution Approach 1:
The patent introduces an AI language model as an intermediary between unstructured investment data and structured analysis outputs. The model transforms unstructured text from sources like news articles, company reports, and meeting notes into structured data with extracted entities, relationships, and insights, enabling automated analysis while maintaining information accuracy through the model's learning capabilities.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with an automated AI-based system. The language model automatically processes unstructured data, extracts relevant information, and generates structured outputs without human intervention, substituting the mechanical process of manual reading, analysis, and data extraction with an intelligent automated system.
2Loss of information
If vast amounts of unstructured data are manually processed, then comprehensive information coverage is achieved, but productivity decreases due to substantial human resource requirements
Solution Approach 1:
The patent enables the system to process and analyze data autonomously without requiring human analysts for each data point. The AI language model self-services by automatically ingesting unstructured data from multiple sources, extracting relevant information, and generating structured analysis outputs, thereby achieving comprehensive information coverage while dramatically improving productivity.
Solution Approach 2:
The patent creates a universal system that can process multiple types of unstructured data from diverse sources (news articles, company reports, meeting notes, financial statements) through a single AI language model platform. This multi-functional approach allows comprehensive information coverage across different data types while maintaining high productivity through automated processing.
3Loss of information
If unstructured information is accumulated from multiple sources, then data completeness is improved, but ease of operation deteriorates due to difficulty in aggregation and presentation
Solution Approach 1:
The patent segments unstructured information into distinct structured components including entities (companies, persons, locations), relationships (investor-company, competitor, partnership), and attributes (financial metrics, market position). This segmentation transforms comprehensive but unmanageable unstructured data into organized, easily operable structured data that can be readily aggregated and presented.
Solution Approach 2:
The patent changes the parameter state of information from unstructured text format to structured data format with defined schemas and relationships. The AI language model transforms free-text information into standardized parameters and fields that are easier to operate with, aggregate, and present, thereby improving ease of operation while maintaining data completeness.
Data Source
AI summary
Embodiments of the present invention provide a novel approach to investment analysis that can automatically accumulate a large amount of relevant company information that can be readily parsed and presented on a dashboard or similar display to support decision making, tracking, and analysis for the purposes of investment in companies and other entities. Some embodiments are tailored to enable firms to easily transform unstructured information into structured (e.g., row/column) data. The transformations are highly accurate and designed to fit seamlessly into an investor's daily workflow (e.g., a private investor, investment firm employee, fund manager or team member, etc.). The resulting structured data can be stored in any suitable format (e.g., Excel, Google Sheet, csv, database, etc.), and can be updated automatically as more information becomes available.