AI Innovation Data Processing System for Automated Scouting
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Solution Overview
Problem
Existing systems for researching innovations are slow and require users to manually scour various data sources, making it difficult to discover and analyze category-specific innovations efficiently.
Innovation Solution
An AI-based innovation data processing system that uses natural language processing and artificial intelligence to automatically gather and analyze data from multiple sources, identifying and tracking innovations through query-based searches, entity-relationship processing, and time series modeling, generating reports and real-time responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data collection and analysis methods are used, then users can access multiple data sources, but the process is slow and time-consuming
Solution Approach 1:
The system performs automatic data collection, processing, and analysis without requiring manual user intervention. The AI-based system autonomously gathers data from multiple sources, processes it through NLP and entity-relationship processing, and generates insights, enabling the system to serve itself rather than requiring continuous human operation.
Solution Approach 2:
The patent replaces manual mechanical data gathering and analysis processes with an automated AI-based system. The mechanical action of manually searching, collecting, and analyzing data is substituted with electronic automation including web scraping, NLP processing, entity-relationship extraction, and machine learning-based analysis.
2Productivity
If automated AI-based processing is implemented, then data analysis speed and efficiency improve, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: data collection module, NLP processing module, entity-relationship processing module, trend analysis module, and reporting module. Each module performs a specific function, making the complex system manageable through clear segmentation of responsibilities and processing stages.
Solution Approach 2:
The patent introduces intermediary components such as knowledge graphs that mediate between raw data and final insights. The entity-relationship processor creates structured knowledge representations that serve as intermediaries, simplifying the connection between unstructured data sources and analytical outputs.
Data Source
AI summary
An Artificial Intelligence (AI)-based innovation data processing system receives at least one query word related to a category. Information material including textual and non-textual data is retrieved from a plurality of data sources using the at least one query word. The information material is tokenized and parsed using a dependency parser for entity recognition, building entity relationships and for generating knowledge graphs. The output of the dependency parser is accessed by a trained classifier for obtaining respective confidence levels for each of the sentences in the textual data. The confidence levels are compared to a predetermined threshold confidence level for determining if the sentences include references to innovations. In addition, trends in the innovations are determined and responses to user queries are generated based on one or more of knowledge graphs and the trends.