Scientometric Model for Technology Readiness Level Assessment
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Solution Overview
Problem
The overwhelming volume and disparate nature of data related to emerging technologies make it difficult to determine the Technology Readiness Level (TRL) of a technology, hindering the identification of trends and maturity assessment, which is crucial for funding, investment, and business decisions.
Innovation Solution
A system and method using a computer to gather, normalize, and analyze data from various sources, applying polynomial fitting and trend analysis to identify initial occurrences and peaks, thereby assigning a TRL indicator, allowing for the development of a technology evolution model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sources are gathered and analyzed to determine TRL, then the accuracy and reliability of TRL assessment is improved, but the complexity of the analysis system and the time required for analysis increases
Solution Approach 1:
The patent segments the analysis system into distinct functional modules: data gathering module that collects data from multiple sources, normalization module that standardizes disparate data formats, analysis module that processes normalized data using polynomial fitting, and TRL determination module that outputs readiness levels. This segmentation manages complexity by making each module independent and specialized.
Solution Approach 2:
The patent introduces normalization as an intermediary process between raw data gathering and TRL analysis. The normalization module acts as a mediator that converts disparate data from patents, papers, articles, and citations into a standardized format, enabling accurate comparison and analysis without requiring complex handling of raw heterogeneous data.
2Loss of information
If comprehensive data from multiple sources are collected, then the completeness of technology trend identification is improved, but the volume of data to be processed increases exponentially
Solution Approach 1:
The patent extracts only the essential features and metrics from comprehensive data sources that are relevant to TRL determination. Instead of processing all raw data, the system identifies and extracts key indicators such as publication counts, citation frequencies, and patent metrics, then applies polynomial fitting to these extracted features to identify trends and inflection points.
Solution Approach 2:
The patent transforms raw data into normalized parameters that capture technology maturity trends. By converting diverse data sources into standardized metrics and applying polynomial transformations, the system changes the parameter representation to reveal underlying trends while reducing the effective data volume requiring detailed analysis.
3Loss of information
If manual analysis of disparate datasets is performed, then the understanding of technology maturity is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent implements self-service automation where the system automatically gathers data from multiple sources, normalizes the data, performs polynomial fitting analysis, identifies peaks and inflection points, and determines TRL levels without requiring manual intervention. This automated pipeline eliminates time-consuming manual analysis while maintaining comprehensive technology maturity assessment.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational methods. Instead of human analysts manually reviewing disparate datasets, the system uses polynomial fitting algorithms and automated trend detection to identify technology maturity patterns, substituting mechanical human analysis with efficient computational processing.
Data Source
AI summary
Provided is a method of generating a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain.