Automated Technology Trend Analysis for Predicting Feasible Designs
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Solution Overview
Problem
Current approaches to anticipating technological advancements are ad-hoc, expensive, and dependent on subject matter experts with broad knowledge, making it difficult to identify surprising or non-intuitive innovations, especially when they arise from enabling technologies outside a company's or government's traditional scope.
Innovation Solution
A system and method that analyzes technological trends to classify currently infeasible designs as predictably feasible based on anticipated technological advancements, using a combination of subject-matter expertise and automated analysis to identify potential future innovations by defining a design space, storing technological trends, and applying morphological and TRIZ frameworks to resolve design conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ad-hoc expert analysis is used to anticipate technological advancements, then subject matter expertise can be leveraged to identify possible areas of technological advancement, but the approach becomes expensive, time-consuming, and dependent on the span of knowledge of individual experts
Solution Approach 1:
The patent replaces manual expert analysis with an automated computer-based system that uses machine learning models and databases to analyze technological trends and predict future advancements. The system automatically processes technical literature, patents, and research papers to identify emerging technologies without requiring human experts to manually review each document, thereby reducing time loss while maintaining prediction accuracy.
Solution Approach 2:
The patent creates a digital replica of expert knowledge by training machine learning models on existing technological data and expert predictions. The system copies the analytical capabilities of human experts into an automated algorithm that can process information at scale, eliminating the time constraint of individual expert analysis while preserving the predictive accuracy through pattern recognition learned from historical data.
2Adaptability or versatility
If broad-scale open-ended exploration by experts is performed to identify future enabling technologies, then surprising or non-intuitive innovation can be detected, but the cost and complexity of the exploration increases significantly
Solution Approach 1:
The patent replaces complex human expert exploration with an automated system that uses natural language processing and machine learning to analyze vast amounts of technical literature across multiple domains. The system automatically identifies surprising innovations by detecting patterns and anomalies in the data that would be difficult for human experts to find, maintaining adaptability while reducing operational complexity.
Solution Approach 2:
The patent creates a universal system that can explore multiple technological domains simultaneously using the same underlying machine learning framework. The system is designed to handle diverse types of technical information (patents, research papers, news articles) and apply consistent analysis methods across different fields, thereby identifying surprising innovations across broad ranges of technology without requiring separate complex exploration processes for each domain.
3Reliability
If anticipation of technological advancements is performed manually by experts, then investment decisions can be informed, but organizations risk being blindsided by unanticipated disruptive technologies
Solution Approach 1:
The patent replaces manual expert anticipation processes with an automated system that continuously monitors and analyzes technological trends at a scale and speed impossible for human experts. The system processes vast amounts of technical information in real-time, providing reliable predictions of disruptive technologies while operating at high productivity, thereby eliminating the trade-off between reliability and speed in technology anticipation.
Data Source
AI summary
A method and system for determining predictably feasible model designs. The method includes defining a plurality of model designs, wherein the plurality of model designs include a plurality of infeasible model designs, wherein one or more of the infeasible model designs are infeasible due to limits in technology; storing information representing a plurality of technological trends; and classifying one or more of the infeasible model designs as predictably feasible model designs, wherein the predictable feasible model designs are those infeasible model designs expected to become feasible model designs if one or more of the plurality of technological trends continues as anticipated.