Patent Network Analysis for Technology Improvement Rate Prediction
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Solution Overview
Problem
Current methods for estimating technological change are limited by relying on indirect and aggregate measures, such as total factor productivity and patent numbers, which fail to provide a systematic and granular understanding of technological improvement rates across various domains, leading to incomplete and inaccurate assessments of future technological importance.
Innovation Solution
A system and method that decompose patents into technological domains using the Classification Overlap Method (COM) and patent network centrality estimation to predict performance improvement rates for a wide range of technologies, enabling instantaneous technology predictions and facilitating decision-making in R&D and policy development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If indirect and aggregate measures (such as total factor productivity and patent numbers) are used to estimate technological change, then the assessment process is simplified, but the granularity and accuracy of technological improvement rates across various domains deteriorates
Solution Approach 1:
The patent applies segmentation by decomposing the patent data into distinct technological domains using classification systems (IPC and CPC). Instead of treating all patents as a single aggregate, the system segments them into specific technology areas, allowing for granular measurement of improvement rates in each domain while maintaining systematic organization. This resolves the contradiction by enabling both efficient processing through structured segmentation and precise measurement through domain-specific analysis.
2Measurement precision
If comprehensive data collection methods are used to estimate technological improvement rates, then the accuracy and completeness of predictions improves, but the time and cost required for data collection increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing patent data into structured technological domains using classification systems before actual analysis is needed. The system pre-calculates improvement rates for multiple technology domains and stores them for rapid retrieval. This allows the system to provide accurate, comprehensive predictions without requiring time-consuming data collection at the point of use, as the heavy lifting of data organization and initial analysis has already been performed.
3Measurement precision
If patent data is decomposed into multiple technological domains using classification systems, then the granularity of technological assessment improves, but the system complexity increases
Solution Approach 1:
The patent applies universality by using standardized patent classification systems (IPC and CPC) that are already widely adopted and recognized globally. These classification systems serve multiple functions: they organize patents into technological domains, enable systematic analysis, facilitate data retrieval, and provide a common framework for comparing different technology areas. By leveraging this existing universal classification infrastructure, the system achieves granular domain-specific measurement without creating a complex custom classification framework from scratch.
Data Source
AI summary
Systems and methods for predicting yearly performance improvement rates for nearly all definable technologies for the first time are provided. In one embodiment, a correspondence of all patents within the U.S. patent system to a set of technology domains is created. From the identified patent sets, the invention may calculate average centrality of the patents in each domain to predict improvement rates, following a patent network-based methodology. Also disclosed is a system to intake a user technology search query and match user intent with the technology domain as well as the corresponding improvement rate.