patent-innovation
AI, Innovation, and Growth Final Project
Science Score: 26.0%
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Low similarity (5.3%) to scientific vocabulary
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AI, Innovation, and Growth Final Project
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Patent-Innovation
This is the final project for the UChicago class: ECON 23050 - Artificial Intelligence, Innovation, and Growth. It looks at patent data to assess firm innovation over time. The following is an abstract from the final paper.
Abstract: The relationship between firm size, patenting behavior, and innovation speed is a critical area of study, particularly as large firms continue to dominate global markets. In the paper “Barriers to Creative Destruction: Large Firms and Non-Productive Strategies”, the argument is made that overtime, the patents firms apply for become increasingly similar as their speed of innovation slows down: “with the help of textual analysis and machine learning tools, patent applications that are too similar to their predecessors could be singled out and their necessity and applicability could be scrutinized” (15). This study explores how the speed and quality of innovation change over time, specifically in the context of patent citations and content similarity. Using large-scale datasets of patent applications and citations, along with advanced textual analysis techniques, we analyze patent citation patterns and CPC subclass similarity to explore speed and quality of innovation over time. We find that patents tend to become more similar as they mature in the market, and the overall impact and novelty of their patents decrease. This study contributes to the literature on creative destruction by highlighting the challenges firms face in maintaining innovation, offering new insights for patent policy, and suggesting ways to improve patent examination practices.
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