Evaluating the disruption of COVID-19 on AI innovation using patent filings
Bibliographic record
Abstract
Economists have long recognized that technological innovation is a key contributor to economic growth due to its impact on productivity. In this paper, we explore the impact of COVID-19 on innovation in artificial intelligence (AI) to better understand future effects on economic growth and productivity. Using patents as a measure of innovation and knowledge production, we analyze monthly patent application filing data from January 2015 to June 2021 to compare and assess trends. Past research has shown that growth in patents in the fields of AI have accelerated since 2012, with 6.5 times more annual filings occurring from 2006 to 2017. Here, we focus specifically on determining if the pandemic has had an impact on this acceleration in AI-related innovation. To accomplish this task we must confront the challenge in using up-to-date patent data for this kind of analysis due to the fact that there are considerable time lags associated with patent filing dates and their ultimate publication dates. In real-time situations such as COVID-19, it is, therefore, difficult to ascertain impact using the publicly available patenting data directly. In this paper, we propose a novel approach for examining existing and up-to-date publicly available patent filing data and use that method to gain new insights into the pandemic’s effects on AI-related innovation. Our findings suggest that the pandemic has had a slowing impact on the rate of innovation in these areas but that the downturn may be reversing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".