Words in Patents: Research Inputs and the Value of Innovativeness in Invention
Bibliographic record
Abstract
Intelligently allocating research effort and funds requires deciding whether to build on recent advances or on more established knowledge.When recent advances create superior opportunities for invention, their adoption as research inputs in the invention process promotes technological progress.The gains from pursuing such innovative research paths may, however, be very limited, due to the undeveloped nature of new knowledge, quick obsolescence of fast-improving knowledge, and the vast scope of the existing knowledge base.In this paper, we first develop a new approach to identifying research inputs in invention.Next, we estimate the value of pursuing innovative research paths that are created by the arrival of new research inputs.We identify research inputs based on a natural language analysis of 10 billion word and word sequence patent pairs in 6 million patents granted during 1920-2010.This novel textual analysis empirically reveals which single and general purpose technologies and scientific discoveries have been popular as research inputs in invention.We estimate the value of innovative research by comparing patents that mention these research inputs early against the value of other patents.For this comparison, we develop also a new measure of patent value.The measure distinguishes between citations that reflect the cumulative nature of invention and citations that may merely reflect similarity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".