How Redeployable are Patent Assets? Evidence from Failed Startups
Bibliographic record
Abstract
Entrepreneurial firms are important sources of patented inventions. Yet little is known about what happens to patents “released” to the market when startups fail. This study provides a first look at the frequency and speed with which patents originating from failed startups are redeployed to new owners, and whether the value of patents is tied to the original venture and team. The evidence is based on 1,766 U.S. patents issued to 285 venture capital-backed startups that disband between 1988 and 2008 in three innovation-intensive sectors: medical devices, semiconductors, and software. At odds with the view that the resale market for patented inventions is illiquid, we find that most patents from these startups are sold, are sold quickly, and remain “alive” through renewal fee payment long after the startups are shuttered. The patents tend to be purchased by other operating companies in the same sector and retain value beyond the original venture and team. We do find, however, that the patents and people sometimes move jointly to a new organization following the dissolution of the original venture, and explore the conditions under which such co-movement is more likely. The study provides new evidence on a phenomenon–of active markets for buying and selling patents–underexplored in the literature and consequential for both entrepreneurial and established firms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".