Three Surveys, A Decade Journey: IPR Tax, Alice Shock, and the Dynamics of Licensing Market as Reflected by LES High Tech Royalty Surveys
Bibliographic record
Abstract
This paper analyzes the combined samples of running royalty rates and lump payments collected by the Licensing Executives Society (USA Canada) (LES USA Canada) through the High Tech Deal Term and Royalty Survey in 2011, 2014, and 2017. The Surveys covered the 10-year period of 2008 to 2017. The analysis presented in this paper is based on 213 sample of royalty rates and 96 samples of lump sum payments. An important caveat is that our analysis is based on the data from licensing market transactions, and accordingly, our conclusions apply only to licensing market and its participants. We want to point this out because an event that has positive effects on those profiting in licensing market may have negative impact on those making revenue from the downstream product or service market. Therefore, it is critical to keep the different perspectives in mind while reading this article and the Report. The economic analysis starts with examining the trend of annual average and median royalty rates over the decade of 2008-2017. By analyzing the annual royalty rates under the backdrop of economic cycle, legislation events, and major relevant Supreme Court cases, this paper sheds light on how the economic down turn, AIA, PTAB, and the Supreme Court landmark rulings such as Alice might have configured the landscape in licensing market. One of the important trends revealed by the analysis is the retreat of corporate business licensors and the crowding-in of academic and governmental entities in the wake of AIA introducing IPR at PTAB. IPR challenges escalate the risk and uncertainty in patent monetization, and increase the patent enforcement costs for private patent owners, both of which depress patent valuation. Since IPR essentially does not affect governmental entities and state universities, it has an effect analogous to an extra tax levied on the private patent owners. The IPR tax discourages private patent owners’ participation in licensing markets, while incentivizes state universities and federal entities to crowd in. Such a shift is further confirmed by the overall licensing market participation data, as well as by the data from the transactions with royalty rates and lump sum payments. Dummy variable regression model is employed to identify and quantify the effects of different deal-specific and market-specific variables. For example, using the combined royalty rate data from the three Surveys, the analysis reports that certain technology types, such as aerospace and software, command statistically significant premiums; and that exclusive deals are typically associated with higher royalty rates. Also, the technologies in the production or fully developed stage carry higher payments than the technologies in earlier stages, and the combination of know-how, designs, and drawings holds a significant premium in licensing market. The regression analysis also demonstrates how the market-specific variables interact with and affect the valuation effects of the deal-specific variables such as exclusivity, licensor organization types, and technology fields. For example, it seems that the average royalty rate in software technology licensing had a sizeable drop during the post-AIA/IPR period, though based on a limited number of samples. Similar regression analysis has been conducted on the samples of lump sum payments.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".