The Tragedy of Prior Art : Lessons from the USPTO
Bibliographic record
Abstract
This paper presents a model for the rate at which the effort required to conduct prior art is increasing and to demonstrate the model's sensitivities. The sensitivities are valuable for informing future Canadian IP policy. The model demonstrates that the increasing amount of information will result in the geometric growth of the effort required by both the USPTO and CIPO to determine novelty by examining the prior art. Model: Effort of Prior Art Assumption 1: Only a subset of concepts or inventions are patentable. Assumption 2: Knowledge and information grows with time. For the sake of argument we will assume that information grows linearly with time i.e., Amount of Information Available is a product of some constant (C ) and time. Furthermore, we assume that patent applications represent a part of the total available information so patent applications scale in with a similar relationship to the total amount of available information. Assumption 3: To grant a patent, a prior art search must be conducted on all available information and the available information increases with time i.e., a patent application granted at a particular time will necessitate a prior art search of all available information. See Assumption 2 regarding the relationship of Amount of Available Information to time. The effort required to conduct a prior art search is a function of a value indicating the amount of available information (C --see Assumption 2), a constant related to the effort required to search information (N), and time. # = t t C N t rt SearchEffo 0 * * ) ( Info. Assumption 4: Solving the integral relationship between time results in a quadratic relationship between time and the effort required to conduct prior art searches: The resulting relationship betwe...
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".