Infringement by Equivalents/Non-Literal Infringement
Bibliographic record
Abstract
Abstract This chapter highlights infringement by equivalents and non-literal infringement. It is possible for an article or process to infringe a patent claim even if it does not fall within the literal meaning of the claims. There are basically two methods by which this is possible: (1) by use of a doctrine of equivalents; and (2) interpretation of the technical scope of the claims to require only essential elements. In each case, the courts have developed guidelines and tests to provide greater certainty about such non-literal infringement. All jurisdictions dealt with in this text apart from the United Kingdom, Canada, and Australia allow non-literal infringement by means of a doctrine of equivalents. Naturally, the doctrine of equivalents in each of these jurisdictions includes a comparison of the function of the accused product or process with that of the claims and the nature of the result achieved. In the United Kingdom, Canada, and Australia, a purposive construction of the claims requires that each of the essential elements of the claim must be taken for infringement to occur. Thus, omission or replacement of inessential elements of the claim will not avoid infringement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".