Hunt for Buried Treasures: Extracting Unclaimed Embodiments from Patent Specifications
Bibliographic record
Abstract
Patent applicants write patent specifications that describe embodiments of inventions.Some embodiments are claimed for a patent, while others may be unclaimed due to strategic considerations.Unclaimed embodiments may be extracted by applicants later and claimed in continuing applications to gain advantages over competitors.Despite being essential for corporate intellectual property (IP) strategies, unclaimed embodiment extraction is conducted manually, and little research has been conducted on its automation.This paper presents a novel task of unclaimed embodiment extraction (UEE) and a novel dataset for the task.Our experiments with Transformer-based models demonstrated that the task was challenging as it required conducting natural language inference on patent specifications, which consisted of technical, long, syntactically and semantically involved sentences.We release the dataset and code to foster this new area of research.1 1 https://github.com/rakutentech/UEE_ ACL23 0001 This invention relates to a system for retrieving ...
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".