Proposal of two-stage patent retrieval method considering the claim structure
Bibliographic record
Abstract
The importance of patents is increasing in global society. In preparing a patent application, it is essential to search for related patents that may invalidate the invention. However, it is time-consuming to identify them among the millions of patents. This article proposes a patent-retrieval method that considers a claim structure for a more accurate search for invalidity. This method uses a claim text as input; it consists of two retrieval stages. In stage 1, general text analysis and retrieval methods are applied to improve recall. In stage 2, the top N documents retrieved in stage 1 are rearranged to improve precision by applying text analysis and retrieval methods using the claim structure. Our two-stage retrieval introduces five precision-oriented analysis and retrieval methods: query-term extraction from a portion of a claim text that describes the characteristics of a claim; query term-weighting without term frequency; query term-weighting with “measurement terms”; text retrieval using only claims as a target; and calculating the relevant score by “partially” adding scores in stage 2 to those in stage 1. Evaluation results using test sets of the NTCIR4 Patent Retrieval Task show that our methods are effective, though the degree of the effectiveness varies depending on the test sets.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".