---
license: mit
dataset_info:
  features:
  - name: 'Unnamed: 0'
    dtype: int64
  - name: claim_input
    dtype: string
  - name: claim_idx
    dtype: int64
  - name: applicationNumber
    dtype: int64
  - name: applicationTypeCategory
    dtype: string
  - name: relatedDocumentData
    dtype: float64
  - name: patentClassification
    dtype: string
  - name: applicantCitedExaminerReferenceIndicatorCount
    dtype: float64
  - name: filingDate
    dtype: string
  - name: publicationDate
    dtype: string
  - name: claimNumberArrayDocument
    dtype: float64
  - name: abstract
    dtype: string
  - name: percentile
    dtype: float64
  - name: claim_label_101
    dtype: int64
  - name: claim_label_102
    dtype: int64
  - name: claim_label_103
    dtype: int64
  - name: claim_label_112
    dtype: int64
  - name: relatedApplicationNumber
    dtype: string
  - name: max_score_x
    dtype: float64
  - name: mean_score
    dtype: float64
  - name: max_citations
    dtype: float64
  - name: max_other_citations
    dtype: float64
  - name: max_article_citations
    dtype: float64
  - name: max_score_y
    dtype: float64
  - name: component
    dtype: int64
  - name: is_closed
    dtype: int64
  - name: is_open
    dtype: int64
  - name: is_half
    dtype: int64
  - name: similarity_product
    dtype: float64
  - name: transitional_phrase
    dtype: string
  - name: app_claim_id
    dtype: string
  - name: bert_scores_102
    dtype: float64
  - name: claim_label_combined
    dtype: int64
  - name: bert_scores_101
    dtype: float64
  - name: combined_pred_scores
    dtype: float64
  - name: claim_label_101_adjusted
    dtype: int64
  - name: bert_score_102_app_feats_no_hinge
    dtype: float64
  - name: bert_score_101_app_feats
    dtype: float64
  - name: bert_score_102_app_feats_w_hinge
    dtype: float64
  - name: bert_score_102_no_app_feats
    dtype: float64
  - name: dataset
    dtype: string
  - name: lexical_diversity
    dtype: float64
  - name: patent_class
    dtype: float64
  - name: foreignPriority
    dtype: bool
  splits:
  - name: train
    num_bytes: 2152439051
    num_examples: 1485693
  - name: validation
    num_bytes: 402312853
    num_examples: 278215
  - name: test
    num_bytes: 267789008
    num_examples: 185477
  download_size: 501745093
  dataset_size: 2822540912
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# PatentAP

<!-- Provide a quick summary of the dataset. -->

A dataset for the task of Patent Approval Prediction, which is proposed in the paper "Beyond Scaling: Predicting Patent Approval with Domain-specific
Fine-grained Claim Dependency Graph"

## Dataset Details

### Dataset Description

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- **Curated by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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### Dataset Sources [optional]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Dataset Structure

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## Dataset Creation

### Curation Rationale

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### Source Data

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#### Data Collection and Processing

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#### Who are the source data producers?

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### Annotations [optional]

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#### Annotation process

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#### Who are the annotators?

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#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

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## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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