PATENT CLAIM ANALYSIS

Application Number: 16043989
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-01
Patent Classification: ["707", "692000"]

Abstract:
Embodiments of the disclosure are directed to providing a single source for adverse event data by taking a layered approach to standardizing, harmonizing and detecting duplicates across multiple data sources at different scales. In one embodiment, a method is provided. The method includes parsing datasets stored in a data store. These datasets are enriched using standardization and normalization. In the candidate duplicates and feature engineering step, the method may join send the data to hashing algorithm to generate candidate duplicates. Features are extracted from each duplicate candidate pair using the term-pair set adjustment technique. These candidates and associate features are sampled using a sampling technique and are labeled as duplicates or non-duplicates. Upon a conflict in labels, a conflict resolution strategy is applied to create a master list of duplicate pairs. A classifier is trained on the master list to classify the rest of the candidate pairs as duplicates/non-duplicates.

Claim (Index 20):
The non-transitory computer-readable medium of  claim 19 , wherein the processing device is further to:\n updating a list of duplicate candidate pairs based on a resolution of the conflict.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 95.0
- Lexical Diversity: 1.6875
- Patent Class: 707.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['12715417', '13928983', '14826575', '12982767', '15257535']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1855164359542481
- 35 USC 102 Novelty (BERT): 0.4962827246992816
- Combined Prediction Score: 0.2165930648287515
- Mean Citation Score: 102.137304
- Max Citation Score: 115.00168
- Similarity Product: 61.61579349005223

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test