PATENT CLAIM ANALYSIS

Application Number: 15889168
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["704", "009000"]

Abstract:
Techniques for improving machine learning and text classification are described. The described techniques include improved processes for collecting training data to train a machine classifier. Some data sets are very large but contain only a small number of positive or negative training examples. The described text classification system obtains training examples by intelligently identifying documents that are likely to present or identify positive or negative training examples. The text classification system employs these techniques to train a classifier to categorize patent claims according some legal rule, such as subject-matter eligibility under 35 U.S.C. 101.

Claim (Index 13):
The method of  claim 11 , further comprising:\n training multiple machine learning models, wherein each model is trained using a different mix of edible and ineligible claims such that each successive model is trained using a mix that has a higher fraction of ineligible claims than its preceding model; receiving an indication of a classifier sensitivity level; selecting one of the machine learning models based on the classifier sensitivity level; and determining whether or not the patent claim is patent eligible based on text of the patent claim evaluated by selected machine learning model.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 88.0
- Lexical Diversity: 1.55385
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13000025', '12658165', '13632943', '13310510', '14630751']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2349549427972209
- 35 USC 102 Novelty (BERT): 0.4665242250759285
- Combined Prediction Score: 0.2581118710250917
- Mean Citation Score: 118.17263
- Max Citation Score: 129.53055
- Similarity Product: 91.22807893549204

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

Dataset: test