PATENT CLAIM ANALYSIS

Application Number: 15912511
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["706", "020000"]

Abstract:
The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Claim (Index 15):
The method of  claim 6 , wherein the analysis of the biological sample from the subject further comprises:\n (a)(2) grouping the measurements of the plurality of event features into a plurality of bins, a bin representing a subset associated with a range of measured values; (a)(3) applying a filter to the plurality of bins, wherein application of the filter comprises:\n (i) identifying the bins populated with no measurements and the bins populated with measurements of undesired event features; and \n (ii) creating a biological data sample of desired bins, wherein the bins identified in (i) are removed from the biological data sample prior to training the artificial neural network.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 90.0
- Lexical Diversity: 1.34375
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15445913', '15532844', '13125054', '11928901', '16085077']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3724595673799202
- 35 USC 102 Novelty (BERT): 0.56476876788674
- Combined Prediction Score: 0.3916904874306022
- Mean Citation Score: 218.638974
- Max Citation Score: 426.39444
- Similarity Product: 271.54774854957816

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

Dataset: test