PATENT CLAIM ANALYSIS

Application Number: 15772454
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-11
Patent Classification: ["381", "067000"]

Abstract:
A digital electronic stethoscope includes an acoustic sensor assembly that includes a body sensor portion and an ambient sensor portion, the body sensor portion being configured to make acoustically coupled contact with a subject while the ambient sensor portion is configured to face away from the body sensor portion so as to capture environmental noise proximate the body sensor portion; a signal processor and data storage system configured to communicate with the acoustic sensor assembly so as to receive detection signals therefrom, the detection signals including an auscultation signal comprising body target sound and a noise signal; and an output device configured to communicate with the signal processor and data storage system to provide at least one of an output signal or information derived from the output signal. The signal processor and data storage system includes a noise reduction system that removes both stationary noise and non-stationary noise from the detection signal to provide a clean auscultation signal substantially free of distortions. The signal processor and data storage system further includes an auscultation sound classification system configured to receive the clean auscultation signal and provide a classification thereof as at least one of a normal breath sound or an abnormal breath sound.

Claim (Index 12):
The digital electronic stethoscope according to  claim 1 , wherein said auscultation sound classification system is a machine learning system that learns from training data.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 91.0
- Lexical Diversity: 2.6125
- Patent Class: 381.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14755954', '12147751', '12054385', '13003627', '12152397']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7233316362010392
- 35 USC 102 Novelty (BERT): 0.5397398977426148
- Combined Prediction Score: 0.7049724623551968
- Mean Citation Score: 264.6686
- Max Citation Score: 387.38763
- Similarity Product: 264.68248662612683

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

Dataset: test