PATENT CLAIM ANALYSIS

Application Number: 16008444
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["356", "318000"]

Abstract:
Systems and methods for diagnosing or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and machine learning are disclosed.

Claim (Index 11):
A system for diagnosing or monitoring progress of a pathology using laser induced breakdown spectroscopy (LIBS) and biomedical fluids, the system comprising:\n a predetermined substrate; the predetermined substrate configured to have a sample of a predetermined biological fluid deposited on the predetermined substrate; a laser light source; a focusing optical subsystem configured to receive light from the laser light source and focus received light on the sample deposited on the predetermined substrate; energy and pulse length of the laser light source being configured to cause ablation of the sample and the predetermined substrate and formation of a plasma; a light collection optical subsystem configured to collect optical emission from the plasma; a spectroscopic acquisition component configured to receive collected optical emission from the light collection optical subsystem and to provide spectral data; the spectroscopic acquisition component comprising a spectrometer and a detector; a processing component configured to receive said spectral data from the spectroscopic acquisition component; the processing component comprising one or more processors; the one or more processors being configured to use a machine learning algorithm to diagnose the pathology or monitor progress of the pathology; wherein the machine learning algorithm is trained on a training set comprising spectral data for LIBS collected optical emission from samples of the predetermined biological fluid on the predetermined substrate which have known pathology or known progress of the pathology; wherein the predetermined substrate is configured to provide higher signal-to-noise ratio than other candidate substrates and to provide higher classification accuracy, obtained using the machine learning algorithm, of greater than a predetermined value.

Metadata:
- Claim Count in Document: 56.0
- Percentile: 94.0
- Lexical Diversity: 1.04167
- Patent Class: 356.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14432873', '12625970', '12403522', '11794838', '12600975']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.678105350215288
- 35 USC 102 Novelty (BERT): 0.4745486249238947
- Combined Prediction Score: 0.6577496776861487
- Mean Citation Score: 172.76267
- Max Citation Score: 188.34277
- Similarity Product: 142.27308480753425

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

Dataset: test