PATENT CLAIM ANALYSIS

Application Number: 15923225
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["382", "128000"]

Abstract:
A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Claim (Index 1):
A method for contactless blood pressure determination of a human subject, the method executed on one or more processors, the method comprising:\n receiving a captured image sequence of light re-emitted from the skin of one or more humans; determining, using a trained hemoglobin concentration (HC) changes machine learning model trained with a HC changes training set, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes training set comprising the captured image sequence; determining a blood flow data signal of one or more predetermined regions of interest (ROIs) of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes; extracting one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set comprising the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure for the human subject; and outputting the determination of blood pressure.

Metadata:
- Claim Count in Document: 13.0
- Percentile: 90.0
- Lexical Diversity: 2.39683
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15923242', '15761891', '16076522', '16076492', '14969300']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3466308243254413
- 35 USC 102 Novelty (BERT): 0.5407421174373453
- Combined Prediction Score: 0.3660419536366317
- Mean Citation Score: 277.991468
- Max Citation Score: 394.2462
- Similarity Product: 327.7059024744272

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

Dataset: test