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 16):
A system for contactless blood pressure determination of a human subject, the system comprising one or more processors and a data storage device, the one or more processors configured to execute:\n a transdermal optical imaging (TOI) module to receive a captured image sequence of light re-emitted from the skin of one or more humans, the TOI module determines, 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, the TOI module determines 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; a profile module to extract one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs; a machine learning module to build 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, the machine learning module determines, using the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure of the human subject; and an output module to output 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): 0.3457670409776678
- 35 USC 102 Novelty (BERT): 0.5418057129361662
- Combined Prediction Score: 0.3653709081735177
- Mean Citation Score: 277.991468
- Max Citation Score: 394.2462
- Similarity Product: 352.03859531621936

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