PATENT CLAIM ANALYSIS

Application Number: 15928992
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-10
Patent Classification: ["382", "128000"]

Abstract:
A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase imaging (TS-QPI) system is described which provides high-throughput quantitative imaging, and utilizing photonic time stretching. In at least one embodiment, TS-QPI is integrated with deep learning to achieve record high accuracies in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The system is particularly well suited for data-driven phenotypic diagnosis and improved understanding of heterogeneous gene expression in cells.

Claim (Index 1):
A computer-based automated method of sorting particles for flow cytometry, the method comprising:\n training, by a training device, an artificial neural network (ANN) according to parameters derived from digitally observed features of known particles that belong to at least two known particle classes; tuning, by the training device, weights of the ANN as a function of at least one property of receiver operating characteristics (ROC) curve representing particle classifications of the known particles generated by the ANN based on the digital observed features; configuring a sorting device with the trained and tuned ANN; storing in a computer readable non-transitory memory, by the sorting device, particle sensor data relating to observed particles that are observed by a sensor; converting, by the sorting device, the particle sensor data into digitally observed particle features representing the observed particles; and sorting, by the sorting device, the observed particles into the at least two classes according to the trained and tuned ANN as a function of the observed particle features; wherein said method is performed by executing instructions on a computer processor, and wherein said instructions are stored on a non-transitory memory readable by said computer processor.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 90.0
- Lexical Diversity: 1.53165
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15016217', '15263419', '13181150', '15672051', '13109640']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2530798720231945
- 35 USC 102 Novelty (BERT): 0.4960163032105307
- Combined Prediction Score: 0.2773735151419281
- Mean Citation Score: 126.8900548
- Max Citation Score: 153.82976000000005
- Similarity Product: 94.85257260147098

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

Dataset: test