# US 2021/0340591 A1 · Microorganism information providing device and method

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- Number: US 2021/0340591 A1
- Document ID: US20210340591A1
- Status: Published application
- Kind: A1
- Application number: 17/273,631
- Filed: 2019-09-06
- Published: 2021-11-04
- Earliest claimed: 2018-09-07
- Assignees: The Wave Talk, Inc.; Korea Advanced Institute of Science and Technology
- Applicants: The Wave Talk, Inc.; Korea Advanced Institute of Science and Technology
- Inventors: YongKeun PARK; KyeoReh LEE; SeungWoo SHIN; Geon KIM; Young Dug KIM
- Web page: https://patentgrep.com/patent/US20210340591A1

## CPC

- G01N 21/4788: {Diffraction (for sizing particles G01N15/0205)}
- C12Q 1/04: Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor {(C12Q1/6897 takes precedence)}
- C12Q 1/06: Quantitative determination
- G01N 15/06: Investigating concentration of particle suspensions (by weighing G01N5/00; investigating sedimentation of particle suspensions G01N15/04; investigating individual particles G01N15/10)
- G01N 21/47: Scattering, i.e. diffuse reflection (G01N21/25, G01N21/41 take precedence {G01N21/55 takes precedence})
- G01N 21/51: inside a container, e.g. in an ampoule (G01N21/53 takes precedence)
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G06V 20/698: {Matching; Classification}
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G01N 15/075: by optical means
- G01N 2021/1765: {Method using an image detector and processing of image signal}
- G01N 2021/1789: {Time resolved}
- G01N 2021/479: {Speckle}
- G01N 2201/1296: using neural networks
- G16H 30/40: for processing medical images, e.g. editing
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

According to an embodiment of the present disclosure, provided is an apparatus for providing microorganism information, including: a receiving unit configured to receive a plurality of images obtained by photographing in time series an outgoing wave emitted from a sample; a detecting unit configured to extract a feature of a change over time from the plurality of images obtained by photographing in time series; a learning unit configured to machine-learn classification criteria based on the extracted feature; and a determining unit configured to classify the type or concentration of a microorganism included in the sample based on the classification criteria, wherein each of the plurality of images includes speckle information generated by multiple scattering by the microorganism due to waves incident on the sample.

## Claims

### Claim 1

An apparatus for providing microorganism information, the apparatus comprising:
a receiving unit configured to receive a plurality of images obtained by photographing in time series an outgoing wave emitted from a sample;
a detecting unit configured to extract a feature of a change over time from the plurality of images obtained in time series;
a learning unit configured to machine-learn classification criteria based on an extracted feature; and
a determining unit configured to classify the type or concentration of a microorganism contained in the sample based on the classification criteria, wherein
each of the plurality of images includes speckle information generated by multiple scattering caused by the microorganism due to a wave entering into the sample.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein
the learning unit learns the classification criteria by using a convolutional neural network (CNN).

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein
the learning unit performs a convolution arithmetic by using a convolution kernel having a size smaller than a size of one speckle.

### Claim 4 (depends on 2)

The apparatus of claim 2, wherein
the learning unit performs a convolution arithmetic by using a convolution kernel having a size of n×n smaller than m when a size of one speckle corresponds to m pixels.

### Claim 5 (depends on 4)

The apparatus of claim 4, wherein
a stride of the convolution arithmetic corresponds to the value n.

### Claim 6 (depends on 4)

The apparatus of claim 4, wherein
the convolution kernel comprises kernel feature maps corresponding to or greater than the number of the plurality of images.

### Claim 7 (depends on 6)

The apparatus of claim 6, wherein
the learning unit performs a convolution arithmetic by using a kernel set including a plurality of convolution kernels corresponding to the number of output channels.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein
the number of output channels by the convolution arithmetic corresponds to the number of the plurality of images.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein
the learning unit learns the classification criteria based on temporal correlation of the plurality of images.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein
the classification criteria is learned by using one of a change in a shape of a speckle pattern in the feature, a temporal correlation coefficient calculated based on an intensity of light of the speckle pattern, and a change in a standard deviation value of the intensity of light of the speckle pattern.

### Claim 11 (depends on 10)

The apparatus of claim 10, wherein
the standard deviation value and the concentration of the microorganism has a linear relationship.

### Claim 12

A method of providing microorganism information, the method comprising:
receiving a plurality of training images which have been obtained by photographing in time series an outgoing wave emitted by irradiating a wave to a sample containing a microorganism of which type or concentration is previously known;
machine-learning classification criteria based on a feature of a change over time from the plurality of training images which have been obtained in time series;
receiving a plurality of images obtained by photographing in time series an outgoing wave which is emitted by irradiating a wave onto a new sample; and
identifying a type or a concentration of a microorganism included in the new sample based on the plurality of images and the classification criteria, wherein
each of the plurality of training images or each of the plurality of images comprises speckle information that is generated by multiple scattering by the microorganism due to waves incident on the sample or the new sample.

### Claim 13 (depends on 12)

The method of claim 12, wherein
the machine-learning of the classification criteria comprises
learning the classification criteria by using a convolutional neural network (CNN).

### Claim 14 (depends on 13)

The method of claim 13, wherein
the machine-learning of the classification criteria comprises
performing a convolution arithmetic by using a convolution kernel having a size smaller than a size of one speckle.

### Claim 15 (depends on 13)

The method of claim 13, wherein
the machine-learning of the classification criteria comprises
performing a convolution arithmetic by using a convolution kernel having a size of n×n smaller than m when a size of one speckle corresponds to m pixels.

### Claim 16 (depends on 15)

The method of claim 15, wherein
a stride of the convolution arithmetic corresponds to the value n.

### Claim 17 (depends on 13)

The method of claim 13, wherein
the convolution kernel comprises kernel feature maps corresponding to or greater than the number of the plurality of images.

### Claim 18 (depends on 17)

The method of claim 17, wherein
the machine-learning of the classification criteria comprises
performing a convolution arithmetic by using a kernel set including a plurality of convolution kernels corresponding to the number of output channels.

### Claim 19 (depends on 18)

The method of claim 18, wherein
the number of output channels by the convolution arithmetic corresponds to the number of the plurality of images.

### Claim 20 (depends on 12)

The method of claim 12, wherein
the learning unit learns the classification criteria based on temporal correlation of the plurality of images.