# US 2022/0044765 A1 · Preprocessing and convolutional operation apparatus for clinical decision-making artificial intelligence development using hypercubic shapes based on bio data

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- Number: US 2022/0044765 A1
- Document ID: US20220044765A1
- Status: Published application
- Kind: A1
- Application number: 17/509,779
- Filed: 2021-10-25
- Published: 2022-02-10
- Earliest claimed: 2019-10-18
- Assignees: UIF (University Industry Foundation), Yonsei University; Itmedic Inc.
- Applicants: UIF (University Industry Foundation), Yonsei University; Itmedic Inc.
- Inventors: Jae Woo SONG; Ju Beam LEE
- Web page: https://patentgrep.com/patent/US20220044765A1

## CPC

- G01N 15/1429: Signal processing
- 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 15/14: Optical investigation techniques, e.g. flow cytometry
- G01N 33/49: Blood {(chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80)}
- G16B 5/20: Probabilistic models
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G01N 15/01: specially adapted for biological cells, e.g. blood cells (investigating sedimentation of particle suspensions in blood G01N15/05)
- G01N 2015/1006: {for cytology}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- 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)
- 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

The present exemplary embodiments provide a data processing device and method which apply a neural network model to hypercubic data by converting a plurality of dimensions of initial data into a table type data structure and calculating between data matching the table and a designed filter.

## Claims

### Claim 1

A data processing method, comprising:
preprocessing initial data with table based conversion data; and
applying a filter of a neural network model to the table based conversion data.

### Claim 2 (depends on 1)

The data processing method according to claim 1, wherein the preprocessing step includes:
converting a first data structure formed by N-dimensional data by N axes (N is a natural number of 2 or larger) into a second data structure formed as a table format.

### Claim 3 (depends on 2)

The data processing method according to claim 2, wherein the first data structure includes a hypercube having depth information of four dimension or higher including two dimension and three dimension.

### Claim 4 (depends on 2)

The data processing method according to claim 2, wherein in the second data structure, (i) coordinate information corresponding to N axes and (ii) value information matching the coordinate information are disposed with reference to a row direction or a column direction.

### Claim 5 (depends on 2)

The data processing method according to claim 2, wherein the first data structure includes bio-extraction data indicating a measurement result of flow cytometry of a clinical sample of blood or a biological analysis sample and an analysis technique using flow cytometry and bio extraction data may be expressed by a predetermined standardized format or a flow cytometry standard (FCS) format, and the second data structure merges measurement values of some parameters of the bio extraction data and transforms the measurement values into data including a coordinate value for a channel and includes the transformed data and a count value.

### Claim 6 (depends on 2)

The data processing method according to claim 2, wherein the preprocessing step includes:
designing a filter frame structure which is computable with a second data structure and expresses a dimension to apply a neural network model to the first data structure.

### Claim 7 (depends on 6)

The data processing method according to claim 6, wherein in the designing of a filter frame structure, a filter center of the filter frame structure is disposed with reference to a predetermined coordinate to set a starting position of the filter frame structure.

### Claim 8 (depends on 7)

The data processing method according to claim 7, wherein in the designing of a filter frame structure, filter weight elements of the filter frame structure expands with a fractal like pattern according to a dimension with reference to the row direction or the column direction in consideration of a dimension of the first data structure.

### Claim 9 (depends on 6)

The data processing method according to claim 6, wherein in the applying of a filter, the calculation is performed between matching elements by moving the filter frame structure with reference to the row direction or the column direction of a table of the second data structure.

### Claim 10 (depends on 9)

The data processing method according to claim 9, wherein in the applying of a filter, when the filter center of the filter frame structure satisfies a predetermined row condition or column condition, the calculation is skipped.

### Claim 11

A data processing device including a processor, wherein the processor preprocesses initial data with table based conversion data and applies a filter of a neural network model to the table based conversion data.

### Claim 12 (depends on 11)

The data processing device according to claim 11, wherein the processor converts a first data structure formed by N-dimensional data by N axes (N is a natural number of 2 or larger) into a second data structure formed as a table format.

### Claim 13 (depends on 12)

The data processing device according to claim 12, wherein the first data structure includes a hypercube having depth information of four dimension or higher including two dimension and three dimension.

### Claim 14 (depends on 12)

The data processing device according to claim 12, wherein in the second data structure, (i) coordinate information corresponding to N axes and (ii) value information matching the coordinate information are disposed with reference to a row direction or a column direction.

### Claim 15 (depends on 12)

The data processing device according to claim 12, wherein the first data structure includes bio-extraction data indicating a measurement result of flow cytometry of a clinical sample of blood or a biological analysis sample and an analysis technique using flow cytometry and bio extraction data is expressed by a predetermined standardized format or a flow cytometry standard (FCS) format, and the second data structure merges measurement values of some parameters of the bio extraction data and transforms the measurement values into data including a coordinate value for a channel and includes the transformed data and a count value.

### Claim 16 (depends on 12)

The data processing device according to claim 12, wherein the processor designs a filter frame structure which is computable with a second data structure and expresses a dimension to apply a neural network model to the first data structure.

### Claim 17 (depends on 16)

The data processing device according to claim 16, wherein the processor disposes a filter center of the filter frame structure with reference to a predetermined coordinate to set a starting position of the filter frame structure.

### Claim 18 (depends on 16)

The data processing device according to claim 16, wherein the processor expands filter weight elements of the filter frame structure with a fractal like pattern according to a dimension with reference to the row direction or the column direction in consideration of a dimension of the first data structure.

### Claim 19 (depends on 16)

The data processing device according to claim 16, wherein the processor performs the calculation between matching elements by moving the filter frame structure with reference to the row direction or the column direction of a table of the second data structure.

### Claim 20 (depends on 19)

The data processing device according to claim 19, wherein when the filter center of the filter frame structure satisfies a predetermined row condition or column condition, the processor skips the calculation.