WO2024049193A1

WO2024049193A1 - Electrocardiogram data transaction service provision method, program, and device

Info

Publication number: WO2024049193A1
Application number: PCT/KR2023/012872
Authority: WO
Inventors: 권준명
Original assignee: 주식회사 메디컬에이아이
Priority date: 2022-09-01
Filing date: 2023-08-30
Publication date: 2024-03-07
Also published as: KR20240032656A

Abstract

The present disclosure relates to an electrocardiogram data transaction service provision method, program, and device, the electrocardiogram data transaction service provision method, carried out by a computing device comprising at least one processor, comprising the steps of: collecting electrocardiogram data of each user; registering ownership of the electrocardiogram data by generating a non-fungible token (NFT) on the basis of the electrocardiogram data; and, according to a request of a buyer terminal, providing sales list information for transactable electrocardiogram data, and if the buyer terminal selects electrocardiogram data on the basis of the sales list information, providing a transaction service between the buyer terminal and a seller terminal having ownership of the NFT for the selected electrocardiogram data.

Description

Method, program, and device for providing transaction services for electrocardiogram data

The present disclosure relates to a method and device for providing a transaction service for ECG data, and specifically relates to a method in which ownership registration and transaction services for ECG data can be provided using a non-fungible token.

An electrocardiogram (ECG) is a signal that measures electrical signals generated in the heart and checks for abnormalities in the conduction system from the heart to the electrodes to determine the presence or absence of disease.

Such an electrocardiogram can be detected through a bipolar lead, which records the potential difference between two parts, and a unipolar lead, which records the potential of the area where the electrode is attached. Methods for measuring an electrocardiogram include the bipolar lead. There is a standard limb lead, a unipolar limb lead, and a unipolar thoracic lead (precordial lead).

The electrical activity stage of the heart is largely divided into atrial depolarization, ventricular depolarization, and ventricular repolarization, and each of these stages is reflected in the form of several waves called P, Q, R, S, and T waves, as shown in Figure 1.

These waves must have a standard shape for the heart's electrical activity to be considered normal. In order to determine whether it is a standard form or not, it is necessary to check whether characteristics such as the time each wave is maintained, the interval between each wave, the amplitude of each wave, and kurtosis are within the normal range.

These electrocardiograms are measured with expensive measuring equipment and used as an auxiliary tool to measure the patient's health status. In general, electrocardiogram measuring equipment only displays measurement results and diagnosis is entirely up to the doctor.

Currently, research is continuing to quickly and accurately diagnose diseases using artificial intelligence based on electrocardiograms to reduce dependence on doctors. In addition, with the development of wearable and lifestyle electrocardiogram measurement devices, the possibility of diagnosing and monitoring not only heart disease but also various other diseases based on electrocardiogram is emerging.

Non-invasively assessing heart function by measuring electrocardiograms helps diagnose numerous heart diseases, including arrhythmia, myocardial infarction, and arterial disease. In all medical facilities, cardiac-related symptoms include electrocardiogram (ECG) measurements, which are stored in daily accumulation of ECG records. Additionally, due to the development of smart devices that include electrocardiogram measurement functions, users can easily measure and save their electrocardiograms even without visiting a medical institution.

In this way, ECG data is highly valuable data that can be used in a variety of ways, such as medical treatment, health management, and heart disease-related research, but the lack of a transaction system for medical data makes it impossible to clearly evaluate its value. In addition, there is a widespread social perception that medical data is a public good, making it difficult to discuss the price of medical data. Due to this, domestic medical-related AI companies are unable to use their own high-quality medical data as the data needed to train AI models, and either purchase and use medical data from overseas or use data sets collected in small quantities as samples. The reality is that it is being artificially created and used by imitating statistical characteristics.

Therefore, in order to increase the competitiveness of the domestic medical data industry, ownership of medical data must be allowed to be owned by the examinee (or patient), and a fair price must be set according to the value of the ECG data. At this time, since medical data, including electrocardiogram data, deals with very sensitive personal information by its nature, a considerable level of reliability and security is required, so it is necessary to be able to trade secure, patient-led medical data in a transaction system.

This disclosure was made in response to the above-described background technology, and utilizes NFT technology to register ownership of ECG data to the measurement subject and to freely and easily transfer ECG data between sellers and buyers on a platform that provides transaction services for ECG data. The purpose is to provide a method to trade data.

However, the problems to be solved by this disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood based on the description below.

According to an embodiment of the present disclosure for realizing the above-described problem, an object is to provide a method of providing a transaction service for electrocardiogram data, which is performed by a computing device including at least one processor. The method includes collecting electrocardiogram data for each user; Generating a non-fungible token (NFT) based on the ECG data and registering ownership of the ECG data; and providing sales list information for tradable ECG data at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, having ownership of a non-fungible token for the selected ECG data. It includes providing a transaction service between the seller terminal and the buyer terminal.

Alternatively, the step of collecting electrocardiogram data for each user may include additionally collecting at least one related information of biological information, reading information, inspection information, or supporting material linked to the electrocardiogram data. .

Alternatively, the step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data may include registering the non-fungible token (NFT) based on the ECG data and at least one piece of associated information. It may include a step of generating a token.

Alternatively, the step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data may be performed according to the type of the related information included in the non-fungible token. It may include setting the level of the non-fungible token.

Alternatively, the rating may include: a first rating corresponding to a non-fungible token generated using the electrocardiogram data; a second level corresponding to a non-fungible token generated using the electrocardiogram data and biological information; a third level corresponding to a non-fungible token generated using the electrocardiogram data, biological information, and readout information; a fourth level corresponding to a non-fungible token generated using the electrocardiogram data, biological information, reading information, and inspection information; and a fifth level corresponding to a non-fungible token generated using the electrocardiogram data, biological information, reading information, inspection information, and supporting materials.

Alternatively, the step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data includes differentially assigning the non-fungible token to the non-fungible token according to the value of the related information. It may include a step of assigning points and adding points.

Alternatively, the step of assigning points differentially to the non-fungible token according to the value of the related information includes a weight according to the type of related information included in the non-fungible token and a weight included in the non-fungible token. It may include the step of differentially assigning points to the non-fungible tokens according to the number of related information.

Alternatively, the step of assigning points to the non-fungible tokens differentially according to the value of the related information is to assign the disease to the biological information based on a preset disease list or a list of diseases that can be read by electrocardiogram. It may include the step of differentially assigning points to the non-fungible tokens according to whether at least one disease exists in the list.

Alternatively, the step of assigning points differentially to the non-fungible token according to the value of the related information includes whether the reading information or inspection information includes expert authentication information for which expert in-depth reading or inspection has been completed; and It may include the step of differentially assigning points to the non-fungible tokens according to the number of expert authentication information included.

Alternatively, the method may further include setting a transaction price for the ECG data according to the grade set for the non-fungible token or the total score added.

Alternatively, sales list information for tradable ECG data is provided at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, a non-fungible token for the selected ECG data The step of providing a transaction service between a seller terminal with ownership and the buyer terminal includes, when the buyer terminal selects a purchase option based on the related information, filtered from the sales list information according to the selected purchase option. It may include providing a matching data list.

Alternatively, sales list information for tradable ECG data is provided at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, a non-fungible token for the selected ECG data The step of providing a transaction service between a seller terminal with ownership and the buyer terminal may include providing transaction alarm information to the seller terminal with ownership of a non-fungible token for the selected ECG data.

Alternatively, the step of providing transaction alarm information to a seller terminal having ownership of a non-fungible token for the selected ECG data may include, when transaction approval of the transaction alarm information is determined based on a user input of the seller terminal. The transaction with the buyer's terminal may be confirmed.

Alternatively, the step of providing transaction alarm information to a seller terminal that has ownership of a non-fungible token for the selected ECG data may include, if a transaction rejection of the transaction alarm information is determined based on a user input of the seller terminal. This may be conveying a transaction impossibility status for the ECG data selected by the purchaser terminal to the purchaser terminal.

Alternatively, sales list information for tradable ECG data is provided at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, a non-fungible token for the selected ECG data The step of providing a transaction service between a seller terminal with ownership and the buyer terminal includes performing user de-identification processing on ECG data for which purchase has been decided, and providing the user de-identified ECG data to the buyer terminal. It may include;

Meanwhile, according to an embodiment of the present disclosure, it is a computer program stored in a computer-readable storage medium, and when the computer program is executed on one or more processors, it performs operations to provide a transaction service for ECG data. The operations include: collecting electrocardiogram data for each user; An operation of generating a non-fungible token (NFT) based on the ECG data and registering ownership of the ECG data; and providing sales list information for tradable ECG data at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, having ownership of a non-fungible token for the selected ECG data. It may include an operation of providing a transaction service between a seller terminal and the buyer terminal.

A computing device for providing a transaction service for electrocardiogram data according to an embodiment of the present disclosure, comprising: a processor including at least one core; and a memory containing program codes executable on the processor; It includes, wherein the processor collects electrocardiogram data for each user according to execution of the program code, and generates a non-fungible token (NFT) based on the electrocardiogram data, for the electrocardiogram data. Registers ownership, provides sales list information for tradable ECG data at the request of the buyer terminal, and when the buyer terminal selects ECG data based on the sales list information, a non-fungible token for the selected ECG data A transaction service can be provided between a seller terminal with ownership of and the buyer terminal.

A method of providing a transaction service for ECG data according to an embodiment of the present disclosure provides ownership of the ECG data to the person being measured using NFT technology, and provides a transaction service for ECG data to sellers and buyers on a platform that provides transaction services for ECG data. It has the effect of allowing ECG data to be traded freely and easily between people.

In addition, the present disclosure allows the grade or differential score of the NFT to be added based on the related information linked to the ECG data, and the transaction price of the ECG data is set according to the grade of the NFT or the added total score, so that the ECG data Ensure that value and transaction prices are assessed fairly.

Meanwhile, this disclosure can effectively record and manage ECG data using NFT, and can reduce the possibility of personal information leakage by making it impossible to forge or falsify ECG data when trading, making medical innovation a reality.

1 is a block diagram of a computing device according to an embodiment of the present disclosure.

Figure 2 is a block diagram illustrating the configuration of a system that provides a transaction service for ECG data according to an embodiment of the present disclosure.

Figure 3 is a flowchart explaining a method of providing a transaction service for ECG data according to an embodiment of the present disclosure.

Figure 4 is a flowchart showing in detail the NFT creation and ownership registration steps for ECG data according to an embodiment of the present invention.

Below, with reference to the attached drawings, embodiments of the present disclosure are described in detail so that those skilled in the art (hereinafter referred to as skilled in the art) can easily implement the present disclosure. The embodiments presented in this disclosure are provided to enable any person skilled in the art to use or practice the subject matter of this disclosure. Accordingly, various modifications to the embodiments of the present disclosure will be apparent to those skilled in the art. That is, the present disclosure can be implemented in various different forms and is not limited to the following embodiments.

The same or similar reference numerals refer to the same or similar elements throughout the specification of this disclosure. Additionally, in order to clearly describe the present disclosure, reference numerals in the drawings may be omitted for parts that are not related to the description of the present disclosure.

As used in this disclosure, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “x uses a or b” should be understood to mean one of natural implicit substitutions. For example, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “x uses a or b” means that x uses a, x uses b, or x uses a and It can be interpreted as one of the cases where both b are used.

The term “and/or” as used in this disclosure should be understood to refer to and include all possible combinations of one or more of the listed related concepts.

The terms “comprise” and/or “comprising” as used in this disclosure should be understood to mean that certain features and/or elements are present. However, the terms "comprise" and/or "including" should be understood as not excluding the presence or addition of one or more other features, other components, and/or combinations thereof.

Unless otherwise specified in this disclosure or the context is clear to indicate a singular form, the singular should generally be construed to include “one or more.”

The term "th nth (n is a natural number)" used in the present disclosure can be understood as an expression used to distinguish the components of the present disclosure according to a predetermined standard such as a functional perspective, a structural perspective, or explanatory convenience. there is. For example, in the present disclosure, components performing different functional roles may be distinguished as first components or second components. However, components that are substantially the same within the technical spirit of the present disclosure but must be distinguished for convenience of explanation may also be distinguished as first components or second components.

The term “acquisition” used in this disclosure is understood to mean not only receiving data through a wired or wireless communication network with an external device or system, but also generating data in an on-device form. It can be.

Meanwhile, the term "module" or "unit" used in this disclosure refers to a computer-related entity, firmware, software or part thereof, hardware or part thereof. , can be understood as a term referring to an independent functional unit that processes computing resources, such as a combination of software and hardware. At this time, the “module” or “unit” may be a unit composed of a single element, or may be a unit expressed as a combination or set of multiple elements. For example, a "module" or "part" in the narrow sense is a hardware element or set of components of a computing device, an application program that performs a specific function of software, a process implemented through the execution of software, or a program. It can refer to a set of instructions for execution, etc. Additionally, as a broad concept, “module” or “unit” may refer to the computing device itself constituting the system, or an application running on the computing device. However, since the above-described concept is only an example, the concept of “module” or “unit” may be defined in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

As used in this disclosure, the term "model" refers to a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or a process to solve a specific problem. It can be understood as an abstract model of a process. For example, a neural network “model” may refer to an overall system implemented as a neural network that has problem-solving capabilities through learning. At this time, the neural network can have problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning. A neural network “model” may include a single neural network or a neural network set in which multiple neural networks are combined.

The term “block” used in the present disclosure can be understood as a set of components divided based on various criteria such as type, function, etc. Accordingly, the configuration classified as one “block” can be changed in various ways depending on the standard. For example, a neural network “block” can be understood as a set of neural networks containing at least one neural network. At this time, it can be assumed that the neural networks included in the neural network “block” perform the same specific operation. The explanation of the foregoing terms is intended to aid understanding of the present disclosure. Therefore, if the above-mentioned terms are not explicitly described as limiting the content of the present disclosure, it should be noted that the content of the present disclosure is not used in the sense of limiting the technical idea.

The computing device 100 according to an embodiment of the present disclosure may be a hardware device or part of a hardware device that performs comprehensive processing and calculation of data, or may be a software-based computing environment connected to a communication network. For example, the computing device 100 may be a server that performs intensive data processing functions and shares resources, or it may be a client that shares resources through interaction with the server. Additionally, the computing device 100 may be a cloud system in which a plurality of servers and clients interact to comprehensively process data. Since the above description is only an example related to the type of computing device 100, the type of computing device 100 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

Referring to FIG. 1, a computing device 100 according to an embodiment of the present disclosure may include a processor 110, a memory 120, and a network unit 130. there is. However, since FIG. 1 is only an example, the computing device 100 may include other components for implementing a computing environment. Additionally, only some of the configurations disclosed above may be included in computing device 100.

The processor 110 according to an embodiment of the present disclosure may be understood as a structural unit including hardware and/or software for performing computing operations. For example, the processor 110 may read a computer program and perform data processing for machine learning. The processor 110 may process computational processes such as processing input data for machine learning, extracting features for machine learning, and calculating errors based on backpropagation. The processor 110 for performing such data processing includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and a custom processing unit (TPU). It may include a semiconductor (ASICc: application specific integrated circuit), or a field programmable gate array (FPGA: field programmable gate array). Since the type of processor 110 described above is only an example, the type of processor 110 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

The processor 110 can generate a non-fungible token (NFT) based on electrocardiogram data, and can provide a transaction service between a seller terminal and a buyer terminal with ownership of a non-fungible token for electrocardiogram data. there is.

At this time, the processor 110 may provide the results of artificial intelligence analysis of the ECG data using a pre-trained neural network model as ECG reading information. To this end, the processor 110 can learn a neural network model that diagnoses heart disease based on electrocardiogram data. For example, the processor 110 may train a neural network model to estimate arrhythmia and other heart diseases based on biological information including information such as gender, age, weight, height, etc., along with electrocardiogram data. Specifically, the processor 110 may input electrocardiogram data and various biological information into the neural network model and train the neural network model to detect changes in the electrocardiogram due to arrhythmia or other heart diseases. At this time, the neural network model can perform learning based on an ECG dataset that includes features extracted from ECG data and diagnostic data for arrhythmia and other heart diseases. The processor 110 may perform an operation representing at least one neural network block included in the neural network model during the learning process of the neural network model.

The processor 110 may estimate ECG reading result data based on ECG data using a neural network model generated through the above-described learning process. The processor 110 inputs electrocardiogram data and biological information including information such as gender, age, weight, height, etc. into a neural network model learned through the above-described process and inferred data representing the result of estimating the probability of heart disease. can be created. For example, the processor 110 can input electrocardiogram data into a trained neural network model to predict the presence or progression of arrhythmia or other heart disease.

In addition to the examples described above, the types of medical data and the output of the neural network model may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

The memory 120 according to an embodiment of the present disclosure may be understood as a structural unit including hardware and/or software for storing and managing data processed in the computing device 100. That is, the memory 120 can store any type of data generated or determined by the processor 110 and any type of data received by the network unit 130. For example, the memory 120 may be a flash memory type, hard disk type, multimedia card micro type, card type memory, or random access memory (RAM). ), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), PROM (prom: programmable read-only memory), magnetic memory , may include at least one type of storage medium among a magnetic disk and an optical disk. Additionally, the memory 120 may include a database system that controls and manages data in a predetermined system. Since the type of memory 120 described above is only an example, the type of memory 120 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

The memory 120 can structure, organize, and manage data necessary for the processor 110 to perform operations, combinations of data, and program codes executable on the processor 110. For example, the memory 120 may store ECG data received through the network unit 130, which will be described later. The memory 120 includes program code that operates the neural network model to receive medical data and perform learning, program code that operates the neural network model to receive medical data and perform inference according to the purpose of use of the computing device 100, and Processed data generated as the program code is executed can be saved.

The network unit 130 according to an embodiment of the present disclosure may be understood as a structural unit that transmits and receives data through any type of known wired or wireless communication system. For example, the network unit 130 may be connected to a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), or wireless (WIBRO). broadband internet, 5th generation mobile communication (5g), ultra wide-band wireless communication, zigbee, radio frequency (RF) communication, wireless LAN, wireless fidelity ), data transmission and reception can be performed using a wired or wireless communication system such as near field communication (NFC), or Bluetooth. Since the above-described communication systems are only examples, the wired and wireless communication systems for data transmission and reception of the network unit 130 may be applied in various ways other than the above-described examples.

The network unit 130 may receive data necessary for the processor 110 to perform calculations through wired or wireless communication with any system or client. Additionally, the network unit 130 may transmit data generated through the calculation of the processor 110 through wired or wireless communication with any system or any client. For example, the network unit 130 may receive medical data through communication with a database in a hospital environment, a cloud server that performs tasks such as standardization of medical data, or a computing device. The network unit 130 may transmit output data of the neural network model, intermediate data derived from the calculation process of the processor 110, processed data, etc. through communication with the above-described database, server, or computing device.

Referring to FIG. 2, the system may include an electrocardiogram measuring device 10, a computing device 100, and an expert terminal 200 for in-depth reading or inspection by an expert.

The electrocardiogram measuring device 10 is worn on the user's body and can not only measure the electrocardiogram, but also measure various biosignals such as blood pressure and pulse rate. This electrocardiogram measuring device 10 may include wearable devices such as electronic accessories and smartwatches, and medical devices such as massage chairs, which have received medical device approval from the Ministry of Food and Drug Safety.

Meanwhile, the electrocardiogram measuring device 10 can measure the electrocardiogram using various electrode combinations, such as a 12-lead method and a 6-lead method, as well as a single-guide method using a wearable device. It is desirable that the electrocardiogram measurement time is also set by adding or subtracting depending on the signal to be obtained.

The computing device 100 may be connected to the ECG meter 10 by wire or wirelessly, obtain ECG data from the ECG meter 10, and provide ECG reading information obtained by analyzing the ECG data. These computing devices 100 include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation, slate PCs, This may include tablet PCs, ultrabooks, etc.

If the electrocardiogram meter 10 is implemented as an intelligent device based on artificial intelligence technology, the computing device 100 may be operated independently or integrated with the electrocardiogram meter 10.

The expert terminal 200 may be an electrocardiogram reading center capable of collaborating with external experts and providing expert in-depth reading services, or a terminal providing diagnostic services by medical experts. This expert terminal 200 can generate expert reading information based on in-depth analysis of ECG data and provide it to the computing device 100.

Additionally, the expert terminal 200 may be a terminal owned by a cardiologist or emergency medicine specialist with expertise in ECG reading in order to inspect the reading information of ECG data.

The computing device 100 may provide a platform that serves as an online market to connect seller terminals and buyer terminals. The expert terminal 200, such as an ECG reading center or a medical institution, can read or inspect ECG data listed for sale through the platform. At this time, the expert terminal 200 may request user consent from the terminal that has ownership of the ECG data to be read or inspected, and may read or inspect only the ECG data for which user consent has been granted. The value of the ECG data read or inspected by the expert terminal 200 increases, and as the value of the ECG data increases, the transaction price may also increase. Accordingly, the expert terminal 200 can share the ownership stake in the ECG data of increased value with the seller terminal in a certain ratio or receive compensation for reading and inspection.

Referring to FIG. 3, the computing device 100 may obtain electrocardiogram data for each user from the electrocardiogram measuring device 10 (S10). At this time, the computing device 100 may additionally collect at least one related information of biological information, reading information, inspection information, or supporting information linked to each ECG data along with the ECG data.

Biological information may include information such as gender, age, weight, and height about the subject of ECG data measurement, and reading information may include ECG reading information using a neural network model or expert reading information.

Here, the neural network model uses a deep learning algorithm to learn ECG data by the characteristics of the ECG, and using the learned model, ECG reading information including classification information related to heart disease can be derived. Specifically, the neural network model may be learned based on a learning dataset including electrocardiograms and diagnostic results of heart disease, and based on correlations between various factors in the learning dataset.

For example, the neural network model includes at least one convolutional neural network (CNN), batch normalization, and ReLU activation function layer, and may include a dropout layer. The neural network model may include a fully connected layer in which biological information such as age, gender, height, and weight is input as auxiliary information.

The neural network model may include a neural network corresponding to each of a plurality of leads of ECG data. That is, the neural network model may include an individual neural network into which electrocardiograms measured with individual leads are input.

Meanwhile, since the structure and type of neural network of the above-described neural network model are only examples, the neural network model according to an embodiment of the present invention may be configured in various ways based on the above-described examples.

The computing device 100 may generate an NFT based on the ECG data and register ownership of the ECG data (S20). The computing device 100 may use NFT to store ownership and related information for the ECG data in a database so that the ECG data can be traded.

The computing device 100 may provide sales list information about tradable ECG data at the request of the purchaser terminal (S30). And, when the buyer terminal selects ECG data based on the sales list information, the computing device 100 can enable a transaction to be made between the seller terminal and the buyer terminal with NFT ownership of the selected ECG data (S40).

The computing device 100 allows the purchaser terminal to select purchase options based on related information linked to electrocardiogram data, and provides a matching data list filtered from the sales list information to the purchaser terminal according to the purchase options selected by the purchaser terminal. can be provided. For example, the buyer terminal may select only ECG data showing myocardial infarction as a purchase option and then set additional options such as gender and age.

Additionally, the computing device 100 may provide transaction alarm information including transaction approval and transaction rejection to the seller terminal that has ownership of the NFT for the ECG data selected by the purchaser terminal. Based on the user input of the seller terminal, the computing device 100 can confirm the completion of a transaction with the buyer terminal if transaction approval of the transaction alarm information is determined, and if transaction rejection of the transaction alarm information is determined, the computing device 100 may determine the ECG selected by the buyer terminal. The status of data being unable to be traded can be transmitted to the buyer terminal.

Additionally, the computing device 100 performs user de-identification on ECG data for which purchase has been determined and provides the user de-identified ECG data to the purchaser terminal. In this way, the computing device can prevent personal information from being collected or used by a third party without the user's consent by performing de-identification processing on information that may infringe personal information, such as a personal identification ID.

The NFT creation and ownership registration step for ECG data according to an embodiment of the present invention (S20) may include steps for classifying and adding points to the NFT in detail.

If there is associated information linked to ECG data (S21), the computing device 100 may generate an NFT based on the ECG data and at least one piece of associated information (S22).

When registering ownership of ECG data, the computing device 100 can set the grade of the NFT according to the type of related information included in the NFT (S23). At this time, the grade of NFT is the first grade corresponding to an NFT created using only electrocardiogram data, the second grade corresponding to an NFT created using electrocardiogram data and biological information, and the second grade corresponding to an NFT created using electrocardiogram data, biological information and reading information. A third class corresponding to the generated NFT, a fourth class corresponding to an NFT created using electrocardiogram data, biological information, reading information, and inspection information, and electrocardiogram data, biological information, reading information, inspection information, and supporting materials. It may include a fifth grade corresponding to the NFT created using.

Additionally, when registering ownership of ECG data, the computing device 100 may differentially assign points according to the value of related information added with the ECG data when creating an NFT (S24). Specifically, the computing device 100 may differentially assign scores to the NFT according to the number of related information included in the NFT and a weight according to the type of related information included in the NFT.

The computing device 100 can differentially score the NFT based on whether at least one disease in the disease list is present in the biological information, based on a preset disease list or a disease list that can be read by an electrocardiogram. there is.

For example, when biological information includes information on gender, age, and race, the computing device 100 may add 0.1 points for each piece of information, for a total of 0.3 points. Alternatively, if the biological information includes information about gender, age, race, and presence of myocardial infarction disease, the computing device 100 may give additional points by giving a total of 0.5 points as 0.1+0.1+0.1+0.2 points. This increases the value of ECG data by setting a low weight for biological information such as user information such as age and gender, and setting a high weight for disease-related information such as myocardial infarction and heart failure, and assigning scores differently according to the weight of each information. It can be raised.

In addition, the computing device 100 may differentially assign points to the NFT depending on whether the reading information or inspection information includes expert authentication information that has been completed by an expert in-depth reading or inspection and the number of included expert authentication information. there is.

For example, if the reading information is ECG reading information using a neural network model, the computing device 100 sets the weight low, and if the reading information is expert reading information through the expert terminal 200 including expert authentication information, sets the weight high to 0.2. Points can be added. Additionally, the computing device 100 may add 0.2 points if the inspection information includes expert authentication information, such as a cardiologist or emergency medicine specialist with expertise in electrocardiogram reading.

The computing device 100 may set the transaction price of the ECG data according to the grade set in the NFT or the total score added (S25).

On the other hand, when there is no relevant information linked to the ECG data, the computing device 100 can generate an NFT using only the ECG data and set the grade of the NFT to grade 1, and in some cases, reading information, inspection information or Supporting materials, etc. may be included (S26).

In this disclosure, ownership of the user's ECG data can be registered using NFT technology, thereby allowing the ECG data to be easily traded. In this way, in the present disclosure, the ownership of ECG data is clearly owned by the user (or seller), so many users are interested in and participate in creating ownership of medical data, including their ECG data, and trading or utilizing the data. This can become possible, and as medical data transactions become easier, active communication between sellers and buyers can take place.

The various embodiments of the present disclosure described above may be combined with additional embodiments and may be changed within the scope understandable to those skilled in the art in light of the above detailed description. The embodiments of the present disclosure should be understood in all respects as illustrative and not restrictive. For example, each component described as unitary may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form. Accordingly, all changes or modified forms derived from the meaning and scope of the claims of the present disclosure and their equivalent concepts should be construed as being included in the scope of the present disclosure.

Claims

1. A method of providing a transaction service for electrocardiogram data, the method performed by a computing device comprising at least one processor, the method comprising:

Collecting electrocardiogram data for each user;

Generating a non-fungible token (NFT) based on the ECG data and registering ownership of the ECG data; and

Upon request from a buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the seller has ownership of a non-fungible token for the selected ECG data. Providing a transaction service between a terminal and the buyer terminal;

containing

method.
According to paragraph 1,

The step of collecting electrocardiogram data for each user is,

Additional collecting at least one related information of biological information, reading information, inspection information, or supporting material linked to the electrocardiogram data;

Including,

method.
According to paragraph 2,

The step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data,

generating the non-fungible token based on the electrocardiogram data and at least one piece of related information;

Including,

method.
According to paragraph 3,

The step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data,

setting a grade of the non-fungible token according to the type of the related information included in the non-fungible token;

Containing more,

method.
According to clause 4,

The above grade is,

a first level corresponding to a non-fungible token generated using the electrocardiogram data;

a second level corresponding to a non-fungible token generated using the electrocardiogram data and biological information;

a third level corresponding to a non-fungible token generated using the electrocardiogram data, biological information, and readout information;

a fourth level corresponding to a non-fungible token generated using the electrocardiogram data, biological information, reading information, and inspection information; and

a fifth level corresponding to a non-fungible token created using the electrocardiogram data, biological information, reading information, inspection information, and supporting materials;

Including,

method.
According to paragraph 3,

The step of registering ownership of the ECG data by generating a non-fungible token (NFT) based on the ECG data,

assigning points differentially to the non-fungible token according to the value of the related information;

Containing more,

method.
According to clause 6,

The step of assigning points differentially to the non-fungible token according to the value of the related information is:

assigning points differentially to the non-fungible tokens according to a weight according to the type of related information included in the non-fungible token and the number of related information included in the non-fungible token;

Including,

method.
According to clause 6,

The step of assigning points differentially to the non-fungible token according to the value of the related information is:

Based on a preset disease list or a list of diseases that can be read by electrocardiogram, differentially assigning points to the non-fungible tokens depending on whether at least one disease in the disease list is present in the biological information. ;

Including,

method.
According to clause 6,

The step of assigning points differentially to the non-fungible token according to the value of the related information is:

A step of differentially assigning points to the non-fungible tokens according to whether the reading information or inspection information includes expert authentication information for which in-depth expert reading or inspection has been completed and the number of expert authentication information included;

Including,

method.
According to clause 6,

Further comprising setting a transaction price of the ECG data according to the level set for the non-fungible token or the total score added,

method.
According to paragraph 2,

At the request of the buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the buyer terminal has ownership of a non-fungible token for the selected ECG data. The step of providing a transaction service between the seller terminal and the buyer terminal is,

When the buyer terminal selects a purchase option based on the related information, providing a matching data list filtered from the sales list information according to the selected purchase option;

Including,

method.
According to paragraph 1,

At the request of the buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the buyer terminal has ownership of a non-fungible token for the selected ECG data. The step of providing a transaction service between the seller terminal and the buyer terminal is,

Providing transaction alarm information to a seller terminal that has ownership of a non-fungible token for the selected ECG data;

Including,

method.
According to clause 11,

The step of providing transaction alarm information to the seller terminal that has ownership of the non-fungible token for the selected ECG data,

Based on the user input of the seller terminal, when transaction approval of the transaction alarm information is determined, the transaction with the buyer terminal is confirmed,

method.
According to clause 11,

The step of providing transaction alarm information to the seller terminal that has ownership of the non-fungible token for the selected ECG data,

Based on the user input of the seller terminal, when the transaction rejection of the transaction alarm information is determined, a transaction impossibility status for the ECG data selected by the buyer terminal is transmitted to the buyer terminal,

method.
According to paragraph 1,

At the request of the buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the buyer terminal has ownership of a non-fungible token for the selected ECG data. The step of providing a transaction service between the seller terminal and the buyer terminal is,

Performing user de-identification processing on ECG data for which a purchase has been determined, and providing the user de-identified ECG data to the purchaser terminal;

Including,

method.
A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, performs operations for providing a transaction service for electrocardiogram data,

The above operations are,

An operation to collect electrocardiogram data for each user;

An operation of generating a non-fungible token (NFT) based on the ECG data and registering ownership of the ECG data; and

Upon request from a buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the seller has ownership of a non-fungible token for the selected ECG data. An operation of providing a transaction service between a terminal and the buyer terminal;

Including,

computer program.
A computing device for providing a transaction service for electrocardiogram data, comprising:

A processor including at least one core; and

a memory containing program codes executable on the processor;

Including,

The processor, according to execution of the program code,

Collects electrocardiogram data for each user,

Create a non-fungible token (NFT) based on the ECG data and register ownership of the ECG data,

Upon request from a buyer terminal, sales list information for tradable ECG data is provided, and when the buyer terminal selects ECG data based on the sales list information, the seller has ownership of a non-fungible token for the selected ECG data. Providing transaction services between a terminal and the buyer terminal,

Device.