Device, method, and user interface for determining bio-signal measurement cycle

Abstract

According to an embodiment of the present disclosure, disclosed is a method for determining a bio-signal measurement cycle. In particular, according to the present disclosure, a computing device receives bio-signals of a user, and determines a personalized bio-signal measurement cycle of the user on the basis of inputting the bio-signals of the user into a bio-signal analysis model, wherein the bio-signal analysis model includes at least one bio-signal analysis sub-model for analyzing the user's health status.

Description

Device, method and user interface for determining a biosignal measurement cycle

[0001] The present disclosure relates to a method for determining a biosignal measurement cycle, and more particularly, to a method for determining a personalized biosignal measurement cycle of a user by inputting the user's biosignal into a biosignal analysis model including at least one biosignal analysis submodel.

[0002] As the services and additional functions provided by personal mobile devices such as smartphones have diversified, various applications that meet the needs of users are being developed.

[0003] Meanwhile, in the mobile healthcare field, a healthcare system that measures the user's bio-signals, including the electrocardiogram, using equipment and then transmits them to a mobile device such as a smartphone or tablet PC, where the device provides various diagnostic information, is gaining attention.

[0004] To effectively operate these health management systems, it is necessary to periodically measure users' biosignals to track ongoing changes in their health status. While periodic monitoring can be achieved by sending alarms to users at regular intervals, these alarms, if repeated at regular intervals, can lead to alarm fatigue. In such situations, users may actually turn off the alarms, making it impossible to track changes in their health status. This can pose a health risk, especially for users who experience significant changes in their biosignals, necessitating more frequent monitoring.

[0005] Therefore, there is a need in the art for a method of determining an optimized biosignal measurement cycle for each user and transmitting an alarm requesting measurement.

[0006]

[0007] Korean Patent No. 10-2525996 discloses a biosignal measuring device.

[0008]

[0009] The present disclosure is conceived in response to the aforementioned background technology, and provides a method for determining a personalized bio-signal measurement cycle of a user by inputting the user's bio-signal into a bio-signal analysis model including at least one bio-signal analysis sub-model, and provides an alarm requesting bio-signal measurement in the form of a user interface.

[0010] Meanwhile, the technical task to be achieved by the present disclosure is not limited to the technical task mentioned above, and may include various technical tasks within a scope obvious to a person skilled in the art from the contents described below.

[0011] According to an embodiment of the present disclosure for achieving the above-described task, a method performed by a computing device for determining a bio-signal measurement cycle is disclosed. The method comprises the steps of receiving a bio-signal of a user, and determining a personalized bio-signal measurement cycle of the user based on inputting the bio-signal of the user into a bio-signal analysis model, wherein the bio-signal analysis model may include at least one bio-signal analysis sub-model for analyzing a health state of the user.

[0012] In one embodiment, the step of determining the personalized bio-signal measurement cycle may include: inputting at least a portion of the user's bio-signal into the at least one bio-signal analysis sub-model; generating comprehensive diagnostic information on the user's health status based on an output value generated by the at least one bio-signal analysis sub-model; and determining the personalized bio-signal measurement cycle based on the comprehensive diagnostic information.

[0013] In one embodiment, the at least one biosignal analysis submodel includes an artificial neural network model, and the comprehensive diagnostic information can be obtained based on disease diagnostic information output by the artificial neural network model.

[0014] In one embodiment, the method further includes a step of displaying a user interface related to the personalized bio-signal measurement cycle; wherein the user interface may include an area indicating whether the personalized bio-signal measurement cycle has changed and an area displaying the personalized bio-signal measurement cycle.

[0015] In one embodiment, the method further includes, in response to a selection input from the user, a step of displaying a user interface related to a variation in the personalized biosignal measurement cycle, wherein the user interface related to a variation in the personalized biosignal measurement cycle may include an area for displaying the content of the variation in the personalized biosignal measurement cycle; and an area for displaying at least one reason for variation in the personalized biosignal measurement cycle.

[0016] In one embodiment, the method further includes, in response to receiving a selection input related to a reason for variation in the personalized bio-signal measurement cycle of the user, a step of displaying a user interface related to the selected reason for variation, wherein the user interface related to the selected reason for variation may include an area for displaying information related to a measurement time point of the bio-signal and an area for displaying disease diagnosis information according to the measurement time point.

[0017] In one embodiment, the method may further include generating alarm information requesting the user to measure a biosignal based on the personalized biosignal measurement cycle.

[0018] In one embodiment, the step of generating the alarm information may include: displaying a user interface related to the alarm, wherein the user interface related to the alarm may include: an area for displaying information related to a request for biosignal measurement; an area for displaying information related to past biosignal measurement results; an area for displaying information related to the personalized biosignal measurement cycle; and an area for displaying information related to a reason for a change in the personalized biosignal measurement cycle.

[0019] According to an embodiment of the present disclosure for realizing the above-described task, a computer program stored in a computer-readable storage medium is disclosed that causes a computing device to perform operations for determining a bio-signal measurement cycle. The operations include: receiving a bio-signal of a user; and determining a personalized bio-signal measurement cycle of the user based on inputting the bio-signal of the user into a bio-signal analysis model, wherein the bio-signal analysis model may include at least one bio-signal analysis sub-model for analyzing a health state of the user.

[0020] According to an embodiment of the present disclosure for achieving the above-described task, a computing device for determining a bio-signal measurement cycle is disclosed. The computing device includes at least one processor and a memory, wherein the at least one processor is configured to receive a bio-signal of a user; and determine a personalized bio-signal measurement cycle of the user based on inputting the bio-signal of the user into a bio-signal analysis model, wherein the bio-signal analysis model may include at least one bio-signal analysis sub-model for analyzing the health status of the user.

[0021] By analyzing a user's biosignal and detecting abnormalities in the biosignal, the present disclosure can determine an optimal biosignal measurement cycle for users at increased risk of disease and requiring continuous monitoring. Furthermore, by providing information on changes in the biosignal measurement cycle, the reasons for these changes, and alarms requesting biosignal measurement in the form of a user interface, user compliance with alarms can be increased. Therefore, the present disclosure can effectively increase user satisfaction with the service in the operation of a healthcare system utilizing a mobile device.

[0022] FIG. 1 is a block diagram of a computing device for determining a biosignal measurement cycle according to one embodiment of the present disclosure.

[0023] FIG. 2 is a schematic diagram illustrating a network function according to one embodiment of the present disclosure.

[0024] FIG. 3 is a flowchart illustrating a process for determining a user's personalized bio-signal measurement cycle according to one embodiment of the present disclosure.

[0025] FIG. 4A is a conceptual diagram illustrating a user interface for displaying information related to a personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0026] FIG. 4b is a conceptual diagram illustrating a user interface related to variation of a personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0027] FIG. 4c is a conceptual diagram illustrating a user interface related to the reason for variation in the personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0028] FIG. 5 is a conceptual diagram illustrating a user interface related to an alarm requesting biosignal measurement according to one embodiment of the present disclosure.

[0029] FIG. 6 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0030] The present disclosure discloses a method for determining a personalized bio-signal measurement cycle of a user by inputting the user's bio-signal into a bio-signal analysis model including at least one bio-signal analysis sub-model, and a method for providing an alarm requesting bio-signal measurement in the form of a user interface.

[0031] Various embodiments are now described with reference to the drawings. In this specification, various descriptions are provided to facilitate understanding of the present disclosure. However, it will be apparent that these embodiments may be practiced without these specific details.

[0032] As used herein, the terms "component," "module," "system," and the like refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device may be a component. One or more components may reside within a processor and / or a thread of execution. A component may be localized within a single computer. One or more components of the present disclosure may be distributed between two or more computers, i.e., execution environments. Furthermore, these components may execute from various computer-readable media having various data structures stored therein. Components may communicate via local and / or remote processes, for example, by signals comprising one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system, and / or data transmitted via signals to another system over a network such as the Internet).

[0033] Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean either of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, "X employs A or B" can apply to any of these cases. Furthermore, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the associated items listed.

[0034] Additionally, the terms "comprises" and / or "comprising" should be understood to imply the presence of the features and / or components in question. However, it should be understood that the terms "comprises" and / or "comprising" do not exclude the presence or addition of one or more other features, components, and / or groups thereof. Furthermore, unless otherwise specified or clear from the context to refer to the singular form, the singular in the specification and claims should generally be construed to mean "one or more."

[0035] And, the term “at least one of A or B” should be interpreted to mean “if it includes only A”, “if it includes only B”, or “if it is combined in the composition of A and B”.

[0036] Those skilled in the art should further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0037] The description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments set forth herein. The present disclosure is to be construed in the widest scope consistent with the principles and novel features disclosed herein.

[0038] In the present disclosure, a biosignal may include a vital sign. Generally, a biosignal may refer to an electrical signal or a non-electrical signal that can be continuously measured from a user (subject). A biosignal may include various bio-related signals such as an electrocardiogram (ECG), an electromyogram (EMG), an electroencephalogram (EEG), an electrodermal activity (EDA), a skin temperature (SKT), a photoplethysmography (PPG), a vital sign, an X-ray image signal, a magnetic resonance imaging (MRI) signal, a computed tomography (CT) image signal, and the like. In addition, a biosignal is not limited to these examples, and may include various information obtained from a user using a medical device, a wearable device, a mobile device, etc.

[0039]

[0040] FIG. 1 is a block diagram of a computing device for determining a biosignal measurement cycle according to one embodiment of the present disclosure.

[0041] The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0042] A computing device (100) may include a processor (110), memory (130), and network unit (150).

[0043] The processor (110) may be configured with one or more cores, and may include a processor for data analysis and deep learning, such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. The processor (110) may read a computer program stored in the memory (130) and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform operations for learning a neural network. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating weights of a neural network using backpropagation.

[0044] At least one of the CPU, GPGPU, and TPU of the processor (110) can process network function learning. For example, the CPU and GPGPU can jointly process network function learning and data classification using the network function. Furthermore, in one embodiment of the present disclosure, processors of multiple computing devices can be used together to process network function learning and data classification using the network function. Furthermore, a computer program executed in a computing device according to one embodiment of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0045]

[0046] In one embodiment of the present disclosure, the processor (110) can receive a user's biosignal. In the present disclosure, the biosignal may be data measured from a separate biosignal measuring device (not shown) connected to the computing device (100) and received through the network unit (150). However, the computing device (100) may include an input unit (not shown) for measuring the biosignal, and in this case, the processor (110) may receive the biosignal measured by the input unit (not shown). In addition to the above examples, the processor (110) may receive the user's biosignal stored in the memory (130), and the method of receiving the biosignal in the present disclosure is not limited to the above examples.

[0047] The processor (110) can determine a personalized biosignal measurement cycle of the user based on inputting the user's biosignal into a biosignal analysis model. In the present disclosure, the biosignal analysis model may include at least one biosignal analysis submodel, and at least one biosignal analysis submodel may include an artificial neural network model. For example, the biosignal analysis model of the present disclosure may include at least one or more of a blood pressure analysis submodel, a blood sugar analysis submodel, or an electrocardiogram analysis submodel. A specific method for determining a personalized biosignal measurement cycle of the user by utilizing the biosignal analysis model will be described below with reference to FIG. 3.

[0048] The processor (110) can display a user interface related to a personalized biosignal measurement cycle through a touch screen (not shown), etc. Specific examples of the user interface related to the personalized biosignal measurement cycle are described below with reference to FIGS. 4A to 4C.

[0049] The processor (110) may generate alarm information requesting the user to measure a biosignal based on a personalized biosignal measurement cycle, and display a user interface related to the alarm on a touch screen (not shown), etc. A specific example of the user interface related to the alarm is described below with reference to FIG. 5.

[0050] Through the present disclosure, by analyzing a user's bio-signal and detecting abnormalities in the bio-signal, it is possible to determine an optimized bio-signal measurement cycle for a user who is at increased risk of disease and requires continuous monitoring, for example, by inputting electrocardiogram data to estimate the heart age at the present time or to predict the heart age at multiple future time points, and for a user whose estimated heart age at the present time differs by a threshold value or more, a user whose predicted heart age change at multiple future time points exceeds a threshold value, or a user whose estimated heart age at the present time differs by a threshold value or more. Alternatively, it is possible to determine an optimized bio-signal measurement cycle for a user with reduced renal function by inputting electrocardiogram data to estimate blood potassium concentration and glomerular filtration rate. In addition, by providing changes in the bio-signal measurement cycle, the reasons for the changes, and alarms requesting bio-signal measurement in the form of a user interface, the user's compliance with the alarms can be increased. Consequently, the operation of a healthcare system utilizing a mobile device has the effect of increasing user satisfaction with the service.

[0051]

[0052] According to one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may also operate in relation to web storage that performs the storage function of the memory (130) on the internet. The description of the above-described memory is merely an example, and the present disclosure is not limited thereto.

[0053] The network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as a public switched telephone network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and a local area network (LAN).

[0054] In addition, the network unit (150) presented in this specification can use various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA) and other systems.

[0055] In the present disclosure, the network unit (150) can use any type of wired or wireless communication system.

[0056] The techniques described in this specification can be used in other networks as well as the networks mentioned above.

[0057]

[0058] FIG. 2 is a schematic diagram illustrating a network function according to one embodiment of the present disclosure.

[0059] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network may be composed of a set of interconnected computational units, generally referred to as nodes. These nodes may also be referred to as neurons. A neural network comprises at least one node. The nodes (or neurons) comprising a neural network may be interconnected by one or more links.

[0060] Within a neural network, one or more nodes connected via links can form a relationship between input nodes and output nodes. The concept of input nodes and output nodes is relative, meaning that any node that is in an output node relationship with one node can also be in an input node relationship with another node, and vice versa. As described above, the relationship between input nodes and output nodes can be created based on links. One input node can be connected to one or more output nodes via links, and vice versa.

[0061] In a relationship between input nodes and output nodes connected through a single link, the data of the output node can have its value determined based on the data input to the input node. Here, the link interconnecting the input nodes and output nodes can have a weight. The weight can be variable and can be varied by the user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node through each link, the output node can determine the output node value based on the values ​​input to the input nodes connected to the output node and the weight set on the link corresponding to each input node.

[0062] As described above, a neural network is a network in which one or more nodes are interconnected through one or more links, forming input and output node relationships within the network. The characteristics of a neural network can be determined based on the number of nodes and links within the network, the relationships between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different weight values ​​for the links, the two neural networks can be perceived as different from each other.

[0063] A neural network can be composed of a set of one or more nodes. A subset of the nodes comprising the neural network can form a layer. Some of the nodes comprising the neural network can form a layer based on their distances from the initial input node. For example, a set of nodes that are n distances from the initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links required to reach the node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a different way than described above. For example, a layer of nodes can be defined by its distance from the final output node.

[0064] An initial input node may refer to one or more nodes within a neural network into which data is directly input without going through links with other nodes. Alternatively, within a neural network, it may refer to nodes that do not have other input nodes connected by links in the relationship between nodes based on links. Similarly, a final output node may refer to one or more nodes within a neural network that do not have output nodes in their relationship with other nodes. Furthermore, a hidden node may refer to nodes that constitute a neural network other than the initial input node and the final output node.

[0065] A neural network according to one embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be the same as the number of nodes in an output layer, and the number of nodes decreases and then increases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be less than the number of nodes in an output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. In addition, a neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in an input layer may be greater than the number of nodes in an output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network in a combined form of the neural networks described above.

[0066] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to input and output layers. Using DNNs, one can identify latent structures in data. For example, one can identify the latent structures of images, text, videos, audio, and music (e.g., what objects are in the image, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). DNNs can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siamese networks, and generative adversarial networks (GANs). The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

[0067] In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called a bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the dimensionality after preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may have a structure in which the number of nodes decreases as it moves away from the input layer. The number of nodes in the bottleneck layer (the layer with the fewest nodes between the encoder and decoder) may be kept above a certain number (e.g., more than half of the input layer), as too few nodes may not transmit enough information.

[0068] Neural networks can learn using at least one of the following methods: supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Neural network learning can be the process of applying knowledge to the neural network to perform a specific action.

[0069] Neural networks can be trained to minimize output errors. This process involves repeatedly inputting training data into the neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error. Supervised learning uses training data with the correct answer labeled for each training data (i.e., labeled training data). Unsupervised learning, on the other hand, may not have the correct answer labeled for each training data. For example, in the case of supervised learning for data classification, the training data may be data with each category labeled. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the training data labels. Alternatively, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each node to be updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of neural network training to quickly achieve a certain level of performance, thereby increasing efficiency. A lower learning rate can be used in the later stages of training to increase accuracy.

[0070] In neural network training, training data can typically be a subset of real-world data (i.e., the data to be processed using the trained neural network). Therefore, there can be a learning cycle where errors on the training data decrease but errors on the real-world data increase. Overfitting is a phenomenon where excessive training on the training data leads to increased errors on the real-world data. For example, a neural network trained on yellow cats may fail to recognize cats when shown non-yellow colors, a type of overfitting. Overfitting can increase errors in machine learning algorithms. Various optimization methods can be used to prevent overfitting. These methods include increasing the training data, regularization, dropout, which disables some nodes in the network during the learning process, and the use of batch normalization layers.

[0071]

[0072] FIG. 3 is a flowchart illustrating a process for determining a user's personalized bio-signal measurement cycle according to one embodiment of the present disclosure.

[0073] According to FIG. 3, the method for determining a personalized bio-signal measurement cycle of the present disclosure may include a step (S110) of inputting at least a portion of a user's bio-signal into at least one bio-signal analysis sub-model, a step (S130) of generating comprehensive diagnostic information on the user's health status based on an output value generated by the at least one bio-signal analysis sub-model, and a step (S150) of determining a personalized bio-signal measurement cycle based on the comprehensive diagnostic information.

[0074] In step S110, the processor (110) may input at least a portion of the user's biosignal into at least one biosignal analysis submodel. In addition, an output value that matches the design purpose of each biosignal analysis submodel may be generated. For example, if the user's biosignal includes information on blood pressure, the processor (110) may input the blood pressure information among the biosignals into the blood pressure analysis submodel included in the biosignal analysis model, and the blood pressure analysis submodel may classify the user's bioinformation as 'normal' or 'abnormal' based on the input blood pressure information. Specifically, the blood pressure analysis submodel may classify the user's bioinformation as 'abnormal' if the systolic blood pressure is 135 mmHg or higher or the diastolic blood pressure is 85 mmHg or higher, and classify the remaining cases as 'normal'.

[0075] As another example, if the user's bio-information includes information about blood sugar, the processor (110) can input the blood sugar information among the bio-signals into a blood sugar analysis sub-model included in the bio-signal analysis model, and the blood sugar analysis sub-model can classify the user's bio-information as 'normal' or 'abnormal' based on the input blood sugar information. Specifically, the blood sugar analysis sub-model can classify the user's bio-information as 'abnormal' if the blood sugar is less than 70 mg / dl or more than 250 mg / dl, and classify the remaining cases as 'normal'.

[0076] As another example, if the user's biometric information includes information about body temperature, the processor (110) can input the body temperature information among the biometric signals into a body temperature analysis submodel included in the biometric signal analysis model, and the body temperature analysis submodel can classify the user's biometric information as 'normal' or 'abnormal' based on the input body temperature information. Specifically, the body temperature analysis submodel can classify the user's biometric information as 'abnormal' if the body temperature is 35°C or lower, and classify the remaining cases as 'normal'.

[0077] As another example, if electrocardiogram data is included among the user's bio-information, the processor (110) can input the electrocardiogram data among the bio-signals into an electrocardiogram analysis sub-model included in the bio-signal analysis model, and the electrocardiogram analysis sub-model can classify the user's bio-information as 'normal' or 'abnormal' through the input electrocardiogram.

[0078] The electrocardiogram analysis submodel of the present disclosure includes a first electrocardiogram analysis submodel that receives an electrocardiogram as input and estimates a heart age at the present time or predicts a heart age at multiple future time points, and classifies the electrocardiogram data as 'abnormal' if the difference between the estimated heart age at the present time point and the actual age is greater than or equal to a threshold value, if the change in the predicted heart age at multiple future time points is greater than or equal to a threshold value, or if the change between the estimated heart age at the past time point and the estimated heart age at the present time point is greater than or equal to a threshold value; a second electrocardiogram analysis submodel that receives an electrocardiogram as input and estimates a serum potassium level and classifies the electrocardiogram data as 'abnormal' if the change in the serum potassium level is greater than or equal to a threshold value; a third electrocardiogram analysis submodel that receives an electrocardiogram as input and estimates an estimated glomerular filtration rate (eGFR) and classifies the electrocardiogram data as 'abnormal' if the decrease in the glomerular filtration rate is greater than or equal to a threshold value; or receives an electrocardiogram as input and estimates a left atrial size and classifies the electrocardiogram data as 'abnormal' if the left atrial size is greater than or equal to a threshold value or If the number of ECG data increases continuously, at least one of the four ECG analysis submodels that classify ECG data as "abnormal" may be included. Each ECG analysis submodel may be an artificial neural network model.

[0079] In step S130, the processor (110) can generate comprehensive diagnostic information on the user's health status based on the output value generated by at least one biosignal analysis submodel.

[0080] For example, if at least one of the output values ​​of the first electrocardiogram analysis submodel that receives an electrocardiogram as input and estimates a heart age, the output values ​​of the second electrocardiogram analysis submodel that receives an electrocardiogram and estimates a blood potassium concentration, the output values ​​of the third electrocardiogram analysis submodel that receives an electrocardiogram and estimates a glomerular filtration rate, and the output values ​​of the fourth electrocardiogram analysis submodel that receives an electrocardiogram and estimates a left atrial volume is 'abnormal', the processor (110) can generate information such as {'increased risk of heart disease', 'increased risk of stroke', 'decreased kidney function'} as comprehensive diagnostic information.

[0081] In step S150, the processor (110) may determine a personalized bio-signal measurement cycle based on comprehensive diagnostic information on the user's health status. For example, if the comprehensive diagnostic information is {'increased risk of heart disease', 'increased risk of stroke', 'decreased kidney function'}, the processor (110) may determine a second measurement cycle, which is shorter than the first measurement cycle, which is the initial measurement cycle, as the personalized bio-signal measurement cycle. As another example, if the comprehensive diagnostic information is {'all bio-signals normal'}, the processor (110) may determine a third measurement cycle, which is longer than the first measurement cycle, which is the initial measurement cycle, as the personalized bio-signal measurement cycle.

[0082] In another embodiment of the present disclosure, the biosignal analysis model is comprised of multiple biosignal analysis submodels, and the multiple biosignal analysis submodels can be used simultaneously to generate comprehensive diagnostic information about the user's health status. For example, multiple different output values ​​are generated by the multiple biosignal analysis submodels, and the processor (110) can be implemented to generate comprehensive diagnostic information about the user's health status based on the different output values.

[0083]

[0084] FIG. 4A is a conceptual diagram illustrating a user interface related to a personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0085] In the present disclosure, the user interface related to the personalized bio-signal measurement cycle (hereinafter referred to as the 'first user interface') may be a user interface displayed on a touch screen (not shown) or the like.

[0086] The first user interface (410) may include a first area (411) indicating whether a personalized bio-signal measurement cycle has changed and a second area (412) displaying the personalized bio-signal measurement cycle. For example, the first area (411) may display text informing the user that the bio-signal measurement cycle has changed ('There has been a change in the recommended measurement cycle'), and the second area (412) may display text corresponding to the past bio-signal measurement cycle ('30 days') and the changed bio-signal measurement cycle ('7 days').

[0087] The first user interface (410) may include a third area (413) for receiving a user's selection input for invoking a user interface related to changing a personalized bio-signal measurement cycle, and may further include a fourth area (414) for receiving a user's selection input for removing the first user interface (410).

[0088]

[0089] FIG. 4b is a conceptual diagram illustrating a user interface related to variation of a personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0090] In the present disclosure, the user interface (hereinafter referred to as the "second user interface") related to the variation of the personalized biosignal measurement cycle may be a user interface displayed on a touch screen (not shown), etc. The second user interface (420) may be displayed by a user's selection input.

[0091] The second user interface (420) may include a fifth area (421) for displaying changes in the bio-signal measurement cycle and a sixth area (422, 423, and 424) for displaying at least one reason for the change in the bio-signal measurement cycle. For example, the fifth area (421) may display text corresponding to a past bio-signal measurement cycle ('30 days') and a changed bio-signal measurement cycle ('7 days'), and the sixth area (422, 423, and 424) may display text indicating at least one reason for the change ('increased risk of heart disease', 'increased risk of stroke', 'decreased kidney function').

[0092] The second user interface (420) may further include a seventh area (425) for receiving a user's selection input to remove the second user interface (420).

[0093]

[0094] FIG. 4c is a conceptual diagram illustrating a user interface related to the reason for variation in the personalized biosignal measurement cycle according to one embodiment of the present disclosure.

[0095] In the present disclosure, the user interface (hereinafter referred to as the "third user interface") related to the reason for the variation in the personalized bio-signal measurement cycle may be a user interface displayed on a touch screen (not shown), etc. The third user interface (430) may be displayed by a selection input related to the reason for the variation in the user's bio-signal measurement cycle.

[0096] The third user interface (430) may include an eighth area (431) for displaying the reason for the change in the biosignal measurement cycle, a ninth area (432) for displaying information related to the time point of measurement of the biosignal, and a tenth area (4331 and 4332) for displaying at least one disease diagnosis information according to the time point of measurement. In this case, the disease diagnosis information displayed in the tenth area (4331 and 4332) may include disease diagnosis information output by an artificial neural network model included in the biosignal analysis model of the present disclosure.

[0097] For example, the 8th area (431) may display text indicating the reason for the change in the bio-signal measurement cycle ('Increased risk of heart disease'), the 9th area (432) may display text indicating the previous measurement time and the last measurement time ('Previous measurement time: 2023. 06. 30 22:18:19', 'Last measurement time: 2023. 07. 30 22:18:19'), and the 10th area (4331 and 4332) may display text indicating disease diagnosis information ('Risk of heart failure increased by 13% compared to the previous measurement time', 'Heart age increased by 1y compared to the previous measurement time').

[0098] The third user interface (430) may further include an eleventh area (434) for receiving a user selection input to remove the third user interface (430).

[0099]

[0100] FIG. 5 is a conceptual diagram illustrating a user interface related to an alarm requesting biosignal measurement according to one embodiment of the present disclosure.

[0101] In the present disclosure, a user interface related to an alarm requesting bio-signal measurement (hereinafter referred to as the "fourth user interface") may be a user interface displayed on a touch screen (not shown), etc. The fourth user interface (510) may include a 12th area (511) that displays information related to a request for bio-signal measurement, a 13th area (512) that displays information related to a past bio-signal measurement time, a 14th area (513) that displays information related to a personalized bio-signal measurement cycle, and a 15th area (514) that displays information related to a reason for a change in the personalized bio-signal measurement cycle.

[0102] For example, a text requesting a bio-signal measurement ('Please measure an electrocardiogram') may be displayed in the 12th area (511), and a text indicating the time elapsed since the last bio-signal measurement ('10 days have passed since the last measurement') may be displayed in the 13th area (512).

[0103] The fourth user interface (510) may include a sixteenth area (515) for receiving an optional input to ignore an alarm or an optional input to remove the fourth user interface, and may further include a seventeenth area (516) for receiving an optional input to measure a biosignal.

[0104]

[0105]

[0106] Meanwhile, a computer-readable medium storing a data structure according to an embodiment of the present disclosure is disclosed.

[0107] A data structure can refer to the organization, management, and storage of data to enable efficient access and modification. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, or data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include connections between user-defined data elements. Physical relationships between data elements can include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., persistent storage). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands applicable to the data. An effectively designed data structure allows a computing device to perform operations while minimizing the use of its resources. Specifically, a computing device can improve the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0108] Data structures can be categorized as linear or nonlinear, depending on their form. A linear data structure can be a structure in which only one data item is linked to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets with an internal order. Lists can also include linked lists. A linked list is a data structure in which data is linked in a single line, each item having a pointer. In a linked list, a pointer can contain information about the next or previous item. Linked lists can be expressed as singly linked lists, doubly linked lists, or circular linked lists, depending on their form. A stack can be a data listing structure with limited data access. A stack can be a linear data structure in which data operations (e.g., insertion or deletion) can only be performed at one end of the data structure. Data stored in a stack can be a Last-in-First-out (LIFO) data structure. A queue is a data structure with limited access to data. Unlike a stack, it can be a first-in, first-out (FIFO) data structure, with later data being retrieved later. A deck can be a data structure that can process data at both ends.

[0109] A nonlinear data structure can be a structure in which multiple pieces of data are connected behind a single piece of data. Nonlinear data structures can include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structures can include tree data structures. A tree data structure can be a data structure in which there is only one path connecting two different vertices among multiple vertices included in the tree. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0110] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they are collectively referred to as neural networks. The data structure may include a neural network. And the data structure including the neural network may be stored on a computer-readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network learning, etc. The data structure including the neural network may include any of the components disclosed above. That is, the data structure including the neural network may be configured to include all or any combination of preprocessed data for processing by the neural network, data input to the neural network, neural network weights, neural network hyperparameters, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for neural network learning, etc. In addition to the aforementioned configurations, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated in the computational process of the neural network, and is not limited to the aforementioned. The computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0111] The data structure may include data input to a neural network. The data structure including the data input to the neural network may be stored on a computer-readable medium. The data input to the neural network may include training data input during the neural network training process and / or input data input to the neural network after training has been completed. The data input to the neural network may include data that has undergone preprocessing and / or data that is the target of preprocessing. Preprocessing may include a data processing process for inputting data to the neural network. Accordingly, the data structure may include data that is the target of preprocessing and data generated by the preprocessing. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0112] The data structure may include weights of the neural network. (In this specification, the terms "weight" and "parameter" may be used interchangeably.) The data structure including the weights of the neural network may be stored in a computer-readable medium. The neural network may include a plurality of weights. The weights may be variable and may be varied by a user or an algorithm so that the neural network can perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node may determine a data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set for links corresponding to each input node. The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0113] By way of example and not limitation, the weights may include weights that vary during the neural network training process and / or weights that have completed neural network training. The weights that vary during the neural network training process may include weights at the start of the training cycle and / or weights that vary during the training cycle. The weights that have completed neural network training may include weights that have completed the training cycle. Accordingly, a data structure including the weights of a neural network may include a data structure including weights that vary during the neural network training process and / or weights that have completed neural network training. Therefore, the above-described weights and / or combinations of each weight are included in the data structure including the weights of a neural network. The above-described data structures are merely examples and the present disclosure is not limited thereto.

[0114] A data structure including neural network weights can be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed and used. A computing device can serialize the data structure to transmit and receive data over a network. The serialized data structure including neural network weights can be reconstructed on the same or different computing devices through deserialization. The data structure including neural network weights is not limited to serialization. Furthermore, the data structure including neural network weights can include a data structure that increases computational efficiency while minimizing the use of computing device resources (e.g., a B-Tree, a Trie, an m-way search tree, an AVL tree, a Red-Black Tree in nonlinear data structures). The foregoing is merely an example, and the present disclosure is not limited thereto.

[0115] The data structure may include hyperparameters of a neural network. Furthermore, the data structure including the hyperparameters of the neural network may be stored on a computer-readable medium. The hyperparameters may be variables that can be varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle repetitions, weight initialization (e.g., setting a range of weight values ​​to be subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layer). The above-described data structure is merely an example, and the present disclosure is not limited thereto.

[0116]

[0117] FIG. 6 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0118] Although the present disclosure has been described above as being generally implemented by a computing device, those skilled in the art will appreciate that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that may be executed on one or more computers and / or as a combination of hardware and software.

[0119] Generally, program modules include routines, programs, components, data structures, and the like that perform particular tasks or implement particular abstract data types. Furthermore, those skilled in the art will appreciate that the methods of the present disclosure can be implemented with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which may be operatively connected to one or more associated devices.

[0120] The described embodiments of the present disclosure can also be practiced in distributed computing environments, where certain tasks are performed by remote processing devices that are connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0121] Computers typically include a variety of computer-readable media. Computer-readable media can be any media that can be accessed by a computer, and includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media. By way of example, and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes both volatile and nonvolatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store the desired information.

[0122] Computer-readable transmission media typically includes any information delivery media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, or other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

[0123] An exemplary environment (1100) implementing various aspects of the present disclosure is illustrated, including a computer (1102) comprising a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be utilized as the processing unit (1104).

[0124] The system bus (1108) may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. The system memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). A basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, or EEPROM, and includes basic routines that help transfer information between components within the computer (1102), such as during start-up. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0125] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA) - which may also be configured for external use within a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from or writing to a CD-ROM disk (1122) or other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0126] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of the computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of media readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

[0127] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or portions of the operating system, applications, modules, and / or data may also be cached in RAM (1112). It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0128] A user may enter commands and information into the computer (1102) via one or more wired / wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) that is connected to the system bus (1108), but may be connected by other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0129] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface, such as a video adapter (1146). In addition to the monitor (1144), the computer typically includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0130] The computer (1102) may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communications. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), although for simplicity, only the memory storage device (1150) is shown. The logical connections shown include wired / wireless connections to a local area network (LAN) (1152) and / or a larger network, such as a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a worldwide computer network, such as the Internet.

[0131] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communications to the LAN (1152), which may also include a wireless access point installed therein for communicating with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communications computing device on the WAN (1154), or have other means of establishing communications over the WAN (1154), such as via the Internet. The modem (1158), which may be internal or external and wired or wireless, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, program modules or portions thereof described for the computer (1102) may be stored in a remote memory / storage device (1150). It will be appreciated that the network connections depicted are exemplary and other means of establishing a communications link between the computers may be used.

[0132] The computer (1102) operates to communicate with any wireless device or object that is arranged and operates via wireless communication, such as a printer, a scanner, a desktop and / or portable computer, a portable data assistant (PDA), a communication satellite, any equipment or location associated with a radio-detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or may simply be an ad hoc communication between at least two devices.

[0133] Wi-Fi (Wireless Fidelity) enables connections to the Internet and other devices without wires. Wi-Fi is a wireless technology that allows devices, such as computers, to send and receive data anywhere within the coverage area of ​​a base station, both indoors and outdoors, similar to cell phones. Wi-Fi networks use wireless technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual-band).

[0134] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0135] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, various forms of programs or design code (referred to herein, for convenience, as software), or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0136] The various embodiments presented herein can be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Furthermore, various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0137] It should be understood that the specific order or hierarchy of steps in the presented processes is merely an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but are not intended to be limited to the specific order or hierarchy presented.

[0138] The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments disclosed herein, but is to be construed in the broadest scope consistent with the principles and novel features disclosed herein.

[0139]

[0140] As described above, the relevant contents have been described in the best possible form for carrying out the invention.

Claims

1. A method performed by a computing device to determine a bio signal measurement cycle, A step of receiving a user's biosignal; and A step of determining a personalized bio-signal measurement cycle of the user based on inputting the bio-signal of the user into a bio-signal analysis model; Including, The above biosignal analysis model is, comprising at least one biosignal analysis submodel that analyzes the health status of the user; method.

2. In paragraph 1, The steps for determining the personalized bio-signal measurement cycle are: A step of inputting at least a portion of the user's biosignal into at least one biosignal analysis submodel; A step of generating comprehensive diagnostic information on the health status of the user based on the output value generated by at least one biosignal analysis submodel; and A step of determining the personalized bio-signal measurement cycle based on the comprehensive diagnostic information; including, method.

3. In paragraph 2, At least a portion of said at least one biosignal analysis submodel comprises an artificial neural network model, The above comprehensive diagnostic information is: Obtained based on disease diagnosis information output by the above artificial neural network model, method.

4. In paragraph 1, The above method, A step of displaying a user interface related to the personalized bio-signal measurement cycle; Including more, The above user interface, An area indicating whether the personalized bio-signal measurement cycle fluctuates; and An area displaying the personalized bio-signal measurement cycle; including, method.

5. In paragraph 1, The above method, In response to the user's selection input, displaying a user interface related to the variation of the personalized bio-signal measurement cycle; Including more, The user interface related to the variation of the above personalized bio-signal measurement cycle is An area displaying the variation of the personalized bio-signal measurement cycle; and An area for indicating at least one reason for variation in the personalized bio-signal measurement cycle; including, method.

6. In paragraph 1, The above method, In response to receiving a selection input related to a reason for variation in the user's bio-signal measurement cycle, a step of displaying a user interface related to the selected reason for variation; Including more, The user interface related to the above selected change reason is: An area displaying information related to the measurement time of the above biosignal; and An area displaying disease diagnosis information according to the above measurement time point; including, method.

7. In paragraph 1, A step of generating alarm information requesting the user to measure a biosignal based on the personalized biosignal measurement cycle; including more, method.

8. In paragraph 7, The steps to generate the above alarm information are: comprising a step of displaying a user interface related to the alarm; The user interface related to the above alarm is: An area displaying information related to a biosignal measurement request; An area that displays information related to past biosignal measurement results; an area displaying information related to the personalized bio-signal measurement cycle; and An area displaying information related to the reason for variation in the personalized bio-signal measurement cycle; including, method.

9. A computer program stored in a computer-readable storage medium, wherein the computer program causes a computing device to perform operations for determining a biosignal measurement cycle, the operations comprising: The action of receiving the user's biosignals; and An action of determining a personalized biosignal measurement cycle of the user based on inputting the biosignal of the user into a biosignal analysis model; Including, The above biosignal analysis model is, comprising at least one biosignal analysis submodel that analyzes the health status of the user; A computer program stored on a computer-readable storage medium.

10. As a computing device, at least one processor; and memory; Including, At least one processor, Receives the user's biosignals; and Based on inputting the user's bio-signal into a bio-signal analysis model, a personalized bio-signal measurement cycle of the user is determined, The above biosignal analysis model is, comprising at least one biosignal analysis submodel that analyzes the health status of the user; Computing device.

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