KR20240097011A - Medical device, method and program for non-face-to-face dementia testing based on artificial intelligence - Google Patents

Abstract

This disclosure relates to a non-face-to-face dementia testing device based on artificial intelligence, which provides a call number for dementia testing to a user's terminal and, when a call is received from a specific terminal, determines whether the specific terminal matches the user's terminal. And, when it is confirmed whether the user has a terminal, a dementia test can be performed and the dementia test results can be provided to the user.
Project number: A1311-22-1008
Ministry name: Ministry of Science and ICT
Name of project management (professional) organization: National IT Industry Promotion Agency
Research Project Name: 2022 Core Industry Cloud Flagship Project
Research project name: Development and commercialization of Altwin Care Call SaaS
Contribution rate: 100%
Name of project carrying out organization: Seven Point One Co., Ltd.
Research period: May 1, 2022 ~ December 31, 2022

Description

Medical device, method and program for non-face-to-face dementia testing based on artificial intelligence}

This disclosure relates to a medical device that performs a non-face-to-face dementia test based on artificial intelligence.

Alzheimer's Disease (AD) is a brain disease that accompanies aging and is a disease caused by abnormalities in the brain that leads to gradual degeneration of memory. Additionally, Alzheimer's disease can lead to dementia, which is a persistent and overall decline in cognitive function that causes difficulties in daily life. Here, cognitive function refers to various intellectual abilities such as memory, language ability, spatial and temporal grasping ability, judgment, and abstract thinking ability, and each cognitive function is closely related to a specific part of the brain.

Mild cognitive impairment (MCI) refers to a condition in which memory and cognitive function are reduced compared to those of the same age, although it has not progressed to dementia. Mild cognitive impairment can develop into Alzheimer's disease, so it is an important step to detect Alzheimer's disease at an early stage and take preventive measures.

Such tests for dementia (or mild cognitive impairment) are generally conducted in the following order: screening tests, diagnostic tests, and differential tests. Here, screening tests are carried out at regional dementia care centers, public health centers, etc., but there are problems in terms of the effectiveness and efficiency of the test because it requires a direct visit by test subjects and the test is performed by a person. Specifically, for people who can visit the testing center in person, the likelihood of being a dementia patient is very low, so the effectiveness of the test is low, and when a person performs the test in person, it takes tens of minutes to test one person, resulting in significant loss in terms of time and cost.

Artificial intelligence call is a phone using artificial intelligence technology, which is a technology in which artificial intelligence makes the call instead of a human. Artificial Intelligence Call automatically connects to multiple people and makes calls according to the purpose, saving the labor required when a person directly calls multiple people.

Public Patent Publication No. 10-2019-0081626, (2019.07.09)

The purpose of the embodiments disclosed in this disclosure is to provide a medical device that performs a non-face-to-face dementia test based on artificial intelligence.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

An artificial intelligence-based non-face-to-face dementia testing device according to an embodiment of the present disclosure for solving the above-described problem includes: a storage unit storing a phone number for at least one user for dementia testing; A communication unit that communicates with the user's terminal; and a processor, wherein the processor provides a call number for dementia testing to the user's terminal, and when a call is received from a specific terminal, determines whether the specific terminal matches the user's terminal, and determines whether the specific terminal matches the user's terminal. Once confirmed, a dementia test is performed and the dementia test results are provided to the user.

In addition, the dementia testing device obtains the user's personal information by conducting a voice question and answer with the terminal, or answers the question through an electronic device owned by the user or an electronic device linked to the user's terminal. The user's personal information is obtained, and the electronic device includes an image output means.

In addition, the dementia testing device obtains the user's local information by conducting a voice question and answer with the terminal, obtains the user's local information by obtaining GPS information from the terminal, or obtains the user's local information by obtaining GPS information from the terminal, or The user's local information is obtained by answering questions through an electronic device or an electronic device linked to the user's terminal, and the electronic device includes an image output means.

Additionally, when performing the dementia test, the processor may perform the dementia test through an electronic device including an image output means and an input means.

In addition, the processor provides a query for the dementia test through an image output means of the electronic device, receives a user input through an input means of the electronic device, and based on the provided query and the received user input. It is possible to generate test results for the dementia test.

In addition, the electronic device is a smart TV, and the input means is a remote control capable of inputting signals to the smart TV and inputting the user's voice.

Additionally, the processor may transmit the dementia test results to the electronic device, generate visualization data based on the dementia test results, and control the visualization data to be output to the image output means.

In addition, the processor determines whether the user needs a follow-up test based on the dementia test results, and when it is determined that the user needs the follow-up test, based on local information acquired about the user, At least one medical institution can be derived, and information about the derived at least one medical institution can be provided to the user.

In addition, the artificial intelligence-based non-face-to-face dementia screening method according to an embodiment of the present disclosure to solve the above-described problem is a method performed by an artificial intelligence-based non-face-to-face dementia screening device, and is a method of dementia testing using the user's terminal. providing a call number for; When a call is received from a specific terminal, determining whether the specific terminal matches the user's terminal; If the user's terminal is confirmed, performing a dementia test; and providing dementia test results to the user.

In addition to this, a computer program stored in a computer-readable recording medium for execution to implement the present disclosure may be further provided.

In addition to this, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the means for solving the above-described problem of the present disclosure, it provides the effect of providing a medical device that performs a non-face-to-face dementia test based on artificial intelligence.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below.

1 is a schematic diagram of an artificial intelligence-based non-face-to-face dementia screening system according to an embodiment of the present disclosure.
Figure 2 is a block diagram of an artificial intelligence-based non-face-to-face dementia screening device according to an embodiment of the present disclosure.
Figure 3 is a flowchart of an artificial intelligence-based non-face-to-face dementia screening method according to an embodiment of the present disclosure.
Figure 4 is a diagram illustrating an electronic device (TV, remote control) used in a non-face-to-face dementia testing device.
Figure 5 is a diagram illustrating a non-face-to-face dementia test using the electronic device of Figure 4.
Figure 6 is a block diagram schematically showing a part of a non-face-to-face dementia testing device according to an embodiment of the present disclosure.
Figure 7 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.
Figure 8 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.
Figure 9 is an exemplary diagram schematically showing the time-series process of the dementia test progress stage according to an embodiment of the present disclosure.
Figure 10 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.

Like reference numerals refer to like elements throughout this disclosure. This disclosure does not describe all elements of the embodiments, and general content or overlapping content between embodiments in the technical field to which this disclosure pertains is omitted. The term 'part, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'part, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part is said to “include” a certain component, this means that it may further include other components, rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In this specification, 'non-face-to-face dementia testing device according to the present disclosure' includes various devices that can perform computational processing and provide results to the user. For example, the non-face-to-face dementia testing device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may take the form of any one.

Here, the computer may include, for example, a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

The server device is a server that processes information by communicating with external devices and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.

The portable terminal is, for example, a wireless communication device that guarantees portability and mobility, such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), and PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smartphone (Smart Phone) ), all types of handheld wireless communication devices, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-device (HMD). may include.

Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may consist of one or multiple processors. At this time, one or more processors may be a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU or VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU. One or more processors control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, when one or more processors are dedicated artificial intelligence processors, the artificial intelligence dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

Predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned using a large number of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform the desired characteristics (or purpose). It means burden. This learning may be accomplished in the device itself that performs the artificial intelligence according to the present disclosure, or may be accomplished through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

An artificial intelligence model may be composed of multiple neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and neural network calculation is performed through calculation between the calculation result of the previous layer and the plurality of weights. Multiple weights of multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, a plurality of weights may be updated so that loss or cost values obtained from the artificial intelligence model are reduced or minimized during the learning process. Artificial neural networks may include deep neural networks (DNN), for example, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, etc., but are not limited to the examples described above.

According to an exemplary embodiment of the present disclosure, a processor may implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that allows machines to learn by imitating human biological neurons. Methodology of artificial intelligence includes supervised learning, in which the answer (output data) to the problem (input data) is determined by providing input data and output data together as training data according to the learning method, and only input data is provided without output data. In unsupervised learning, in which the solution (output data) to the problem (input data) is not determined, and a reward is given from the external environment whenever an action is taken in the current state, , It can be divided into reinforcement learning, which conducts learning in the direction of maximizing these rewards. In addition, artificial intelligence methodologies can be divided according to the architecture, which is the structure of the learning model. The architecture of widely used deep learning technology is convolutional neural network (CNN) and recurrent neural network (RNN). , Transformer, generative adversarial networks (GAN), etc.

The device may include an artificial intelligence model. An artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. Artificial intelligence models may be composed of neural networks (or artificial neural networks) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network can refer to an overall model in which artificial neurons (nodes), which form a network through the combination of synapses, change the strength of the synapse connection through learning and have problem-solving capabilities. Neurons in a neural network can contain combinations of weights or biases. A neural network may include one or more layers consisting of one or more neurons or nodes. By way of example, a device may include an input layer, a hidden layer, and an output layer. The neural network that makes up the device can infer the result (output) to be predicted from arbitrary input (input) by changing the weight of neurons through learning.

The processor creates a neural network, trains or learns a neural network, performs calculations based on received input data, generates an information signal based on the results, or generates a neural network. The network can be retrained. Neural network models include CNN (Convolution Neural Network), R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), and RNN, such as GoogleNet, AlexNet, and VGG Network. (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network , LSTM (Long Short-Term Memory) Network, Classification Network, etc., but the processor is not limited thereto and may include one or more processors for performing operations according to neural network models. For example, a neural network may include a deep neural network.

Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), and LSTM. (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield) Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network) ), Liquid State Machine (LSM), Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Differential Neural Computer (DNC), Neural Turning Machine (NTM), Capsule Network (CN), Those skilled in the art will understand that it may include any neural network, including, but not limited to, KN (Kohonen Network) and AN (Attention Network).

According to an exemplary embodiment of the present disclosure, the processor may support a Convolution Neural Network (CNN), a Region with Convolution Neural Network (R-CNN), a Region Proposal Network (RPN), a Recurrent Neural Network (RNN), such as GoogleNet, AlexNet, VGG Network, etc. ), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT for natural language processing, SP-BERT, MRC/QA, Text Analysis, Dialog System, GPT-3 , GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, and Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation for ResNet data intelligence. , but is not limited to this. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

Figure 1 is a schematic diagram of an artificial intelligence-based non-face-to-face dementia screening system 10 according to an embodiment of the present disclosure.

Referring to FIG. 1, an artificial intelligence-based non-face-to-face dementia screening system 10 according to an embodiment of the present disclosure includes a dementia screening device 100, a user terminal 200, and an electronic device 300.

The dementia testing device 100 operates as the main agent of the artificial intelligence-based non-face-to-face dementia testing system 10 according to an embodiment of the present disclosure, and communicates with the user terminal 200 and the electronic device 300 to provide non-face-to-face dementia to the user. Dementia tests are conducted face-to-face.

In an embodiment of the present disclosure, the dementia testing device 100 provides a call number for dementia testing to the user, and when a call is received from the user's terminal 200, the process proceeds with starting the dementia testing.

At this time, the dementia testing device 100 according to an embodiment of the present disclosure provides an image for a dementia test through the user's electronic device 300 and performs the dementia test through the image, thereby significantly improving the test effect. It becomes effective.

Below, with reference to other drawings, the artificial intelligence-based non-face-to-face dementia screening device 100 according to an embodiment of the present disclosure will describe in more detail how to perform a non-face-to-face dementia screening on a user using the electronic device 300. Let me explain.

Figure 2 is a block diagram of an artificial intelligence-based non-face-to-face dementia screening device 100 according to an embodiment of the present disclosure.

The processor 120 includes a memory that stores data for an algorithm for controlling the operation of components within the device 100 or a program that reproduces the algorithm, and at least one device that performs the above-described operations using the data stored in the memory. It can be implemented with the processor 120. At this time, the memory and processor 120 may each be implemented as separate chips. Alternatively, the memory and processor 120 may be implemented as a single chip.

In addition, the processor 120 may control any one or a combination of the components described above in order to implement various embodiments according to the present disclosure described in the drawings below on the device 100.

In addition to operations related to the application program, the processor 120 may generally control the overall operation of the device 100. The processor 120 can provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components discussed above, or by running an application program stored in memory.

Additionally, the processor 120 may control at least some of the components of the device 100 to run an application program stored in the memory. Furthermore, the processor 120 may operate in combination with at least two or more of the components included in the device 100 in order to run the application program.

The communication unit 110 may include one or more modules that connect the dementia testing device 100 to one or more networks.

The communication unit 110 may include one or more components that enable communication with the external device 100, for example, a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module. It can contain at least one.

Wired communication modules include various wired communication modules such as Local Area Network (LAN) modules, Wide Area Network (WAN) modules, or Value Added Network (VAN) modules, as well as USB (Universal Serial Bus) modules. ), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).

In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, wireless communication modules include GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), and UMTS (universal mobile telecommunications system). ), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, 6G, etc. may include a wireless communication module that supports various wireless communication methods.

The wireless communication module may include a wireless communication interface including an antenna and a transmitter that transmits a signal (for example, mobile communication). In addition, the wireless communication module may further include a signal conversion module that modulates a digital control signal output from the processor 120 through a wireless communication interface into an analog wireless signal under the control of the processor 120.

The short-range communication module is for short-range communication and includes Bluetooth®, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, and NFC ( Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technology can be used to support short-distance communication.

The storage unit 150 can store data supporting various functions of the device 100. The storage unit 150 may store a number of application programs (application programs or applications) running on the device 100, data for operating the device 100, and commands. At least some of these application programs may exist for the basic functions of the device 100. Meanwhile, the application program may be stored in the storage unit 150, installed in the device 100, and driven to perform an operation (or function) by the processor 120.

The storage unit 150 can store data supporting various functions of the device 100 and a program for the operation of the processor 120, and can store input/output data (e.g., music files, still images, etc.). , videos, etc.) can be stored, and a plurality of application programs (application programs or applications) running on the device 100, data for operation of the device 100, and commands can be stored. At least some of these applications may be downloaded from an external server via wireless communication.

The storage unit 150 includes a flash memory type, hard disk type, solid state disk type, SDD type (Silicon Disk Drive type), and multimedia card micro type. card micro type), card type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically It may include at least one type of storage medium among erasable programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. Additionally, the storage unit 150 is separate from the device 100, but may be a database connected by wire or wirelessly.

Additionally, the memory may be provided with a plurality of processes for executing the artificial intelligence-based non-face-to-face dementia testing device 100 and method.

In an embodiment of the present disclosure, the electronic device 300 and the terminal 200 may include a display unit, an audio output unit, a communication module, an interface unit, etc.

The display unit displays (outputs) information processed in the device 100. For example, the display unit may display execution screen information of an application program (for example, an application) running on the device 100, or UI (User Interface) and GUI (Graphic User Interface) information according to this execution screen information. You can.

The audio output unit may output audio data received through the communication unit 110 or stored in the memory, or output audio signals related to functions performed in the device 100. This sound output unit may include a receiver, speaker, buzzer, etc.

The interface unit serves as a passageway for various types of external devices connected to the device 100. This interface unit connects the device 100 equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module (SIM). It may include at least one of a port, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. This device 100 can perform appropriate control related to external devices connected to the interface unit.

Figure 3 is a flowchart of an artificial intelligence-based non-face-to-face dementia screening method according to an embodiment of the present disclosure.

The processor 120 provides a call number for a non-face-to-face dementia test to the user's terminal 200. (S10)

In an embodiment of the present disclosure, the storage unit 150 stores a list of user phone numbers for dementia testing.

In an extended embodiment, the storage unit 150 can store the ages of users (residents, citizens) in the area in charge, and can automatically register users who fall within a preset age range on a list of subjects to be tested for non-face-to-face dementia. .

Additionally, the storage unit 150 may store information such as the user's phone number, region, age, gender, etc., and a non-face-to-face dementia examination schedule for each user.

When a call is received from a specific terminal 200, the processor 120 determines whether the corresponding terminal 200 is the user's terminal 200. (S20)

The processor 120 can determine whether the corresponding terminal 200 is the user's terminal 200 by matching it with the phone book DB stored in the storage unit 150.

However, the method of checking the user's terminal 200 is not limited to this, and various embodiments are applicable depending on the situation.

If it is confirmed that the processor 120 is the user's terminal 200 in S20, a non-face-to-face dementia test is performed. (S30)

The processor 120 provides dementia test results to the user. (S40)

FIG. 4 is a diagram illustrating an electronic device 300 (TV, remote control) used in the non-face-to-face dementia testing device 100.

Referring to FIG. 4 , the electronic device 300 used in the non-face-to-face dementia testing device 100 in the embodiment of the present disclosure may include at least one of an image output means and an input means.

And, as shown in FIG. 4, a TV can be used as an image output means, and a remote controller linked to the TV can be used as an input means.

However, in the embodiment of the present disclosure, the electronic device 300 is not necessarily applicable to only the TV and the TV's remote control, and can output images for performing a non-face-to-face dementia test and input voice signals and user input signals. Any device 100 that can be used can be applied.

In an embodiment of the present disclosure, the dementia testing device 100 may obtain the user's personal information by conducting a voice question and answer using the terminal 200.

In addition, the dementia testing device 100 can obtain the user's personal information by conducting a question-and-answer process through the electronic device 300 owned by the user or the electronic device 300 linked to the user's terminal 200. there is.

In an embodiment of the present disclosure, the dementia testing device 100 may obtain the user's local information by conducting a voice question and answer using the terminal 200.

In addition, the dementia testing device 100 can acquire the user's local information by conducting a question-and-answer process through the electronic device 300 owned by the user or the electronic device 300 linked to the user's terminal 200. there is.

FIG. 5 is a diagram illustrating a non-face-to-face dementia test using the electronic device 300 of FIG. 4.

In one embodiment, the processor 120 may use the electronic device 300 when performing a non-face-to-face dementia test.

Referring to FIG. 5 , there is an example of a user subject to a dementia test performing a dementia test non-face-to-face using a TV and remote control at home.

In an embodiment of the present disclosure, the user's electronic device 300 is preset and the dementia testing device 100 can control a non-face-to-face dementia test to proceed through the preset electronic device 300.

In another embodiment, the user's electronic device 300 is paired with the user's terminal 200, and the dementia testing device 100 recognizes the electronic device 300 paired with the user's terminal 200. , the non-face-to-face dementia test can be controlled to proceed through the recognized electronic device 300.

In this way, various embodiments are applicable to recognizing/identifying the electronic device 300 on which the user wishes to perform a non-face-to-face dementia test and performing a non-face-to-face dementia test through the recognized/identified electronic device 300.

In one embodiment, the processor 120 provides a query for dementia testing through the image output means of the electronic device 300, receives the user's input through the input means of the electronic device 300, and answers the provided query and Test results for dementia testing can be generated based on received user input.

In an embodiment of the present disclosure, the electronic device 300 is a smart TV, and the input means may be a remote control capable of inputting signals to the smart TV and capable of inputting a user's voice.

The processor 120 performs a non-face-to-face dementia test through the electronic device 300, and depending on the query, requests to select a response on the screen through an input means (remote control) or responds by voice through an input means (remote control). You can request it.

For this function, the input means may have an IMU sensor built in to input values to a video output means (TV). Additionally, the input means may have a built-in microphone so that the user can input voice.

Additionally, the processor 120 can perform a dementia screening service by linking the electronic device 300 and the user's terminal 200.

In one embodiment, the processor 120 transmits the dementia test results to the electronic device 300 and provides them to the user, generates visualization data based on the dementia test results, and controls the visualization data to be output to the image output means. You can.

The processor 120 may generate and provide visualization data that compares the user's dementia test results with the results within a group similar to the user. In this case, the similar group may be of the same age, same educational level, or same region as the user.

Additionally, the processor 120 may compare the user's previous dementia test results and generate the comparison results as visualization data such as a graph.

In one embodiment, the processor 120 determines whether the user needs a follow-up test based on the dementia test results, and if it is determined that the user needs a follow-up test, based on local information acquired about the user, at least one Medical institutions can be derived and information about at least one derived medical institution can be provided to the user.

In one embodiment, the processor 120 may determine whether the user should proceed with face-to-face medical treatment or remote medical treatment service based on the results of the user's dementia test.

In one embodiment, if it is determined that the user needs face-to-face medical treatment, the processor 120 may provide information about medical institutions in the user's area as described above.

In one embodiment, when it is determined that the user needs to proceed with a remote medical service, the processor 120 searches for medical staff available for remote medical treatment based on the current time, determines a medical staff based on the search results, and then determines the medical staff for the user. Remote medical treatment can be performed by connecting to the terminal 200 or the user's electronic device 300.

In one embodiment, when the user's non-face-to-face dementia test is completed, the processor 120 may deliver the test time and test results to the user's guardian.

In an embodiment of the present disclosure, the storage unit 150 stores the terminal 200 number for at least one guardian of the user, and the processor 120 stores the stored guardian terminal ( Dementia test results can be provided at number 200).

Figure 6 is a block diagram schematically showing a portion of a non-face-to-face dementia screening device 100 according to an embodiment of the present disclosure.

As shown in FIG. 6, the dementia testing device 100 according to an embodiment of the present invention may include a communication unit 110, a processor 120, a dementia test processing unit 130, and an analysis unit 140. there is.

In the following embodiments, providing something to the user means providing it to at least one of the user's terminal 200 or the electronic device 300, and obtaining something from the user means providing it to the user's terminal 200 or the electronic device 300. It may mean obtaining from at least one of (300).

Meanwhile, some of the components included in the dementia testing device 100 may be deleted or implemented integrally with other components as needed. Additionally, it is possible for other components not shown in FIG. 3 to be added. In other words, the types of components included in the dementia testing apparatus 100 and their mutual positions may be changed in various ways as needed.

Since the processor 120, communication unit 110, and storage unit 150 are the same as described above, redundant description will be omitted.

The communication unit 110 functions to communicate with the user terminal 200 and/or the institutional server. The communication unit 110 may include at least one of a short-range communication module, a wired communication module, and a wireless communication module.

The dementia test progress unit 130 performs a dementia test on the test subject of the user terminal 200 to which the artificial intelligence call is connected. The dementia test performed by the dementia test progress unit 130 may be conducted based on voice questions and answers. For example, the dementia test processing unit 130 may perform the dementia test by providing questions for the test to the user and obtaining the test subject's answer voice from the user. Specifically, the dementia test progress unit 130 provides a specific topic (or standard, category, etc.) to the test subject and provides a question voice requesting that the test subject answer as many words as possible during a preset time. You can. The dementia test processing unit 130 may acquire an answer voice that the user answers in response to the question voice.

In one embodiment, the dementia test performed by the dementia test processing unit 130 may include a first test and a second test that are performed sequentially. Here, the “first test” may be a practice test to increase understanding of the test before proceeding with the second test. In addition, the “second test” may be the main test that is actually used to determine the presence or absence of dementia disease or the level of dementia symptoms in the test subject.

Detailed information on the dementia test performed by the dementia test progress unit 130 will be described later with reference to FIGS. 7 to 9.

The dementia test processing unit 130 performs the dementia test, and the test subject's response voice obtained from the user may be transmitted to the analysis unit 140.

The analysis unit 140 analyzes the test content based on the answer voice received from the dementia test progress unit 130. In one embodiment, as shown in FIG. 6, the analysis unit 140 may include a voice conversion unit 141, a word extraction unit 143, and a test content analysis unit 145.

The voice conversion unit 141 may perform the role of converting the answer voice received from the dementia test progress unit 130 into text data. The voice conversion unit 141 may be equipped with speech-to-text (STT) technology, and there is no limitation on the type of speech-to-text conversion technology.

In one embodiment, the voice conversion unit 141 divides the answer voice into segments, converts each segment into text, and then integrates the converted text segments to convert the answer voice into text data. . To this end, the voice conversion unit 141 may include, but is not limited to, a voice separation unit (not shown), a text conversion unit (not shown), and a text integration unit (not shown).

The word extraction unit 143 serves to extract at least one word from text data converted by the voice conversion unit 141 into the test subject's answer voice.

The word extraction unit 143 may extract at least one word related to the test content from the entire text data converted from the answer voice obtained during the dementia test. For example, when the dementia test performed by the dementia test progress unit 130 is conducted by answering as many words as possible corresponding to a specific topic during a preset time, the word extraction unit 143 extracts the test subject's answer voice. Words related to the test contents can be extracted from the converted text data. Specifically, the word extraction unit 143 can extract only words corresponding to answers to topics given in the test from the test subject's response voice.

The test content analysis unit 145 serves to analyze test content based on at least one word extracted by the word extraction unit 143.

The test content analysis unit 145 calculates a language fluency value based on at least one word extracted by the word extraction unit 143, and analyzes the test content by comparing and analyzing the language fluency value with a preset reference value. there is.

Here, the “verbal fluency value” is a numerical value of related abilities (semantic memory, executive function, working memory, etc.) to determine the presence and/or progress of dementia disease. The verbal fluency value is calculated by applying an addition/subtraction standard for at least one of the total number of words extracted from the test subject's answers, the number of words in the first half, the number of words in the second half, the number of letters per word, the number of category changes, the number of words per category, and the number of duplicate words. It can be calculated by scoring. Here, the type, number, and weight of variables used to calculate the language fluency value can be modified in various ways.

As a specific example, the test content analysis unit 145 can calculate the language fluency value through the following equation.

[Equation 1]

LFV=a1*A+a2*B+a3*C+a4*D+a5*E+a6*F+a7*G

LFV: Verbal Fluency Value

A: Total word count

B: First half word count

C: Number of words in the second half

D: Average number of characters per word

E: Number of category changes

F: Average number of words per category

G: Number of duplicate words

a1~a7: Weight variable (satisfies a1+a2+a3+a4+a5+a6+a7=1, and a1~a7 are constants between 0 and 1 respectively)

In one embodiment, the language fluency value may be calculated by reflecting not only the test subject's answers but also auxiliary information. Here, “auxiliary information” is basic information such as personal information about the test subject, such as gender (male, female), age (teens, 20s, 30s, 40s, 50s, 60s, 70s, etc.) 80s, 90s, etc.), education level (elementary school graduation, middle school graduation, high school graduation, master's degree, bachelor's degree, doctoral degree, etc.), number of people living together (1 person, 2 people, 3 people, 4 people, etc.), etc. . The test content analysis unit 145 differentially applies the type, number, and weight of variables used to calculate the language fluency value based on auxiliary information such as age, gender, education level, and number of people living together to determine the language fluency value. can be calculated accurately.

The test content analysis unit 145 can compare and analyze the calculated language fluency value with a preset value to determine the presence or progress of the test subject's dementia disease. Here, the “preset value” may be a preset comparison standard to determine the level of language fluency value calculated as a result of the test. These preset values can be grouped according to gender, age, education level, number of people living together, etc. and set differentially for each group. For example, gender (male, female), age (teens, 20s, 30s, 40s, 50s, 60s, 70s, 80s, 90s, etc.), education level (elementary school graduation, middle school graduation, high school graduation) The standard value of language fluency may be preset depending on the degree of graduation, master's degree, bachelor's degree, doctorate, etc.) and the number of people living together (1, 2, 3, 4, etc.). This standard value of language fluency can be set based on the test results of test completers belonging to the corresponding group, and can be continuously updated and managed based on information on additional test completers.

Figure 7 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.

Figure 8 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.

As shown in Figures 7 and 8, the dementia testing method according to an embodiment of the present invention includes a guidance voice providing step (S310), a first testing step (S330), a second testing step (S350), and a testing step. It may include a content analysis step (S400) and a test result transmission step (S500).

The guidance voice provision step (S310) is a step of providing a guidance voice to the user connected to the artificial intelligence call sent by the test server.

The guidance voice may include content to prevent the test subject from recognizing the call as a spam call, etc. and hanging up without proceeding with the test. In one embodiment, the guidance voice may include test subject (recipient) information, test agency information, test solution (program) information, recommender information, contact information acquisition path, compensation information provided upon test completion, etc. in the first half.

At this time, the test subject (recipient) information is inserted based on the test subject database described above, so that the guidance voice can be customized for each test subject. Through this, the test participation rate of test subjects connected by phone can be improved.

After the guidance voice is provided, a dementia test may be performed, and the dementia test may consist of a first test and a second test performed sequentially.

The first test progress step (S330) is a step in which the first test, which is a practice test, is performed and the test subject's (user)'s understanding of the test progress method is improved.

Referring to FIG. 8, the first test progress step (S330) includes a step of the test server providing a first question voice to the user (S331) and a step of the test server obtaining a first answer voice from the user (S333). can do.

Here, the first question voice may include a voice requesting an answer to the first topic during the first time, and the first answer voice may include a voice that the test subject answers in response to the first question voice.

The test subject can participate in the first test by answering in response to the first question voice, and the test subject's first answer voice is acquired through the microphone of the terminal 200 or the microphone of the input means of the electronic device 300, It may be transmitted to the dementia testing device 100 through the communication unit 110.

In one embodiment, the first answer voice obtained through the first test can be used as data to determine whether the test subject properly understood the test procedure. That is, the non-face-to-face dementia testing method according to an embodiment of the present invention may further include the step of determining the test subject's level of understanding of the test proceeding method based on the first answer voice.

For example, the dementia testing apparatus 100 may calculate a test subject's understanding of the test procedure method and determine whether to proceed with the test based on that value. Here, the “understanding value” is a value that quantifies the degree to which the test subject understands the test procedure method.

As a specific example, the dementia testing device 100 calculates a comprehension value based on the ratio of the word voice answered in response to the first question voice among the first answer voices and the other voices, and the calculated comprehension value is less than a preset value. In this case, the second test may not be performed.

As another specific example, the dementia testing device 100 calculates a comprehension value based on the number of words the test subject answers for the first topic presented in the first test, and if the calculated comprehension value is less than a preset value, the second The test may not proceed.

If the test subject does not proceed with the second test because it is determined that he or she does not properly understand the test progress method, the dementia test device 100 may provide a voice guiding the test progress method or proceed with scheduling the test thereafter. It is not limited.

In this case, the dementia testing device 100 may provide a voice guiding how the test is performed or may make a reservation for a later test, but is not limited thereto.

In another embodiment, the step of obtaining the first answer voice from the user in the first test progress step (S330) may be omitted.

The first test is only a test to increase understanding of the test method, and is not used as data to determine the language fluency of the test subject, so obtaining a voice response to the first test can be omitted.

Through this, the total amount of data transmitted and received during the inspection process can be reduced.

The second test progress step (S350) is the step of conducting the second test, which is the main test, and securing voice data used to determine the presence or absence of dementia disease and the level of dementia symptoms.

Referring to FIG. 7, the second test progress step (S350) may include providing a second question voice to the user (S351) and obtaining a second answer voice from the user (S353).

Here, the second question voice may include a voice requesting an answer to the second topic during a second time, and the second answer voice may include a voice that the test subject answers in response to the second question voice.

The test subject may participate in the second test by answering in response to the second question voice, and the test subject's second answer voice is acquired through the microphone of the user terminal 200 or the microphone of the electronic device 300, and the communication unit ( It may be transmitted to the dementia testing device 100 through 110).

The test content analysis step (S400) is a step in which the dementia testing device 100 analyzes the test content based on the second answer voice obtained from the user.

In the test content analysis step (S400), the analysis unit 140 of the dementia testing device 100 converts the second answer voice received from the user into text data, calculates a language fluency value based on the converted text data, and performs the test. Content can be analyzed. Specifically, the test content analysis step (S400) includes converting the second answer voice received from the user into text data, and extracting at least one word corresponding to the second subject of the second test from the converted text data. , calculating a language fluency value based on at least one word corresponding to the extracted second topic, comparing and analyzing the language fluency value with a preset reference value, and if the language fluency value is less than the preset reference value, a follow-up test subject. It may include the step of determining that the person is not eligible for follow-up testing if the language fluency value is higher than a preset standard value.

The step of calculating the language fluency value is a step of scoring by applying an addition or subtraction standard for at least one of the total number of words, the number of words in the first half, the number of words in the second half, the number of letters per word, the number of category changes, the number of words per category, and the number of duplicate words. may include. In one embodiment, the language fluency value can be calculated using Equation 1 described above.

Additionally, the preset reference value for comparing the calculated language fluency values may be differentially set for each of the test groups classified based on auxiliary information including at least one of gender, age, education level, and number of people living together. The calculated language fluency value can be compared and analyzed with the standard value set for the group to which the test subject belongs.

Meanwhile, in one embodiment, the test content analysis step (S400) is performed if the test subject has a test history, that is, if the user connected to the phone has a test history, the test subject's existing test data and the current test are obtained. A step of analyzing changes in speaking style based on the second answer voice may be further included. Here, “speech style” may include speech speed, accuracy of pronunciation, etc., but is not limited thereto.

The analysis unit 140 of the dementia testing device 100 analyzes the speech style of the second answer voice obtained in this test, compares it with the data on which the speech style of the answer voice obtained in the existing test was analyzed, and changes the speech style. The value can be calculated.

The analysis unit 140 of the dementia testing device 100 may determine a person to be subject to a follow-up test if the speech style change value is greater than or equal to a specific value (preset value).

In addition, the analysis unit 140 of the dementia testing device 100 determines whether the follow-up test subject is selected if the difference between the language fluency value calculated in the existing test and the language fluency value calculated in this test is greater than a specific value (preset value). It can be judged as follows.

This has the effect of preventing rapid worsening of symptoms by conducting a follow-up test not only in terms of the absolute language fluency value, but also in cases where language fluency is relatively reduced compared to the time of the existing test.

In one embodiment, the test content analysis step (S400) may further include determining the degree of understanding of the test progress method based on the first answer voice.

Specifically, the dementia testing device 100 can analyze the first answer voice to determine whether the test subject fully understands the test procedure and is answering, or whether the test subject does not understand and is giving an answer that is unrelated to the test content.

For example, the dementia testing device 100 determines that the test subject correctly understands the test procedure if the test subject answers more than a preset number of words for the first topic presented in the first test, and if not, It may be judged that the test subject does not properly understand how the test is conducted.

Even if the language fluency value calculated by analyzing the second answer voice is less than a preset standard value, the dementia test device 100 determines whether the comprehension value of the test progress method calculated by analyzing the first answer voice is less than a specific value (preset value). In this case, the patient may not be judged to be subject to a follow-up test immediately, but may proceed with a re-test after re-instruction on the test method. Through this, it is possible to improve the accuracy of the test by being able to distinguish cases where test subjects with excellent language fluency (without symptoms of dementia) received a low language fluency value due to a lack of understanding of the test process.

The test result transmission step (S500) is a step in which the test results analyzed in the test content analysis step (S400) are provided to the user.

In one embodiment, the test results may be transmitted in the form of a message, and the message may include a link to check the test results. The method of transmitting test results is not limited to the above examples and may be provided in various forms such as phone, mail, or message. In addition, the subject receiving the test results is not limited to the test subject, and can be applied to a variety of people around the test subject, such as housemates and guardians.

In one embodiment, the test result may include whether the patient is a subject for follow-up testing and information on predicting future symptoms. Here, the subsequent symptom prediction information is derived based on the symptom information of similar groups of people with similar auxiliary information (e.g., age, gender, education level, number of people living together, etc.) as the test subject, and the language fluency value decreases over time. It may include trends, follow-up test results, etc.

Figure 9 is an exemplary diagram schematically showing the time-series process of the dementia test progress stage according to an embodiment of the present disclosure.

Figure 9 is an exemplary diagram schematically showing the time-series process of the inspection progress stage according to an embodiment of the present invention.

As shown in FIG. 9, the inspection progress stage according to an embodiment of the present invention may include an introduction section, a first inspection, a second inspection, and a finishing section.

The introduction part corresponds to the guidance voice provision step (S310, see Figure 7), the first test part corresponds to the first test progress stage (S330, see Figure 7), and the second test part corresponds to the second test progress step (S330, see Figure 7). S350, see FIG. 7). Additionally, the finishing part may be a step in which a finishing voice is provided after the second test is completed. This finishing part may be omitted as needed.

The “first test” may be a practice test to increase understanding of the test before proceeding with the second test. Additionally, the “second test” may be the main test that is actually used to determine the presence or absence of dementia symptoms in the test subject.

The first test and the second test may have the same test procedure. For example, both the first test and the second test may be tests in which the user answers as many words as possible during a preset time period corresponding to a given topic. Test subjects can improve their understanding of the test process by taking the first test, which is a practice test, and can avoid variables due to lack of understanding of the test process when performing the second test, which is the main test. Accordingly, the accuracy of dementia testing can be improved.

Meanwhile, the first test and the second test performed by the dementia test progress unit 130 have the same test progress method, but the topics presented may be different.

For example, the first test may ask you to answer as many words as possible corresponding to the first topic, and the second test may ask you to answer as many words as possible corresponding to the second topic.

At this time, the first topic of the first test and the second topic of the second test may be different from each other. As a specific example, the first subject of the first test may be "plants", and the second subject of the second test may be "animals", but this is not limited, and the first subject and the second subject may be modified in various ways. and may include arbitrary, different topics.

Through this, it is possible to prevent the accuracy of the test from decreasing as the test subject directly applies the words that came to mind during the first test to the second test.

In other words, the test subject only increases his or her understanding of the test process through the first test, which is a practice test, but cannot train in advance to answer the second test, which is the main test. Therefore, the test subject also improves memory ability, agility, etc. in the second test. Since you have to use your skills to answer, the accuracy of the test can be improved.

Additionally, the progress time of the first test and the progress time of the second test may be different from each other.

Specifically, the first test may be conducted by requesting an answer to a first topic during a first time, and the second test may be conducted by requesting an answer to a second topic during a second time.

At this time, the first time, which is the progress time of the first test, may be shorter than the second time, which is the progress time of the second test. For example, the first time, which is the progress time of the first test, may be 10 seconds or more and 30 seconds or less, and the second time, which is the progress time of the second test, may be 30 seconds or more and 60 seconds or less, but is not limited thereto, and the first time is It can be modified in various ways to satisfy something shorter than the second time.

Through this, it is possible to improve the efficiency and concentration of the inspection by preventing the overall inspection time from being unnecessarily long by only improving the understanding of the inspection process method, which is the purpose of the first inspection.

Figure 10 is a flowchart schematically showing a portion of a dementia testing method according to an embodiment of the present disclosure.

Figure 107 is a flowchart schematically showing part of a dementia testing method according to an embodiment of the present invention. Hereinafter, since the same reference numbers in the drawings indicate the same components, descriptions of content that overlaps with the above-mentioned content will be omitted.

As shown in FIG. 10, the artificial intelligence call-based dementia testing method according to an embodiment of the present invention may further include a step of confirming the intention to proceed with the test (S100) and a step of collecting auxiliary information (S200). Meanwhile, it is also possible to include only one of the S100 step and the S200 step and omit the other step.

The intention to proceed with the test confirmation step (S100) is a step to confirm the intention to proceed with the test before proceeding with the dementia test to the test subject to whom the artificial intelligence call is connected.

The confirmation step (S100) of the intention to proceed with the test may be performed based on voice questions and answers. Specifically, the step of confirming the intention to proceed with the test (S100) is a step of providing a voice asking the user connected to the artificial intelligence call whether he or she wants to proceed with the test, obtaining a voice answering the intention to proceed with the test from the user, and based on the voice answering the intention to proceed with the test. It may include a step of confirming the test subject's intention to proceed with the test.

The dementia testing device 100 may perform the above-described dementia test when the doctor's response to the test is positive, and if the doctor's response to the test is negative, the dementia test device 100 may end the call or proceed with the procedure for reserving a future test schedule. Meanwhile, the voice answering the intention to proceed with the test can be converted to text data by applying speech to text (STT) technology and then analyzed.

The auxiliary information collection step (S200) is a step of collecting auxiliary information that can be used to analyze test contents before proceeding with the test.

The auxiliary information collection step (S200) may include providing an auxiliary information question voice asking one of gender, age, education level, and number of people living with the user, and obtaining an auxiliary information answer voice from the user. .

By proceeding with this auxiliary information collection step (S200), incompleteness of the test subject database can be compensated for. Meanwhile, the auxiliary information answer voice can be converted to text data by applying speech to text (STT) technology and then analyzed.

The auxiliary information collected through the auxiliary information collection step (S200) can be used to analyze test contents. Specifically, the artificial intelligence call-based dementia testing method according to an embodiment of the present invention may further include the step of classifying test groups. The step of classifying the test group may include converting the auxiliary information answer voice into text data and classifying the test group based on the text data. In other words, the dementia test device 100 can classify the group to which the test subject belongs based on the collected auxiliary information and apply a preset reference value for comparing the language fluency value set for the group to derive dementia test results more accurately. there is.

The method according to an embodiment of the present disclosure described above may be implemented as a program (or application) and stored in a medium in order to be executed in combination with a server, which is hardware.

The above-mentioned program is C, C++, JAVA, machine language, etc. that can be read by the processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented in the program. It may include code coded in a computer language. These codes may include functional codes related to functions that define the necessary functions for executing the methods, and include control codes related to execution procedures necessary for the computer's processor to execute the functions according to predetermined procedures. can do. In addition, these codes may further include memory reference-related codes that indicate at which location (address address) in the computer's internal or external memory additional information or media required for the computer's processor to execute the above functions should be referenced. there is. In addition, if the computer's processor needs to communicate with any other remote computer or server in order to execute the above functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes regarding whether communication should be performed and what information or media should be transmitted and received during communication.

The storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers that the computer can access or on various recording media on the user's computer. Additionally, the medium may be distributed to computer systems connected to a network, and computer-readable code may be stored in a distributed manner.

The steps of the method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which this disclosure pertains.

Above, embodiments of the present disclosure have been described with reference to the attached drawings, but those skilled in the art will understand that the present disclosure can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

10: Non-face-to-face dementia screening system based on artificial intelligence
100: Dementia testing device
110: Department of Communications
120: processor
130: Dementia test progress section
140: analysis department
150: storage unit
200: user terminal
300: electronic device

Claims (10)

a storage unit storing the phone number of at least one user for dementia testing;
A communication unit that communicates with the user's terminal; and
Contains a processor,
The processor,
Provides a call number for dementia testing to the user's terminal,
When a call is received from a specific terminal, determine whether the specific terminal matches the user's terminal,
Once the user's terminal is confirmed, a dementia test is performed,
Providing dementia test results to the user,
Non-face-to-face dementia screening device based on artificial intelligence.
According to paragraph 1,
The dementia testing device,
Obtain the user's personal information by conducting a voice question and answer using the terminal, or
Obtain the user's personal information by conducting questions and answers through an electronic device owned by the user or an electronic device linked to the user's terminal,
The electronic device includes an image output means,
Non-face-to-face dementia screening device based on artificial intelligence.
According to paragraph 1,
The dementia testing device,
Obtain the user's local information by conducting a voice question and answer using the terminal, or
Obtain the user's local information by obtaining GPS information from the terminal, or
Obtain local information about the user by conducting a question-and-answer process through an electronic device owned by the user or an electronic device linked to the user's terminal,
The electronic device includes an image output means,
Non-face-to-face dementia screening device based on artificial intelligence.
According to paragraph 1,
The processor,
When performing the dementia test, the dementia test is performed through an electronic device including an image output means and an input means,
Non-face-to-face dementia screening device based on artificial intelligence.
According to clause 4,
The processor,
Providing a query for the dementia test through an image output means of the electronic device,
Receiving user input through an input means of the electronic device,
Characterized in generating a test result for the dementia test based on the provided query and the received user input,
Non-face-to-face dementia screening device based on artificial intelligence.
According to clause 5,
The electronic device is a smart TV,
The input means is a remote control capable of inputting a signal to the smart TV and inputting the user's voice,
Non-face-to-face dementia screening device based on artificial intelligence.
According to clause 4,
The processor,
Characterized in that transmitting the dementia test results to the electronic device, generating visualization data based on the dementia test results, and controlling the visualization data to be output to the image output means.
Non-face-to-face dementia screening device based on artificial intelligence.
In clause 7,
The processor,
Based on the dementia test results, determine whether the user needs a follow-up test,
When it is determined that the user needs the follow-up examination, deriving at least one medical institution based on local information obtained for the user, and providing information about the derived at least one medical institution to the user Characterized by,
Non-face-to-face dementia screening device based on artificial intelligence.
A method performed by an artificial intelligence-based non-face-to-face dementia screening device,
Providing a call number for dementia testing to the user's terminal;
When a call is received from a specific terminal, determining whether the specific terminal matches the user's terminal;
If the user's terminal is confirmed, performing a dementia test; and
Providing dementia test results to the user;
Including, artificial intelligence-based non-face-to-face dementia screening method.
A computer-readable recording medium that is combined with a hardware computer and stores a program for executing the artificial intelligence-based non-face-to-face dementia screening method of paragraph 9.

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