KR102374780B1 - Method, server and computer program for providing digital care solution based on game for improving mental disorder - Google Patents

Abstract

Provided are a method, a server, and a computer program for providing a game-based digital treatment solution for alleviating mental disorders. A method for providing a game-based digital treatment solution for alleviating mental disorders according to various embodiments of the present invention is a method performed by a computing device. The method comprises a step of determining a mental disorder level for a user, and a step of providing a digital treatment solution for treating mental disorders based on the determined mental disorder level, wherein the step of providing the digital treatment solution includes a step of providing game-based learning content to a user terminal.

Description

Method of providing game-based digital treatment solution for improvement of mental disorders, server and computer program

Various embodiments of the present invention relate to a method, a server, and a computer program for providing a game-based digital treatment solution for the improvement of mental disorders.

Digital therapeutics are software medical devices that provide evidence-based therapeutic interventions to patients to prevent, manage, and treat medical disorders or diseases.

As chronic diseases increase due to the aging population, interest in digital therapeutics is increasing in order to solve unmet demand due to increased social burden such as health insurance finance and problems with medical access.

Due to the rapid increase in medical demand due to the aging of the population and the improvement of living standards, the ratio of ordinary medical expenses to GDP continues to rise, and social burdens such as health insurance finance are increasing. As the number of depressed patients is rapidly increasing, the demand for overall digital health care is increasing, and interest in digital therapeutics is also increasing along the lines of this.

Digital therapeutics have fewer toxic and side effects compared to conventional therapeutics due to their SW characteristics, and do not require manufacturing, transport, and storage like over-the-counter drugs. It has the advantage of being able to manage a large number of patients irrespective of health insurance finances, medical supply shortage, and regional ubiquity. This can reduce the risk of infection, so it is attracting attention in the recent COVID-19 situation.

On the other hand, the conventional digital therapeutics industry has mainly developed technologies and products that improve the physical and mental health of users through connection with hardware devices (eg, rehabilitation devices, VR devices, sensor devices, etc.), and accordingly, digital therapeutics There is a problem that a separate hardware device must be prepared to obtain the .

Korean Patent Laid-Open Patent No. 10-2019-0025549 (2019.03.11)

The problem to be solved by the present invention is for the purpose of solving the problems of the conventional method for providing a digital therapeutic agent described above, in consideration of the user's level of mental disability, a digital treatment solution including user interaction game-based learning content in the user's terminal By providing a game-based digital treatment solution for the improvement of mental disorders that can provide a digital treatment solution for the treatment of mental disorders only with software configuration without a separate hardware device, it relates to a server and a computer program.

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

A method of providing a game-based digital treatment solution for improvement of mental disorders according to an embodiment of the present invention for solving the above-mentioned problems, in a method performed by a computing device, determining the level of mental disability for a user and providing a digital treatment solution for the treatment of a mental disorder based on the determined level of mental disorder, wherein the providing of the digital treatment solution includes: based on the determined level of mental disorder, the user It may include providing the game-based learning content to the terminal of.

In various embodiments, the step of determining the level of mental disability includes providing test content to the user's terminal, and a pre-learned artificial intelligence model - the pre-learned artificial intelligence model is each of a plurality of users performing the test As the user performs the provided test content using a model learned using a plurality of log data collected as the content is performed and medical diagnosis result data for each of the plurality of users as training data The method may include determining the level of mental disability of the user by analyzing the collected log data.

In various embodiments, the providing of the learning content may include classifying the user's mental disability level into any one of a plurality of grades based on the determined mental disability level, and any one of the user's classified based on the grade of , providing learning content including query content of difficulty corresponding to any one grade and guide information for the query content, wherein as the collected log data is analyzed, the user The step of determining the level of mental disability of the user may include updating the level of mental disability of the first user by using log data collected as the first user classified into the first grade performs the provided learning content, and 1 When the first user achieves the rating adjustment criterion as the mental disability level of the user is updated, the test content is provided to the first user, and log data collected by performing the provided test content determining to adjust the rating of the first user from the first rating to a second rating that is the next rating of the first rating using It may include a step of including limited guide information including only the content or including only the query content of the difficulty corresponding to the second grade.

In various embodiments, the providing of the learning content may include, based on the determined level of mental disability, generating first learning content including an arbitrary topic, and providing the generated first learning content; Provide the contents of a query on the arbitrary topic through the provided first learning contents, and answer contents from the user in response to the provided query contents - The contents of the answer are the opinions and feelings of the user on the arbitrary topic Including information - receiving an input and analyzing the inputted answer content to determine the appropriateness of the inputted answer content, and to analyze the inputted answer content based on the result of determining the appropriateness of the inputted answer content It may include the step of processing the correct answer or providing guide information for input of the correct answer.

In various embodiments, the step of receiving the answer content from the user may include, for the content of the query on the arbitrary subject, a plurality of options - each of the plurality of options provides different opinions and emotion information corresponding to the content of the query. including - provided, wherein the type and number of the plurality of options is determined based on the determined level of mental disorder, and receiving a selection of any one of the plurality of options from the user as an answer content may include steps.

In various embodiments, the receiving of answers from the user may include receiving one or more keywords from the user with respect to the content of the query on the arbitrary topic and the one or more input keywords through an auto-complete function. At least a part of the answer content is automatically completed using the, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability, it may include a step.

In various embodiments, the providing of the guide information includes, when it is determined that the inputted answer content is not appropriate, analyzes the inputted answer content to determine the user's intention to write the answer content, and One or more templates are selected from among a plurality of templates on the basis of an intention to write answer content and provided to the user as the guide information, wherein the selected one or more templates include one or more recommended words and a recommended sentence structure according to the determined answer content writing intention. , which includes information about the recommended expression method, may include a step.

In various embodiments, the providing of the learning content may include, based on the determined level of mental disability, generating second learning content including an arbitrary situation, and providing the generated second learning content; The content of a query for the arbitrary situation is provided through the provided second learning content, and the content of an answer from the user in response to the content of the provided question - The content of the answer is the user's behavior information for the random situation Including - receiving input and analyzing the inputted answer content to determine the appropriateness of the inputted answer content, and processing the inputted answer content as a correct answer based on the result of determining the appropriateness of the inputted answer content Alternatively, it may include providing guide information for inputting a correct answer.

In various embodiments, the step of receiving the answer content from the user may include a plurality of options including one or more incorrect answer options and one or more correct answer options for the query content for the arbitrary situation - each of the plurality of options is the Including different behavioral information corresponding to the content of the query - provided, but the type of behavior information included in each of the plurality of options and the number of the plurality of options are determined based on the determined level of mental disorder , and receiving a selection of any one of the plurality of options from the user as an answer content.

In various embodiments, the receiving of the answer from the user may include: receiving one or more keywords from the user with respect to the query content for the arbitrary situation, and the one or more input keywords through an auto-complete function At least a part of the answer content is automatically completed using the, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability, it may include a step.

In various embodiments, the providing of the guide information includes providing, as the guide information, a hint about a correct answer of the query content, when it is determined that the input answer content is not appropriate, wherein the hint is Providing game content based on user interaction to the user's terminal based on the determined level of mental disability, and providing the game content based on a result of the user performing the provided game content, may include a step.

In various embodiments, the providing of the learning content may include receiving a scenario for a specific situation from the user, and based on the determined level of mental disability, one or more corresponding to the input scenario for the specific situation. The method may include generating a query content and generating learning content including a scenario for the input specific situation and the one or more generated query content, and providing the generated learning content to a user.

A game-based digital treatment solution providing server for the improvement of mental disorders according to another embodiment of the present invention for solving the above problems is loaded into the processor, the network interface, the memory, and the memory, and the processor Including a computer program executed by, wherein the computer program provides a digital treatment solution for the treatment of mental disorders based on instructions for determining the level of mental disorder for the user and the determined level of mental disorder Including instructions, the instructions for providing the digital treatment solution may include instructions for providing game-based learning content to the user's terminal, based on the determined level of mental disability.

A computer program recorded on a computer-readable recording medium according to another embodiment of the present invention for solving the above-described problems is combined with a computing device, the step of determining the level of mental disability for the user and the determined Comprising the step of providing a digital treatment solution for the treatment of a mental disorder based on a level of mental disorder, wherein the step of providing the digital treatment solution is based on a game to the user's terminal based on the determined level of mental disorder It may be stored in a computer-readable recording medium to execute a method for providing a game-based digital treatment solution for the improvement of mental disorders including the step of providing learning contents of

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention, by providing a digital treatment solution including user interactive game-based learning content to the user's terminal in consideration of the user's level of mental disability, only a software configuration without a separate hardware device It has the advantage of being able to provide a digital treatment solution for treatment.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a game-based digital treatment solution providing system for improving mental disorders according to an embodiment of the present invention.
2 is a hardware configuration diagram of a game-based digital treatment solution providing server for improving mental disorders according to another embodiment of the present invention.
3 is a flowchart of a method for providing a game-based digital treatment solution for improving mental disorders according to another embodiment of the present invention.
4 is a flowchart of a method of determining a user's level of mental disability, in various embodiments.
5 is a flowchart of a method of providing first learning content, in various embodiments.
6 is a flowchart of a method of providing second learning content, in various embodiments.
7 is a flowchart of a method of updating a user's mental disability level according to a result of performing learning content, according to various embodiments of the present disclosure;

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, as an example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a diagram illustrating a game-based digital treatment solution providing system for improving mental disorders according to an embodiment of the present invention.

Referring to FIG. 1 , a game-based digital treatment solution providing system for improving mental disorders according to an embodiment of the present invention includes a digital treatment solution providing server 100 , a user terminal 200 , and an external server 300 . and a network 400 .

Here, the game-based digital treatment solution providing system for the improvement of mental disorders shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and additional components may be added as necessary. , may be changed or deleted.

In an embodiment, the digital treatment solution providing server 100 may provide a digital treatment solution as a digital treatment for the treatment of a user's mental disorder (eg, developmental disability, intellectual disability, autism, etc.).

In various embodiments, the digital treatment solution providing server 100 determines the level of mental disability of the user, and based on the determination result, a game-based digital treatment solution providing service for improving mental disorders that provides learning content as a digital treatment. can provide Here, the game-based digital treatment solution providing service for the improvement of mental disorders may be implemented in the form of a web or an application, but is not limited thereto.

Here, the learning content may be content in the form of a game. For example, the learning content may be a metaverse-based game in which a virtual world is constructed to constitute a society by itself, and a plurality of users can act as members of a society in the virtual world, and mental disorders It may include detailed content for treatment and learning (eg, question-and-answer content, user interaction game content, etc.).

In one embodiment, the user terminal 200 may be connected to the digital treatment solution providing server 100 through the network 400 , and a game-based system for improving mental disorders provided by the digital treatment solution providing server 100 . You can receive a digital treatment solution through the digital treatment solution provision service.

Here, the user terminal 200 includes an operating system (OS) capable of driving a game-based digital treatment solution providing service for improving mental disorders provided in the form of an application, and a smartphone including a display in at least some areas ( Smartphone), but is not limited thereto, and the user terminal 200 is a wireless communication device that guarantees portability and mobility, and includes navigation, Personal Communication System (PCS), Global System for Mobile communications (GSM), and Personal Digital (PDC). Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro ( It may include all kinds of handheld-based wireless communication devices such as a Wireless Broadband Internet terminal, a smart pad, and a tablet PC.

In addition, here, the network 400 refers to a connection structure in which information exchange is possible between each node such as a plurality of terminals and servers, and an example of such a network includes a local area network (LAN), a wide area It may include a communication network (WAN: Wide Area Network), the Internet (WWW: World Wide Web), a wired/wireless data communication network, a telephone network, a wired/wireless television communication network, and the like. In addition, wireless data communication networks include 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), and Wi-Fi. , Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (Bluetooth) network, NFC ( A Near-Field Communication) network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, etc. may be included, but are not limited thereto.

In one embodiment, the external server 300 may be connected to the digital treatment solution providing server 100 through the network 400, and the digital treatment solution providing server 100 is a game-based digital treatment for improving mental disorders. Various information and data required to perform the solution providing method or various information and data generated by performing the game-based digital treatment solution providing method for improving mental disorders may be stored and managed. For example, the external server 300 may be a storage server separately provided outside the digital treatment solution providing server 100, but is not limited thereto. Hereinafter, a hardware configuration of the digital treatment solution providing server 100 performing a game-based digital treatment solution providing method for improving mental disorders will be described with reference to FIG. 2 .

2 is a hardware configuration diagram of a game-based digital treatment solution providing server for improving mental disorders according to another embodiment of the present invention.

Referring to FIG. 2 , in various embodiments, the digital treatment solution providing server 100 (hereinafter, “server 100”) includes one or more processors 110 and a computer program 151 executed by the processor 110 . It may include a memory 120 for loading (Load), a bus 130 , a communication interface 140 , and a storage 150 for storing the computer program 151 . Here, only the components related to the embodiment of the present invention are shown in FIG. 2 . Accordingly, one of ordinary skill in the art to which the present invention pertains can see that other general-purpose components other than those shown in FIG. 2 may be further included.

The processor 110 controls the overall operation of each component of the server 100 . The processor 110 includes a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art. can be

In addition, the processor 110 may perform an operation for at least one application or program for executing the method according to the embodiments of the present invention, and the server 100 may include one or more processors.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110 , a random access memory (RAM) and a read access memory (ROM). -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 120 stores various data, commands and/or information. The memory 120 may load the computer program 151 from the storage 150 to execute methods/operations according to various embodiments of the present disclosure. When the computer program 151 is loaded into the memory 120 , the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151 . The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 130 provides a communication function between the components of the server 100 . The bus 130 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired/wireless Internet communication of the server 100 . In addition, the communication interface 140 may support various communication methods other than Internet communication. To this end, the communication interface 140 may be configured to include a communication module well known in the art. In some embodiments, the communication interface 140 may be omitted.

The storage 150 may non-temporarily store the computer program 151 . When performing the game-based digital treatment solution providing process for the improvement of mental disorders through the server 100, the storage 150 provides a game-based digital treatment solution providing process for the improvement of mental disorders. information can be stored.

The storage 150 is a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or well in the art to which the present invention pertains. It may be configured to include any known computer-readable recording medium.

The computer program 151 may include one or more instructions that, when loaded into the memory 120 , cause the processor 110 to perform methods/operations according to various embodiments of the present invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present disclosure by executing the one or more instructions.

In one embodiment, the computer program 151 ameliorates a mental disorder comprising determining a level of mental disorder for the user and providing a digital therapeutic solution for treatment of the mental disorder based on the determined level of mental disorder. It may include one or more instructions for performing a method of providing a game-based digital treatment solution for

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, a method of providing a game-based digital treatment solution for improving mental disorders performed by the server 100 will be described with reference to FIGS. 3 to 7 .

3 is a flowchart of a method for providing a game-based digital treatment solution for improving mental disorders according to another embodiment of the present invention.

Referring to FIG. 3 , in step S110 , the server 100 may determine the level of mental disability for the user.

In various embodiments, the server 100 may provide test content for determining the user's mental disability level to the user terminal 200 , and may determine the user's mental disability level through the test content. In this case, the server 100 may classify the user based on the mental disorder the user suffers from, for the purpose of determining the level of the mental disorder more accurately.

In various embodiments, the server 100 may collect log data generated as the user performs content for testing, and analyze the collected log data using a pre-learned artificial intelligence model, thereby causing the user's mental disorder. level can be judged. Hereinafter, with reference to FIG. 4 , a method of determining a user's level of mental disability will be described in detail.

4 is a flowchart of a method of determining a user's level of mental disability, in various embodiments.

Referring to FIG. 4 , in step S210 , the server 100 may provide test content, and log data generated as a user performs test content (eg, user input input during test content execution process). values and test results) can be collected.

Here, the test content is content for determining the level of mental disability of the user, and the content for the test is presented to the user performing the test content and the content of the question about the random situation and the answer is inputted in response, It may be content in which a user is presented with an arbitrary topic and a question about the arbitrary topic, and an answer is received in response thereto.

At this time, in the case of learning content for the treatment of mental disorders, since it has the purpose of treatment and learning for users with mental disabilities, appropriate answers to the questions as well as the content of questions about any situation and any subject are provided. By providing input guide information together, it is implemented in a form that assists the user in entering the correct answer to the question, whereas in the case of test content, the user is asked to determine and evaluate the user's level of mental disability. Since it has the purpose of conducting a test for can be implemented.

In addition, here, in the case of test content that the server 100 provides to adjust the rating for the user's mental disability level, since the current user's mental disability level knows the rating, the current user's mental disability level is Test content of appropriate difficulty is provided, but in the case of test content that is provided to users for the first time to determine the user's level of mental disability, since there is no history of determining the user's level of mental disability, test content with arbitrary difficulty It may be implemented in the form of adjusting the difficulty of the test content to be provided to the user by using the log data collected as the test content is performed.

In step S220 , the server 100 may determine the level of mental disability of the user by analyzing the log data collected through step S210 using the pre-learned artificial intelligence model.

Here, the pre-learned artificial intelligence model includes a plurality of log data collected as each of a plurality of users perform content for testing and medical diagnosis result data for each of a plurality of users (eg, psychiatric analysis results, brain images). Neurological analysis results, etc.) as learning data may be a learned model.

An artificial intelligence model (or computational model, neural network, network function, neural network) consists of one or more network functions, one or more network functions being a set of interconnected computational units that can generally be referred to as 'nodes'. can be composed of These ‘nodes’ may also be referred to as ‘neurons’. The one or more network functions are configured by including at least one or more nodes. Nodes (or neurons) constituting one or more network functions may be interconnected by one or more ‘links’.

In the AI model, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the AI model to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the respective input nodes. An output node value may be determined based on the weight.

As described above, in the AI model, one or more nodes are interconnected through one or more links to form an input node and an output node relationship within the AI model. Characteristics of the AI model may be determined according to the number of nodes and links in the AI model, the correlation between nodes and links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two AI models having different weight values between the links, the two AI models may be recognized as different from each other.

Some of the nodes constituting the artificial intelligence model may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance of n from the initial input node may constitute n layers. The distance from the initial input node may be defined by the minimum number of links that must be passed to reach the corresponding node from the initial input node. However, the definition of such a layer is arbitrary for the purpose of explanation, and the order of the layer in the AI model may be defined in a different way from that described above. For example, a layer of nodes may be defined by a distance from the final output node.

The initial input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the AI model. Alternatively, in the relationship between nodes based on the link in the artificial intelligence model network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the artificial intelligence model. In addition, the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node. The artificial intelligence model according to an embodiment of the present disclosure may have more nodes in the input layer than nodes in the hidden layer close to the output layer, and is an artificial intelligence model in which the number of nodes decreases as the input layer progresses to the hidden layer. can

An AI model may include one or more hidden layers. The hidden node of the hidden layer may have the output of the previous layer and the output of the neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be calculated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In various embodiments, the server 100 may build learning data for learning the artificial intelligence model, and using the constructed learning data, teacher learning (supervised learning), non-supervised learning (unsupervised learning), and anti-teacher The artificial intelligence model may be trained by at least one method of semi-supervised learning.

The training of the AI model is to minimize the error in the output. In the learning of the AI model, iteratively input the training data into the AI model, calculate the output of the AI model for the training data and the error of the target, and reduce the error of the AI model in the direction of reducing the error. This is the process of updating the weight of each node of the AI model by backpropagating from the output layer to the input layer.

In the case of teacher learning, learning data in which correct answers are labeled in each learning data is used (ie, labeled learning data), and in the case of comparative learning, the correct answers may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning related to data classification may be data in which categories are labeled in each of the learning data. The labeled training data is input to the AI model, and an error can be calculated by comparing the output (category) of the AI model with the label of the training data.

As another example, in the case of comparative learning about data classification, an error may be calculated by comparing the input, the training data, with the output of the artificial intelligence model. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the AI model, and the connection weight of each node of each layer of the AI model may be updated according to the back propagation. The change amount of the connection weight of each node to be updated may be determined according to a learning rate.

The computation of the AI model on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the AI model. For example, in the early stages of learning an AI model, a high learning rate can be used to allow the AI model to quickly achieve a certain level of performance, thereby increasing efficiency, and using a low learning rate at the end of learning can increase accuracy.

In the training of artificial intelligence models, in general, the training data may be a subset of the real data (that is, the data to be processed using the trained artificial intelligence model), and thus the error for the training data is reduced, but for the real data There may be learning cycles in which errors increase. Overfitting is a phenomenon in which errors on actual data increase by over-learning on training data as described above. For example, a phenomenon in which an AI model that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting.

Overfitting can act as a cause of increasing errors in machine learning algorithms. In order to prevent such overfitting, various optimization methods can be used. In order to prevent overfitting, methods such as increasing training data, regularization, or dropout in which a part of nodes in the network are omitted in the process of learning, may be applied.

In various embodiments, the server 100 trains an artificial intelligence model using log data labeled with medical diagnosis result data as training data, thereby generating a model learning the association between log data and medical diagnosis result, that is, for testing. It is possible to create a model that extracts a medical diagnosis result as result data by using log data collected as content is performed as input data.

In various embodiments, the server 100 analyzes log data using a pre-learned artificial intelligence model, so that for each of a plurality of items (eg, social adaptation ability, communication ability and expression ability, etc.) For example: deficiency, disability, etc.) can be extracted, and the mental disability level score for each of the plurality of items can be calculated by using the extracted probability value to score the mental disability level for each of the plurality of items. can In addition, the server 100 may determine the mental disability level for each item by using the mental disability level score for each of the plurality of items.

Here, the mental disability level score is a numerical value so that the high and low mental disability level can be intuitively recognized. A higher score may mean a lower mental disability level, and a lower score may mean a higher mental disability level.

More specifically, the server 100 can calculate the probability that an abnormality occurs in each of social adaptation ability, communication ability, and expression ability as a value within the range of 0 to 1 by analyzing log data using the previously learned artificial intelligence model. there is.

Thereafter, the server 100 may calculate a mental disability level score within a range of 0 to 100 points based on the calculated probability value. For example, the server 100 points when the probability value is 0 to 0.2, 80 to 100 points, 0.2 to 0.4, 60 to 80 points, 0.4 to 0.6, 40 to 60 points, and 0.6 to 0.8, 20 to 40 points. And when it is 0.8 to 1, it is possible to calculate a mental disability level score from 0 to 20 points. However, the present invention is not limited thereto.

In addition, the server 100 may set a plurality of grades according to the mental disability level score in advance, and a plurality of mental disability grades for each of social adaptation ability, communication ability and expression ability using the user's mental disability level score It can be classified into any one of the grades. For example, the server 100 has a first grade when the mental disability level score is 0 to 20 points, a second grade when 20 to 40 points, a third grade when 40 to 60 points, a fourth grade when 60 to 80 points, and When the score is 80-100, it can be classified as 5th grade. However, the present invention is not limited thereto.

Later, the server 100 may update the mental disability level score for the user (add or subtract a score of a predetermined size) using the log data collected as the user performs specific learning content, and the updated mental When the disability level score reaches a preset rating adjustment criterion (eg, 20 points, 40 points, 60 points, and 80 points, which are the reference scores for classifying each grade), the content for testing is provided by providing test content. By using the log data collected accordingly, it is possible to determine the rating adjustment for the user. The method of updating the mental disability level score and adjusting the grade will be described later with reference to FIG. 7 .

Referring again to FIG. 3 , in step S120 , the server 100 may provide a digital treatment solution that provides learning content for the treatment of mental disorders based on the level of mental disorder of the user determined through step S110. .

In various embodiments, the server 100 may generate game-based learning content based on the user's level of mental disability, and may provide the generated learning content to the user terminal 200 .

In various embodiments, the server 100 may determine an area in need of improvement and treatment based on the level of mental disability for each of the user's social adaptation ability, communication ability, and expression ability, and learning based on the determined area You can create and provide content. For example, the server 100 may generate and provide learning content (eg, first learning content) for improving and treating the expressive ability part when the level of mental disability for the expressive ability part of the user is less than or equal to the standard, and , when the level of mental disability for the social adaptation ability and communication ability part of the user is below the standard, learning content (eg, second learning content) for improving and treating the social adaptation ability and communication ability part may be provided. However, the present invention is not limited thereto, and the server 100 may obtain a request for provision of specific learning content from the user, and may generate and provide the type of learning content that the user desires to be provided according to the user's needs. . Hereinafter, a method of providing the first learning content or the second learning content to the user will be described with reference to FIGS. 5 and 6 .

5 is a flowchart of a method of providing first learning content, in various embodiments.

Referring to FIG. 5 , referring to FIG. 5 , in step S310 , the server 100 obtains a request for providing learning content for improving expressive ability from the user or when the level of mental impairment for the expressive ability part of the user is less than or equal to the standard In this case, based on the user's mental disability level, the first learning content may be generated, and the generated first learning content may be provided to the user terminal 200 .

Here, the first learning content may be learning content for improving the user's expressive ability, that is, content that allows users to learn how to express their emotions and utilize them for users who are not good at expressing their ideas and emotions. For example, the first learning content inquires about a user's opinion on a specific topic and a specific situation, or asks for a feeling or emotion when encountering a specific work (picture, music, etc.), and in response, It may be learning content that is conducted in such a way that the content of the answer expressing the emotion of the person is input. However, the present invention is not limited thereto.

In addition, here, the first learning content provided by the server 100 is described as being conducted in a form in which the server 100 provides a query to the user and receives an answer from the user on a specific topic, etc. , but not limited thereto, and in some cases, users belonging to the same class as the specific user or matching a specific user with another user whose difference in mental disability level score with the specific user is less than a preset value, and matching each other on the same subject It can be carried out in the form of alternately inputting questions and answers between each other.

At this time, when the first learning content provided by the server 100 is implemented in a form in which a plurality of users match and alternately input questions and answers on the same topic, the answers input from other users are shared. Instead, if it is determined that the answers of other users are not appropriate, the learning effect can be increased by modifying and rewriting other users' answers in an appropriate form or performing the process of modifying and rewriting them according to one's own style. can

In step S320, the server 100 may provide the contents of a query on any topic through the first learning content provided through the step S310, and the contents of answers to the questions from the user (eg, the user on any topic) opinion and emotion information, etc.) can be input.

In various embodiments, the server 100 may provide a plurality of options for the query content on an arbitrary topic, and may receive any one option among the plurality of options as the answer content. For example, the server 100 may generate and provide a plurality of options including different opinions and emotional information to the user with respect to the content of the query on a specific topic, and select any one of the plurality of options from the user can receive

In various embodiments, the server 100 may determine the type and number of a plurality of options (types of opinion and emotion information included in each of the plurality of options) and the number based on the user's mental disability level. For example, when it is determined that the server 100 is the first grade, which is the lowest grade, the mental disability level for the user's expressive ability, the server 100 may generate and provide two options to the user, and each of the two options The included opinion and emotion information may be composed of words with low difficulty or short phrases, clauses, and sentences in which words with low difficulty are combined. On the other hand, when it is determined that the server 100 is the fifth grade, which is the highest grade, the mental disability level for the user's expressive ability can generate and provide five options to the user, and the five options are included in each of the five options. The opinion and emotion information may be configured in the form of a long sentence in which words of high difficulty are combined or a paragraph in which a plurality of sentences are combined.

In various embodiments, the server 100 may directly receive an answer from the user for the content of a query on an arbitrary subject. For example, the server 100 may provide a user interface (UI) that outputs the contents of a query on a specific topic, and provides information on the contents of the query directly from the user through the answer contents input area output on the UI. You can input your answer.

In various embodiments, when receiving one or more keywords from the user as answer content, the server 100 may automatically complete at least a portion of the answer content using one or more keywords through an auto-complete function.

In various embodiments, the server 100 may determine the degree of auto-completion of the answer content through the auto-completion function, based on the user's level of mental disability. For example, when the server 100 is determined to be the first grade, which is the lowest grade, the level of mental disability for the user's expressive ability, the phrase including one or more keywords as it receives one or more keywords from the user; It can be implemented in the form of automatic completion of clauses or sentences. On the other hand, when it is determined that the server 100 is the fifth grade, which is the highest grade, the mental disability level for the user's expressive ability, the server 100 recommends words including one or more keywords or other words having the same meaning as the corresponding words. It may be provided as , and as the user selects any one recommended word, it may be implemented in a form in which only the corresponding recommended word is reflected in the answer content.

In step S330, the server 100 may analyze the content of the answer input by the user through step S320, and determine the appropriateness of the content of the input answer. For example, the server 100 may store a relationship between the preset model answer content for the first query content provided to the user and the attributes of the answer content input from the user (eg, sentence structure, type of word used, expression method, etc.) The degree of similarity can be calculated, and by comparing the calculated degree of similarity with a reference value, it is possible to determine the appropriateness of the answer content input by the user.

Here, the exemplary answer content may mean answer content generated together with the query content while the server 100 generates the query content, but is not limited thereto, and a plurality of input from other users provided with the same query content Among the answers, it may be an answer that has been processed as a correct answer, or it may be an answer entered as a response to a question provided in advance to the user's guardian (eg, a parent).

In addition, although it is described here that the server 100 compares the similarity between the answer content input by the user and the model answer content to determine the appropriateness, it is not limited thereto, and the By providing the answer content input by the user, it can be implemented in a form in which the guardian of the specific user directly determines whether the content of the answer of the specific user is appropriate.

In step S340, if the server 100 determines that the answer input from the user is appropriate (eg, if the similarity between the answer content and the model answer is greater than or equal to the reference value), the server 100 may process the answer input from the user as the correct answer. And, it is possible to provide a reward according to the input of the correct answer.

Here, the reward may be goods that can be used on the game content provided by the server 100 or a predetermined experience value for growing the user's game character on the game content, but is not limited thereto.

In addition, here, the server 100 compares the word included in the answer content input from the user with the word included in the model answer content to determine the similarity, but to the word included in the answer content input from the user and the model answer content. When the meanings of the included words are the same, the similarity can be determined by processing them as the same word.

In various embodiments, the server 100 may set different reference values for similarity comparison according to the user's mental disability level. For example, the server 100 may set a reference value for a user with a low level of mental disability to be higher than a reference value for a user with a high level of mental disability. However, the present invention is not limited thereto.

In step S350, when the server 100 determines that the answer input from the user is not appropriate (eg, the similarity between the answer content and the model answer content is less than the reference value), for input of the correct answer to the query content Guide information can be provided.

In various embodiments, when it is determined that the answer content input by the user is not appropriate, the server 100 determines the user's answer content writing intention by analyzing the answer content, and based on the determined answer content writing intention, a plurality of It is possible to select one or more templates from among the templates and provide it to the user as guide information.

Here, the template is meant to include guide information on which words are good to use, what sentence structure is good to write, or what expression methods are good to use according to the user's intention to write the answer content can do. For example, the server 100 provides a template for inputting a positive answer when the user wants to input a positive answer on a specific topic, that is, a word, sentence structure, and expression frequently used in the content of a positive answer. A template for guiding the method may be provided to the user as guide information. In this case, the template provided to the user may be implemented in various forms such as video (image, video), text, and voice.

In addition, the server 100 selects answer content written with the same intent as the user from among a plurality of answers input from a plurality of users who provided the same query content as the query content provided to the user, and uses the selected answer content as a template and can be provided to users as guide information. For example, the server 100 analyzes the answer content written with the same intention as the user, sets the word used in the answer content as a recommended word, sets the expression method of the answer content as the recommended expression method, By setting the sentence structure of the corresponding answer as the recommended sentence structure, it is possible to provide information on a recommended word, a recommended expression method, and a recommended sentence structure as a template.

In various embodiments, the server 100 may analyze the answer content to determine the user's answer content writing intention, and recommend a plurality of templates corresponding to the determined answer content writing intention to the user as candidate templates, and the user In response to selecting any one candidate template from among the plurality of candidate templates, guide information for writing answer content according to the selected candidate template may be provided.

6 is a flowchart of a method of providing second learning content, in various embodiments.

Referring to FIG. 6 , in step S410 , the server 100 receives a request for providing learning content for improving social adaptability and communication ability from the user or when the level of mental disability for the social adaptation ability and communication ability part of the user is below the standard or from the user In the case of acquisition, on the basis of the user's mental disability level, the second learning content may be generated, and the generated second learning content may be provided to the user terminal 200 .

Here, the second learning content is learning content for improving the social adaptation ability and communication ability of the user, that is, behavior and communication for users who have difficulty in adapting to living in a community with a large number of people or who have poor communication with users It may be content that allows you to learn a method and utilize it. For example, the second learning content simulates a specific situation, and inquires about the user's behavior and communication, such as how to behave or how to act on issues occurring in the process of simulating the specific situation, and As an answer, it may be learning content that proceeds in such a way that the user decides how to act or how to communicate in a specific situation. However, the present invention is not limited thereto.

In addition, here, the second learning content provided by the server 100 is described as being conducted in a form in which the server 100 provides the user with a query with respect to a specific situation and receives an answer from the user. , but not limited thereto, and in some cases, users belonging to the same class as the specific user or matching a specific user with another user whose mental disability level score difference with the specific user is less than a preset value, and matching each other with respect to the same situation It can be implemented in the form of alternately inputting questions and answers between each other.

At this time, when the second learning content provided by the server 100 is implemented in a form in which a plurality of users match and alternately input the query and answer for the same situation, the answer content input from other users is shared. Instead, if it is determined that the answers of other users are not appropriate, the learning effect can be increased by modifying and rewriting other users' answers in an appropriate form or performing the process of modifying and rewriting them according to one's own style. can

In step S420 , the server 100 may provide the content of a query for an arbitrary situation through the second learning content provided through the step S410 , and the content of an answer to the question from the user (eg, the user for an arbitrary situation) behavior information) can be input.

In various embodiments, the server 100 may provide a plurality of options for the query content for an arbitrary situation, and any one of the plurality of options may be selected as the answer content. For example, the server 100 selects a plurality of options (including different behavioral information corresponding to the query content) including one or more incorrect answer options and one or more correct answer options with respect to the query content for a specific situation, and the user may be provided to the user, and any one of a plurality of options may be selected by the user.

In various embodiments, the server 100, based on the level of mental disability of the user, the type of a plurality of options (eg, the type of behavior information included in each of the plurality of options) and the number (eg, incorrect answer option and correct answer option) number, the total number of options) can be determined.

In various embodiments, the server 100 may directly receive an answer from the user for the content of the query for an arbitrary situation. For example, the server 100 may provide a user interface (UI) that outputs the contents of a query on a specific topic, and provides information on the contents of the query directly from the user through the answer contents input area output on the UI. You can input your answer.

In various embodiments, when receiving one or more keywords from the user as answer content, the server 100 may automatically complete at least a portion of the answer content using one or more keywords through an auto-complete function.

In various embodiments, the server 100 may determine the degree of auto-completion of the answer content through the auto-completion function, based on the user's level of mental disability.

In various embodiments, when the server 100 provides the user with a query about the user's behavior through the metaverse-based second learning content in the game form, the user regarding the movement of the game character input from the user in the game The input may be obtained as the content of the answer.

In step S430 , the server 100 may analyze the content of the answer input by the user through step S420 , and determine the appropriateness of the content of the input answer. Here, the method of determining the relevance of the answer content input by the user may be implemented in the same form as the method of determining the relevance performed in step S330 of FIG. 5 , but is not limited thereto.

In step S440, when the server 100 determines that the answer input from the user is appropriate (eg, when the similarity between the answer content and the model answer is greater than or equal to the reference value), the server 100 may process the answer input from the user as the correct answer. And, it is possible to provide a reward according to the input of the correct answer.

In step S450, when the server 100 determines that the answer input from the user is not appropriate (eg, when the similarity between the answer content and the model answer content is less than the reference value), for input of the correct answer to the query content Guide information can be provided.

In various embodiments, when it is determined that the answer content input by the user is inappropriate, the server 100 may provide a hint about the correct answer of the query content as guide information.

In this case, the hint may be provided based on a result of providing the user interaction-based game content to the user terminal 200 based on the user's mental disability level, and the user performing the game content. For example, the server 100 sets the difficulty level of the game content based on the user's mental disability level when it is determined that the answer input from the user is not appropriate or when a hint provision request is obtained from the user, According to the difficulty level, user interaction-based game content can be created and provided to the user.

Here, the user interaction-based game content may include a puzzle game and a shooting game. For example, the server 100 generates puzzle game content by dividing an image containing the contents of a hint to be provided to the user into an arbitrary number and arbitrary shape according to the level of difficulty set based on the user's level of mental disability. and by providing it to the user, the user can complete the puzzle to obtain hint information. In addition, the server 100 may generate a shooting game content that obtains a score by matching a specific object to the user, and provide it to the user, thereby allowing the user to acquire hint information according to the score obtained through the shooting game.

In various embodiments, the server 100 may generate and provide learning content by reflecting a user's desired situation or desired subject.

First, the server 100 may receive a scenario or a specific topic for a specific situation from the user.

Thereafter, the server 100 may generate one or more query contents corresponding to a scenario for a specific situation or a specific topic input by the user. For example, the server 100 may generate one or more query contents corresponding to a specific topic or scenario for a specific situation input from the user based on the user's level of mental disability.

Thereafter, the server 100 generates learning content including a scenario for a specific situation or a specific topic and one or more query contents, and provides the generated learning content to the user, so that the user can learn about the situation he or she wants. can

In various embodiments, the server 100 analyzes a plurality of log data collected as the user performs a plurality of learning content, and an answer determined to be inappropriate for a query among a plurality of answers input from the user An incorrect answer note may be generated by extracting only the content, and learning content may be created and provided by using the answers included in the incorrect answer note at every preset period. For example, the server 100 provides the user with the answer content of any one of the plurality of answers included in the incorrect answer note and the question content corresponding to the answer content, and at the same time requests to change the content of the answer into an appropriate form. Learning content can be provided. Thereafter, the server 100 may delete the content of the answer from the incorrect answer note when the user changes the content of the answer to an appropriate form, and when the user fails to change the content of the answer to the appropriate form, the content of the answer is returned to the incorrect answer note Write it down so that the user can learn what was repeatedly wrong.

In various embodiments, when the user performs the first learning content and the second learning content according to the above method, the server 100 uses log data collected as the first learning content and the second learning content are performed. to update the user's mental disability level. Hereinafter, it will be described with reference to FIG.

7 is a flowchart of a method of updating a user's mental disability level according to a result of performing learning content, according to various embodiments of the present disclosure;

Referring to FIG. 7 , in steps S510 to S540 , the server 100 may provide the first learning content and the second learning content to the user, and may provide query content through the provided learning content, In response, you can get the content of the answer. In addition, the server 100 may determine the appropriateness of the content of the answer obtained from the user, and if it is determined that the content of the answer is not appropriate, the server 100 may provide guide information for input of the correct answer. Here, steps S510 to S540 of FIG. 7 may be implemented in the same form as steps S310 to S340 of FIG. 5 and steps S410 to S440 of FIG. 6 , but are not limited thereto.

In step S550 , when it is determined that the obtained answer is appropriate, the server 100 may update the user's mental disability level. For example, when it is determined that the user's answer is appropriate, the server 100 may update the user's mental disability level score by adding a predetermined score to the previously calculated user's mental disability level score.

Here, the learning content provided by the server 100 is a game-based learning content, a metaverse in which a virtual world is built to constitute a society by itself, and a plurality of users can act as members of society in the virtual world. Based on a game form, for example, a role-playing game (RPG), a game character corresponding to each user may exist, and the user's mental disability level score indicates the degree of growth of the game character. It may be set in the form of a level that is an indicator (eg, when the user's mental disability level score is 50, the level of the character corresponding to the user is set to 50).

That is, when it is determined that the answer content obtained from the user is appropriate, the server 100 may provide an experience value (eg, n%) of a predetermined size for the growth of a game character corresponding to the user as a reward, When a character achieves 100% of the acquired experience, the level of the game character can be raised by 1 level, and in response to the level of the game character rising by 1 level, the mental disability level score for the user of the game character is 1 point can elevate

Here, the size of the experience value provided to the game character may be differentially applied according to the number of times the user inputs the answer content until the user inputs an appropriate answer to the question content. For example, the server 100 may provide an experience of 10% when the user inputs an appropriate answer to the question only once, and 5 when the user inputs an appropriate answer after 2 to 3 times % of the experience can be provided, and 2% of the experience can be provided if the answer is entered more than 3 times. However, the present invention is not limited thereto.

In step S560, the server 100 may determine whether the user's mental disability level score has achieved a rating adjustment criterion. For example, the server 100 determines whether the user's mental disability level score updated through step S550 reaches 20 points, 40 points, 60 points, and 80 points, which are the reference scores for classifying each grade, It may be determined whether the user's mental disability level score has achieved a rating adjustment criterion.

In step S570, when it is determined that the user's mental disability level score has achieved the grade adjustment criterion through step S560, the server 100 may provide test content for determining grade adjustment, and in response thereto It is possible to obtain answers to questions included in the test content from the user.

In various embodiments, the server 100 may provide test content including query content of a difficulty level corresponding to a level to be adjusted for the user. For example, if the user currently belongs to the first grade and the user wants to upgrade from the first grade to the second grade as the mental disability level score is updated, the level of difficulty corresponding to the first grade Content for testing including query content can be provided.

In various embodiments, the server 100 includes limited guide information including only a part of the query content and at least part of the difficulty level corresponding to the level to be adjusted for the user, or the query of the difficulty level corresponding to the level to be adjusted for the user. You can provide content for testing that contains only content. For example, the server 100 adjusts the user's grade from the lowest grade, first grade to the second grade, or from the second grade to the third grade, the query content of the difficulty corresponding to each grade On the other hand, if you want to adjust the 3rd grade to 4th grade or 4th grade to 5th grade while providing test content including limited guide information for the content of the query, the query content of the difficulty corresponding to each grade You can provide content for testing that includes only

On the other hand, if the server 100 determines that the user's mental disability level score has not achieved the grade adjustment criterion through step S560, steps S510 to S540 until the user's mental disability level score achieves the grade adjustment criterion can be repeated.

In step S580, the server 100 may calculate a similarity by comparing the response contents of the user input through step S570 with the model responses to the query contents included in the test contents, and based on the calculated similarity, the user You can decide whether or not to adjust the rating. For example, the server 100 may adjust the user's rating if the similarity between the user's answer and the best answer is greater than or equal to the reference value, and if the similarity between the user's answer and the best answer is less than the reference value, the user's rating can be maintained at the current level.

In various embodiments, when the similarity between the user's answer content and the exemplary answer content is less than the reference value, the server 100 determines the difference between the user's answer content and the model answer content based on the similarity between the user's answer content and the model answer content and the magnitude of the difference between the reference value. If the value is less than 1, it is possible to re-determine whether to adjust the rating by providing test content once again. When the value is greater than 2, the user's mental disability score is lowered by the second size larger than the first size, so that the rating adjustment review can be performed again after the user performs more learning content.

In this way, the server 100 expresses the level of mental disability for the user as the level of the user's game character, so that the user can intuitively check his/her mental disability level.

In various embodiments, the server 100 differentiates and provides functions in the game according to the user's game character level, thereby improving the learning effect, such as performing the learning content harder for the user to raise the level of the game character. can

For example, the server 100 may provide a service in which the user can decorate his or her game character within the game, but may differentiate a range in which decoration is possible according to the level of the game character. For example, in the server 100, when the level of the game character is low, only basic clothing items such as tops, bottoms, and shoes can be worn, whereas when the level of the game character is high, not only basic clothing items but also hats, glasses, watches, necklaces, It is possible to wear accessory items such as rings, and it can be set to change the shape or color of the head.

As another example, the server 100 may differentiate the limit of the number of times the learning content can be provided according to the level of the game character, the size of a reward provided as the learning content is performed, and the like.

As another example, when the level of the game character is low according to the level of the game character, the server 100 may unilaterally receive and perform only the learning content generated by the server 100, while the level of the game character is high. Learning content can be created in consideration of a user's desired situation and topic, and can be provided and performed.

A method of providing a game-based digital treatment solution for the improvement of the aforementioned mental disorders has been described with reference to the flowchart shown in the drawings. For a brief description, the method of providing a game-based digital treatment solution for the improvement of mental disorders has been described by showing a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are shown in the present specification It may be performed in a different order or may be performed concurrently. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: digital treatment solution providing server
200: user terminal
300 : external server
400: network

Claims (14)

A method performed by a computing device, comprising:
determining a level of mental disability for the user; and
Comprising the step of providing a digital treatment solution for the treatment of mental disorders based on the determined level of mental disorder,
Providing the digital treatment solution comprises:
Based on the determined level of mental disability, comprising the step of providing a game-based learning content to the user's terminal,
The step of providing the learning content,
based on the determined level of mental disorder, generating first learning content including a random subject, and providing the generated first learning content;
Provide the contents of a query on the arbitrary topic through the provided first learning contents, and answer contents from the user in response to the provided query contents - The contents of the answer are the opinions and feelings of the user on the arbitrary topic including information - receiving an input; and
Guide information for analyzing the input answer content to determine the appropriateness of the input answer content, and processing the input answer content as a correct answer based on the result of determining the adequacy of the input answer content or for inputting the correct answer comprising the step of providing
The step of receiving the answer content from the user,
With respect to the content of the query on the arbitrary subject, a plurality of options are provided, each of the plurality of options including different opinions and emotion information corresponding to the query content, wherein the number of the plurality of options is determined by the user. The higher the grade classified based on the level of mental disability, the higher the number of the plurality of options, the higher the grade, the higher the difficulty option is provided; and
Comprising the step of receiving a selection of any one of the plurality of options from the user as an answer content,
The step of receiving the answer content from the user,
receiving one or more keywords from the user with respect to the content of the query on the arbitrary subject; and
Through the auto-complete function, at least a part of the answer content is automatically completed using the input one or more keywords, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability comprising the steps of
A method of providing a game-based digital treatment solution for the improvement of mental disorders. According to claim 1,
The step of determining the level of mental disorder,
providing test content to the user's terminal; and
Pre-trained artificial intelligence model - The pre-trained artificial intelligence model uses a plurality of log data collected as each of a plurality of users perform the test content and medical diagnosis result data for each of the plurality of users as learning data. and determining the level of mental disability of the user by analyzing log data collected as the user performs the provided test content using -
A method of providing a game-based digital treatment solution for the improvement of mental disorders. 3. The method of claim 2,
The step of providing the learning content,
classifying the user's mental disability level into any one of a plurality of grades based on the determined mental disability level; and
Based on any one of the ratings classified by the user, providing learning content including query content of difficulty corresponding to the one rating and guide information for the query content,
The step of determining the level of mental disability of the user by analyzing the collected log data,
updating the mental disability level of the first user by using log data collected as the first user classified into the first grade performs the provided learning content; and
Logs collected by providing the test content to the first user and performing the provided test content when the first user achieves the rating adjustment criterion as the first user's mental disability level is updated It is decided to adjust the rating of the first user from the first rating to a second rating that is the next rating of the first rating by using data, wherein the test content includes a query content of a difficulty corresponding to the second rating and Including a step that includes limited guide information that includes only at least a portion of the content or includes only the query content of the difficulty corresponding to the second grade,
A method of providing a game-based digital treatment solution for the improvement of mental disorders. delete delete delete According to claim 1,
The step of providing the guide information includes:
When it is determined that the input answer content is not appropriate, the input answer content is analyzed to determine the user's intention to write the answer content, and based on the determined answer content writing intention, one or more templates among a plurality of templates is selected and provided to the user as the guide information, wherein the selected one or more templates include one or more recommended words according to the determined answer content writing intention, information on a recommended sentence structure, and information on a recommended expression method containing,
A method of providing a game-based digital treatment solution for the improvement of mental disorders. According to claim 1,
The step of providing the learning content,
based on the determined level of mental disorder, generating second learning content including an arbitrary situation, and providing the generated second learning content;
The content of a query for the arbitrary situation is provided through the provided second learning content, and the content of an answer from the user in response to the content of the provided question - The content of the answer is the user's behavior information for the random situation including - receiving an input; and
Guide information for analyzing the input answer content to determine the appropriateness of the input answer content, and processing the input answer content as a correct answer based on the result of determining the adequacy of the input answer content or for inputting the correct answer comprising the step of providing
A method of providing a game-based digital treatment solution for the improvement of mental disorders. 9. The method of claim 8,
The step of receiving the answer content from the user,
Provide a plurality of options including one or more incorrect answer options and one or more correct answer options for the content of the query for the arbitrary situation, each of the plurality of options including different behavior information corresponding to the content of the query. , The type of behavior information included in each of the plurality of options and the number of the plurality of options will be determined based on the determined level of mental disorder; and
Comprising the step of receiving a selection of any one of the plurality of options from the user as an answer content,
A method of providing a game-based digital treatment solution for the improvement of mental disorders. 9. The method of claim 8,
The step of receiving the answer content from the user,
receiving one or more keywords from the user with respect to the content of the query for the arbitrary situation; and
Through the auto-completion function, at least a portion of the answer content is automatically completed using the input one or more keywords, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability comprising the steps of
A method of providing a game-based digital treatment solution for the improvement of mental disorders. 9. The method of claim 8,
The step of providing the guide information includes:
When it is determined that the inputted answer is not appropriate, a hint about the correct answer of the query is provided as the guide information, and the hint is sent to the user's terminal based on the determined level of mental disability. Providing game content based on user interaction, comprising the step of being provided based on a result of the user performing the provided game content, comprising the steps of,
A method of providing a game-based digital treatment solution for the improvement of mental disorders. According to claim 1,
The step of providing the learning content,
receiving a scenario for a specific situation from the user;
generating one or more query contents corresponding to the input scenario for the specific situation based on the determined level of mental disorder; and
generating learning content including a scenario for the input specific situation and the one or more generated query contents, and providing the generated learning content to a user,
A method of providing a game-based digital treatment solution for the improvement of mental disorders. processor;
network interface;
Memory; and
A computer program loaded into the memory and executed by the processor,
The computer program is
instructions for determining the level of mental disability for the user; and
Including instructions for providing a digital treatment solution for the treatment of a mental disorder based on the determined level of mental disorder,
The instructions for providing the digital treatment solution include,
On the basis of the determined level of mental disability, including instructions for providing game-based learning content to the user's terminal,
Instructions for providing the learning content,
instructions for generating a first learning content including an arbitrary subject based on the determined level of mental disorder, and providing the generated first learning content;
Provide the contents of a query on the arbitrary topic through the provided first learning contents, and answer contents from the user in response to the provided query contents - The contents of the answer are the opinions and feelings of the user on the arbitrary topic contains information - an instruction to receive input; and
Guide information for analyzing the input answer content to determine the appropriateness of the input answer content, and processing the input answer content as a correct answer based on the result of determining the adequacy of the input answer content or for inputting the correct answer including instructions to provide
The instruction for receiving the answer content from the user is,
With respect to the content of the query on the arbitrary subject, a plurality of options are provided, each of the plurality of options including different opinions and emotion information corresponding to the query content, wherein the number of the plurality of options is determined by the user. The higher the grade, the higher the grade classified based on the level of mental disability, the higher the grade, the higher the difficulty of the options are provided. and
Including an instruction for receiving any one of the plurality of options from the user as an answer content,
The instruction for receiving the answer content from the user is,
instructions for receiving one or more keywords from the user with respect to the content of the query on the arbitrary subject; and
Through the auto-completion function, at least a portion of the answer content is automatically completed using the input one or more keywords, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability which includes instructions,
A server that provides game-based digital treatment solutions for the improvement of mental disorders. combined with a computing device,
determining a level of mental disability for the user; and
Comprising the step of providing a digital treatment solution for the treatment of mental disorders based on the determined level of mental disorder,
Providing the digital treatment solution comprises:
Based on the determined level of mental disability, comprising the step of providing a game-based learning content to the user's terminal,
The step of providing the learning content,
based on the determined level of mental disorder, generating first learning content including a random subject, and providing the generated first learning content;
Provide the contents of a query on the arbitrary topic through the provided first learning contents, and answer contents from the user in response to the provided query contents - The contents of the answer are the opinions and feelings of the user on the arbitrary topic including information - receiving an input; and
Guide information for analyzing the input answer content to determine the appropriateness of the input answer content, and processing the input answer content as a correct answer based on the result of determining the adequacy of the input answer content or for inputting the correct answer comprising the step of providing
The step of receiving the answer content from the user,
With respect to the content of the query on the arbitrary subject, a plurality of options are provided, each of the plurality of options including different opinions and emotion information corresponding to the query content, wherein the number of the plurality of options is determined by the user. The higher the grade classified based on the level of mental disability, the higher the number of the plurality of options, the higher the grade, the higher the difficulty option is provided; and
Comprising the step of receiving a selection of any one of the plurality of options from the user as an answer content,
The step of receiving the answer content from the user,
receiving one or more keywords from the user with respect to the content of the query on the arbitrary subject; and
Through the auto-completion function, at least a portion of the answer content is automatically completed using the input one or more keywords, but the degree of automatic completion of the answer content through the auto-completion function is determined based on the determined level of mental disability Stored in a computer-readable recording medium to execute a method for providing a game-based digital treatment solution for the improvement of mental disorders comprising the steps of,
A computer program recorded on a computer-readable recording medium.

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