KR102686964B1 - Apparatus and method for supporting psychological treatment using five minds mutual restriction therapy based on artificial intelligence - Google Patents

Abstract

The present disclosure relates to a device and method for supporting psychological treatment using artificial intelligence-based Oji Ascension, which includes a communication module that communicates with an external device; For each of the five emotions of anger, distress, fear, joy, and sadness, storing at least one process to provide psychotherapy support services based on the Oji Ascension method that seeks to change emotions based on complementary corresponding emotions. storage module; and a control module that provides the psychological treatment support service based on the at least one process, wherein the control module stores emotional information including changes in each user's emotional state and corresponding emotions accumulated and stored during a preset period. Collects, analyzes the emotional state of the user using an artificial intelligence-based pre-trained emotional treatment model based on the emotional information, and provides solution information that generates at least one corresponding emotion corresponding to the emotion determined according to the analysis results. After creation, the solution information can be provided to the user terminal.

Description

Psychotherapy support device and method using artificial intelligence-based ascension method {APPARATUS AND METHOD FOR SUPPORTING PSYCHOLOGICAL TREATMENT USING FIVE MINDS MUTUAL RESTRICTION THERAPY BASED ON ARTIFICIAL INTELLIGENCE}

The present disclosure relates to a psychological treatment support and method, and more specifically, the present disclosure relates to a psychological treatment support device and method using artificial intelligence-based Oji Ascension.

Oji Ascension is one of the psychotherapies used in Oriental medicine, and is based on the idea that the cause of disease originates in the mind.

This is an application of the theory of coexistence and opposites in the theory of the five elements. In the theory of the five elements, harmony is achieved through coexistence and opposites, and connected to this are the seven emotions (seven psychological actions of a person, namely joy, anger, If emotional problems arise due to excessive sadness, joy, love, hate, or greed, these should be treated by suppressing the emotions. In other words, the purpose is to bring emotions into harmony.

Therefore, there is a need to develop technology that can support psychological treatment by analyzing the user's emotional state and purifying the user's emotional state into corresponding emotions based on the Oji Ascension method.

Korean Patent Publication No. 10-2023-0103326 (Publication date: July 7, 2023)

The present disclosure uses an artificial intelligence-based pre-trained emotion treatment model to collect data about the user, analyze the emotional state, and purify the user's emotion into an emotion corresponding to the user's emotion determined according to the analysis results based on the Oji Ascension method. The aim is to provide a psychological treatment support device and method using artificial intelligence-based Oji Ascension method to support psychological treatment.

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

A psychological treatment support device using an artificial intelligence-based Oji ascending method according to an aspect of the present disclosure for achieving the above-described technical problem includes a communication module that communicates with an external device; For each of the five emotions of anger, distress, fear, joy, and sadness, storing at least one process to provide psychotherapy support services based on the Oji Ascension method that seeks to change emotions based on complementary corresponding emotions. storage module; and a control module that provides the psychological treatment support service based on the at least one process, wherein the control module stores emotional information including changes in each user's emotional state and corresponding emotions accumulated and stored during a preset period. Collects, analyzes the emotional state of the user using an artificial intelligence-based pre-trained emotional treatment model based on the emotional information, and provides solution information that generates at least one corresponding emotion corresponding to the emotion determined according to the analysis results. After creation, the solution information can be provided to the user terminal.

On the other hand, a method of supporting psychological treatment using an artificial intelligence-based augmentation method according to one aspect of the present disclosure includes the steps of collecting emotional information including changes according to emotional states and corresponding emotions for each user accumulated and stored over a preset period of time. ; Analyzing the user's emotional state using an artificial intelligence-based pre-trained emotional treatment model based on the emotional information; generating solution information that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis results; and providing the solution information to a user terminal, wherein the change information is based on at least one of a keyword and a sentence entered by the user through the user terminal during the preset period, and the determined emotion is, It can include at least one of five emotions: anger, distress, fear, joy, and sadness.

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

According to the means for solving the above-described problem of the present disclosure, data on the user is collected and the emotional state is analyzed using an artificial intelligence-based pre-trained emotional treatment model, and the user's emotional state is determined based on the analysis results based on the Oji Ascent method. It can support psychotherapy by purifying emotions with corresponding emotions.

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

1 is a diagram illustrating the network structure of a psychological treatment support system using an artificial intelligence-based Oji ascending method according to an embodiment of the present disclosure.
Figure 2 is a flowchart showing the operation of a psychological treatment support system using an artificial intelligence-based Oji ascending method according to an embodiment of the present disclosure.
Figure 3 is a diagram showing the configuration of a psychological treatment support device using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure.
Figure 4 is a diagram illustrating a method of supporting psychological treatment using an artificial intelligence-based remote elevation method according to an embodiment of the present disclosure.
FIG. 5 is a diagram illustrating in detail the operation of classifying groups for a plurality of users in a method of supporting psychological treatment using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure.
Figure 6 is a diagram illustrating in detail the operation of selecting content and generating solution information in a method of supporting psychological treatment using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure.
FIG. 7 is a diagram illustrating in detail the operation of processing feedback information about solution information in a method of supporting psychological treatment using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure.
Figure 8 is a diagram showing a method for pre-learning an emotion therapy model based on artificial intelligence according to an embodiment of the present disclosure.

The advantages and features of the present disclosure and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the present disclosure is complete and to those skilled in the art to which the present disclosure pertains. It is provided to fully convey the scope of the present disclosure, and the present disclosure is defined only by the scope of the claims.

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

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless clearly specifically defined.

Like reference numerals refer to like elements throughout this disclosure. This disclosure does not describe all elements of the embodiments, and general content or overlapping content between embodiments in the technical field to which this disclosure pertains is omitted. As used in the specification, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and the “unit” or “module” performs certain roles. However, “part” or “module” is not limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a “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, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and “parts” or “modules” can be combined into smaller components and “parts” or “modules” or into additional components and “parts” or “modules”. Could be further separated.

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

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

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

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

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

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

The terms used in the following description are defined as follows.

Although the description is limited to 'support device' in this specification, it is based on a separate platform and/or web page, and is based on complementary emotions in each of the five emotions of anger, distress, fear, joy, and sadness. It is a terminal of a service provider who wants to provide psychological treatment support services based on the Oji Ascension method that promotes change, and can include a variety of devices capable of performing computational processing. That is, the support device 100 may further include various devices such as computers, servers, and/or portable terminals, or may take the form of any one of them, but is not limited thereto.

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

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

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

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

Figure 1 is a diagram illustrating the network structure of a psychological treatment support system using an artificial intelligence-based remote ascent method according to an embodiment of the present disclosure.

Referring to FIG. 1, a psychotherapy support system (hereinafter referred to as 'support system') based on the Oji Ascent method according to an embodiment of the present disclosure 10 includes a support device 100 and at least one user terminal 200. It may be configured to include.

First, as described above, the support device 100 is a device (terminal) of a service provider who wishes to provide psychological treatment support services based on a separate platform and/or web page, and each of the support devices 100 includes at least one user terminal 200. We create and provide solution information for psychological treatment.

Specifically, the support device 100 collects emotional information including changes according to each user's emotional state and corresponding emotions accumulated and stored over a preset period of time, and provides artificial intelligence-based pre-learned emotional treatment based on the emotional information. The model is used to analyze the emotional state of the user. Here, emotional information is information generated using at least one data that may include changes according to each user's emotional state and corresponding emotions. For example, the emotional information is input by the user through his or her user terminal for a preset period of time. It may be generated based on at least one keyword or sentence.

Thereafter, the support device 100 generates solution information that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis result, and then provides the solution information to the user terminal of the corresponding user. At this time, the determined emotion may be at least one of the five emotions of anger, distress, fear, joy, and sadness, but this is only one example, and the emotion may be classified into more types of emotions, and is not limited thereto. .

Meanwhile, when feedback information is received from the user terminal 200 that has received the solution information, the support device 100 calculates and stores a feedback score for each of at least one content included in the solution information based on the feedback information. do. Here, the feedback information includes the results of the user performing psychological treatment based on at least one content included in the solution information through the user terminal 200 (hereinafter referred to as 'psychotherapy performance results'). , It may include the time the user performed psychological treatment based on each of at least one piece of content, emotional state changed accordingly, user satisfaction, etc. At this time, the changed emotional state and user satisfaction can be confirmed through a question tree or survey.

Additionally, the support device 100 may analyze the results of the psychological treatment, generate performance information, and transmit the performance information to at least one preset stakeholder terminal (not shown). At this time, at least one stakeholder terminal may be a terminal owned by a stakeholder related to the user (user himself, medical staff in charge, guardian, etc.). Additionally, the performance information may include at least one of the user's performance history, whether there is a change in emotions, psychological treatment judged to be necessary, at least one recommended content for the psychological treatment, and recommended medical staff/medical institutions. However, this performance information may be automatically generated and provided in response to the feedback information each time the user performs psychological treatment, or may be set to be generated and provided only at preset intervals or when the user requests it. The form and cycle of provision are not limited.

To this end, the support device 100 may generate and provide an emotional treatment model by learning an original model using the emotional information of each at least one user and solution information corresponding to the emotional information as learning data. As a result, solution information optimized for the user's emotional information is provided. This emotion therapy model will be described in detail below based on Figure 8.

Meanwhile, at least one user terminal (200-1, ...200-n) is one of the terminals to receive psychological treatment support services through a separate platform or web page provided by the support device (100). Psychotherapy is performed based on at least one content included in the solution information provided by ). At this time, the at least one content includes at least one of game data, video data, and audio data. Additionally, the solution information may include at least one content or information about a link or path through which the at least one content can be provided.

To this end, each of the at least one user terminal 200-1, ... 200-n may register a user by making a service provision request to the support device 100 in order to receive this psychological treatment support service.

Meanwhile, each of the at least one user terminal 200-1, ... 200-n may transmit feedback information about the psychological treatment to the support device 100, and receive the performance information from the support device 100. can be provided.

Here, at least one user terminal 200 is a computer, UMPC (Ultra Mobile PC), workstation, net-book, etc. that can install and run a plurality of applications (i.e. applications) desired by each user. PDAs (Personal Digital Assistants), portable computers, web tablets, wireless phones, mobile phones, smart phones, Pads, smart watches ( Smart watch, wearable terminal, e-book, PMP (portable multimedia player), portable game console, navigation device, black box or digital camera, etc. It may be a mobile communication terminal, etc. That is, each of the at least one user terminal 200 may be provided in various forms, and the number, type, and form are not limited.

Figure 2 is a flowchart showing the operation of a psychological treatment support system using the artificial intelligence-based Oji Ascension method according to an embodiment of the present disclosure, and only one user terminal is shown for convenience of explanation. , As described above, it may consist of at least one or more, and the number and type are not limited.

Referring to FIG. 2, first, the support device 100 builds a support system for the psychological treatment support service by generating and providing a pre-trained emotional treatment model based on artificial intelligence (S101).

Next, the user terminal 200 installs and executes the application (S103) and requests service provision to receive psychological treatment support services (S105).

Next, the support device 100 registers (new user) or confirms (existing user) the user according to the request in step S105 (S107) and collects emotional information about the user (S109).

Next, the support device 100 generates solution information based on the emotional information collected in step S109 (S111) and transmits the solution information to the user terminal 200 of the corresponding user (S113).

Next, the user terminal 200 performs a solution (psychotherapy) based on the solution information received in step S113 (S115) and generates feedback information including the results of the performance (S117), The feedback information is transmitted to the support device 100 (S119).

Next, the support device 100 calculates, stores, and manages a feedback score based on the feedback information received in step S119 (S121), and generates performance information using the feedback information according to need or settings ( S123).

Meanwhile, although not shown in FIG. 2, the support device 100 transmits the performance information generated in step S123 to the user terminal 200 of the corresponding user or to at least one stakeholder terminal (not shown) preset for the corresponding user. Can be sent.

Below, we will look at the specific configuration and operation of the support device 100.

Figure 3 is a diagram showing the configuration of a psychological treatment support device using the artificial intelligence-based Oji ascending method according to an embodiment of the present disclosure.

Referring to FIG. 3, the karaoke service providing device, that is, the support device 100, according to an embodiment of the present disclosure may be configured to include a communication module 110, a storage module 130, and a control module 150. there is.

The communication module 110 transmits and receives at least one information or data to at least one device/terminal. Here, the at least one device/terminal may be a device/terminal that wishes to use (or receive) a psychological treatment support service in addition to the at least one user terminal 200 described above, and its type and form are not limited.

In addition, this communication module 110 can perform communication with other devices and transmits and receives wireless signals in a communication network based on wireless Internet technologies.

Wireless Internet technologies include, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), and WiMAX (Worldwide). Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), etc. Supported devices (100) transmits and receives data according to at least one wireless Internet technology, including Internet technologies not listed above.

For short range communication, Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, NFC (Near Field Communication), Wi -Short-distance communication can be supported using at least one of Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies. Such wireless communication between the device 100 and at least one user terminal 200 may be supported using wireless area networks. At this time, the short-range wireless communication network may be a short-range wireless personal area network.

Meanwhile, the storage module 130 may store data about at least one process (algorithm) for providing a psychological treatment support service or a program that reproduces the process. In addition, the storage module 130 may further store processes for performing other operations, but this is not limited.

In addition, the storage module 130 can store a plurality of application programs (application programs) running on the support device 100, data and commands for operating the production device 100. These application programs At least some of them may be downloaded from an external server through wireless communication. Meanwhile, the application program is stored in at least one memory provided in the storage module 130 and installed on the support device 100 for control. It can be driven to perform an operation (or function) by at least one processor stored in the storage module 130 through the module 150.

Meanwhile, at least one memory is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g., SD or XD memory, etc.). , RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) magnetic memory, It may include at least one type of storage medium among magnetic disks and optical disks. In addition, memory can store information temporarily, permanently, or semi-permanently, and can be provided as a built-in or removable type.

Additionally, the storage module 130 may be further linked with a separate external server (including a cloud server).

In addition to operations related to the application program, the control module 150 controls all components within the support device 100 based on at least one processor to process input or output signals, data, information, etc., or store signals in at least one memory. Various processes can be performed by executing stored commands, algorithms, and applications, and appropriate information or functions can be provided or processed to provide services that enable the production of user-participatory content.

Specifically, the control module 150 collects emotional information including changes according to each user's emotional state and corresponding emotions accumulated and stored during a preset period, and provides artificial intelligence-based pre-learned emotional treatment based on the emotional information. The user's emotional state is analyzed using the model, solution information that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis result is generated, and then the solution information is provided to the user's user terminal.

At this time, when generating solution information, the control module 150 generates solution information by searching for at least one content that causes at least one corresponding emotion corresponding to each emotion determined by analyzing the emotional information of the user. . As previously explained, the present disclosure is based on the Oji Ascension method, which seeks to change emotions based on complementary corresponding emotions of various human emotions, and for each emotion, there are no-seung-sa (怒勝思) and hee-seung (怒勝思) Corresponding emotions can be determined based on sorrow, sorrow, sorrow, suffering, and joy.

As an example, the control module 150 confirms a group corresponding to the user's tendency among a plurality of groups, and within the confirmed group, there is at least one group that has a history of being provided to another user having the same emotion as the user's determined emotion. Solution information can be generated by selecting only the top content with a high feedback score among one content.

To this end, the control module 150 collects user data for each of at least one user who receives psychological treatment support services, and determines each user's tendency based on the user data, thereby dividing users with the same tendency between users. Grouping is possible. In addition, at least one content suitable for each group's tendencies can be searched, matched, and managed.

In other words, even users in the same situation may feel different emotional states depending on their tendencies, so the tendency identified based on each user's user data should be taken into further consideration.

Meanwhile, when generating solution information, the control module 150 can check the emotional stage of each of the user's determined emotions and set the exposure time for at least one content corresponding to each emotion according to the emotional stage. there is. For example, if the level of emotion is divided into three levels, the higher the level, the deeper the level of the emotion, and the exposure time can be increased. In addition, when there are multiple emotions determined, the ratio of the emotions can be determined, and the exposure time for at least one content corresponding to each emotion can also be determined according to the ratio.

Meanwhile, when feedback information about the solution information is received from the user terminal 200, the control module 150 calculates a feedback score for each of at least one content previously provided through the solution information based on the feedback information. The feedback score of each of the at least one pieces of content may be stored and managed for the group to which the user belongs, that is, for each group.

In addition, specific operations of the control module 150 will be described below based on each drawing.

FIG. 4 is a diagram illustrating a method of supporting psychological treatment using an artificial intelligence-based Oji ascending method according to an embodiment of the present disclosure.

Referring to FIG. 4, the support device 100 collects emotional information for at least one user (S210). At this time, emotional information for each user may be collected at preset intervals or may be collected upon each user's service provision request. Additionally, the support device 100 can accumulate and manage the collected emotional information for each user.

Next, the support device 100 analyzes the user's emotional state using an artificial intelligence-based pre-trained emotional treatment model based on the user's emotional information from the emotional information collected in step S210 (S220 ).

Next, the support device 100 generates solution information that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis result in step S220 (S230), and sends the solution information to the user terminal 200 of the corresponding user. ) (S240).

At this time, the emotion determined according to the analysis result may be one or multiple, and if it is determined that the user has multiple emotions in combination, at least one corresponding emotion for each emotion is confirmed, and the Solution information can be generated by searching and selecting at least one content for each of the at least one identified corresponding emotion.

FIG. 5 is a diagram illustrating in detail the operation of classifying groups for a plurality of users in a method of supporting psychological treatment using an artificial intelligence-based Ojimyeong method according to an embodiment of the present disclosure.

First, the support device 100 may group at least one user who receives psychological treatment services. In this case, the grouping may be performed based on the tendencies of each user.

Referring to FIG. 5, the support device 100 collects user data for each of at least one user who receives psychological treatment support services (S310), and analyzes (identifies) each user's tendency based on the user data. Do it (S320).

Here, user data includes at least one user's age, gender, education, property details, expenditure details, eating habits, occupation, work type, working hours, residence, family relationship, MBTI (Myers-Briggs Type Indicator), and SNS data. It can contain at least one.

Next, the support device 100 groups users with the same tendency (S330), and searches for and manages at least one content suitable for each group's tendency (S340).

This series of operations according to FIG. 5 may be performed when the user is registered, or may be performed when step S210 of FIG. 4 is performed.

FIG. 6 is a diagram specifically illustrating the operation of selecting content and generating solution information in a psychological treatment support method using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure, showing step S230 of FIG. 4 This drawing is for detailed explanation.

Referring to FIG. 6, the support device 100 analyzes the user's preference based on the user data (S231), and according to the analysis result, selects a group corresponding to (mapped to) the user's preference among a plurality of groups. Confirm (S232).

Next, the support device 100 selects only appropriate content among at least one content that has been provided to another user with the same emotion as the emotion determined for the user within the group identified in step S232 (S233) .

For example, among at least one content that has been provided to other users, only top content with a high feedback score may be selected, or only top content with a high number of provision times may be selected.

In other words, the selection criteria are not limited, and the content selected may vary depending on what criteria are set. The standard may be set by the support device 100, that is, the service provider, or may be set individually by each user terminal 200 that receives the service.

FIG. 7 is a diagram illustrating in detail the operation of processing feedback information about solution information in a method of supporting psychological treatment using the artificial intelligence-based Oji Ascent method according to an embodiment of the present disclosure.

Referring to FIG. 7, when feedback information about the solution information is received from the user terminal 200 that has received the solution information (S250), the support device 100 provides the solution information based on the feedback information. A feedback score for each piece of at least one content is calculated (S260).

At this time, the feedback information includes the results of the user performing a solution (psychotherapy) using each of the at least one content provided through the solution information, and the user's emotions are expressed as intended through the at least one content. Whether or not there has been a change may be generated based on information entered by the user through a question tree or survey.

Figure 8 is a diagram showing a method for pre-learning an emotion therapy model based on artificial intelligence according to an embodiment of the present disclosure.

Referring to FIG. 8, the support device 100 checks emotional information and corresponding solution information for each group (S410) and generates learning data based on the emotional information and solution information (S420).

At this time, when generating learning data, all solution information corresponding to emotional information may be used, but only content for which the feedback score corresponding to at least one piece of content is more than a preset threshold based on feedback information about the solution information is used. It can also be filtered and used as solution information corresponding to the relevant emotional information.

Next, the support device 100 can generate (construct) an emotional therapy model by inputting the learning data generated in step S420 into the original model and performing learning (S430).

Meanwhile, the present disclosure may perform inference for a predetermined purpose using a model implemented in an artificial neural network method.

Below, we will look at artificial neural networks. A model in this specification may refer to any type of computer program that operates based on a network function, artificial neural network, and/or neural network. Throughout this specification, model, neural network, network function, and neural network may be used interchangeably. In a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer, and the hidden layer in the middle is preferably one or more in a deep neural network. consists of two or more.

These deep neural networks include convolutional neural network (CNN), vision transformer, recurrent neural network (RNN), Long Short Term Memory (LSTM) network, and Generative Pre-trained (GPT). Transformer, auto encoder, GAN (Generative Adversarial Networks), restricted Boltzmann machine (RBM), deep belief network (DBN), Q network, U network, Siamese network, adversarial generation. It may include a network (GAN: Generative Adversarial Network), a transformer, etc.

Alternatively, depending on the embodiment, the deep neural network may be a model learned using transfer learning. Here, transfer learning pre-trains a large amount of unlabeled learning data using a semi-supervised learning or self-learning method to create a pre-trained model (or base unit) with the first task. A learning method that implements a target model by learning labeled training data using a supervised learning method in order to obtain it through various techniques (MLM, NSP, etc.) and fine-tune the pre-trained model to be suitable for the second task. represents. One of the models learned using this transfer learning method is BERT (Bidirectional Encoder Representations from Transformers), but it is not limited to this.

The description of the deep neural network described above is only an example and the present disclosure is not limited thereto. In the case of the convolutional neural network described above, it consists of a feature extraction unit (feature learning) that extracts features from the image, and a classification unit that performs classification using the extracted features. The feature extraction unit may include, but is not limited to, a convolution layer in which features are extracted from the image using a kernel, a ReLU layer as one of the activation functions, and a pooling layer to reduce the dimensionality of the data. In addition, the classification unit may include, but is not limited to, a flatten layer that lines up the features extracted from the feature extraction unit, a fully connected layer where classification is actually performed, and a softmax function.

A neural network can be learned in at least one of supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. You can. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of supervised learning, data in which the correct answer is labeled (labeled data) can be used in each learning data, and in the case of unsupervised learning, data in which the correct answer is not labeled in each learning data (unlabeled data) can be used. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and 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 neural network. Additionally, to prevent overfitting, methods such as increasing learning data, regularization, dropout to disable some nodes, and batch normalization layer can be applied.

Meanwhile, the model disclosed in one embodiment may borrow at least part of a transformer. The transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. A transformer may have a structure that receives a series of data, goes through encoding and decoding steps, and outputs a series of data of different types. In one embodiment, a series of data can be processed into a form that can be operated by a transformer. The process of processing a series of data into a form that can be operated by a transformer may include an embedding process. Expressions such as data token, embedding vector, embedding token, etc. may refer to data embedded in a form that a transformer can process.

In order for the transformer to encode and decode a series of data, the encoders and decoders within the transformer can be processed using an attention algorithm. The attention algorithm calculates the similarity of one or more keys for a given query, reflects the given similarity to the value corresponding to each key, and returns the reflected similarity value ( It may refer to an algorithm that calculates the attention value by weighting the values.

Depending on how queries, keys, and values are set, various types of attention algorithms can be classified. For example, if attention is obtained by setting the query, key, and value all the same, this may mean a self-attention algorithm. In order to process a series of input data in parallel, when the dimension of the embedding vector is reduced and attention is obtained by obtaining individual attention heads for each divided embedding vector, this refers to a multi-head attention algorithm. can do.

In one embodiment, the transformer may be composed of modules that perform a plurality of multi-head self-attention algorithms or a multi-head encoder-decoder algorithm. In one embodiment, the transformer may also include additional components other than the attention algorithm, such as embedding, normalization, and softmax. A method of configuring a transformer using an attention algorithm may include the method disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

Transformers can be applied to various data domains such as embedded natural language, segmented image data, and audio waveforms to convert a series of input data into a series of output data. In order to convert data with various data domains into a series of data that can be input to the transformer, the transformer can embed the data. Transformers can process additional data expressing relative positional or phase relationships between a series of input data. Alternatively, a series of input data may be embedded by additionally reflecting vectors expressing relative positional relationships or phase relationships between the input data. In one example, the relative positional relationship between a series of input data may include, but is not limited to, word order within a natural language sentence, relative positional relationship of each segmented image, temporal order of segmented audio waveforms, etc. . The process of adding information expressing relative positional or phase relationships between a series of input data may be referred to as positional encoding.

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

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

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

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

10: Support system 100: Support device
110: communication module 130: storage module
150: Control module 200: At least one user terminal

Claims (10)

A communication module that communicates with an external device;
For each of the five emotions of anger, distress, fear, joy, and sadness, storing at least one process to provide psychotherapy support services based on the Oji Ascension method that seeks to change emotions based on complementary corresponding emotions. storage module; and
It includes a control module that provides the psychological treatment support service based on the at least one process,
The control module is,
Collect emotional information including changes in each user's emotional state and corresponding emotions accumulated and stored over a preset period of time, and use an artificial intelligence-based pre-trained emotional treatment model based on the emotional information to determine the emotional state of the user. After analyzing and generating solution information by searching for at least one content that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis result, providing the solution information to the user terminal,
When feedback information about the solution information is received from the user terminal, a feedback score for each of the at least one content is calculated and stored based on the feedback information, and performance information corresponding to the feedback information is generated to determine at least a preset value. Provided through one stakeholder terminal,
Each user's tendencies are identified through a combination of at least two of the following: property details, expenditure details, eating habits, occupation, work type, working hours, residence, family relationships, MBTI (Myers-Briggs Type Indicator), and SNS data, and the identified Users with the same tendency are grouped and managed by matching at least one content to each group,
When generating the solution information, a group corresponding to the user's tendency among a plurality of groups is identified, and within the identified group, at least one content that has a history of being provided to another user having the same emotion as the determined emotion is selected. The solution information is generated by selecting only content whose calculated feedback score is higher than a preset threshold,
Characterized in that the type of the generated performance information is determined dependent on the type or state of emotion as the user of the user terminal performs the solution according to the solution information.
Psychotherapy support device using artificial intelligence-based Oji Ascent method. According to paragraph 1,
The emotional information is,
Based on at least one of keywords and sentences entered by the user through the user terminal during the preset period,
The emotions determined above are,
Contains at least one of the five emotions above,
Psychotherapy support device using artificial intelligence-based Oji Ascent method.
delete delete delete According to paragraph 1,
The control module is,
Characterized in that confirming the emotional stage of each of the determined emotions and setting the exposure time for the at least one content corresponding to each emotion according to the emotional stage,
Psychotherapy support device using artificial intelligence-based Oji Ascent method.
delete According to paragraph 1,
The emotional therapy model is,
Characterized in that the original model is pre-learned based on emotional information for each of at least one user belonging to each group and solution information provided in response to the emotional information,
Psychotherapy support device using artificial intelligence-based Oji Ascent method.
In a method of supporting psychological treatment using an artificial intelligence-based ascension method performed by a device,
Collecting emotional information including changes according to emotional states and corresponding emotions for each user accumulated and stored over a preset period of time;
Analyzing the user's emotional state using an artificial intelligence-based pre-trained emotional treatment model based on the emotional information;
generating solution information by searching for at least one content that causes at least one corresponding emotion corresponding to the emotion determined according to the analysis result; and
Including providing the solution information to the user terminal,
When feedback information about the solution information is received from the user terminal, calculating and storing a feedback score for each of the at least one content based on the feedback information; and
Further comprising generating performance information corresponding to the feedback information and providing it to at least one preset stakeholder terminal,
The step of generating the solution information is,
Confirming a group corresponding to the user's tendency among a plurality of groups;
selecting only content for which the calculated feedback score is equal to or higher than a preset threshold among at least one content that has been provided to another user within the identified group having the same emotion as the determined emotion; and
It includes generating the solution information based on the selected high-level contents,
The plurality of groups are,
Each user's tendencies are identified through a combination of at least two of the following: property details, spending details, eating habits, occupation, work type, working hours, residence, family relations, MBTI (Myers-Briggs Type Indicator), and SNS data, and identify users with the same tendency. It is classified by grouping users,
The types of performance information generated above are:
It is determined depending on the type or state of emotion as the user of the user terminal performs a solution according to the solution information,
The emotional information is,
Based on at least one of keywords and sentences entered by the user through the user terminal during the preset period,
The emotions determined above are,
Characterized by including at least one of the following five emotions: anger, distress, fear, joy, and sadness,
Psychotherapy support method using artificial intelligence-based Oji Ascent method. delete