KR20230151425A - Method and apparatus for providing information related to a psychological state based on an order in which color images are selected and the color images using neural network - Google Patents

Abstract

Embodiments present a method and device in which a server provides information related to a psychological state based on a color image to a terminal using a neural network. The method according to one embodiment includes receiving a message requesting psychological diagnosis based on a color image from a terminal and information about the user of the terminal - the information about the user of the terminal includes information about the user's date of birth, the user Contains at least one of information about the user's name, information about the user's gender, information about the user's occupation, information about the user's residence, or information about the user's family relationship -; Transmitting information related to a plurality of color images to the terminal based on the message - the information related to the plurality of color images includes a plurality of color images and the number of color images to be selected by the terminal -; Receiving a color image selected according to the number of color images from the terminal; determining a value for a psychological state through a diagnostic model using a neural network based on the selected color image and information about the user of the terminal; And it may include providing information related to the psychological state to the terminal based on the value for the psychological state. For example, the color image may be an image that combines two or more colors. For example, the number of color images may be determined based on information about the user of the terminal.

Description

Method and device for providing information related to color images and psychological states based on the order in which color images are selected using a neural network COLOR IMAGES USING NEURAL NETWORK}

Embodiments of the present disclosure relate to a technology for providing* information related to a psychological state based on a color image, and to a technology for providing information related to a psychological state based on a color image using a neural network.

Due to rapid changes in modern society resulting from rapid industrial growth, more modern people are experiencing stress than before. As a result, the demand for psychological counseling and psychiatric treatment for modern people is increasing, but due to a social atmosphere that considers psychological counseling or psychiatric treatment to be shameful, modern people who need counseling or treatment may avoid visiting and receiving treatment in person. . Furthermore, recently, due to the outbreak of the coronavirus, the demand for non-face-to-face psychological counseling is increasing.

Meanwhile, color psychology is a type of psychology that focuses on the impact of colors on human behavior. In other words, by analyzing the effects and principles of color on human psychology and emotions through color psychology, the counselor can diagnose the client's current psychological state and find psychological balance through colors that can affect the client. .

At this time, when clients conduct non-face-to-face psychological counseling through a terminal, they may be reluctant to transmit information about themselves due to security issues, and smooth information exchange may be difficult depending on the communication status of the terminal.

Accordingly, in order to efficiently provide non-face-to-face psychological diagnosis based on color psychology, the number of color images to be selected is determined according to information about the user received from the terminal, and the color images selected by the user and information about the user are determined. Based on this, it is necessary to determine a more accurate psychological state through a neural network and provide information related to the psychological state. Additionally, it is necessary to receive additional image information from the user, derive a color image from the user's clothing through a neural network, and determine the psychological state by also considering the color image.

Public Patent Publication No. 10-2021-0088991

Embodiments of the present disclosure may provide a method and device for providing information related to a psychological state based on a color image using a neural network.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

According to one embodiment, a method in which a server provides information related to a psychological state based on a color image to a terminal using a neural network includes a message requesting psychological diagnosis based on a color image from the terminal and the terminal. Receiving information about the user of the terminal - Information about the user of the terminal includes information about the user's date of birth, information about the user's name, information about the user's gender, information about the user's occupation, and information about the user's place of residence. Contains at least one of information about the user or information about the user's family relationship -; Transmitting information related to a plurality of color images to the terminal based on the message - the information related to the plurality of color images includes a plurality of color images and the number of color images to be selected by the terminal -; Receiving a color image selected according to the number of color images from the terminal; determining a value for a psychological state through a diagnostic model using a neural network based on the selected color image and information about the user of the terminal; And it may include providing information related to the psychological state to the terminal based on the value for the psychological state. For example, the color image may be an image that combines two or more colors. For example, the number of color images may be determined based on information about the user of the terminal.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of a plurality of color images, information about a plurality of users, and a correct answer psychological state is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer, and is output. A vector is output, the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data. And, as the parameters of the neural network are learned in a direction that reduces the loss value, the diagnostic model can be generated through the neural network.

Additionally, according to one embodiment, the server may receive a selected color image and a selection order for the color image from the terminal.

Or, for example, each learning data consisting of a plurality of color images, an order of the plurality of color images, information about a plurality of users, and a correct answer psychological state is input to the input layer of the neural network, An output vector is output by passing through the one or more hidden layers and the output layer, the output vector is input to a loss function layer connected to the output layer, and the loss function layer is a correct answer vector for the output vector and each training data. A loss value is output using a loss function that compares, and as the parameters of the neural network are learned in a direction in which the loss value decreases, the diagnostic model can be generated through the neural network. Additionally, a diagnostic model may be created that takes into account the order of multiple color images.

For example, the weight may be different depending on the type of information about the user of the terminal, and the number of color images may be determined differently based on the sum of the weights for each piece of information about the user of the terminal. For example, the number of color images may be two or more.

For example, among the information about the user of the terminal, a first weight is applied to information about the user's date of birth, information about the user's gender, and information about the user's family relationship, and the user of the terminal Among the information about the user, a second weight may be applied to information about the user's occupation and information about the user's residence, and a third weight may be applied to information about the user's name among the information about the user of the terminal. . For example, based on the fact that the sum of the weights for each piece of information about the user of the terminal is included in the threshold range, the number of color images may be determined to be three.

For example, based on the fact that the sum of the weights for each piece of information about the user of the terminal exceeds the maximum value of the threshold range, the number of color images may be determined to be four. For example, the maximum value of the threshold range can be set by the equation below.

In the above equation, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the number of maximum weights, n _total is the total number of weights, n _min is the number of minimum weights, and w _max is the number of minimum weights. may be the maximum weight value, w _min may be the minimum weight value, and R may be the reference signal received power (RSRP) value of the terminal.

Additionally, according to one embodiment, based on the fact that the sum of the weights for each piece of information about the user of the terminal is less than the minimum value of the threshold range, the number of color images may be determined to be two. At this time, the minimum value of the critical range can be set by the equation below.

In the above equation, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the number of maximum weights, n _total is the total number of weights, n _min is the number of minimum weights, and w _max is the number of minimum weights. is the maximum weight value, w _min is the minimum weight value, and R may be the RSRP value of the terminal.

Additionally, according to one embodiment, the server may receive information about the user of the terminal from the terminal as well as image information about the user captured through a camera provided in the terminal. For example, the server may determine the user's clothing information based on the user's image information through a clothing classification model using a neural network. Here, clothing information may be information about clothing worn by the user. That is, for example, clothing information may include two or more types of color information about clothing worn by the user and information about the style of clothing worn by the user.

For example, based on the user's image information, the server may determine values for the color that accounts for the largest proportion of the user's clothing, the color that accounts for the next largest proportion, and the clothing style through the clothing classification model. .

For example, when the clothing information is determined, the server may determine a value for the psychological state through a neural network based on the determined color image, a color image matching the clothing information, and information about the user.

According to embodiments, the server can derive the number of color images suitable for the user by transmitting to the terminal the number of color images to be selected by the terminal according to information about the user received from the terminal.

According to embodiments, a server may more accurately determine a value for a psychological state based on a color image selected by the user and information about the user through a neural network.

According to embodiments, the server determines clothing information worn by the user through a neural network based on image information received from the terminal, and derives a color image based on the clothing information to determine psychological state by also taking the clothing information into consideration. The value for can be determined.

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
1 is a diagram showing the configuration of an electronic device according to an embodiment.
Figure 2 is a diagram showing the configuration of a program according to one embodiment.
Figure 3 shows a method in which a server provides information related to a psychological state based on a color image to a terminal, according to an embodiment.
Figure 4 is a flowchart illustrating the operation of a method in which a server provides information related to a psychological state based on a color image to a terminal, according to an embodiment.
Figure 5 shows examples of multiple color images according to one embodiment.
Figure 6 is a block diagram showing the configuration of a server according to one embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or WAP server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Visual Basic, and Visual C, and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 uses a variety of server programs provided depending on the operating system such as DOS, Windows, Linux, UNIX, and Macintosh on general server hardware. Representative examples include Website, IIS (Internet Information Server) used in a Windows environment, and CERN, NCSA, and APPACH used in a Unix environment. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet uses the TCP/IP protocol and several services existing at its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), and SNMP ( It refers to a worldwide open computer first network (198) and second network (199) structure that provides Simple Network Management Protocol (NFS), Network File Service (NFS), and Network Information Service (NIS).

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own functions. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Throughout this specification, neural network, neural network, and network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as “nodes.” These “nodes” may also be referred to as “neurons.” A neural network is composed of at least two or more nodes. Nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, two or more nodes connected through a link can relatively form a relationship as an input node and an output node. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, nodes connecting the input node and the output node may have weights. Weights may be variable and may be varied by a user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, two 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 in the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

Figure 3 shows a method in which a server provides information related to a psychological state based on a color image to a terminal, according to an embodiment. One embodiment of FIG. 3 may be combined with various embodiments of the present disclosure.

Referring to FIG. 3, in step S301, a server (e.g., server 108 in FIG. 1) performs psychological diagnosis based on a color image from a terminal (e.g., electronic device 101 and electronic device 120 in FIG. 1). It is possible to receive a request message and information about the user of the terminal.

Here, the color image may be an image that combines two or more colors. For example, the color image may be an image of a container containing two or more aroma oils without mixing, and may represent two or more colors through two or more aroma oils contained in the one container. Additionally, the color image may include not only information about the color, but also information about the vividness of the color and information about the brightness of the color.

Therefore, unlike selecting one color, since the image is a combination of two or more colors, it is possible to accurately determine the value of the psychological state by responding to more cases than one color.

For example, information about the user of the terminal includes information about the user's date of birth, information about the user's name, information about the user's gender, information about the user's occupation, information about the user's residence, or the user's family relationship. It may include at least one of the information about.

For example, a message requesting psychological diagnosis based on a color image may include information about the user of the terminal.

Additionally, according to one embodiment, the server receives the user's image information captured through a camera (e.g., the camera module 180 in FIG. 1) provided in the terminal along with information about the user of the terminal. You can receive it. For example, the server may determine the user's clothing information based on the user's image information through a clothing classification model using a neural network. Here, clothing information may be information about clothing worn by the user. That is, for example, clothing information may include two or more types of color information about clothing worn by the user and information about the style of clothing worn by the user. For example, the information about the style may be a value for a clothing style that includes the clothing worn by the user among a plurality of clothing styles. For example, clothing styles can be categorized into casual styles, street styles, sporty styles, and office styles, and the office styles can be categorized into classic styles and semi-classic styles. And, the value mapped to each style may be preset for the server.

For example, based on the user's image information, the server may determine values for the color that accounts for the largest proportion of the user's clothing, the color that accounts for the next largest proportion, and the clothing style through the clothing classification model. . For example, in the user's video information, if the user is wearing jeans and a white shirt, the server uses the clothing classification model based on the user's video information to select blue, which accounts for the largest proportion, and blue, which accounts for the next largest proportion. Color information can be determined as white, and a value for a clothing style corresponding to light clothing (for example, casual style) can be determined. Thereafter, the server may determine a color image that matches the clothing information (value for color information and clothing style) among a plurality of color images.

For example, the user's image information may represent parts of the user's clothing in pixel units of RGB values. For example, RGB can be expressed as coordinate values for three colors: red, green, and blue.

For example, a neural network may include an input layer, one or more hidden layers, and an output layer. At this time, for example, the neural network may learn in advance based on the image information of a plurality of users to generate a feature vector corresponding to clothing information when the user's image information is input. For example, based on the input image information of a plurality of users, the neural network selects the color that accounts for the largest proportion of the user's clothing and the color that accounts for the next largest proportion to any one of the plurality of color images. Matching can be learned in advance, and the clothing styles worn by users can be classified into various style categories based on the input video information of multiple users.

For example, the style category may be learned in advance through a neural network based on metadata about clothing images. In other words, the server can determine the top 8 most frequent characteristics (e.g. characteristics such as color, tone, color scheme, pattern, material, etc.) based on metadata about the clothing image through a neural network. Here, for example, the colors may be classified into a plurality of first groups such as a natural color group, a red group, a green group, a yellow group, and a blue group, and the tones may be a light tone group, a light tone group, It may be classified into a plurality of second groups, such as a gray tone group and a black tone group. For example, color combinations may be classified into a plurality of third groups, such as a same color group, a similar color group, and an opposite color group. For example, patterns may be classified into a plurality of fourth groups such as geometric patterns, animal patterns, striped patterns, check patterns, dot patterns, floral patterns, abstract patterns, and logo patterns. For example, materials may be classified into a plurality of fifth groups such as cotton, wool, polyester, nylon, silk, and chiffon.

For example, the server may generate the various style categories by clustering clothing images with similar values based on the plurality of groups classified according to a plurality of characteristics through a neural network. Here, the clustering operation can be performed through various machine learning techniques for unsupervised learning.

For example, when the user's image information is input to the input layer, the neural network may pass through one or more hidden layers and an output layer to output a feature vector. Additionally, the neural network can extract feature vectors for color and feature vectors for clothing style based on the inputted user's image information, and match the extracted feature vector values to features corresponding to them. Then, the server can determine a color image corresponding to the matched feature.

Accordingly, the server can secure more information for diagnosing the user's psychological state by deriving a color image based on the user's image information. Additionally, if information about the user is insufficient, it can be supplemented through the user's image information.

Additionally, for example, if the color of the clothing worn by the user included in the user's image information is a single color, the server may basically set another color as the default value. Here, the default value may mean transparent without color.

In step S302, the server may transmit information related to a plurality of color images to the terminal based on the message. For example, information related to a plurality of color images may include a plurality of color images and the number of color images to be selected by the terminal.

For example, the number of color images may be determined based on information about the user of the terminal. For example, the number of color images may be two or more.

For example, the weight may be different depending on the type of information about the user of the terminal, and the number of color images may be determined differently based on the sum of the weights for each piece of information about the user of the terminal. . That is, the weight may be different depending on the type of information included in the information about the user of the terminal.

For example, among the information about the user of the terminal, a first weight is applied to information about the user's date of birth, information about the user's gender, and information about the user's family relationship, and the user of the terminal Among the information about the user, a second weight may be applied to information about the user's occupation and information about the user's residence, and a third weight may be applied to information about the user's name among the information about the user of the terminal. .

That is, rather than simply fixing the color image, the importance is determined according to the type of information about the user of the terminal, and the number of color images is set to reflect the weight according to the importance, so that the psychology is tailored to the information about the user. A value for the state can be determined.

For example, based on the fact that the sum of the weights for each piece of information about the user of the terminal is included in the threshold range, the number of color images may be determined to be three.

For example, based on the fact that the sum of the weights for each piece of information about the user of the terminal exceeds the maximum value of the threshold range, the number of color images may be determined to be four.

The maximum value of the threshold range is set based on the number of different weight values, the total number of weights, the number of maximum weights, the number of minimum weights, the maximum weight value, the minimum weight value, and the RSRP (reference signal received power) value of the terminal. It can be.

At this time, for example, the maximum value of the threshold range may be set by Equation 1 below.

In Equation 1, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the number of maximum weights, n _total is the total number of weights, n _min is the number of minimum weights, w _max may be the maximum weight value, w _min may be the minimum weight value, and R may be the reference signal received power (RSRP) value of the terminal. Here, RSRP is a value indicating the communication state of the terminal and may have a negative dB value. For example, the larger the absolute value of RSRP, the poorer the communication state of the terminal may be determined. Therefore, when the absolute value of the RSRP is large, the T _max value increases, thereby reducing the number of color images to be selected by the terminal. That is, the number of color images to be transmitted to the server can be adjusted considering the communication status of the terminal.

For example, based on the fact that the sum of the weights for each piece of information about the user of the terminal is less than the minimum value of the threshold range, the number of color images may be determined to be two. At this time, the minimum value of the critical range can be set by Equation 2 below.

In Equation 2, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the number of maximum weights, n _total is the total number of weights, n _min is the number of minimum weights, w _max may be the maximum weight value, w _min may be the minimum weight value, and R may be the RSRP value of the terminal.

Additionally, for example, when the clothing information is determined, the server may adjust the number of the determined color images. That is, the server may store color images matching the clothing information and adjust the number of color images by subtracting one from the determined number of color images. Thereafter, the server may transmit information related to the plurality of color images, including the plurality of color images and the number of the adjusted color images, to the terminal.

Therefore, rather than fixing the number of color images to be selected, the server flexibly adjusts the number of color images to be selected according to the communication status of the terminal or the presence or absence of other auxiliary information, thereby determining the value of the psychological state more efficiently. Information can be transmitted and received.

In step S303, the server may receive a color image selected according to the number of color images from the terminal.

Additionally, for example, the server may receive a selected color image and a selection order for the color image from the terminal.

In step S304, the server may determine a value for the psychological state through a diagnostic model using a neural network based on the selected color image and information about the user of the terminal.

For example, each learning data consisting of a plurality of color images, information about a plurality of users, and a correct answer psychological state is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer, and is output. A vector is output, the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data. And, as the parameters of the neural network are learned in a direction that reduces the loss value, the diagnostic model can be generated through the neural network.

Additionally, for example, each learning data consisting of a plurality of color images, an order for the plurality of color images, information on a plurality of users, and a correct answer psychological state is input to the input layer of the neural network. An output vector is output by passing through the one or more hidden layers and the output layer, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer is connected to the output vector and each training data. A loss value is output using a loss function that compares the correct answer vector, and as the parameters of the neural network are learned in a direction that reduces the loss value, the diagnostic model can be created through the neural network. That is, a diagnostic model may be created that additionally considers the order of a plurality of color images.

For example, when determining a value for psychological state through the diagnostic model, the server receives information about the user of the terminal including information about the user's date of birth, information about the user's gender, and information about the user's occupation. When receiving information, the server may apply a first weight to information about the user's date of birth and information about the user's gender, and apply a second weight to information about the user's occupation.

Here, the value for the psychological state is a different value depending on the psychological state of the user. For example, information about the user's psychological state that matches the value for the psychological state may be preset in the server. For example, if the value for the psychological state is 1, the psychological state of the user may match a depressed and anxious state, and if the value for the psychological state is 2, the psychological state of the user may be slightly depressed. Can be matched to status. For example, the value for the psychological state may be determined differently depending on information about the user even if the color image is the same.

Or, for example, when the clothing information is determined, the server may determine a value for the psychological state through a neural network based on the determined color image, a color image matching the clothing information, and information about the user. .

As described above, by determining the value of the psychological state through a diagnostic model using a neural network, a more accurate psychological state can be provided to the user of the terminal.

In step S305, the server may provide information related to the psychological state to the terminal based on the value for the psychological state. For example, the information related to the psychological state may include information about the user's psychological state, information about recommended colors, advice information for the user, and information about a counseling program. For example, depending on the psychological state that matches the value for the psychological state, information about the user's psychological state, information about recommended colors, advice information for the user, and information about the counseling program may be determined differently. . For example, a matching relationship between a psychological state value and information about the user's psychological state, information about recommended colors, advice information for the user, and information about a counseling program may be preset in the server.

Figure 4 is a flowchart illustrating the operation of a method in which a server provides information related to a psychological state based on a color image to a terminal, according to an embodiment. One embodiment of FIG. 4 may be combined with various embodiments of the present disclosure.

Referring to FIG. 4, in step S401, the server may determine a weight for information about the user of the terminal. For example, the server may determine different weights depending on the type of information about the user of the terminal. For example, the server may receive information about the user of the terminal from the terminal, and the information about the user of the terminal includes information about the user's date of birth, information about the user's name, information about the user's gender, It may include at least one of information about the user's occupation, information about the user's residence, or information about the user's family relationship.

For example, the server may apply a first weight to information about the user's date of birth, information about the user's gender, and information about the user's family relationship among the information about the user of the terminal, and the user of the terminal A second weight can be applied to information about the user's occupation and information about the user's residence among the information about the user, and a third weight can be applied to the information about the user's name among the information about the user of the terminal. there is.

For example, the server may determine the number of color images differently based on the sum of weights for each piece of information about the user of the terminal.

In step S402, the server may determine whether the sum of the weights is within the threshold range. For example, the maximum value of the critical range can be determined by Equation 1 described above. For example, the maximum value of the critical range can be determined by Equation 2 described above.

In step S403, based on the sum of the weights being within the threshold range, the server may determine whether the user's image received from the terminal exists. In the subsequent process, as described above, when there is a user image received from the terminal, the server may determine the user's clothing information based on the user's image through a clothing classification model using a neural network.

In step S404, based on the summed value of the weights falling outside the threshold range, the server may determine whether the summed value of the weights exceeds the maximum value of the threshold range. That is, if the sum of the weights is not included in the critical range, the server can determine whether the sum of the weights exceeds the maximum value of the threshold range.

In step S405, based on the sum of the weights exceeding the maximum value of the threshold range, the server may determine whether the user's image received from the terminal exists. In the subsequent process, as described above, when there is a user image received from the terminal, the server may determine the user's clothing information based on the user's image through a clothing classification model using a neural network.

In step S406, based on the fact that the sum of the weights does not exceed the maximum value of the threshold range, the server may determine whether the user's image information received from the terminal exists. That is, based on the fact that the sum of the weights does not exceed the maximum value of the threshold range, the server may determine that the sum of the weights is less than the minimum value of the threshold range because the sum of the weights is not included in the threshold range. For example, based on the fact that the sum of the weights is less than the minimum value of the threshold range, the server can determine whether the user's image information received from the terminal exists.

In step S407, based on the fact that the sum of the weights is within the threshold range and that the user's image information exists (S403), the server may determine the number of color images to be two.

Also, for example, in step S407, based on the fact that the sum of the weights is less than the minimum value and that the user's image information does not exist (S406), the server may determine the number of color images to be two.

In step S408, based on the fact that the sum of the weights is within the threshold range and that the user's image information does not exist (S403), the server may determine the number of color images to be three.

Also, for example, in step S408, based on the fact that the sum of the weights exceeds the maximum value and that the user's image information exists (S405), the server may determine the number of color images to be three.

In step S409, based on the fact that the sum of the weights exceeds the maximum value and that the user's image information does not exist (S405), the server may determine the number of color images to be four.

In step S410, based on the fact that the sum of the weights is less than the minimum value and that the user's image information exists (S406), the server may determine the number of color images to be 1.

According to one embodiment, when there is user image information received from the terminal, the server may reduce the number of color images determined by one by comparing the sum of the weights and the threshold range. Additionally, for example, if there is no user image information received from the terminal, the server may use the number of color images determined through comparison of the sum of the weights and the threshold range.

That is, the server may extract one color image based on the user's image information received from the terminal and replace one color image among the color images to be selected from the terminal with the extracted color image.

Various embodiments of the present disclosure are not limited with respect to the number of color images. It is an example that the number of color images described above is determined to be 1, 2, 3, and 4, and the number of color images can be set in various ways, such as 2, 4, 6, and 8. there is.

In step S411, the server may provide information related to a plurality of color images to the terminal. For example, information related to a plurality of color images may include a plurality of color images and the number of color images to be selected by the terminal.

For example, in step S407, if the number of color images is determined to be two, the server may provide the terminal with a plurality of color images and information setting the number of color images to be selected by the terminal to two.

For example, in step S408, if the number of color images is determined to be three, the server may provide the terminal with a plurality of color images and information setting the number of color images to be selected by the terminal to three.

For example, in step S409, if the number of color images is determined to be 4, the server may provide the terminal with a plurality of color images and information setting the number of color images to be selected by the terminal to 4.

*For example, in step S410, if the number of color images is determined to be 1, the server may provide the terminal with a plurality of color images and information setting the number of color images to be selected by the terminal to 1. .

Figure 5 shows examples of multiple color images according to one embodiment. One embodiment of FIG. 5 may be combined with various embodiments of the present disclosure.

As described above, the terminal may transmit a message requesting psychological diagnosis based on a color image and information about the user of the terminal to the server. Additionally, the terminal may transmit image information about the user captured through a camera provided in the terminal along with information about the user of the terminal.

The server may transmit information related to the plurality of color images including a plurality of color images and the number of color images to be selected by the terminal to the terminal based on the psychological diagnosis request message.

At this time, for example, the server may determine the number of color images to be selected by the terminal based on information about the user of the terminal, and when receiving the user's image information, the server may determine the number of color images to be selected by the terminal. can be adjusted. That is, the server determines clothing information based on the user's image information, and replaces the color image matching the clothing information with one of the color images to be selected, thereby reducing the number of color images determined by one. .

Referring to FIG. 5, the terminal can display a plurality of color images 511 and the number of color images 512 on the screen 510 of the terminal based on information related to the received plurality of color images. there is. For example, each of the plurality of color images 511 may be an image of a container containing two or more aroma oils without mixing them. For example, the user of the terminal may select as many color images as the number 512 of the color images 511.

The terminal may transmit the selected color images to the server, and the server may determine a value for the psychological state through the above-described diagnostic model based on the selected color images and information about the user.

Additionally, the terminal may transmit the selected color images and the selection order of the color images to the server, and the server may perform the above-described diagnosis based on the selected color images, the selection order of the color images, and information about the user. Through the model, values for psychological states can be determined.

Figure 6 is a block diagram showing the configuration of a server according to one embodiment. One embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

As shown in FIG. 6, the server 600 may include a processor 610, a communication unit 620, and a memory 630. However, not all of the components shown in FIG. 6 are essential components of the server 600. The server 600 may be implemented with more components than those shown in FIG. 6, or the service providing server 600 may be implemented with fewer components than those shown in FIG. 6. For example, the server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610, the communication unit 620, and the memory 630. .

The processor 610 typically controls the overall operation of the server 600. The processor 610 may include one or more processors and control other components included in the server 600. For example, the processor 610 can generally control the communication unit 620 and the memory 630 by executing programs stored in the memory 630. Additionally, the processor 610 may perform the functions of the server 600 shown in FIGS. 3 to 5 by executing programs stored in the memory 630.

According to one embodiment, the processor 610 may set a style category in advance based on metadata for the clothing image input to the server 600.

According to one embodiment, the processor 610 may receive a message requesting psychological diagnosis based on a color image and information about the user of the terminal from the terminal through the communication unit 620. The processor 610 may receive the user's image information from the terminal through the communication unit 620.

According to one embodiment, the processor 610 may determine the user's clothing information based on the user's image information through a clothing classification model using a neural network. For example, the processor 610 determines values for the color that accounts for the largest proportion of the user's clothing, the color that accounts for the next largest proportion, and clothing style through the clothing classification model based on the user's image information. You can decide.

According to one embodiment, the processor 610 may transmit information related to a plurality of color images to the terminal through the communication unit 620 based on the message.

For example, the processor 610 may determine the number of color images based on information about the user of the terminal. For example, the processor 610 may determine different weights depending on the type of information included in the information about the user of the terminal through the memory 630. For example, the processor 610 may determine the number of color images differently based on the sum of weights for each piece of information about the user of the terminal through the memory 630. For example, the processor 610 may determine the number of color images to be three, based on the fact that the sum of the weights for each piece of information about the user of the terminal is included in the threshold range. For example, the processor 610 may determine the maximum value of the critical range using Equation 1 described above, and the minimum value of the critical range may be determined using Equation 2 described above.

For example, the processor 610 applies a first weight to information about the user's date of birth, information about the user's gender, and information about the user's family relationship among the information about the user of the terminal, A second weight is applied to information about the user's occupation and information about the user's residence among the information about the user of the terminal, and a third weight is applied to information about the user's name among the information about the user of the terminal. Weighting can be applied.

For example, when the clothing information is determined, the processor 610 may adjust the number of the determined color images. That is, the processor 610 may store color images matching the clothing information through the memory 630 and adjust the number of color images by subtracting one from the determined number of color images. Thereafter, the processor 610 may transmit a plurality of color images and information related to the plurality of color images including the number of the adjusted color images to the terminal through the communication unit 620.

According to one embodiment, the processor 610 may receive a color image selected according to the number of color images from the terminal through the communication unit 620.

For example, the processor 610 may receive a color image selected according to the number of color images and a selection order for the color image from the terminal through the communication unit 620.

According to one embodiment, the processor 610 may determine a value for the psychological state through a diagnostic model using a neural network based on the selected color image and information about the user of the terminal through the memory 630.

For example, the processor 610 inputs each learning data consisting of a plurality of color images, information about a plurality of users, and a correct answer psychological state into the input layer of the neural network through the memory 630, and An output vector is output by passing through one or more hidden layers and an output layer, the output vector is input to a loss function layer connected to the output layer, and the output vector and the correct answer vector for each training data are generated by the loss function layer. A loss value is output using a loss function that compares, and as the parameters of the neural network are learned in a direction in which the loss value decreases, the diagnostic model can be generated through the neural network.

Or, for example, the processor 610 may output a plurality of color images through the memory 630, an order for the plurality of color images, information on a plurality of users, and each learning data consisting of the psychological state of the correct answer. It is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is input to the loss function layer connected to the output layer, and the loss function layer is A loss value is output using a loss function that compares the output vector with the correct answer vector for each training data, and the parameters of the neural network are learned in a direction in which the loss value decreases, so that the diagnostic model uses the neural network. It can be created through a network. That is, the processor 610 may additionally generate a diagnostic model that takes into account the order of a plurality of color images through the memory 630.

For example, when determining a value for a psychological state through the diagnostic model, the processor 610 receives information about the user's date of birth, information about the user's gender, and the user's occupation from the terminal through the communication unit 620. When receiving information about the user of the terminal containing the information, the processor 610 applies a first weight to the information about the user's date of birth and the information about the user's gender, and to the information about the user's occupation. A second weight may be applied.

According to one embodiment, the processor 610 may provide information related to the psychological state to the terminal through the communication unit 620 based on the value of the psychological state.

The communication unit 620 may include one or more components that allow the server 600 to communicate with other devices (not shown) and servers (not shown). The other device (not shown) may be a computing device such as the server 600 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or receive data stored in an external device from an external device through a network.

For example, the communication unit 620 may receive a message requesting psychological diagnosis based on a color image and information about the user of the terminal from the terminal. The communication unit 620 may transmit information related to a plurality of color images to the terminal based on the message. The communication unit 620 may receive a color image selected according to the number of color images from the terminal. For example, the communication unit 620 may additionally receive the selection order of the color image from the terminal. The communication unit 620 may provide information related to the psychological state to the terminal. For example, the communication unit 620 may receive the user's image information from the terminal.

The memory 630 may store programs for processing and control of the processor 610. The memory 630 may store information input by the user or information received from the terminal through a network. The memory 630 may store information input to or output from the server 600. For example, the memory 620 may include information about the user's date of birth, information about the user's name, information about the user's gender, information about the user's occupation, information about the user's residence, or information about the user's residence. At least one piece of information about family relationships can be stored. Additionally, the memory 630 can store various data generated by the processor 610. For example, the memory 620 may store a clothing classification model and a diagnostic model generated by the processor 610. For example, the memory 630 may store the number of color images to be selected generated by the processor 610 and values for the psychological state. For example, the memory 630 may pre-store a plurality of color images. For example, the memory 630 may store style categories previously learned by the processor 610 through a neural network based on metadata about the clothing image. For example, the memory 630 may pre-store values mapped to each clothing style. For example, the memory 630 may pre-store information about recommended colors, advice information for users, and information about counseling programs. For example, the memory 630 may pre-store a matching relationship between a psychological state value, information about the user's psychological state, information about recommended colors, advice information for the user, and information about a counseling program. .

The memory 630 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory, etc.), and RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , and may include at least one type of storage medium among optical disks.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (3)

In a method where a server provides information related to a psychological state based on a color image to a terminal using a neural network,
Receiving a message requesting psychological diagnosis based on a color image from a terminal and information about the user of the terminal;
transmitting information related to a plurality of color images to the terminal based on the message;
Receiving a color image selected according to the number of color images and a selection order for the color image from the terminal;
determining a value for a psychological state through a diagnostic model using a neural network based on the selected color image, a selection order for the color image, and information about the user of the terminal; and
Providing information related to the psychological state to the terminal based on the value for the psychological state,
The color image is a combination of two or more colors,
The number of color images is determined based on information about the user of the terminal,
Information about the user of the terminal includes information about the user's date of birth, information about the user's name, information about the user's gender, information about the user's occupation, information about the user's residence, or information about the user's family relationship. Contains at least one of
The information related to the plurality of color images includes a plurality of color images and the number of color images to be selected by the terminal,
The weight is different depending on the type of information about the user of the terminal,
The number of color images is determined differently based on the sum of the weights for each piece of information about the user of the terminal,
The number of color images is two or more,
Among the information about the user of the terminal, a first weight is applied to information about the user's date of birth, information about the user's gender, and information about the user's family relationship,
Among the information about the user of the terminal, a second weight is applied to information about the user's occupation and information about the user's residence,
A third weight is applied to information about the user's name among the information about the user of the terminal,
Based on the fact that the sum of the weights for each piece of information about the user of the terminal is included in the threshold range, the number of color images is determined to be three,
Based on the fact that the sum of the weights for each piece of information about the user of the terminal exceeds the maximum value of the threshold range, the number of color images is determined to be 4,
The maximum value of the critical range is set by Equation 1 below,
[Equation 1]

In Equation 1, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the maximum number of weights, n _total is the total number of weights, and n _min is the minimum weight. where w _max is the maximum weight value, w _min is the minimum weight value, and R is the RSRP (reference signal received power) value of the terminal.
method.
According to clause 1,
The neural network includes an input layer, one or more hidden layers, and an output layer,
Each learning data consisting of a plurality of color images, information about a plurality of users, the order of the plurality of color images, and the correct answer psychological state is input to the input layer of the neural network, the one or more hidden layers, and the output An output vector is output through the layer, and the output vector is input to a loss function layer connected to the output layer. The loss function layer uses a loss function that compares the output vector with the correct answer vector for each training data. outputs a loss value, and as the parameters of the neural network are learned in a direction in which the loss value decreases, the diagnostic model is generated through the neural network,
method.
According to clause 1,
Based on the fact that the sum of the weights for each piece of information about the user of the terminal is less than the minimum value of the threshold range, the number of color images is determined to be two,
The minimum value of the critical range is set by Equation 2 below,
[Equation 2]

In Equation 2, T _max is the maximum value of the threshold range, N is the number of different weight values, n _max is the maximum number of weights, n _total is the total number of weights, and n _min is the minimum weight. where w _max is the maximum weight value, w _min is the minimum weight value, and R is the RSRP value of the terminal,
method.