System

Abstract

An object of a system according to an embodiment is to analyze data related to behavioral psychology of a child and propose an appropriate approach method.SOLUTION: A system according to an embodiment includes an inspection execution unit, a data analysis unit, a proposal generation unit, and a cooperation unit. The examination conducting unit conducts a behavioral psychological test of the child. The data analysis unit analyzes the inspection data collected by the inspection execution unit. The proposal generation unit proposes an analysis result and an approach method to a guardian or an education site based on the inspection result analyzed by the data analysis unit. The cooperation unit cooperates with a medical institution or an education-related institution as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of not being able to effectively collect and analyze data on children's behavioral psychology and propose appropriate approaches.

[0005] The system according to the embodiment aims to analyze data relating to children's behavioral psychology and propose an appropriate approach. [Means for solving the problem]

[0006] The system according to the embodiment includes a test implementation unit, a data analysis unit, a proposal generation unit, and a collaboration unit. The test implementation unit implements behavioral psychology tests on children. The data analysis unit analyzes the test data collected by the test implementation unit. The proposal generation unit proposes analysis results and approaches to parents or educational institutions based on the test results analyzed by the data analysis unit. The collaboration unit collaborates with medical institutions and education-related institutions as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data relating to children's behavioral psychology and suggest appropriate approaches. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The behavioral psychology testing system according to an embodiment of the present invention uses generative AI to conduct behavioral psychology tests on children and analyze the results. Based on the test results, the system proposes analysis results and approaches to children to parents and educators. The system also collaborates with medical institutions and other educational institutions as needed. This allows the behavioral psychology testing system to accurately evaluate children's behavior and psychological state and propose appropriate approaches.

[0029] A behavioral psychology testing system according to an embodiment includes a test implementation unit, a data analysis unit, a proposal generation unit, and a linking unit. The test implementation unit implements a behavioral psychology test on a child. For example, a generation AI asks the child questions and collects their responses. The test implementation unit can also collect data by observing the child's behavior in a game format. For example, the generation AI asks a question such as, "What have you enjoyed doing at school recently?" and collects the child's responses. The data analysis unit analyzes the test data collected by the test implementation unit. For example, the generation AI analyzes the child's responses and behavioral patterns to detect signs of stress or anxiety. The data analysis unit can also analyze the test data using a pre-finished model. For example, the generation AI analyzes the child's responses using natural language processing technology to extract potential psychological states and behavioral patterns. The proposal generation unit proposes analysis results and approaches to parents and educators based on the test results analyzed by the data analysis unit. For example, it provides specific advice such as, "Your child seems to be feeling stressed recently. We recommend that you spend more time relaxing." The suggestion generation unit can also output the suggestion content in text format and send it to parents or educational institutions. The collaboration unit collaborates with medical institutions and education-related institutions as necessary. For example, if professional diagnosis or treatment is required based on the test results, the generation AI contacts a medical institution and requests appropriate measures. The collaboration unit can also collaborate with education-related institutions to provide learning support and counseling to children. This allows the behavioral psychology testing system according to the embodiment to accurately evaluate children's behavior and psychological state and suggest appropriate approaches. For example, it can reduce stress for children at school and provide a better learning environment. Furthermore, by detecting problems early and taking appropriate measures, it can support children's healthy development.

[0030] The test implementation unit can use motion capture technology to analyze a child's movement patterns and evaluate their psychological state. The test implementation unit, for example, uses motion capture technology to record the child's movements in detail. For example, it captures hand movements, walking patterns, etc. and saves them as data. In addition, the test implementation unit has a generation AI analyze the motion capture data and extract movement patterns. For example, it analyzes the speed of hand movements and the rhythm of walking to evaluate the child's psychological state. In addition, the test implementation unit has the generation AI evaluate the psychological state based on the movement patterns. For example, if movements are slow, it may determine that the child is feeling stressed. In this way, by analyzing the movement patterns, the child's psychological state can be evaluated in more detail.

[0031] The test implementation unit can assess a child's psychological state not only through questions, but also through creative activities using art or music. For example, the test implementation unit may have the generation AI have the child draw a picture and analyze the content. For example, the test implementation unit may evaluate the child's psychological state based on the colors used and the motifs drawn. The test implementation unit may also have the generation AI have the child play music and analyze the performance. For example, the test implementation unit may evaluate the child's psychological state based on the rhythm and tempo of the performance. The test implementation unit may also have the generation AI have the child make a craft and observe the process. For example, the test implementation unit may evaluate the child's psychological state based on the progress and completion of the craft. This makes it possible to evaluate a child's psychological state from multiple angles through creative activities.

[0032] The test implementation unit allows the generation AI to automatically generate customizable test protocols for different age groups and cultural backgrounds. For example, the generation AI automatically generates questions based on the child's age and cultural background. For example, it includes age-appropriate language and culturally relevant topics. The test implementation unit also allows the generation AI to select test methods based on different age groups and cultural backgrounds. For example, it may conduct tests using picture books for young children and interview-style tests for teenagers. The test implementation unit also allows the generation AI to automatically generate customizable test protocols. For example, it may adjust the question content and test methods to suit the child's characteristics. This allows for more appropriate psychological evaluations by automatically generating test protocols based on different age groups and cultural backgrounds.

[0033] The data analysis unit analyzes the child's answers using natural language processing technology and can extract latent psychological states and behavioral patterns. For example, the generation AI analyzes the child's answers using natural language processing technology to extract keywords and phrases. For example, it evaluates the child's psychological state based on frequently used words. The data analysis unit also has the generation AI analyze the context of the answers and extract latent psychological states. For example, it analyzes the context of the answers to detect signs of stress or anxiety. The data analysis unit also has the generation AI analyze the emotional nuances of the answers and extract behavioral patterns. For example, it evaluates behavioral patterns based on the frequency of positive and negative emotions. In this way, natural language processing technology can accurately extract a child's latent psychological states and behavioral patterns.

[0034] The data analysis unit analyzes the child's response data in chronological order, allowing long-term changes in psychological state to be tracked. In the data analysis unit, for example, the generation AI analyzes the child's response data in chronological order to track changes in psychological state. For example, it compares periodic test results. The data analysis unit also allows the generation AI to analyze trends in the response data and evaluate the long-term psychological state. For example, it analyzes fluctuations in stress levels. The data analysis unit also allows the generation AI to analyze patterns in the response data and identify long-term changes in psychological state. For example, it evaluates the psychological state based on the consistency and fluctuations in responses. This allows for accurate tracking of long-term changes in psychological state through chronological analysis.

[0035] The data analysis unit can compare the test data with other children's data and detect outliers or peculiar patterns. For example, the generation AI compares the test data with other children's data and detects outliers. For example, it identifies data that deviates significantly from the average. The data analysis unit also compares the test data with other children's data and detects peculiar patterns. For example, it detects a specific behavioral pattern that differs from other children as an peculiar pattern. The data analysis unit also evaluates the generation AI's psychological state based on outliers or peculiar patterns. For example, if there are many outliers, it determines that stress or anxiety is high. This allows outliers and peculiar patterns to be accurately detected by comparing with other children's data.

[0036] The data analysis unit can integrate the results of different test methods and perform a comprehensive psychological evaluation. For example, the generation AI integrates the results of question-style and game-style tests to perform a comprehensive psychological evaluation. For example, it evaluates stress levels by taking both results into account. The data analysis unit also integrates the results of observation-style tests to perform a comprehensive psychological evaluation. For example, it evaluates psychological states by combining observation data and question data. The data analysis unit also develops an algorithm for the generation AI to integrate the results of different test methods and perform a comprehensive psychological evaluation. For example, it integrates multiple test results to perform a consistent evaluation. This enables a more accurate psychological evaluation by integrating the results of different test methods.

[0037] The proposal generation unit can collect feedback from parents and educators and continuously improve the proposal content based on that feedback. In the proposal generation unit, for example, the generation AI collects feedback from parents and educators. For example, it uses an online survey or comment function. In addition, in the proposal generation unit, the generation AI continuously improves the proposal content based on the feedback. For example, it analyzes the content of the feedback and adjusts the proposal content. In addition, in the proposal generation unit, the generation AI updates the proposal content based on the feedback. For example, it reflects new information and opinions. This enables a more effective approach by continuously improving the proposal content based on feedback.

[0038] The proposal generation unit can visualize the proposal content and provide it in an intuitively understandable format using graphs and charts. In the proposal generation unit, for example, the generation AI visualizes the proposal content and provides it using graphs and charts. For example, changes in stress levels are displayed in a line graph. In addition, the proposal generation unit provides the proposal content in the form of infographics. For example, the evaluation results of psychological state are visually represented. In addition, the proposal generation unit visualizes the proposal content and provides it in an intuitively understandable format using charts and diagrams. For example, information is organized using charts and diagrams. In this way, providing visualized proposal content makes it easier for parents and educators to intuitively understand.

[0039] The proposal generation unit automatically translates the proposal content into different languages ​​and can obtain feedback from an international perspective. In the proposal generation unit, for example, the generation AI automatically translates the proposal content into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. In addition, the proposal generation unit uses the generation AI's automatic translation function to provide the proposal content in multiple languages. For example, it displays the proposal content in multiple languages. In addition, the proposal generation unit collects feedback based on the proposal content translated by the generation AI into different languages. For example, it reflects opinions from an international perspective. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0040] The proposal generation unit provides the proposal content in video format, deepening understanding through visual and auditory means. For example, the proposal generation unit uses a generation AI to provide the proposal content in video format, deepening understanding through visual and auditory means. For example, the proposal content is explained using a video with animation or narration. The proposal generation unit also uses a generation AI to provide the proposal content in an interactive video format. For example, the proposal content changes when the viewer selects an option in the video. The proposal generation unit also uses a generation AI to provide the proposal content in video format, conveying information through visual and auditory means. For example, information is provided by combining video and audio. In this way, providing the proposal content in video format allows for a deeper understanding of the proposal content through visual and auditory means.

[0041] The collaboration unit can collaborate with experts at medical institutions and educational institutions to create customized treatment plans based on test results. In the collaboration unit, for example, the generation AI collaborates with experts at medical institutions to create treatment plans based on test results. For example, it may propose psychological counseling or drug therapy. In addition, the collaboration unit can collaborate with experts at educational institutions to create learning support plans. For example, it may propose special needs education or counseling. In addition, the collaboration unit can collaborate with experts to create customized treatment plans based on expert opinions. For example, it may create a treatment plan based on individual needs. This allows collaboration with experts to create more appropriate treatment plans.

[0042] The Collaboration Department can collect feedback from collaborating institutions and continuously improve the testing protocols and proposals. In the Collaboration Department, for example, the generation AI collects feedback from medical institutions and educational institutions. For example, it uses online surveys and comment functions. The Collaboration Department also has the generation AI improve the testing protocols based on the feedback. For example, it analyzes the content of the feedback and adjusts the testing method. The Collaboration Department also has the generation AI update the proposals based on the feedback. For example, it reflects new information and opinions. This allows the testing protocols and proposals to be continuously improved based on feedback from collaborating institutions.

[0043] The collaboration unit can integrate data from different medical institutions and educational institutions and perform comprehensive analysis. In the collaboration unit, for example, the generation AI integrates data from different medical institutions and performs comprehensive analysis. For example, it centralizes diagnostic data from multiple hospitals. In addition, the collaboration unit integrates data from different educational institutions and performs comprehensive analysis. For example, it centralizes learning data from multiple schools. In addition, the collaboration unit allows the generation AI to perform comprehensive analysis based on the integrated data. For example, it combines data from different institutions to perform a consistent evaluation. This makes it possible to integrate data from different institutions and perform a more accurate comprehensive analysis.

[0044] The collaboration department automates communication with collaborating organizations, enabling the sharing of information in real time. For example, the collaboration department's generative AI automates communication with medical institutions and educational institutions. For example, it builds a system that automatically shares test results. The collaboration department also uses the generative AI to share information in real time. For example, it immediately sends test results and proposals to collaborating organizations. The collaboration department also improves the efficiency of collaboration through the automation of communication by the generative AI. For example, it uses chatbots and automatic email sending systems. This automates communication and shares information in real time, improving the efficiency of collaboration.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The test implementation unit can use virtual reality (VR) technology to observe the child's behavior. For example, the generation AI has the child wear a VR headset and observe their behavior in the virtual environment. The test implementation unit can also have the generation AI record the child's reactions in the virtual environment and save the data. For example, the generation AI can observe the child's behavior in a virtual classroom and evaluate their social skills and stress responses. The test implementation unit can also have the generation AI analyze the child's behavioral patterns in the virtual environment and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on their choices and changes in behavior in the virtual environment. In this way, using virtual reality technology makes it possible to observe the child's behavior in more detail and evaluate their psychological state.

[0047] The test implementation unit can use drone technology to observe children's behavior. For example, the generation AI uses a drone to observe children's behavior from the air and collect data. The test implementation unit can also analyze video data obtained by the generation AI from the drone to extract behavioral patterns. For example, the generation AI can observe children's behavior in a playground and evaluate their social skills and stress responses. The test implementation unit can also use drone technology to monitor children's behavior in real time and evaluate their psychological state. For example, the generation AI can track children's movements using a drone camera and evaluate their psychological state based on changes in their behavior. In this way, using drone technology makes it possible to observe children's behavior in more detail and evaluate their psychological state.

[0048] The test implementation unit can use a wearable device to observe a child's behavior. For example, the generation AI has the child wear a wearable device and collect biometric data such as heart rate and body temperature. The test implementation unit can also analyze the data obtained by the generation AI from the wearable device and extract behavioral patterns. For example, the generation AI can analyze fluctuations in heart rate during exercise and evaluate stress levels. The test implementation unit can also use the generation AI to monitor a child's behavior in real time and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on changes in heart rate and body temperature. In this way, using a wearable device makes it possible to observe a child's behavior in more detail and evaluate their psychological state.

[0049] The test implementation unit can use smart home technology to observe a child's behavior. For example, the generation AI uses smart home devices to observe a child's behavior and collect data. The test implementation unit can also analyze the data obtained by the generation AI from the smart home devices and extract behavioral patterns. For example, the generation AI can analyze movement patterns within the home and evaluate stress levels. The test implementation unit can also use smart home technology to monitor a child's behavior in real time and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on changes in their behavior within the home. In this way, by using smart home technology, it is possible to observe a child's behavior in more detail and evaluate their psychological state.

[0050] The test implementation unit can use robot technology to observe children's behavior. For example, the generation AI uses a robot to observe children's behavior and collect data. The test implementation unit can also analyze the data obtained by the generation AI from the robot and extract behavioral patterns. For example, the robot can interact with the child and analyze their responses to evaluate their psychological state. The test implementation unit can also use the generation AI to monitor children's behavior in real time and evaluate their psychological state using robot technology. For example, the robot's sensors can be used to track the child's movements and evaluate their psychological state based on changes in behavior. In this way, using robot technology makes it possible to observe children's behavior in more detail and evaluate their psychological state.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The test implementation unit conducts a behavioral psychology test on the child. For example, the generation AI asks the child questions and collects their responses. The test implementation unit can also collect data by observing the child's behavior in a game format. For example, the generation AI asks questions such as, "What have you enjoyed doing at school recently?" and collects the child's responses. Step 2: The data analysis unit analyzes the test data collected by the test implementation unit. For example, the generation AI analyzes the child's answers and behavioral patterns to detect signs of stress or anxiety. The data analysis unit can also analyze the test data using a pre-fine-tuned model. For example, the generation AI analyzes the child's answers using natural language processing technology to extract underlying psychological states and behavioral patterns. Step 3: The proposal generation unit proposes analysis results and approaches to parents and educational institutions based on the test results analyzed by the data analysis unit. For example, it provides specific advice such as, "Your child seems to be feeling stressed recently. We recommend that you increase the amount of time they spend relaxing." The proposal generation unit can also output the proposal content in text format and send it to parents and educational institutions. Step 4: The Collaboration Department will collaborate with medical institutions and educational institutions as needed. For example, if the test results indicate that specialized diagnosis or treatment is required, the AI ​​will contact a medical institution and request appropriate action. The Collaboration Department can also collaborate with educational institutions to provide learning support and counseling to children.

[0053] (Example 2) The behavioral psychology testing system according to an embodiment of the present invention uses generative AI to conduct behavioral psychology tests on children and analyze the results. Based on the test results, the system proposes analysis results and approaches to children to parents and educators. The system also collaborates with medical institutions and other educational institutions as needed. This allows the behavioral psychology testing system to accurately evaluate children's behavior and psychological state and propose appropriate approaches.

[0054] A behavioral psychology testing system according to an embodiment includes a test implementation unit, a data analysis unit, a proposal generation unit, and a linking unit. The test implementation unit implements a behavioral psychology test on a child. For example, a generation AI asks the child questions and collects their responses. The test implementation unit can also collect data by observing the child's behavior in a game format. For example, the generation AI asks a question such as, "What have you enjoyed doing at school recently?" and collects the child's responses. The data analysis unit analyzes the test data collected by the test implementation unit. For example, the generation AI analyzes the child's responses and behavioral patterns to detect signs of stress or anxiety. The data analysis unit can also analyze the test data using a pre-finished model. For example, the generation AI analyzes the child's responses using natural language processing technology to extract potential psychological states and behavioral patterns. The proposal generation unit proposes analysis results and approaches to parents and educators based on the test results analyzed by the data analysis unit. For example, it provides specific advice such as, "Your child seems to be feeling stressed recently. We recommend that you spend more time relaxing." The suggestion generation unit can also output the suggestion content in text format and send it to parents or educational institutions. The collaboration unit collaborates with medical institutions and education-related institutions as necessary. For example, if professional diagnosis or treatment is required based on the test results, the generation AI contacts a medical institution and requests appropriate measures. The collaboration unit can also collaborate with education-related institutions to provide learning support and counseling to children. This allows the behavioral psychology testing system according to the embodiment to accurately evaluate children's behavior and psychological state and suggest appropriate approaches. For example, it can reduce stress for children at school and provide a better learning environment. Furthermore, by detecting problems early and taking appropriate measures, it can support children's healthy development.

[0055] The test implementation unit can analyze the child's facial expressions and tone of voice in real time and adjust the questions while estimating their emotional state. In the test implementation unit, for example, the generation AI captures the child's facial expressions with a camera and analyzes them in real time. For example, it analyzes facial expressions such as smiling and furrowed brows to determine whether the child is relaxed or nervous. In the test implementation unit, the generation AI analyzes the child's tone of voice to estimate their emotional state. For example, it analyzes the pitch and speed of the voice to determine whether the child is excited or calm. In the test implementation unit, the generation AI adjusts the questions according to the child's emotional state. For example, if the child is nervous, it asks questions that will help them relax. This allows for more accurate psychological evaluation by asking questions that are appropriate to the child's emotional state.

[0056] The test implementation unit can use motion capture technology to analyze a child's movement patterns and evaluate their psychological state. The test implementation unit, for example, uses motion capture technology to record the child's movements in detail. For example, it captures hand movements, walking patterns, etc. and saves them as data. In addition, the test implementation unit has a generation AI analyze the motion capture data and extract movement patterns. For example, it analyzes the speed of hand movements and the rhythm of walking to evaluate the child's psychological state. In addition, the test implementation unit has the generation AI evaluate the psychological state based on the movement patterns. For example, if movements are slow, it may determine that the child is feeling stressed. In this way, by analyzing the movement patterns, the child's psychological state can be evaluated in more detail.

[0057] The test implementation unit uses the emotion estimation function to estimate in real time how the child is feeling in response to the questions and can dynamically change the content of the questions. In the test implementation unit, for example, the generation AI estimates the child's emotions in response to the answers in real time. For example, it analyzes facial expressions and tone of voice when answering and calculates an emotion score. In addition, in the test implementation unit, the generation AI dynamically changes the content of the questions based on the emotion score. For example, if the child is feeling anxious, it changes the questions to ones that give a sense of security. In addition, in the test implementation unit, the generation AI uses the emotion estimation function to monitor the child's emotional state in real time. For example, it continuously evaluates the child's emotional state based on fluctuations in the emotion score. This enables more accurate psychological evaluation by asking questions that correspond to the child's emotions.

[0058] The test implementation unit can assess a child's psychological state not only through questions, but also through creative activities using art or music. For example, the test implementation unit may have the generation AI have the child draw a picture and analyze the content. For example, the test implementation unit may evaluate the child's psychological state based on the colors used and the motifs drawn. The test implementation unit may also have the generation AI have the child play music and analyze the performance. For example, the test implementation unit may evaluate the child's psychological state based on the rhythm and tempo of the performance. The test implementation unit may also have the generation AI have the child make a craft and observe the process. For example, the test implementation unit may evaluate the child's psychological state based on the progress and completion of the craft. This makes it possible to evaluate a child's psychological state from multiple angles through creative activities.

[0059] The test implementation unit allows the generation AI to automatically generate customizable test protocols for different age groups and cultural backgrounds. For example, the generation AI automatically generates questions based on the child's age and cultural background. For example, it includes age-appropriate language and culturally relevant topics. The test implementation unit also allows the generation AI to select test methods based on different age groups and cultural backgrounds. For example, it may conduct tests using picture books for young children and interview-style tests for teenagers. The test implementation unit also allows the generation AI to automatically generate customizable test protocols. For example, it may adjust the question content and test methods to suit the child's characteristics. This allows for more appropriate psychological evaluations by automatically generating test protocols based on different age groups and cultural backgrounds.

[0060] The test implementation unit uses the emotion estimation function to provide a child with the most relaxing environment and conduct the test in that environment. For example, the test implementation unit uses the generation AI to analyze the child's emotional state in real time and provide the most relaxing environment. For example, the lighting and music may be adjusted. The test implementation unit also uses the generation AI to dynamically change the environment according to the child's emotional state. For example, if the child is nervous, relaxing music may be played. The test implementation unit also uses the generation AI to monitor the child's emotional state and provide the optimal environment. For example, the temperature and humidity may be adjusted. By conducting the test in a relaxing environment, the test can be brought out to elicit the child's natural reactions, enabling a more accurate psychological evaluation.

[0061] The data analysis unit analyzes the child's answers using natural language processing technology and can extract latent psychological states and behavioral patterns. For example, the generation AI analyzes the child's answers using natural language processing technology to extract keywords and phrases. For example, it evaluates the child's psychological state based on frequently used words. The data analysis unit also has the generation AI analyze the context of the answers and extract latent psychological states. For example, it analyzes the context of the answers to detect signs of stress or anxiety. The data analysis unit also has the generation AI analyze the emotional nuances of the answers and extract behavioral patterns. For example, it evaluates behavioral patterns based on the frequency of positive and negative emotions. In this way, natural language processing technology can accurately extract a child's latent psychological states and behavioral patterns.

[0062] The data analysis unit analyzes the child's response data in chronological order, allowing long-term changes in psychological state to be tracked. In the data analysis unit, for example, the generation AI analyzes the child's response data in chronological order to track changes in psychological state. For example, it compares periodic test results. The data analysis unit also allows the generation AI to analyze trends in the response data and evaluate the long-term psychological state. For example, it analyzes fluctuations in stress levels. The data analysis unit also allows the generation AI to analyze patterns in the response data and identify long-term changes in psychological state. For example, it evaluates the psychological state based on the consistency and fluctuations in responses. This allows for accurate tracking of long-term changes in psychological state through chronological analysis.

[0063] The data analysis unit uses the emotion estimation function to analyze the emotional nuances contained in the child's answers and detect signs of stress or anxiety. In the data analysis unit, for example, the generation AI analyzes the emotional nuances contained in the child's answers. For example, it calculates an emotion score based on facial expressions and tone of voice when answering. The data analysis unit also allows the generation AI to detect signs of stress or anxiety based on the emotion score. For example, it analyzes fluctuations in the emotion score to evaluate the stress level. The data analysis unit also allows the generation AI to analyze the emotional nuances in the answers and evaluate the psychological state. For example, it evaluates the psychological state based on the frequency of positive and negative emotions. In this way, by analyzing emotional nuances, signs of stress and anxiety can be detected early.

[0064] The data analysis unit can compare the test data with other children's data and detect outliers or peculiar patterns. For example, the generation AI compares the test data with other children's data and detects outliers. For example, it identifies data that deviates significantly from the average. The data analysis unit also compares the test data with other children's data and detects peculiar patterns. For example, it detects a specific behavioral pattern that differs from other children as an peculiar pattern. The data analysis unit also evaluates the generation AI's psychological state based on outliers or peculiar patterns. For example, if there are many outliers, it determines that stress or anxiety is high. This allows outliers and peculiar patterns to be accurately detected by comparing with other children's data.

[0065] The data analysis unit can integrate the results of different test methods and perform a comprehensive psychological evaluation. For example, the generation AI integrates the results of question-style and game-style tests to perform a comprehensive psychological evaluation. For example, it evaluates stress levels by taking both results into account. The data analysis unit also integrates the results of observation-style tests to perform a comprehensive psychological evaluation. For example, it evaluates psychological states by combining observation data and question data. The data analysis unit also develops an algorithm for the generation AI to integrate the results of different test methods and perform a comprehensive psychological evaluation. For example, it integrates multiple test results to perform a consistent evaluation. This enables a more accurate psychological evaluation by integrating the results of different test methods.

[0066] The data analysis unit uses the emotion estimation function to analyze the emotional reactions of parents and educators to the child's answers and can suggest a more appropriate approach. In the data analysis unit, for example, the generation AI analyzes the emotional reactions of parents and educators to the child's answers. For example, it analyzes facial expressions and tone of voice in response to the answer content. In addition, in the data analysis unit, the generation AI uses the emotion estimation function to analyze the emotional reactions of parents and educators. For example, it calculates an emotion score for the answer content. In addition, the data analysis unit suggests a more appropriate approach based on the emotional reactions of parents and educators. For example, if a parent is feeling anxious, it suggests an approach that gives them a sense of security. In this way, by analyzing the emotional reactions of parents and educators, a more appropriate approach can be suggested.

[0067] The proposal generation unit can collect feedback from parents and educators and continuously improve the proposal content based on that feedback. In the proposal generation unit, for example, the generation AI collects feedback from parents and educators. For example, it uses an online survey or comment function. In addition, in the proposal generation unit, the generation AI continuously improves the proposal content based on the feedback. For example, it analyzes the content of the feedback and adjusts the proposal content. In addition, in the proposal generation unit, the generation AI updates the proposal content based on the feedback. For example, it reflects new information and opinions. This enables a more effective approach by continuously improving the proposal content based on feedback.

[0068] The proposal generation unit can visualize the proposal content and provide it in an intuitively understandable format using graphs and charts. In the proposal generation unit, for example, the generation AI visualizes the proposal content and provides it using graphs and charts. For example, changes in stress levels are displayed in a line graph. In addition, the proposal generation unit provides the proposal content in the form of infographics. For example, the evaluation results of psychological state are visually represented. In addition, the proposal generation unit visualizes the proposal content and provides it in an intuitively understandable format using charts and diagrams. For example, information is organized using charts and diagrams. In this way, providing visualized proposal content makes it easier for parents and educators to intuitively understand.

[0069] The proposal generation unit uses the emotion estimation function to analyze how parents and educators feel about the proposal content and can adjust the proposal content accordingly. In the proposal generation unit, for example, the generation AI analyzes the emotions of parents and educators about the proposal content. For example, it analyzes facial expressions and tone of voice when reading the proposal content. In addition, the proposal generation unit uses the emotion estimation function to analyze the emotions of parents and educators. For example, it adjusts the proposal content based on the emotion score. In addition, the proposal generation unit updates the proposal content based on the emotional reactions of parents and educators. For example, it analyzes fluctuations in the emotion score and optimizes the proposal content. In this way, by analyzing the emotions of parents and educators, more appropriate proposal content can be provided.

[0070] The proposal generation unit automatically translates the proposal content into different languages ​​and can obtain feedback from an international perspective. In the proposal generation unit, for example, the generation AI automatically translates the proposal content into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. In addition, the proposal generation unit uses the generation AI's automatic translation function to provide the proposal content in multiple languages. For example, it displays the proposal content in multiple languages. In addition, the proposal generation unit collects feedback based on the proposal content translated by the generation AI into different languages. For example, it reflects opinions from an international perspective. In this way, by automatically translating into different languages, feedback from an international perspective can be obtained.

[0071] The proposal generation unit provides the proposal content in video format, deepening understanding through visual and auditory means. For example, the proposal generation unit uses a generation AI to provide the proposal content in video format, deepening understanding through visual and auditory means. For example, the proposal content is explained using a video with animation or narration. The proposal generation unit also uses a generation AI to provide the proposal content in an interactive video format. For example, the proposal content changes when the viewer selects an option in the video. The proposal generation unit also uses a generation AI to provide the proposal content in video format, conveying information through visual and auditory means. For example, information is provided by combining video and audio. In this way, providing the proposal content in video format allows for a deeper understanding of the proposal content through visual and auditory means.

[0072] The proposal generation unit can use the emotion estimation function to collect children's emotional reactions to the proposal content and provide feedback to parents and educators. In the proposal generation unit, for example, the generation AI collects children's emotional reactions to the proposal content. For example, it analyzes facial expressions and tone of voice when listening to the proposal content. In addition, in the proposal generation unit, the generation AI uses the emotion estimation function to analyze children's emotional reactions. For example, it evaluates the emotional reaction based on an emotion score. In addition, in the proposal generation unit, the generation AI provides feedback to parents and educators based on the children's emotional reactions. For example, it reports how the child feels about the proposal content. In this way, a more appropriate approach can be achieved by collecting children's emotional reactions and providing feedback to parents and educators.

[0073] The collaboration unit can collaborate with experts at medical institutions and educational institutions to create customized treatment plans based on test results. In the collaboration unit, for example, the generation AI collaborates with experts at medical institutions to create treatment plans based on test results. For example, it may propose psychological counseling or drug therapy. In addition, the collaboration unit can collaborate with experts at educational institutions to create learning support plans. For example, it may propose special needs education or counseling. In addition, the collaboration unit can collaborate with experts to create customized treatment plans based on expert opinions. For example, it may create a treatment plan based on individual needs. This allows collaboration with experts to create more appropriate treatment plans.

[0074] The Collaboration Department can collect feedback from collaborating institutions and continuously improve the testing protocols and proposals. In the Collaboration Department, for example, the generation AI collects feedback from medical institutions and educational institutions. For example, it uses online surveys and comment functions. The Collaboration Department also has the generation AI improve the testing protocols based on the feedback. For example, it analyzes the content of the feedback and adjusts the testing method. The Collaboration Department also has the generation AI update the proposals based on the feedback. For example, it reflects new information and opinions. This allows the testing protocols and proposals to be continuously improved based on feedback from collaborating institutions.

[0075] The collaboration unit uses the emotion estimation function to analyze how experts at collaborating institutions feel about the test results, thereby improving the quality of collaboration. In the collaboration unit, for example, the generation AI analyzes the experts' emotions about the test results. For example, it analyzes their facial expressions and tone of voice when they see the test results. In addition, the collaboration unit uses the emotion estimation function to analyze the experts' emotions. For example, it evaluates their emotional reactions based on an emotion score. In addition, the collaboration unit improves the quality of collaboration based on the experts' emotional reactions. For example, if the expert is feeling anxious, it communicates in a way that gives them a sense of security. In this way, the quality of collaboration can be improved by analyzing the experts' emotions.

[0076] The collaboration unit can integrate data from different medical institutions and educational institutions and perform comprehensive analysis. In the collaboration unit, for example, the generation AI integrates data from different medical institutions and performs comprehensive analysis. For example, it centralizes diagnostic data from multiple hospitals. In addition, the collaboration unit integrates data from different educational institutions and performs comprehensive analysis. For example, it centralizes learning data from multiple schools. In addition, the collaboration unit allows the generation AI to perform comprehensive analysis based on the integrated data. For example, it combines data from different institutions to perform a consistent evaluation. This makes it possible to integrate data from different institutions and perform a more accurate comprehensive analysis.

[0077] The collaboration department automates communication with collaborating organizations, enabling the sharing of information in real time. For example, the collaboration department's generative AI automates communication with medical institutions and educational institutions. For example, it builds a system that automatically shares test results. The collaboration department also uses the generative AI to share information in real time. For example, it immediately sends test results and proposals to collaborating organizations. The collaboration department also improves the efficiency of collaboration through the automation of communication by the generative AI. For example, it uses chatbots and automatic email sending systems. This automates communication and shares information in real time, improving the efficiency of collaboration.

[0078] The collaboration unit uses the emotion estimation function to analyze how experts at collaborating institutions feel about the proposal content, thereby improving the quality of collaboration. In the collaboration unit, for example, the generation AI analyzes the experts' emotions about the proposal content. For example, it analyzes facial expressions and tone of voice when reading the proposal content. In addition, in the collaboration unit, the generation AI uses the emotion estimation function to analyze the experts' emotions. For example, it evaluates the emotional response based on an emotion score. In addition, the collaboration unit improves the quality of collaboration based on the experts' emotional responses. For example, if the expert is feeling anxious, it communicates in a way that gives them a sense of security. In this way, the quality of collaboration can be improved by analyzing the experts' emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The test implementation unit can use virtual reality (VR) technology to observe the child's behavior. For example, the generation AI has the child wear a VR headset and observe their behavior in the virtual environment. The test implementation unit can also have the generation AI record the child's reactions in the virtual environment and save the data. For example, the generation AI can observe the child's behavior in a virtual classroom and evaluate their social skills and stress responses. The test implementation unit can also have the generation AI analyze the child's behavioral patterns in the virtual environment and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on their choices and changes in behavior in the virtual environment. In this way, using virtual reality technology makes it possible to observe the child's behavior in more detail and evaluate their psychological state.

[0081] The test implementation unit uses the emotion estimation function to estimate in real time how a child is feeling in response to a specific question and adjust the difficulty of the question. For example, the generation AI estimates the child's emotions in response to their answer in real time and calculates an emotion score. The test implementation unit also dynamically changes the difficulty of the questions based on the emotion score. For example, if the child is feeling anxious, the question is changed to an easier one. The test implementation unit also uses the emotion estimation function to monitor the child's emotional state in real time. For example, the child's emotional state is continuously evaluated based on fluctuations in the emotion score. This allows for more accurate psychological evaluation by asking questions that correspond to the child's emotions.

[0082] The test implementation unit can use drone technology to observe children's behavior. For example, the generation AI uses a drone to observe children's behavior from the air and collect data. The test implementation unit can also analyze video data obtained by the generation AI from the drone to extract behavioral patterns. For example, the generation AI can observe children's behavior in a playground and evaluate their social skills and stress responses. The test implementation unit can also use drone technology to monitor children's behavior in real time and evaluate their psychological state. For example, the generation AI can track children's movements using a drone camera and evaluate their psychological state based on changes in their behavior. In this way, using drone technology makes it possible to observe children's behavior in more detail and evaluate their psychological state.

[0083] The test implementation unit uses the emotion estimation function to estimate in real time how a child is feeling about a specific activity and adjust the activity accordingly. For example, the generation AI analyzes the child's facial expressions and tone of voice to calculate an emotion score. The test implementation unit then dynamically changes the activity based on the emotion score. For example, if the child is excited, the activity will be changed to a calmer one. The test implementation unit also uses the emotion estimation function to monitor the child's emotional state in real time. For example, the child's emotional state can be continuously evaluated based on fluctuations in the emotion score. This allows for more accurate psychological evaluation by performing activities that correspond to the child's emotions.

[0084] The test implementation unit can use a wearable device to observe a child's behavior. For example, the generation AI has the child wear a wearable device and collect biometric data such as heart rate and body temperature. The test implementation unit can also analyze the data obtained by the generation AI from the wearable device and extract behavioral patterns. For example, the generation AI can analyze fluctuations in heart rate during exercise and evaluate stress levels. The test implementation unit can also use the generation AI to monitor a child's behavior in real time and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on changes in heart rate and body temperature. In this way, using a wearable device makes it possible to observe a child's behavior in more detail and evaluate their psychological state.

[0085] The test implementation unit can use the emotion estimation function to estimate in real time how a child feels about specific music and adjust the music selection accordingly. For example, the generation AI analyzes the child's facial expressions and tone of voice to calculate an emotion score. The test implementation unit then dynamically changes the music selection based on the emotion score. For example, if the child is relaxed, the generation AI selects relaxing music. The test implementation unit also uses the emotion estimation function to monitor the child's emotional state in real time. For example, the generation AI continuously evaluates the child's emotional state based on fluctuations in the emotion score. This allows for more accurate psychological evaluation by providing music that matches the child's emotions.

[0086] The test implementation unit can use smart home technology to observe a child's behavior. For example, the generation AI uses smart home devices to observe a child's behavior and collect data. The test implementation unit can also analyze the data obtained by the generation AI from the smart home devices and extract behavioral patterns. For example, the generation AI can analyze movement patterns within the home and evaluate stress levels. The test implementation unit can also use smart home technology to monitor a child's behavior in real time and evaluate their psychological state. For example, the generation AI can evaluate their psychological state based on changes in their behavior within the home. In this way, by using smart home technology, it is possible to observe a child's behavior in more detail and evaluate their psychological state.

[0087] The test implementation unit uses the emotion estimation function to estimate in real time how a child is feeling in response to a specific video and can adjust the content of the video. For example, the generation AI analyzes the child's facial expressions and tone of voice to calculate an emotion score. The test implementation unit then dynamically changes the content of the video based on the emotion score. For example, if the child is excited, the video will be changed to a calmer one. The test implementation unit also uses the emotion estimation function to monitor the child's emotional state in real time. For example, the test implementation unit continuously evaluates the child's emotional state based on fluctuations in the emotion score. This allows for more accurate psychological evaluation by providing video that matches the child's emotions.

[0088] The test implementation unit can use robot technology to observe children's behavior. For example, the generation AI uses a robot to observe children's behavior and collect data. The test implementation unit can also analyze the data obtained by the generation AI from the robot and extract behavioral patterns. For example, the robot can interact with the child and analyze their responses to evaluate their psychological state. The test implementation unit can also use the generation AI to monitor children's behavior in real time and evaluate their psychological state using robot technology. For example, the robot's sensors can be used to track the child's movements and evaluate their psychological state based on changes in behavior. In this way, using robot technology makes it possible to observe children's behavior in more detail and evaluate their psychological state.

[0089] The test implementation unit uses the emotion estimation function to estimate in real time how a child is feeling about a particular game and adjust the game's difficulty level accordingly. For example, the generation AI analyzes the child's facial expressions and tone of voice to calculate an emotion score. The test implementation unit then dynamically changes the game's difficulty level based on the emotion score. For example, if the child is feeling stressed, the generation AI changes the difficulty level to an easier level. The test implementation unit also uses the emotion estimation function to monitor the child's emotional state in real time. For example, the generation AI continuously evaluates the child's emotional state based on fluctuations in the emotion score. This allows for a more accurate psychological assessment by providing games that match the child's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The test implementation unit conducts a behavioral psychology test on the child. For example, the generation AI asks the child questions and collects their responses. The test implementation unit can also collect data by observing the child's behavior in a game format. For example, the generation AI asks questions such as, "What have you enjoyed doing at school recently?" and collects the child's responses. Step 2: The data analysis unit analyzes the test data collected by the test implementation unit. For example, the generation AI analyzes the child's answers and behavioral patterns to detect signs of stress or anxiety. The data analysis unit can also analyze the test data using a pre-fine-tuned model. For example, the generation AI analyzes the child's answers using natural language processing technology to extract underlying psychological states and behavioral patterns. Step 3: The proposal generation unit proposes analysis results and approaches to parents and educational institutions based on the test results analyzed by the data analysis unit. For example, it provides specific advice such as, "Your child seems to be feeling stressed recently. We recommend that you increase the amount of time they spend relaxing." The proposal generation unit can also output the proposal content in text format and send it to parents and educational institutions. Step 4: The Collaboration Department will collaborate with medical institutions and educational institutions as needed. For example, if the test results indicate that specialized diagnosis or treatment is required, the AI ​​will contact a medical institution and request appropriate action. The Collaboration Department can also collaborate with educational institutions to provide learning support and counseling to children.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A testing department that conducts behavioral and psychological tests for children; a data analysis unit that analyzes the test data collected by the test execution unit; a proposal generation unit that proposes analysis results and approaches to parents or educational institutions based on the test results analyzed by the data analysis unit; A liaison department will be established to collaborate with medical institutions and educational institutions as needed. A system characterized by:

2. The inspection execution unit Analyzes the child's facial expressions and tone of voice in real time to estimate their emotional state and adjust questions accordingly 2. The system of claim 1.

3. The inspection execution unit Assessing mental health through creative activities using art or music, rather than just questionnaires 2. The system of claim 1.

4. The data analysis unit Children's responses are analyzed using natural language processing technology to extract underlying psychological states or behavioral patterns.

2. The system of claim 1.

5. The proposal generation unit Collect feedback from said parents or educators and use it to continually improve our offerings 2. The system of claim 1.

6. The linking unit is Collaborate with experts from the medical institution or educational institution to create a customized treatment plan based on the test results.

2. The system of claim 1.

7. The inspection execution unit Dynamically change questions based on real-time estimation of how the child feels about the question 2. The system of claim 1.

8. The data analysis unit Analyze the emotional nuances in your child's responses to detect signs of stress or anxiety 2. The system of claim 1.

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