system

Abstract

The system according to this embodiment aims to analyze a mandala drawn by the user and objectively evaluate their mental state. [Solution] The system according to this embodiment comprises a scanning unit, an analysis unit, an evaluation unit, and a provision unit. The scanning unit scans the mandala drawn by the user. The analysis unit analyzes the mandala scanned by the scanning unit. The evaluation unit evaluates the mental state based on the results analyzed by the analysis unit. The provision unit provides the results evaluated by the evaluation unit as a report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the discovery of mental disorders depends on self-reporting and subjective diagnosis, and there is a problem that it is difficult to discover.

[0005] The system according to the embodiment aims to analyze a mandala drawn by a user and objectively evaluate the mental state.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a scanning unit, an analysis unit, an evaluation unit, and a provision unit. The scanning unit scans the mandala drawn by the user. The analysis unit analyzes the mandala scanned by the scanning unit. The evaluation unit evaluates the mental state based on the results analyzed by the analysis unit. The provision unit provides the results evaluated by the evaluation unit as a report. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a mandala drawn by the user and objectively evaluate their mental state. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mental state analysis system according to an embodiment of the present invention is a system in which an AI analyzes a mandala drawn by a user, analyzes the user's mental state based on data of color, shape, and pattern, and provides a report. The mental state analysis system scans the mandala drawn by the user, analyzes the color, shape, and pattern, evaluates the user's mental state, and generates a report. For example, the mental state analysis system allows the user to freely draw a mandala using colors, shapes, and patterns. For example, the user can draw colorful circles and lines, geometric patterns, etc. This mandala is said to reflect the user's unconscious signs. Next, the mental state analysis system has the AI ​​scan the drawn mandala and analyze the color, shape, and pattern. The AI ​​analyzes each element of the mandala using image recognition technology and evaluates the use of colors, the arrangement of shapes, the repetition of patterns, etc. For example, it can detect cases where a particular color is used frequently or where a particular shape appears frequently. Next, the mental state analysis system evaluates the user's mental state based on the analysis results. The AI ​​determines the user's mental state using a mandala analysis method based on Jungian psychology. For example, if certain colors or shapes are used frequently, it can be determined that this may indicate the user's stress or anxiety. Finally, the mental state analysis system provides the user with an AI-generated report. This report describes the user's mental state and risk of depression, leading to early detection and appropriate care. For example, if the user is experiencing stress, the report includes advice on the causes and countermeasures. In this way, users can objectively understand their own mental state and receive appropriate care. This mechanism goes beyond the limitations of self-reporting and subjective diagnosis, capturing unconscious signs to enable early detection of mental health and depression risks and lead to appropriate care. For example, the AI ​​can detect stress and anxiety that the user is unaware of, allowing for early intervention. Furthermore, if a company prioritizes employee mental health care, this tool can be used to regularly monitor employees' mental states and provide appropriate support.This allows the mental state analysis system to objectively evaluate the user's mental state and provide appropriate care early on.

[0029] The mental state analysis system according to this embodiment comprises a scanning unit, an analysis unit, an evaluation unit, and a provision unit. The scanning unit scans the mandala drawn by the user. The scanning unit can, for example, scan the mandala using a smartphone camera. The scanning unit can, for example, scan the mandala at high resolution and acquire detailed image data. The scanning unit can also, for example, perform scans under different lighting conditions to ensure accurate acquisition of colors. The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit can, for example, analyze colors, shapes, and patterns. The analysis unit can, for example, determine if the frequent use of certain colors or shapes may indicate the user's stress or anxiety. The analysis unit can, for example, analyze the speed and order of drawing the mandala and evaluate the user's mental state. The evaluation unit evaluates the mental state based on the results analyzed by the analysis unit. The evaluation unit can, for example, evaluate the mental state based on Jungian psychology. The evaluation unit can, for example, refer to the user's past mental state data and compare it with the current evaluation result. The service provider provides the results evaluated by the evaluation provider as a report. The service provider can, for example, provide the report through an app. The service provider can, for example, estimate the user's emotions and adjust the way the report is presented based on the estimated user emotions. As a result, the mental state analysis system according to this embodiment can objectively evaluate the user's mental state and provide appropriate care at an early stage.

[0030] The scanning unit scans mandalas drawn by the user. The scanning unit can, for example, scan mandalas using a smartphone camera. Specifically, it uses a smartphone camera to scan mandalas at high resolution, acquiring detailed image data. The scanning unit can perform scans under different lighting conditions and utilize multiple light sources to ensure accurate color acquisition. For example, it can perform scans using different light sources such as natural light, fluorescent lights, and LED lights, and then compare and integrate color data under each light source to obtain more accurate color information. Furthermore, the scanning unit incorporates algorithms to correct image distortion and blur, ensuring accurate image data even when the user performs the scan handheld. Additionally, the scanning unit can improve scanning accuracy by displaying guidelines and frames when the user begins scanning, instructing them on the appropriate scanning position and angle. This allows the scanning unit to scan user-drawn mandalas with high precision and acquire detailed image data for the analysis unit.

[0031] The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit can analyze, for example, color, shape, and pattern. Specifically, it uses an image analysis algorithm to extract color information from each part of the mandala and analyze the distribution and frequency of use of colors. For shape and pattern analysis, it uses a shape recognition algorithm to detect specific shapes and patterns within the mandala and analyze their arrangement and repeating patterns. Furthermore, the analysis unit can track the progress of drawing by adding timestamps to the scanned data to analyze the drawing speed and order when the user draws the mandala. This allows for inferring the user's mental state from their drawing behavior. For example, if certain colors or shapes are frequently used, it can be determined that this may indicate the user's stress or anxiety. Also, a fast drawing speed may indicate impatience or tension, while a slow speed may indicate relaxation or concentration. The analysis unit comprehensively analyzes this data to provide basic data for evaluating the user's mental state.

[0032] The evaluation unit assesses the user's mental state based on the results analyzed by the analysis unit. For example, the evaluation unit can assess the mental state based on Jungian psychology. Specifically, it comprehensively evaluates the user's mental state based on data such as color, shape, pattern, and drawing speed provided by the analysis unit. In evaluations based on Jungian psychology, the psychological meanings of specific colors and shapes are considered to infer the user's inner state. For example, frequent use of red may indicate passion or anger, while frequent use of blue may indicate calmness or sadness. Furthermore, the evaluation unit can refer to the user's past mental state data and compare it to the current evaluation results. This allows for the understanding of changes and trends in the user's mental state, supporting long-term mental state management. In addition, the evaluation unit can provide specific advice and care suggestions to the user based on the analysis results. For example, if high stress levels are detected, relaxation techniques or counseling can be suggested.

[0033] The service provider provides the results evaluated by the evaluation provider as a report. The service provider can, for example, provide the report through an app. Specifically, it visualizes the evaluation results in an easy-to-understand manner and explains them to the user using graphs and charts. The service provider can also estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is feeling stressed, the report will be displayed using relaxing colors and designs so that the user can receive the content with peace of mind. Furthermore, the service provider can adjust the content of the report according to the user's level of understanding and explain it in simple language, avoiding technical jargon. This allows the user to accurately understand their own mental state and take appropriate measures. The service provider can also update the report regularly so that the user can continuously monitor their own mental state. As a result, the mental state analysis system according to this embodiment can objectively evaluate the user's mental state and provide appropriate care early on.

[0034] The scanning unit can scan a mandala using a smartphone camera. The scanning unit can, for example, scan a mandala using a smartphone camera and save it as image data. The scanning unit can, for example, scan a mandala in real time using a smartphone camera and acquire detailed image data. The scanning unit can, for example, scan a mandala using a smartphone camera and save the image data to the cloud. This makes it easy to scan a mandala using a smartphone camera. Smartphone cameras include, for example, high resolution, autofocus, and HDR shooting functions, but are not limited to these examples. Some or all of the above processing in the scanning unit may be performed using, for example, AI, or not using AI. For example, the scanning unit can input image data acquired by a smartphone camera into a generating AI and have the generating AI perform analysis of the image data.

[0035] The analysis unit can analyze color, shape, and pattern. For example, the analysis unit can clarify specific analysis methods and criteria for color. For example, it can analyze hue, saturation, and brightness. For example, the analysis unit can clarify specific analysis methods and criteria for shape. For example, it can analyze geometric shapes and symmetry. For example, the analysis unit can clarify specific analysis methods and criteria for pattern. For example, it can analyze repeating patterns and fractal patterns. By analyzing color, shape, and pattern, the user's mental state can be evaluated in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the scanning unit into a generating AI and have the generating AI perform the analysis of color, shape, and pattern.

[0036] The evaluation unit can assess the mental state based on Jungian psychology. The evaluation unit can clarify specific theories and evaluation methods of Jungian psychology, for example. For example, it can evaluate archetypes, shadows, etc. The evaluation unit can accurately grasp the user's mental state through evaluation based on Jungian psychology, for example. The evaluation unit can use questionnaires based on Jungian psychology to assess the user's mental state, for example. The evaluation unit can conduct interviews based on Jungian psychology to assess the user's mental state, for example. This allows the evaluation unit to accurately grasp the user's mental state through evaluation based on Jungian psychology. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data acquired by the analysis unit into a generating AI and have the generating AI perform the evaluation of the mental state.

[0037] The service provider can provide reports through an app. For example, by providing reports through an app, the service provider can make it easy for users to check the results. For example, when providing reports through an app, the service provider can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, when providing reports through an app, the service provider can refer to the user's past feedback to select the optimal report format. For example, when providing reports through an app, the service provider can provide customized advice by considering the user's lifestyle and interests. This makes it easy for users to check the results by providing reports through an app. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the report generated by the evaluation unit into a generating AI and have the generating AI adjust the way the report is presented.

[0038] The analysis unit can determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, the analysis unit can clarify specific color analysis methods and criteria to determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, it can analyze hue, saturation, and brightness. For example, the analysis unit can clarify specific shape analysis methods and criteria to determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, it can analyze geometric shapes and symmetry. This allows for early detection of user stress and anxiety by detecting the frequent use of certain colors and shapes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the scanning unit into a generating AI and have the generating AI perform an analysis to detect the frequent use of certain colors and shapes.

[0039] The scanning unit can detect the user's hand movements and pen pressure during scanning and record the mandala drawing process. For example, the scanning unit can detect the user's hand movements and record the speed and direction of drawing. For example, the scanning unit can detect the user's pen pressure and record the strength of the drawing. For example, the scanning unit can combine the user's hand movements and pen pressure to record the detailed drawing process. This allows for a detailed understanding of the drawing process by recording the user's hand movements and pen pressure. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's hand movement data into a generating AI and have the generating AI record the drawing process.

[0040] The scanning unit can perform scans under different lighting conditions during scanning to ensure accurate color acquisition. For example, the scanning unit can perform scans under natural light to ensure accurate color acquisition. For example, the scanning unit can perform scans under artificial light to ensure accurate color acquisition. For example, the scanning unit can perform scans using multiple light sources to ensure accurate color acquisition. This makes it possible to acquire accurate colors by performing scans under different lighting conditions. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input image data acquired under different lighting conditions into a generating AI and have the generating AI perform accurate color acquisition.

[0041] The scanning unit can prioritize the analysis of region-specific colors and patterns by considering the user's geographical location information during scanning. For example, the scanning unit can acquire the user's geographical location information and prioritize the analysis of region-specific colors. For example, the scanning unit can acquire the user's geographical location information and prioritize the analysis of region-specific patterns. For example, the scanning unit can acquire the user's geographical location information and analyze it in combination with region-specific colors and patterns. This allows for more accurate analysis by prioritizing the analysis of region-specific colors and patterns. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of region-specific colors and patterns.

[0042] The scanning unit can analyze the user's social media activity during scanning and obtain relevant mandala designs. For example, the scanning unit can analyze the user's social media activity and obtain relevant colors. For example, the scanning unit can analyze the user's social media activity and obtain relevant shapes. For example, the scanning unit can analyze the user's social media activity and obtain relevant patterns. In this way, designs related to the user can be obtained by analyzing social media activity. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's social media data into a generating AI and cause the generating AI to perform the acquisition of relevant mandala designs.

[0043] The analysis unit can analyze the mandala's drawing speed and sequence during the analysis process to evaluate the user's mental state. For example, the analysis unit can analyze the mandala's drawing speed to evaluate the user's stress level. For example, the analysis unit can analyze the mandala's drawing sequence to evaluate the user's thought patterns. For example, the analysis unit can analyze the mandala's drawing speed and sequence in combination to comprehensively evaluate the user's mental state. This allows for a detailed evaluation of the user's mental state by analyzing the drawing speed and sequence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the drawing speed and sequence data acquired by the scanning unit into a generating AI and have the generating AI perform the evaluation of the mental state.

[0044] The analysis unit can perform a more detailed evaluation of the mental state by considering the interrelationships of each element of the mandala during analysis. For example, the analysis unit can analyze the interrelationships of the mandala's colors and shapes to evaluate the user's emotions. For example, the analysis unit can analyze the interrelationships of the mandala's patterns and arrangements to evaluate the user's thought patterns. For example, the analysis unit can comprehensively analyze the interrelationships of the mandala's colors, shapes, and patterns to evaluate the user's mental state in detail. This makes it possible to evaluate the mental state in detail by considering the interrelationships of each element. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data of each element acquired by the scanning unit into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0045] The analysis unit can analyze trends in change by referring to the user's past mandala data during analysis. For example, the analysis unit can analyze changes in color by referring to the user's past mandala data. For example, the analysis unit can analyze changes in shape by referring to the user's past mandala data. For example, the analysis unit can analyze changes in pattern by referring to the user's past mandala data. This allows the system to understand changes in the user's mental state by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past mandala data into a generating AI and have the generating AI perform an analysis to analyze trends in change.

[0046] The analysis unit can refer to the user's lifestyle data during analysis and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's sleep data and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's exercise data and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's dietary data and reflect it in the evaluation of their mental state. This makes it possible to evaluate the mental state more accurately by referring to lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the evaluation of the mental state.

[0047] The evaluation unit can refer to the user's past mental state data during the evaluation and compare it with the current evaluation result. For example, the evaluation unit can refer to the user's past mental state data and compare it with the current evaluation result. For example, the evaluation unit can refer to the user's past mental state data and analyze trends in change. For example, the evaluation unit can refer to the user's past mental state data and identify areas for improvement. This allows for a more accurate evaluation of the current mental state by referring to past data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past mental state data into a generating AI and have the generating AI perform a comparison with the current evaluation result.

[0048] The evaluation unit can perform a more accurate evaluation by considering the user's living environment and stress factors during the evaluation process. For example, the evaluation unit can consider the user's living environment and reflect it in the evaluation results. For example, the evaluation unit can consider the user's stress factors and reflect them in the evaluation results. For example, the evaluation unit can consider the user's living environment and stress factors in combination and reflect them in the evaluation results. This makes it possible to evaluate the mental state more accurately by considering the living environment and stress factors. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's living environment data into a generating AI and have the generating AI perform the evaluation of the mental state.

[0049] The evaluation unit can take into account the user's geographical location information during the evaluation process and reflect region-specific stress factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific stress factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific living environment factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific cultural background factors in the evaluation. This makes it possible to evaluate mental state more accurately by taking region-specific stress factors into consideration. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform an evaluation of region-specific stress factors.

[0050] The evaluation unit can analyze the user's social media activity during the evaluation process and reflect this in the assessment of their mental state. For example, the evaluation unit can analyze the user's social media activity and assess their stress level. For example, the evaluation unit can analyze the user's social media activity and assess changes in their emotions. For example, the evaluation unit can analyze the user's social media activity and assess trends in their mental state. This allows for a more accurate assessment of the user's mental state by analyzing their social media activity. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the assessment of their mental state.

[0051] The service provider can select the most suitable report format by referring to the user's past feedback when providing a report. For example, if the user previously preferred a detailed report, the service provider can provide a detailed report format. For example, if the user previously preferred a simplified report, the service provider can provide a simplified report format. The service provider can, for example, analyze the user's past feedback and select the most suitable report format. This allows the service provider to provide the user with the most suitable report format by referring to past feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the most suitable report format.

[0052] The service provider can provide customized advice when delivering reports, taking into account the user's lifestyle and interests. For example, the service provider can provide appropriate advice by considering the user's lifestyle. For example, the service provider can provide relevant advice by considering the user's interests. For example, the service provider can provide customized advice by considering a combination of the user's lifestyle and interests. This allows for the provision of more appropriate advice by taking into account lifestyle and interests. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data and interest data into a generating AI and have the generating AI perform the task of providing customized advice.

[0053] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, by taking device information into account, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0054] The service provider can analyze the user's social media activity and provide relevant advice when providing reports. For example, the service provider can analyze the user's social media activity and obtain relevant colors. For example, the service provider can analyze the user's social media activity and obtain relevant shapes. For example, the service provider can analyze the user's social media activity and obtain relevant patterns. This allows the service provider to provide relevant advice to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant advice.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A mental state analysis system can include the ability to record and later replay the user's drawing process. For example, after a user completes a drawing, they can play a video of the drawing process to see how they chose colors and shapes. It can also focus on specific moments during the drawing process and provide commentary on the emotions and mental state at those times. Furthermore, it can compare a mandala drawn by the user in the past with a mandala drawn now, visually demonstrating changes in their mental state. This allows users to gain a deeper understanding of their changing mental state and receive appropriate care.

[0057] The mental state analysis system can provide comparative data with other users during the user's drawing process. For example, it can show what colors and shapes other users of the same age group and gender have chosen. It can also show how the user's drawing differs from that of other users. Furthermore, it can show how the user's mental state differs from that of other users. This allows users to compare their own mental state with that of other users and gain a more objective understanding of it.

[0058] The mental state analysis system can provide advice to users during their drawing process, based on their progress. For example, it can offer advice on basic color and shape selection when the user has just started drawing. As the drawing progresses, it can provide advice on more advanced patterns and designs. Furthermore, after the drawing is completed, it can provide an evaluation of the entire drawing along with advice for the next drawing. This allows users to improve their skills and gain a deeper understanding of their mental state throughout the drawing process.

[0059] The mental state analysis system can provide music during the user's drawing process, according to the progress of the drawing. For example, it can provide calming music to help the user relax before starting to draw. It can also provide music to enhance concentration as the drawing progresses. Furthermore, it can provide music to allow time for reflection after the drawing is completed. This allows the user to enjoy the drawing process more through music and to gain a deeper understanding of their mental state.

[0060] The mental state analysis system can provide visual effects during the user's drawing process, depending on the progress of the drawing. For example, it can provide simple visual effects when the user has just started drawing. As the drawing progresses, it can provide more complex visual effects. Furthermore, after the drawing is completed, it can provide a visual effect of the entire drawing, indicating which parts were particularly important. This allows the user to enjoy the drawing process more through the visual effects and to gain a deeper understanding of their mental state.

[0061] The mental state analysis system can provide interactive guidance during the user's drawing process, adapting to the progress of the drawing. For example, at the beginning of the drawing process, it can provide interactive guidance on basic color and shape selection. As the drawing progresses, it can provide interactive guidance on more advanced patterns and designs. Furthermore, after the drawing is completed, it can provide an evaluation of the entire drawing, along with interactive guidance for the next drawing. This allows users to enjoy the drawing process more and gain a deeper understanding of their mental state through the interactive guidance.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The scanning unit scans the mandala drawn by the user. The scanning unit uses, for example, a smartphone camera to scan the mandala at high resolution and obtain detailed image data. It also performs scans under different lighting conditions to ensure accurate color acquisition. Step 2: The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit analyzes the colors, shapes, and patterns, and determines if the frequent use of certain colors or shapes may indicate the user's stress or anxiety. It also analyzes the speed and order of drawing the mandala to evaluate the user's mental state. Step 3: The evaluation unit assesses the mental state based on the results analyzed by the analysis unit. The evaluation unit assesses the mental state based on Jungian psychology and compares the current evaluation result with the user's past mental state data. Step 4: The service provider provides a report based on the evaluation results from the evaluation department. The service provider delivers the report through the app, estimates the user's sentiment, and adjusts the way the report is presented based on the estimated user sentiment.

[0064] (Example of form 2) The mental state analysis system according to an embodiment of the present invention is a system in which an AI analyzes a mandala drawn by a user, analyzes the user's mental state based on data of color, shape, and pattern, and provides a report. The mental state analysis system scans the mandala drawn by the user, analyzes the color, shape, and pattern, evaluates the user's mental state, and generates a report. For example, the mental state analysis system allows the user to freely draw a mandala using colors, shapes, and patterns. For example, the user can draw colorful circles and lines, geometric patterns, etc. This mandala is said to reflect the user's unconscious signs. Next, the mental state analysis system has the AI ​​scan the drawn mandala and analyze the color, shape, and pattern. The AI ​​analyzes each element of the mandala using image recognition technology and evaluates the use of colors, the arrangement of shapes, the repetition of patterns, etc. For example, it can detect cases where a particular color is used frequently or where a particular shape appears frequently. Next, the mental state analysis system evaluates the user's mental state based on the analysis results. The AI ​​determines the user's mental state using a mandala analysis method based on Jungian psychology. For example, if certain colors or shapes are used frequently, it can be determined that this may indicate the user's stress or anxiety. Finally, the mental state analysis system provides the user with an AI-generated report. This report describes the user's mental state and risk of depression, leading to early detection and appropriate care. For example, if the user is experiencing stress, the report includes advice on the causes and countermeasures. In this way, users can objectively understand their own mental state and receive appropriate care. This mechanism goes beyond the limitations of self-reporting and subjective diagnosis, capturing unconscious signs to enable early detection of mental health and depression risks and lead to appropriate care. For example, the AI ​​can detect stress and anxiety that the user is unaware of, allowing for early intervention. Furthermore, if a company prioritizes employee mental health care, this tool can be used to regularly monitor employees' mental states and provide appropriate support.This allows the mental state analysis system to objectively evaluate the user's mental state and provide appropriate care early on.

[0065] The mental state analysis system according to this embodiment comprises a scanning unit, an analysis unit, an evaluation unit, and a provision unit. The scanning unit scans the mandala drawn by the user. The scanning unit can, for example, scan the mandala using a smartphone camera. The scanning unit can, for example, scan the mandala at high resolution and acquire detailed image data. The scanning unit can also, for example, perform scans under different lighting conditions to ensure accurate acquisition of colors. The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit can, for example, analyze colors, shapes, and patterns. The analysis unit can, for example, determine if the frequent use of certain colors or shapes may indicate the user's stress or anxiety. The analysis unit can, for example, analyze the speed and order of drawing the mandala and evaluate the user's mental state. The evaluation unit evaluates the mental state based on the results analyzed by the analysis unit. The evaluation unit can, for example, evaluate the mental state based on Jungian psychology. The evaluation unit can, for example, refer to the user's past mental state data and compare it with the current evaluation result. The service provider provides the results evaluated by the evaluation provider as a report. The service provider can, for example, provide the report through an app. The service provider can, for example, estimate the user's emotions and adjust the way the report is presented based on the estimated user emotions. As a result, the mental state analysis system according to this embodiment can objectively evaluate the user's mental state and provide appropriate care at an early stage.

[0066] The scanning unit scans mandalas drawn by the user. The scanning unit can, for example, scan mandalas using a smartphone camera. Specifically, it uses a smartphone camera to scan mandalas at high resolution, acquiring detailed image data. The scanning unit can perform scans under different lighting conditions and utilize multiple light sources to ensure accurate color acquisition. For example, it can perform scans using different light sources such as natural light, fluorescent lights, and LED lights, and then compare and integrate color data under each light source to obtain more accurate color information. Furthermore, the scanning unit incorporates algorithms to correct image distortion and blur, ensuring accurate image data even when the user performs the scan handheld. Additionally, the scanning unit can improve scanning accuracy by displaying guidelines and frames when the user begins scanning, instructing them on the appropriate scanning position and angle. This allows the scanning unit to scan user-drawn mandalas with high precision and acquire detailed image data for the analysis unit.

[0067] The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit can analyze, for example, color, shape, and pattern. Specifically, it uses an image analysis algorithm to extract color information from each part of the mandala and analyze the distribution and frequency of use of colors. For shape and pattern analysis, it uses a shape recognition algorithm to detect specific shapes and patterns within the mandala and analyze their arrangement and repeating patterns. Furthermore, the analysis unit can track the progress of drawing by adding timestamps to the scanned data to analyze the drawing speed and order when the user draws the mandala. This allows for inferring the user's mental state from their drawing behavior. For example, if certain colors or shapes are frequently used, it can be determined that this may indicate the user's stress or anxiety. Also, a fast drawing speed may indicate impatience or tension, while a slow speed may indicate relaxation or concentration. The analysis unit comprehensively analyzes this data to provide basic data for evaluating the user's mental state.

[0068] The evaluation unit assesses the user's mental state based on the results analyzed by the analysis unit. For example, the evaluation unit can assess the mental state based on Jungian psychology. Specifically, it comprehensively evaluates the user's mental state based on data such as color, shape, pattern, and drawing speed provided by the analysis unit. In evaluations based on Jungian psychology, the psychological meanings of specific colors and shapes are considered to infer the user's inner state. For example, frequent use of red may indicate passion or anger, while frequent use of blue may indicate calmness or sadness. Furthermore, the evaluation unit can refer to the user's past mental state data and compare it to the current evaluation results. This allows for the understanding of changes and trends in the user's mental state, supporting long-term mental state management. In addition, the evaluation unit can provide specific advice and care suggestions to the user based on the analysis results. For example, if high stress levels are detected, relaxation techniques or counseling can be suggested.

[0069] The service provider provides the results evaluated by the evaluation provider as a report. The service provider can, for example, provide the report through an app. Specifically, it visualizes the evaluation results in an easy-to-understand manner and explains them to the user using graphs and charts. The service provider can also estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is feeling stressed, the report will be displayed using relaxing colors and designs so that the user can receive the content with peace of mind. Furthermore, the service provider can adjust the content of the report according to the user's level of understanding and explain it in simple language, avoiding technical jargon. This allows the user to accurately understand their own mental state and take appropriate measures. The service provider can also update the report regularly so that the user can continuously monitor their own mental state. As a result, the mental state analysis system according to this embodiment can objectively evaluate the user's mental state and provide appropriate care early on.

[0070] The scanning unit can scan a mandala using a smartphone camera. The scanning unit can, for example, scan a mandala using a smartphone camera and save it as image data. The scanning unit can, for example, scan a mandala in real time using a smartphone camera and acquire detailed image data. The scanning unit can, for example, scan a mandala using a smartphone camera and save the image data to the cloud. This makes it easy to scan a mandala using a smartphone camera. Smartphone cameras include, for example, high resolution, autofocus, and HDR shooting functions, but are not limited to these examples. Some or all of the above processing in the scanning unit may be performed using, for example, AI, or not using AI. For example, the scanning unit can input image data acquired by a smartphone camera into a generating AI and have the generating AI perform analysis of the image data.

[0071] The analysis unit can analyze color, shape, and pattern. For example, the analysis unit can clarify specific analysis methods and criteria for color. For example, it can analyze hue, saturation, and brightness. For example, the analysis unit can clarify specific analysis methods and criteria for shape. For example, it can analyze geometric shapes and symmetry. For example, the analysis unit can clarify specific analysis methods and criteria for pattern. For example, it can analyze repeating patterns and fractal patterns. By analyzing color, shape, and pattern, the user's mental state can be evaluated in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the scanning unit into a generating AI and have the generating AI perform the analysis of color, shape, and pattern.

[0072] The evaluation unit can assess the mental state based on Jungian psychology. The evaluation unit can clarify specific theories and evaluation methods of Jungian psychology, for example. For example, it can evaluate archetypes, shadows, etc. The evaluation unit can accurately grasp the user's mental state through evaluation based on Jungian psychology, for example. The evaluation unit can use questionnaires based on Jungian psychology to assess the user's mental state, for example. The evaluation unit can conduct interviews based on Jungian psychology to assess the user's mental state, for example. This allows the evaluation unit to accurately grasp the user's mental state through evaluation based on Jungian psychology. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data acquired by the analysis unit into a generating AI and have the generating AI perform the evaluation of the mental state.

[0073] The service provider can provide reports through an app. For example, by providing reports through an app, the service provider can make it easy for users to check the results. For example, when providing reports through an app, the service provider can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, when providing reports through an app, the service provider can refer to the user's past feedback to select the optimal report format. For example, when providing reports through an app, the service provider can provide customized advice by considering the user's lifestyle and interests. This makes it easy for users to check the results by providing reports through an app. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the report generated by the evaluation unit into a generating AI and have the generating AI adjust the way the report is presented.

[0074] The analysis unit can determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, the analysis unit can clarify specific color analysis methods and criteria to determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, it can analyze hue, saturation, and brightness. For example, the analysis unit can clarify specific shape analysis methods and criteria to determine if the frequent use of certain colors or shapes may indicate user stress or anxiety. For example, it can analyze geometric shapes and symmetry. This allows for early detection of user stress and anxiety by detecting the frequent use of certain colors and shapes. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data acquired by the scanning unit into a generating AI and have the generating AI perform an analysis to detect the frequent use of certain colors and shapes.

[0075] The scanning unit can estimate the user's emotions and adjust the scanning timing based on the estimated emotions. For example, if the user is relaxed, the scanning unit can slow down the scanning timing to acquire a detailed image. For example, if the user is tense, the scanning unit can speed up the scanning timing to acquire an image quickly. For example, if the user is focused, the scanning unit can adjust the scanning timing to match the user's drawing pace. By adjusting the scanning timing according to the user's emotions, more accurate scanning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The scanning unit can detect the user's hand movements and pen pressure during scanning and record the mandala drawing process. For example, the scanning unit can detect the user's hand movements and record the speed and direction of drawing. For example, the scanning unit can detect the user's pen pressure and record the strength of the drawing. For example, the scanning unit can combine the user's hand movements and pen pressure to record the detailed drawing process. This allows for a detailed understanding of the drawing process by recording the user's hand movements and pen pressure. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's hand movement data into a generating AI and have the generating AI record the drawing process.

[0077] The scanning unit can perform scans under different lighting conditions during scanning to ensure accurate color acquisition. For example, the scanning unit can perform scans under natural light to ensure accurate color acquisition. For example, the scanning unit can perform scans under artificial light to ensure accurate color acquisition. For example, the scanning unit can perform scans using multiple light sources to ensure accurate color acquisition. This makes it possible to acquire accurate colors by performing scans under different lighting conditions. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input image data acquired under different lighting conditions into a generating AI and have the generating AI perform accurate color acquisition.

[0078] The scanning unit can estimate the user's emotions and adjust the scan resolution based on the estimated emotions. For example, the scanning unit can perform a high-resolution scan when the user is relaxed. For example, the scanning unit can perform a low-resolution scan when the user is tense. For example, the scanning unit can adjust the resolution to match the user's drawing pace when the user is focused. This allows for more appropriate scanning by adjusting the scan resolution according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scanning unit may be performed using AI or not using AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The scanning unit can prioritize the analysis of region-specific colors and patterns by considering the user's geographical location information during scanning. For example, the scanning unit can acquire the user's geographical location information and prioritize the analysis of region-specific colors. For example, the scanning unit can acquire the user's geographical location information and prioritize the analysis of region-specific patterns. For example, the scanning unit can acquire the user's geographical location information and analyze it in combination with region-specific colors and patterns. This allows for more accurate analysis by prioritizing the analysis of region-specific colors and patterns. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of region-specific colors and patterns.

[0080] The scanning unit can analyze the user's social media activity during scanning and obtain relevant mandala designs. For example, the scanning unit can analyze the user's social media activity and obtain relevant colors. For example, the scanning unit can analyze the user's social media activity and obtain relevant shapes. For example, the scanning unit can analyze the user's social media activity and obtain relevant patterns. In this way, designs related to the user can be obtained by analyzing social media activity. Some or all of the above processing in the scanning unit may be performed using AI, for example, or without AI. For example, the scanning unit can input the user's social media data into a generating AI and cause the generating AI to perform the acquisition of relevant mandala designs.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. For example, if the user is tense, the analysis unit can use an algorithm that performs a simplified analysis. For example, if the user is focused, the analysis unit can use an algorithm that performs an analysis that focuses on specific elements. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can analyze the mandala's drawing speed and sequence during the analysis process to evaluate the user's mental state. For example, the analysis unit can analyze the mandala's drawing speed to evaluate the user's stress level. For example, the analysis unit can analyze the mandala's drawing sequence to evaluate the user's thought patterns. For example, the analysis unit can analyze the mandala's drawing speed and sequence in combination to comprehensively evaluate the user's mental state. This allows for a detailed evaluation of the user's mental state by analyzing the drawing speed and sequence. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the drawing speed and sequence data acquired by the scanning unit into a generating AI and have the generating AI perform the evaluation of the mental state.

[0083] The analysis unit can perform a more detailed evaluation of the mental state by considering the interrelationships of each element of the mandala during analysis. For example, the analysis unit can analyze the interrelationships of the mandala's colors and shapes to evaluate the user's emotions. For example, the analysis unit can analyze the interrelationships of the mandala's patterns and arrangements to evaluate the user's thought patterns. For example, the analysis unit can comprehensively analyze the interrelationships of the mandala's colors, shapes, and patterns to evaluate the user's mental state in detail. This makes it possible to evaluate the mental state in detail by considering the interrelationships of each element. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data of each element acquired by the scanning unit into a generating AI and have the generating AI perform the analysis of the interrelationships.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is tense, the analysis unit can display simplified analysis results. For example, if the user is focused, the analysis unit can display analysis results that focus on specific elements. By adjusting the display method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can analyze trends in change by referring to the user's past mandala data during analysis. For example, the analysis unit can analyze changes in color by referring to the user's past mandala data. For example, the analysis unit can analyze changes in shape by referring to the user's past mandala data. For example, the analysis unit can analyze changes in pattern by referring to the user's past mandala data. This allows the system to understand changes in the user's mental state by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past mandala data into a generating AI and have the generating AI perform an analysis to analyze trends in change.

[0086] The analysis unit can refer to the user's lifestyle data during analysis and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's sleep data and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's exercise data and reflect it in the evaluation of their mental state. For example, the analysis unit can refer to the user's dietary data and reflect it in the evaluation of their mental state. This makes it possible to evaluate the mental state more accurately by referring to lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the evaluation of the mental state.

[0087] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can use detailed evaluation criteria. For example, if the user is tense, the evaluation unit can use simplified evaluation criteria. For example, if the user is focused, the evaluation unit can use evaluation criteria that focus on specific elements. By adjusting the evaluation criteria according to the user's emotions, a more accurate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The evaluation unit can refer to the user's past mental state data during the evaluation and compare it with the current evaluation result. For example, the evaluation unit can refer to the user's past mental state data and compare it with the current evaluation result. For example, the evaluation unit can refer to the user's past mental state data and analyze trends in change. For example, the evaluation unit can refer to the user's past mental state data and identify areas for improvement. This allows for a more accurate evaluation of the current mental state by referring to past data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past mental state data into a generating AI and have the generating AI perform a comparison with the current evaluation result.

[0089] The evaluation unit can perform a more accurate evaluation by considering the user's living environment and stress factors during the evaluation process. For example, the evaluation unit can consider the user's living environment and reflect it in the evaluation results. For example, the evaluation unit can consider the user's stress factors and reflect them in the evaluation results. For example, the evaluation unit can consider the user's living environment and stress factors in combination and reflect them in the evaluation results. This makes it possible to evaluate the mental state more accurately by considering the living environment and stress factors. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's living environment data into a generating AI and have the generating AI perform the evaluation of the mental state.

[0090] The evaluation unit can estimate the user's emotions and determine the priority of evaluation results based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can prioritize displaying detailed evaluation results. For example, if the user is tense, the evaluation unit can prioritize displaying simplified evaluation results. For example, if the user is focused, the evaluation unit can prioritize displaying evaluation results that focus on specific elements. This allows for a more appropriate evaluation by prioritizing evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The evaluation unit can take into account the user's geographical location information during the evaluation process and reflect region-specific stress factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific stress factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific living environment factors in the evaluation. For example, the evaluation unit can acquire the user's geographical location information and reflect region-specific cultural background factors in the evaluation. This makes it possible to evaluate mental state more accurately by taking region-specific stress factors into consideration. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform an evaluation of region-specific stress factors.

[0092] The evaluation unit can analyze the user's social media activity during the evaluation process and reflect this in the assessment of their mental state. For example, the evaluation unit can analyze the user's social media activity and assess their stress level. For example, the evaluation unit can analyze the user's social media activity and assess changes in their emotions. For example, the evaluation unit can analyze the user's social media activity and assess trends in their mental state. This allows for a more accurate assessment of the user's mental state by analyzing their social media activity. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media data into a generating AI and have the generating AI perform the assessment of their mental state.

[0093] The service provider can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed report. For example, if the user is tense, the service provider can provide a simplified report. For example, if the user is focused, the service provider can provide a report that focuses on specific elements. This allows for more appropriate information to be provided by adjusting the presentation of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can select the most suitable report format by referring to the user's past feedback when providing a report. For example, if the user previously preferred a detailed report, the service provider can provide a detailed report format. For example, if the user previously preferred a simplified report, the service provider can provide a simplified report format. The service provider can, for example, analyze the user's past feedback and select the most suitable report format. This allows the service provider to provide the user with the most suitable report format by referring to past feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the most suitable report format.

[0095] The service provider can provide customized advice when delivering reports, taking into account the user's lifestyle and interests. For example, the service provider can provide appropriate advice by considering the user's lifestyle. For example, the service provider can provide relevant advice by considering the user's interests. For example, the service provider can provide customized advice by considering a combination of the user's lifestyle and interests. This allows for the provision of more appropriate advice by taking into account lifestyle and interests. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data and interest data into a generating AI and have the generating AI perform the task of providing customized advice.

[0096] The service provider can estimate the user's emotions and prioritize reports based on the estimated emotions. For example, if the user is relaxed, the service provider can prioritize displaying detailed reports. For example, if the user is tense, the service provider can prioritize displaying simplified reports. For example, if the user is focused, the service provider can prioritize displaying reports that focus on specific elements. This allows for more appropriate information to be provided by prioritizing reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, by taking device information into account, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0098] The service provider can analyze the user's social media activity and provide relevant advice when providing reports. For example, the service provider can analyze the user's social media activity and obtain relevant colors. For example, the service provider can analyze the user's social media activity and obtain relevant shapes. For example, the service provider can analyze the user's social media activity and obtain relevant patterns. This allows the service provider to provide relevant advice to the user by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant advice.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The mental state analysis system can provide real-time feedback during the user's drawing process. For example, if a user frequently uses a particular color, it can instantly provide information about the emotions or mental states that color might represent. Furthermore, if the system estimates that the user is experiencing stress during drawing, it can provide real-time advice and guidance on how to relax. After the user completes their drawing, it can provide feedback on the entire drawing process, highlighting which parts were particularly important. This allows users to understand their mental state in real time and take appropriate measures.

[0101] A mental state analysis system can include the ability to record and later replay the user's drawing process. For example, after a user completes a drawing, they can play a video of the drawing process to see how they chose colors and shapes. It can also focus on specific moments during the drawing process and provide commentary on the emotions and mental state at those times. Furthermore, it can compare a mandala drawn by the user in the past with a mandala drawn now, visually demonstrating changes in their mental state. This allows users to gain a deeper understanding of their changing mental state and receive appropriate care.

[0102] The mental state analysis system can provide audio guidance during the user's drawing process. For example, it can offer guidance on breathing techniques or meditation to help the user relax before they begin drawing. Furthermore, if the user selects specific colors or shapes during the drawing process, it can provide audio information about the emotions or mental states that those selections might represent. Finally, after the drawing is complete, it can provide audio feedback on the entire drawing process, explaining which parts were particularly important. This allows the user to gain a deeper understanding of their own mental state and take appropriate action.

[0103] The mental state analysis system can provide biofeedback during the user's drawing process. For example, it can monitor the user's heart rate and skin electrical activity to provide real-time feedback on their stress and relaxation levels. Furthermore, if the user chooses a specific color or shape, it can provide biofeedback on the emotions and mental states that this choice might represent. After the drawing is complete, it can provide biofeedback on the entire drawing process, highlighting which parts were particularly important. This allows the user to understand their mental state in real time and take appropriate action.

[0104] The mental state analysis system can analyze the user's facial expressions and tone of voice during the user's drawing process using emotion recognition technology. For example, if the user smiles while drawing, the system can record that moment's emotion as positive. Furthermore, if the user's tone of voice changes during drawing, the system can provide information about the emotions and mental state that the change might indicate. After the drawing is completed, the system can provide the emotion recognition results for the entire drawing process, highlighting which parts were particularly important. This allows the user to gain a deeper understanding of their own mental state and take appropriate action.

[0105] The mental state analysis system can provide comparative data with other users during the user's drawing process. For example, it can show what colors and shapes other users of the same age group and gender have chosen. It can also show how the user's drawing differs from that of other users. Furthermore, it can show how the user's mental state differs from that of other users. This allows users to compare their own mental state with that of other users and gain a more objective understanding of it.

[0106] The mental state analysis system can provide advice to users during their drawing process, based on their progress. For example, it can offer advice on basic color and shape selection when the user has just started drawing. As the drawing progresses, it can provide advice on more advanced patterns and designs. Furthermore, after the drawing is completed, it can provide an evaluation of the entire drawing along with advice for the next drawing. This allows users to improve their skills and gain a deeper understanding of their mental state throughout the drawing process.

[0107] The mental state analysis system can provide music during the user's drawing process, according to the progress of the drawing. For example, it can provide calming music to help the user relax before starting to draw. It can also provide music to enhance concentration as the drawing progresses. Furthermore, it can provide music to allow time for reflection after the drawing is completed. This allows the user to enjoy the drawing process more through music and to gain a deeper understanding of their mental state.

[0108] The mental state analysis system can provide visual effects during the user's drawing process, depending on the progress of the drawing. For example, it can provide simple visual effects when the user has just started drawing. As the drawing progresses, it can provide more complex visual effects. Furthermore, after the drawing is completed, it can provide a visual effect of the entire drawing, indicating which parts were particularly important. This allows the user to enjoy the drawing process more through the visual effects and to gain a deeper understanding of their mental state.

[0109] The mental state analysis system can provide interactive guidance during the user's drawing process, adapting to the progress of the drawing. For example, at the beginning of the drawing process, it can provide interactive guidance on basic color and shape selection. As the drawing progresses, it can provide interactive guidance on more advanced patterns and designs. Furthermore, after the drawing is completed, it can provide an evaluation of the entire drawing, along with interactive guidance for the next drawing. This allows users to enjoy the drawing process more and gain a deeper understanding of their mental state through the interactive guidance.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The scanning unit scans the mandala drawn by the user. The scanning unit uses, for example, a smartphone camera to scan the mandala at high resolution and obtain detailed image data. It also performs scans under different lighting conditions to ensure accurate color acquisition. Step 2: The analysis unit analyzes the mandala scanned by the scanning unit. The analysis unit analyzes the colors, shapes, and patterns, and determines if the frequent use of certain colors or shapes may indicate the user's stress or anxiety. It also analyzes the speed and order of drawing the mandala to evaluate the user's mental state. Step 3: The evaluation unit assesses the mental state based on the results analyzed by the analysis unit. The evaluation unit assesses the mental state based on Jungian psychology and compares the current evaluation result with the user's past mental state data. Step 4: The service provider provides a report based on the evaluation results from the evaluation department. The service provider delivers the report through the app, estimates the user's sentiment, and adjusts the way the report is presented based on the estimated user sentiment.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the scanning unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit can scan the mandala using the camera 42 of the smart device 14. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the colors, shapes, and patterns of the scanned mandala. The evaluation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the mental state based on the analysis results. The provision unit is implemented in, for example, the control unit 46A of the smart device 14 and provides the evaluation results to the user as a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the scanning unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit can scan the mandala using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the colors, shapes, and patterns of the scanned mandala. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and evaluates the mental state based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the evaluation results to the user as a report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the scanning unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit can scan the mandala using the camera 42 of the headset terminal 314. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the colors, shapes, and patterns of the scanned mandala. The evaluation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the mental state based on the analysis results. The provision unit is implemented in, for example, the control unit 46A of the headset terminal 314, and provides the evaluation results to the user as a report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the scanning unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the scanning unit can scan the mandala using the camera 42 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the colors, shapes, and patterns of the scanned mandala. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and evaluates the mental state based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides the evaluation results to the user as a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A scanning unit that scans mandalas drawn by the user, An analysis unit analyzes the mandala scanned by the aforementioned scanning unit, An evaluation unit that evaluates the mental state based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the results evaluated by the evaluation unit as a report. A system characterized by the following features. (Note 2) The scanning unit is Scan the mandala using your smartphone's camera. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze colors, shapes, and patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluating mental states based on Jungian psychology The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide reports through the app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, If certain colors or shapes are used frequently, it may indicate that the user is experiencing stress or anxiety. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is During scanning, the system detects the user's hand movements and pen pressure, and records the mandala drawing process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is During scanning, we perform scans under different lighting conditions to ensure accurate color reproduction. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and adjusts the scan resolution based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is During scanning, the system prioritizes analyzing region-specific colors and patterns, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is During the scan, the system analyzes the user's social media activity and retrieves relevant mandala designs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the drawing speed and order of the mandala are analyzed to evaluate the user's mental state. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the interrelationships of each element of the mandala are taken into consideration to perform a more detailed assessment of the mental state. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's past mandala data is referenced to analyze trends in change. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the user's lifestyle data is referenced and reflected in the assessment of their mental state. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation, the system references the user's past mental state data and compares it to the current evaluation result. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, we take into account the user's living environment and stress factors to provide a more accurate assessment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the user's emotions and prioritizes evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation process, the user's geographical location information will be taken into consideration, and region-specific stress factors will be reflected in the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, the user's social media activity will be analyzed and reflected in the assessment of their mental state. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing reports, we refer to past user feedback to select the most suitable report format. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing reports, we offer customized advice that takes into account the user's lifestyle and interests. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing reports, the optimal display method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing reports, we analyze users' social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A scanning unit that scans mandalas drawn by the user, An analysis unit analyzes the mandala scanned by the aforementioned scanning unit, An evaluation unit that evaluates the mental state based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the results evaluated by the evaluation unit as a report. A system characterized by the following features.

2. The scanning unit is Scan the mandala using your smartphone's camera. The system according to feature 1.

3. The aforementioned analysis unit, Analyze colors, shapes, and patterns. The system according to feature 1.

4. The evaluation unit, Evaluating mental states based on Jungian psychology The system according to feature 1.

5. The aforementioned supply unit is, Provide reports through the app. The system according to feature 1.

6. The aforementioned analysis unit, If certain colors or shapes are used frequently, it may indicate that the user is experiencing stress or anxiety. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is During scanning, the system detects the user's hand movements and pen pressure, and records the mandala drawing process. The system according to feature 1.

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