System

Abstract

An object of a system according to an embodiment is to analyze a user's mental state and provide personalized positive advice.SOLUTION: A system includes a mental state collection unit, an analysis unit, and an advice provision unit. The mental state collection unit collects a mental state and performance data of the user. The analysis unit analyzes the data collected by the mental state collection unit. The advice providing unit provides personalized positive advice based on a result of the analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to properly evaluate the impact of mental state on performance and respond to each individual.

[0005] The system according to the embodiment aims to analyze the mental state of the user and provide personalized positive advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a mental state collection unit, an analysis unit, and an advice provision unit. The mental state collection unit collects data on the user's mental state and performance. The analysis unit analyzes the data collected by the mental state collection unit. The advice provision unit provides personalized positive advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the mental state of the user and provide personalized positive advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mental health improvement system according to an embodiment of the present invention collects a user's mental state and performance data, analyzes it using a generation AI, and provides personalized positive advice. This allows the mental health improvement system to improve the user's mental health, thereby improving performance and building mental toughness.

[0029] A mental health improvement system according to an embodiment includes a mental state collection unit, an analysis unit, and an advice provision unit. The mental state collection unit collects data on a user's mental state and performance. For example, the mental state collection unit uses a wearable device that measures the user's heart rate and stress level. The mental state collection unit may also use an application that records the user's mood score and work efficiency. The mental state collection unit may also use sensors that measure the user's reaction time and concentration. The analysis unit analyzes the collected data. For example, the analysis unit may use a generation AI to analyze the data and evaluate the user's mental state. The analysis unit may also use the generation AI to analyze performance data and generate advice for improving the user's performance. The analysis unit may also use the generation AI to analyze fluctuations in the user's mental state and identify trends. The advice provision unit provides personalized positive advice based on the analysis results. For example, the advice provision unit may use the generation AI to provide the user with specific advice such as, "Take a deep breath and relax." The advice providing unit can also use the generation AI to provide encouraging words to the user, such as, "You did a great job today. I'm sure you'll do better next time." The advice providing unit can also use the generation AI to provide advice to the user, such as, "You've had many successes in the past. Use that experience to continue working with confidence." This allows the mental health improvement system according to the embodiment to provide personalized advice based on the user's mental state. For example, the mental health improvement system can monitor the user's mental state in real time and immediately issue an alert and provide appropriate advice if there is a sudden change. The mental health improvement system can also track the user's past mental state and performance data over the long term, analyze trends, predict future mental state, and provide preventive advice.In addition, the mental health improvement system collects the user's lifestyle data (sleep, diet, exercise) and provides mental advice that takes into account the user's overall health condition.

[0030] The advice providing unit can track the user's past mental state and performance data over the long term, analyze trends to predict future mental states, and provide preventive advice. The advice providing unit, for example, collects the user's past mental state and performance data and analyzes long-term trends. For example, based on data from the past year, it identifies patterns of mental state fluctuations and predicts future mental states. The advice providing unit can also use a generation AI to analyze trends and predict future mental states. For example, the generation AI applies a predictive algorithm based on past data to predict future mental states. The advice providing unit also provides preventive advice based on the prediction results. For example, the advice providing unit uses a generation AI to provide preventive advice such as, "Take a short break before stress increases." This makes it possible to predict future mental states and provide preventive advice.

[0031] The mental state collection unit monitors the user's mental state in real time and can immediately issue an alert and provide appropriate advice if there is a sudden change. The mental state collection unit, for example, builds a system that monitors the user's mental state in real time. For example, it uses a wearable device that measures heart rate and stress level. The mental state collection unit can also use an application that records the user's mood score and work efficiency in real time. The mental state collection unit can also use sensors that measure the user's reaction time and concentration level in real time. The advice provision unit immediately issues an alert and provides appropriate advice if there is a sudden change. For example, the advice provision unit can use a generation AI to provide advice such as, "Your stress is rapidly increasing. Take a deep breath and relax." The advice provision unit can also use a generation AI to provide advice such as, "Your heart rate is increasing. Take a short break." The advice provision unit can also use a generation AI to provide advice such as, "Your concentration level is decreasing. Take a short walk to refresh yourself." This allows for immediate response to sudden changes in mental state.

[0032] The advice providing unit can also provide mental advice to improve performance in fields other than sports. For example, in the field of music, the advice providing unit provides mental advice to relieve tension before a performance. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a deep breath and relax." The advice providing unit can also provide mental advice to reduce stress in creative activities in the field of art. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a short break. Refreshing yourself will help you create more smoothly." The advice providing unit can also provide mental advice to relieve tension before an exam in the academic field. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a deep breath and relax. This will help you concentrate on the exam." This makes it possible to provide mental advice to improve performance in fields other than sports.

[0033] The mental state collection unit can collect the user's lifestyle data and provide mental advice that takes into account the user's overall health condition. For example, the mental state collection unit can collect the user's sleep data and provide mental advice to improve the quality of sleep. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try some relaxation techniques before bed." The mental state collection unit can also collect the user's dietary data and provide mental advice to promote healthy eating habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try eating a balanced diet." The mental state collection unit can also collect the user's exercise data and provide mental advice to promote appropriate exercise habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Continue to exercise regularly. Exercise also has a positive effect on mental health." This allows for comprehensive mental advice to be provided based on the user's lifestyle data.

[0034] The advice providing unit can provide advice on stress management and time management to business people. The advice providing unit, for example, provides advice on stress management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Take a short break. Refreshing yourself will improve your work efficiency." The advice providing unit can also provide advice on time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Review your schedule and set priorities." The advice providing unit can also provide advice that combines stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Leave some leeway in your schedule to reduce stress." This makes it possible to provide advice on stress management and time management to business people.

[0035] The advice providing unit can provide students with advice to reduce tension and stress before an exam. The advice providing unit provides, for example, advice to reduce tension before an exam to students. For example, the advice providing unit uses a generating AI to provide advice such as, "Take a deep breath and relax." The advice providing unit can also provide students with advice to reduce stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as, "Take a short break. Refreshing yourself will help you concentrate on the exam." The advice providing unit can also provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as, "Try some relaxation techniques to relax before the exam." This makes it possible to provide students with advice to reduce tension and stress before an exam.

[0036] The advice providing unit can analyze the user's past success experiences and positive feedback and, based on that, provide specific advice to improve self-evaluation. For example, the advice providing unit stores the user's past success experiences in a database and, based on that, provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Reflect on your past success experiences and gain confidence." The advice providing unit can also analyze the user's positive feedback and, based on that, provide advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the praise you received from others and gain confidence." The advice providing unit can also provide advice by combining the user's past success experiences and positive feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Based on your past success experiences and praise from others, gain confidence." This makes it possible to provide specific advice to improve self-evaluation based on past success experiences and positive feedback.

[0037] The mental state collection unit can track fluctuations in a user's self-evaluation over the long term and immediately issue an alert and provide appropriate advice when there is a decline. The mental state collection unit, for example, builds a system that tracks fluctuations in a user's self-evaluation over the long term. For example, the mental state collection unit uses a generation AI to conduct regular self-evaluation surveys and store the results in a database. The mental state collection unit can also analyze fluctuations in a user's self-evaluation score and immediately issue an alert when there is a decline. For example, the generation AI detects a decrease in the self-evaluation score and issues an alert. The advice provision unit provides appropriate advice. For example, the advice provision unit can use a generation AI to provide advice such as, "Your self-evaluation is declining. Remember your past successes and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "Your self-evaluation is declining. Remember the praise you've received from others and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "Your self-evaluation is declining. Try relaxation techniques." This allows us to track fluctuations in self-evaluation over the long term, immediately alerting you when it declines and providing appropriate advice.

[0038] The advice providing unit can collect positive feedback from other users and provide advice based on that feedback in order to improve the user's self-evaluation. For example, the advice providing unit collects positive feedback from other users and provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-esteem based on words of praise and gratitude from others." The advice providing unit can also analyze positive feedback from other users and provide advice based on that. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the positive feedback from others and gain confidence." The advice providing unit can also build a system that collects positive feedback from other users and provides advice based on that feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-evaluation based on words of praise and gratitude from others." This makes it possible to provide advice to improve self-evaluation based on positive feedback from other users.

[0039] The advice providing unit can visualize the user's past successful experiences to make them easier to understand visually in order to improve the user's self-evaluation. For example, the advice providing unit visualizes the user's past successful experiences and provides advice to improve self-evaluation. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-evaluation." The advice providing unit can also provide advice by visualizing past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Visualize your past successful experiences and use them to improve self-evaluation." The advice providing unit can also build a system that visualizes past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-esteem." In this way, advice to improve self-evaluation can be provided by visualizing past successful experiences to make them easier to understand visually.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The advice providing unit can also provide mental advice to improve performance in fields other than sports. For example, in the field of music, the advice providing unit can provide mental advice to relieve tension before a performance. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a deep breath and relax." In the field of art, the advice providing unit can also provide mental advice to reduce stress during creative activities. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a short break. Refreshing yourself will help you create more smoothly." In the field of academics, the advice providing unit can also provide mental advice to relieve tension before an exam. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a deep breath and relax. This will help you concentrate on the exam." This makes it possible to provide mental advice to improve performance in fields other than sports.

[0042] The mental state collection unit can collect the user's lifestyle data and provide mental advice that takes into account the user's overall health condition. For example, the mental state collection unit can collect the user's sleep data and provide mental advice to improve the quality of sleep. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try some relaxation techniques before bed." The mental state collection unit can also collect the user's dietary data and provide mental advice to promote healthy eating habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try eating a balanced diet." The mental state collection unit can also collect the user's exercise data and provide mental advice to promote appropriate exercise habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Continue to exercise regularly. Exercise also has a positive effect on mental health." This makes it possible to provide comprehensive mental advice based on the user's lifestyle data.

[0043] The advice providing unit can provide advice on stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Take a short break. Refreshing yourself will improve your work efficiency." The advice providing unit can also provide advice on time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Review your schedule and set priorities." The advice providing unit can also provide advice that combines stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Leave some leeway in your schedule to reduce stress." This makes it possible to provide advice on stress management and time management to business people.

[0044] The advice providing unit can provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Take a deep breath and relax." The advice providing unit can also provide students with advice to reduce stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Take a short break. Refreshing yourself will help you concentrate on the exam." The advice providing unit can also provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Try some relaxation techniques to relax before the exam." This makes it possible to provide students with advice to reduce tension and stress before an exam.

[0045] The advice providing unit can collect positive feedback from other users and provide advice based on that feedback in order to improve the user's self-evaluation. For example, the advice providing unit collects positive feedback from other users and provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-esteem based on words of praise and gratitude from others." The advice providing unit can also analyze positive feedback from other users and provide advice based on that. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the positive feedback from others and gain confidence." The advice providing unit can also build a system that collects positive feedback from other users and provides advice based on that feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-evaluation based on words of praise and gratitude from others." This makes it possible to provide advice to improve self-evaluation based on positive feedback from other users.

[0046] The advice providing unit can visualize the user's past successful experiences to make them easier to understand visually in order to improve the user's self-evaluation. For example, the advice providing unit visualizes the user's past successful experiences and provides advice to improve self-evaluation. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-evaluation." The advice providing unit can also provide advice by visualizing past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Visualize your past successful experiences and use them to improve self-evaluation." The advice providing unit can also build a system that visualizes past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-esteem." In this way, advice to improve self-evaluation can be provided by visualizing past successful experiences to make them easier to understand visually.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The mental state collection unit collects the user's mental state and performance data. For example, the mental state collection unit may use a wearable device that measures the user's heart rate and stress level. It may also use an application that records the user's mood score and work efficiency, or a sensor that measures reaction time and concentration. Step 2: The analysis unit analyzes the collected data. For example, the analysis unit may use generative AI to analyze the data and evaluate the user's mental state. It may also analyze performance data and generate advice to improve the user's performance. It may also analyze fluctuations in the user's mental state and identify trends. Step 3: The advice provider provides personalized positive advice based on the analysis results. For example, the generative AI can provide specific advice such as "Take a deep breath and relax" or encouraging words such as "You did a good job today. You'll do better next time." It can also provide advice such as "You've had many successes in the past. Use that experience to continue working with confidence."

[0049] (Example 2) A mental health improvement system according to an embodiment of the present invention collects a user's mental state and performance data, analyzes it using a generation AI, and provides personalized positive advice. This allows the mental health improvement system to improve the user's mental health, thereby improving performance and building mental toughness.

[0050] A mental health improvement system according to an embodiment includes a mental state collection unit, an analysis unit, and an advice provision unit. The mental state collection unit collects data on a user's mental state and performance. For example, the mental state collection unit uses a wearable device that measures the user's heart rate and stress level. The mental state collection unit may also use an application that records the user's mood score and work efficiency. The mental state collection unit may also use sensors that measure the user's reaction time and concentration. The analysis unit analyzes the collected data. For example, the analysis unit may use a generation AI to analyze the data and evaluate the user's mental state. The analysis unit may also use the generation AI to analyze performance data and generate advice for improving the user's performance. The analysis unit may also use the generation AI to analyze fluctuations in the user's mental state and identify trends. The advice provision unit provides personalized positive advice based on the analysis results. For example, the advice provision unit may use the generation AI to provide the user with specific advice such as, "Take a deep breath and relax." The advice providing unit can also use the generation AI to provide encouraging words to the user, such as, "You did a great job today. I'm sure you'll do better next time." The advice providing unit can also use the generation AI to provide advice to the user, such as, "You've had many successes in the past. Use that experience to continue working with confidence." This allows the mental health improvement system according to the embodiment to provide personalized advice based on the user's mental state. For example, the mental health improvement system can monitor the user's mental state in real time and immediately issue an alert and provide appropriate advice if there is a sudden change. The mental health improvement system can also track the user's past mental state and performance data over the long term, analyze trends, predict future mental state, and provide preventive advice.In addition, the mental health improvement system collects the user's lifestyle data (sleep, diet, exercise) and provides mental advice that takes into account the user's overall health condition.

[0051] The advice providing unit can track the user's past mental state and performance data over the long term, analyze trends to predict future mental states, and provide preventive advice. The advice providing unit, for example, collects the user's past mental state and performance data and analyzes long-term trends. For example, based on data from the past year, it identifies patterns of mental state fluctuations and predicts future mental states. The advice providing unit can also use a generation AI to analyze trends and predict future mental states. For example, the generation AI applies a predictive algorithm based on past data to predict future mental states. The advice providing unit also provides preventive advice based on the prediction results. For example, the advice providing unit uses a generation AI to provide preventive advice such as, "Take a short break before stress increases." This makes it possible to predict future mental states and provide preventive advice.

[0052] The mental state collection unit monitors the user's mental state in real time and can immediately issue an alert and provide appropriate advice if there is a sudden change. The mental state collection unit, for example, builds a system that monitors the user's mental state in real time. For example, it uses a wearable device that measures heart rate and stress level. The mental state collection unit can also use an application that records the user's mood score and work efficiency in real time. The mental state collection unit can also use sensors that measure the user's reaction time and concentration level in real time. The advice provision unit immediately issues an alert and provides appropriate advice if there is a sudden change. For example, the advice provision unit can use a generation AI to provide advice such as, "Your stress is rapidly increasing. Take a deep breath and relax." The advice provision unit can also use a generation AI to provide advice such as, "Your heart rate is increasing. Take a short break." The advice provision unit can also use a generation AI to provide advice such as, "Your concentration level is decreasing. Take a short walk to refresh yourself." This allows for immediate response to sudden changes in mental state.

[0053] The analysis unit can use an emotion estimation function to analyze the user's emotions in real time and provide optimal positive advice based on those emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's facial expressions and voice in real time to estimate emotions. For example, the analysis unit can use a camera or microphone to analyze the user's emotional state. The analysis unit can also use a generation AI to apply an emotion estimation algorithm to analyze the user's emotions in real time. For example, the generation AI can analyze the user's facial expressions using facial expression recognition technology to calculate an emotion score. The generation AI can also analyze the user's voice using voice analysis technology to calculate an emotion score. The advice providing unit can provide optimal positive advice based on the emotion. For example, the advice providing unit can use the generation AI to provide advice such as, "You are feeling a little anxious right now. Take a deep breath to relax." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little tense right now. Take a short break to relax." This allows the system to provide optimal advice in real time based on the user's emotions.

[0054] The advice providing unit can also provide mental advice to improve performance in fields other than sports. For example, in the field of music, the advice providing unit provides mental advice to relieve tension before a performance. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a deep breath and relax." The advice providing unit can also provide mental advice to reduce stress in creative activities in the field of art. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a short break. Refreshing yourself will help you create more smoothly." The advice providing unit can also provide mental advice to relieve tension before an exam in the academic field. For example, the advice providing unit uses a generation AI to provide advice such as, "Take a deep breath and relax. This will help you concentrate on the exam." This makes it possible to provide mental advice to improve performance in fields other than sports.

[0055] The mental state collection unit can collect the user's lifestyle data and provide mental advice that takes into account the user's overall health condition. For example, the mental state collection unit can collect the user's sleep data and provide mental advice to improve the quality of sleep. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try some relaxation techniques before bed." The mental state collection unit can also collect the user's dietary data and provide mental advice to promote healthy eating habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try eating a balanced diet." The mental state collection unit can also collect the user's exercise data and provide mental advice to promote appropriate exercise habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Continue to exercise regularly. Exercise also has a positive effect on mental health." This allows for comprehensive mental advice to be provided based on the user's lifestyle data.

[0056] The analysis unit can use the emotion estimation function to predict stress and anxiety a user may feel in specific situations and provide preventative advice. For example, the analysis unit uses the emotion estimation function to analyze stress and anxiety a user may feel in specific situations in real time. For example, the analysis unit can use the generation AI to detect tension before a meeting. The analysis unit can also use the generation AI to detect anxiety before an exam. For example, the generation AI can analyze a user's heart rate and facial expressions to detect signs of stress and anxiety. The advice providing unit provides preventative advice. For example, the advice providing unit can use the generation AI to provide advice such as, "Take a deep breath and relax before a meeting." The advice providing unit can also use the generation AI to provide advice such as, "Take a short break to relax before an exam." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques before you feel stressed." This allows for predicting stress and anxiety in specific situations and providing preventative advice.

[0057] The mental state collection unit analyzes the user's voice tone and facial expression to more accurately grasp the user's emotional state and provide advice. The mental state collection unit, for example, analyzes the user's voice tone and builds a system that estimates the user's emotional state. For example, the mental state collection unit uses a generation AI to analyze the pitch and speed of the voice to identify the user's emotions. The mental state collection unit can also analyze the user's facial expression to estimate the user's emotional state. For example, the mental state collection unit uses a generation AI to analyze facial muscle movements to identify emotions. The advice provision unit more accurately grasps the user's emotional state and provides advice. For example, the advice provision unit can use a generation AI to provide advice such as, "You are feeling a little nervous right now. Take a deep breath to relax." The advice provision unit can also use a generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "You are feeling a little anxious right now. Take a short break to relax." This allows the user's emotional state to be more accurately grasped and advice to be provided.

[0058] The analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and provide encouragement or advice at the optimal time. For example, the analysis unit uses the emotion estimation function to build a system that analyzes the user's emotional state in real time. For example, the analysis unit uses the generation AI to analyze the user's facial expressions and voice and estimate their emotions. The analysis unit can also use the generation AI to apply an emotion estimation algorithm to analyze the user's emotional state in real time. For example, the generation AI can analyze the user's facial expressions using facial expression recognition technology and calculate an emotion score. The generation AI can also analyze the user's voice using voice analysis technology and calculate an emotion score. The advice providing unit can provide encouragement or advice at the optimal time. For example, the advice providing unit can use the generation AI to provide advice such as, "You are feeling a little anxious right now. Take a deep breath to relax." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice-providing unit can also use the generation AI to provide advice such as, "You're feeling a little tense right now. Take a short break to relax." This allows encouragement and advice to be provided at the optimal time depending on the user's emotional state.

[0059] The advice providing unit can provide advice on stress management and time management to business people. The advice providing unit, for example, provides advice on stress management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Take a short break. Refreshing yourself will improve your work efficiency." The advice providing unit can also provide advice on time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Review your schedule and set priorities." The advice providing unit can also provide advice that combines stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Leave some leeway in your schedule to reduce stress." This makes it possible to provide advice on stress management and time management to business people.

[0060] The advice providing unit can provide students with advice to reduce tension and stress before an exam. The advice providing unit provides, for example, advice to reduce tension before an exam to students. For example, the advice providing unit uses a generating AI to provide advice such as, "Take a deep breath and relax." The advice providing unit can also provide students with advice to reduce stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as, "Take a short break. Refreshing yourself will help you concentrate on the exam." The advice providing unit can also provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as, "Try some relaxation techniques to relax before the exam." This makes it possible to provide students with advice to reduce tension and stress before an exam.

[0061] The analysis unit can use the emotion estimation function to predict stress a user may feel during specific time periods or situations in advance and provide preventive advice. For example, the analysis unit uses the emotion estimation function to analyze stress a user may feel during specific time periods or situations in real time. For example, the analysis unit can use the generation AI to predict stress during the morning commute. The analysis unit can also use the generation AI to predict stress during evening relaxation time. For example, the generation AI can analyze the user's heart rate and facial expressions to detect signs of stress. The advice providing unit provides preventive advice. For example, the advice providing unit can use the generation AI to provide advice such as, "Take a deep breath and relax before commuting." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques during your relaxation time." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques before you feel stressed." This makes it possible to predict stress felt during specific time periods or situations in advance and provide preventive advice.

[0062] The advice providing unit can analyze the user's past success experiences and positive feedback and, based on that, provide specific advice to improve self-evaluation. For example, the advice providing unit stores the user's past success experiences in a database and, based on that, provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Reflect on your past success experiences and gain confidence." The advice providing unit can also analyze the user's positive feedback and, based on that, provide advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the praise you received from others and gain confidence." The advice providing unit can also provide advice by combining the user's past success experiences and positive feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Based on your past success experiences and praise from others, gain confidence." This makes it possible to provide specific advice to improve self-evaluation based on past success experiences and positive feedback.

[0063] The mental state collection unit can track fluctuations in a user's self-evaluation over the long term and immediately issue an alert and provide appropriate advice when there is a decline. The mental state collection unit, for example, builds a system that tracks fluctuations in a user's self-evaluation over the long term. For example, the mental state collection unit uses a generation AI to conduct regular self-evaluation surveys and store the results in a database. The mental state collection unit can also analyze fluctuations in a user's self-evaluation score and immediately issue an alert when there is a decline. For example, the generation AI detects a decrease in the self-evaluation score and issues an alert. The advice provision unit provides appropriate advice. For example, the advice provision unit can use a generation AI to provide advice such as, "Your self-evaluation is declining. Remember your past successes and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "Your self-evaluation is declining. Remember the praise you've received from others and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "Your self-evaluation is declining. Try relaxation techniques." This allows us to track fluctuations in self-evaluation over the long term, immediately alerting you when it declines and providing appropriate advice.

[0064] The advice providing unit can collect positive feedback from other users and provide advice based on that feedback in order to improve the user's self-evaluation. For example, the advice providing unit collects positive feedback from other users and provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-esteem based on words of praise and gratitude from others." The advice providing unit can also analyze positive feedback from other users and provide advice based on that. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the positive feedback from others and gain confidence." The advice providing unit can also build a system that collects positive feedback from other users and provides advice based on that feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-evaluation based on words of praise and gratitude from others." This makes it possible to provide advice to improve self-evaluation based on positive feedback from other users.

[0065] The advice providing unit can visualize the user's past successful experiences to make them easier to understand visually in order to improve the user's self-evaluation. For example, the advice providing unit visualizes the user's past successful experiences and provides advice to improve self-evaluation. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-evaluation." The advice providing unit can also provide advice by visualizing past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Visualize your past successful experiences and use them to improve self-evaluation." The advice providing unit can also build a system that visualizes past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-esteem." In this way, advice to improve self-evaluation can be provided by visualizing past successful experiences to make them easier to understand visually.

[0066] The analysis unit can use the emotion estimation function to predict in advance whether a user will experience a decline in self-esteem in certain situations and provide preventative advice. For example, the analysis unit can use the emotion estimation function to analyze in real time whether a user will experience a decline in self-esteem in certain situations. For example, the analysis unit can use the generation AI to detect nervousness before a presentation. The analysis unit can also use the generation AI to detect anxiety before an exam. For example, the generation AI can analyze a user's heart rate and facial expressions to detect signs of a decline in self-esteem. The advice providing unit can provide preventative advice. For example, the advice providing unit can use the generation AI to provide advice such as, "Take a deep breath and relax before a presentation." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques before your self-esteem declines." This allows for predicting a decline in self-esteem in certain situations and providing preventative advice.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The mental state collection unit collects the user's mental state and performance data. For example, the mental state collection unit may use a wearable device that measures the user's heart rate and stress level. The mental state collection unit may also use an application that records the user's mood score and work efficiency. The mental state collection unit may also use sensors that measure the user's reaction time and concentration. The analysis unit analyzes the collected data. For example, the analysis unit may use a generation AI to analyze the data and evaluate the user's mental state. The analysis unit may also use a generation AI to analyze performance data and generate advice to improve the user's performance. The analysis unit may also use a generation AI to analyze fluctuations in the user's mental state and identify trends. The advice provision unit provides personalized positive advice based on the analysis results. For example, the advice provision unit may use a generation AI to provide the user with specific advice such as "Take a deep breath and relax." The advice provision unit may also use a generation AI to provide the user with encouraging words such as "You worked hard today. I'm sure you'll do better next time." The advice providing unit can also use the generation AI to provide the user with advice such as, "You have had many successes in the past. Use that experience to continue working with confidence." This allows the mental health improvement system according to the embodiment to provide personalized advice based on the user's mental state. For example, the mental health improvement system monitors the user's mental state in real time and immediately issues an alert and provides appropriate advice if there is a sudden change. The mental health improvement system also tracks the user's past mental state and performance data over the long term, analyzes trends, predicts future mental state, and provides preventative advice. Furthermore, the mental health improvement system collects the user's lifestyle data (sleep, diet, exercise) and provides mental advice that takes into account the user's overall health.

[0069] The advice providing unit can track the user's past mental state and performance data over the long term, analyze trends to predict future mental states, and provide preventive advice. For example, the advice providing unit collects the user's past mental state and performance data and analyzes long-term trends. For example, based on data from the past year, it identifies patterns of mental state fluctuations and predicts future mental states. The advice providing unit can also use a generation AI to analyze trends and predict future mental states. For example, the generation AI applies a predictive algorithm based on past data to predict future mental states. The advice providing unit also provides preventive advice based on the prediction results. For example, the advice providing unit uses a generation AI to provide preventive advice such as, "Take a short break before stress increases." This makes it possible to predict future mental states and provide preventive advice.

[0070] The mental state collection unit monitors the user's mental state in real time and can immediately issue an alert and provide appropriate advice if there is a sudden change. For example, the mental state collection unit builds a system that monitors the user's mental state in real time. For example, a wearable device that measures heart rate and stress level can be used. The mental state collection unit can also use an application that records the user's mood score and work efficiency in real time. The mental state collection unit can also use sensors that measure the user's reaction time and concentration level in real time. The advice provision unit immediately issues an alert and provides appropriate advice if there is a sudden change. For example, the advice provision unit can use a generation AI to provide advice such as, "Your stress is rapidly increasing. Take a deep breath and relax." The advice provision unit can also use a generation AI to provide advice such as, "Your heart rate is increasing. Take a short break." The advice provision unit can also use a generation AI to provide advice such as, "Your concentration level is decreasing. Take a short walk to refresh yourself." This allows for immediate response to sudden changes in mental state.

[0071] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time and provide optimal positive advice based on those emotions. For example, the analysis unit can use the emotion estimation function to analyze the user's facial expressions and voice in real time to estimate emotions. For example, the analysis unit can use a camera or microphone to analyze the user's emotional state. The analysis unit can also use a generation AI to apply an emotion estimation algorithm to analyze the user's emotions in real time. For example, the generation AI can analyze the user's facial expressions using facial expression recognition technology to calculate an emotion score. The generation AI can also analyze the user's voice using voice analysis technology to calculate an emotion score. The advice providing unit can provide optimal positive advice based on the emotion. For example, the advice providing unit can use the generation AI to provide advice such as, "You are feeling a little anxious right now. Take a deep breath to relax." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little tense right now. Take a short break to relax." This allows the system to provide optimal advice in real time based on the user's emotions.

[0072] The advice providing unit can also provide mental advice to improve performance in fields other than sports. For example, in the field of music, the advice providing unit can provide mental advice to relieve tension before a performance. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a deep breath and relax." In the field of art, the advice providing unit can also provide mental advice to reduce stress during creative activities. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a short break. Refreshing yourself will help you create more smoothly." In the field of academics, the advice providing unit can also provide mental advice to relieve tension before an exam. For example, the advice providing unit can use a generation AI to provide advice such as, "Take a deep breath and relax. This will help you concentrate on the exam." This makes it possible to provide mental advice to improve performance in fields other than sports.

[0073] The mental state collection unit can collect the user's lifestyle data and provide mental advice that takes into account the user's overall health condition. For example, the mental state collection unit can collect the user's sleep data and provide mental advice to improve the quality of sleep. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try some relaxation techniques before bed." The mental state collection unit can also collect the user's dietary data and provide mental advice to promote healthy eating habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Try eating a balanced diet." The mental state collection unit can also collect the user's exercise data and provide mental advice to promote appropriate exercise habits. For example, the mental state collection unit can use a generation AI to provide advice such as, "Continue to exercise regularly. Exercise also has a positive effect on mental health." This makes it possible to provide comprehensive mental advice based on the user's lifestyle data.

[0074] The analysis unit can use the emotion estimation function to predict stress and anxiety a user may feel in specific situations and provide preventative advice. For example, the analysis unit uses the emotion estimation function to analyze the stress and anxiety a user may feel in specific situations in real time. For example, the analysis unit can use the generation AI to detect tension before a meeting. The analysis unit can also use the generation AI to detect anxiety before an exam. For example, the generation AI can analyze a user's heart rate and facial expressions to detect signs of stress and anxiety. The advice providing unit provides preventative advice. For example, the advice providing unit can use the generation AI to provide advice such as, "Take a deep breath and relax before a meeting." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques before you feel stressed." This allows for predicting stress and anxiety in specific situations and providing preventative advice.

[0075] The advice providing unit can provide advice on stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Take a short break. Refreshing yourself will improve your work efficiency." The advice providing unit can also provide advice on time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Review your schedule and set priorities." The advice providing unit can also provide advice that combines stress management and time management to business people. For example, the advice providing unit uses the generation AI to provide advice such as, "Leave some leeway in your schedule to reduce stress." This makes it possible to provide advice on stress management and time management to business people.

[0076] The mental state collection unit analyzes the user's voice tone and facial expression to more accurately grasp the user's emotional state and provide advice. For example, the mental state collection unit analyzes the user's voice tone and builds a system that estimates the user's emotional state. For example, the mental state collection unit uses a generation AI to analyze the pitch and speed of the voice to identify the user's emotions. The mental state collection unit can also analyze the user's facial expression to estimate the user's emotional state. For example, the mental state collection unit uses a generation AI to analyze facial muscle movements to identify emotions. The advice provision unit more accurately grasps the user's emotional state and provides advice. For example, the advice provision unit can use a generation AI to provide advice such as, "You are feeling a little nervous right now. Take a deep breath to relax." The advice provision unit can also use a generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice provision unit can also use a generation AI to provide advice such as, "You are feeling a little anxious right now. Take a short break to relax." This allows the user's emotional state to be more accurately grasped and advice to be provided.

[0077] The analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and provide encouragement or advice at the optimal time. For example, the analysis unit can use the emotion estimation function to build a system that analyzes the user's emotional state in real time. For example, the analysis unit can use the generation AI to analyze the user's facial expressions and voice to estimate emotions. The analysis unit can also use the generation AI to apply an emotion estimation algorithm to analyze the user's emotional state in real time. For example, the generation AI can analyze the user's facial expressions using facial expression recognition technology and calculate an emotion score. The generation AI can also analyze the user's voice using voice analysis technology and calculate an emotion score. The advice providing unit can provide encouragement or advice at the optimal time. For example, the advice providing unit can use the generation AI to provide advice such as, "You are feeling a little anxious right now. Take a deep breath to relax." The advice providing unit can also use the generation AI to provide advice such as, "You are feeling a little depressed right now. Remember your past successes and gain confidence." The advice-providing unit can also use the generation AI to provide advice such as, "You're feeling a little tense right now. Take a short break to relax." This allows encouragement and advice to be provided at the optimal time depending on the user's emotional state.

[0078] The advice providing unit can provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Take a deep breath and relax." The advice providing unit can also provide students with advice to reduce stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Take a short break. Refreshing yourself will help you concentrate on the exam." The advice providing unit can also provide students with advice to reduce tension and stress before an exam. For example, the advice providing unit uses a generating AI to provide advice such as "Try some relaxation techniques to relax before the exam." This makes it possible to provide students with advice to reduce tension and stress before an exam.

[0079] The advice providing unit can collect positive feedback from other users and provide advice based on that feedback in order to improve the user's self-evaluation. For example, the advice providing unit collects positive feedback from other users and provides advice to improve self-evaluation. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-esteem based on words of praise and gratitude from others." The advice providing unit can also analyze positive feedback from other users and provide advice based on that. For example, the advice providing unit uses a generation AI to provide advice such as, "Remember the positive feedback from others and gain confidence." The advice providing unit can also build a system that collects positive feedback from other users and provides advice based on that feedback. For example, the advice providing unit uses a generation AI to provide advice such as, "Improve your self-evaluation based on words of praise and gratitude from others." This makes it possible to provide advice to improve self-evaluation based on positive feedback from other users.

[0080] The advice providing unit can visualize the user's past successful experiences to make them easier to understand visually in order to improve the user's self-evaluation. For example, the advice providing unit visualizes the user's past successful experiences and provides advice to improve self-evaluation. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-evaluation." The advice providing unit can also provide advice by visualizing past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Visualize your past successful experiences and use them to improve self-evaluation." The advice providing unit can also build a system that visualizes past successful experiences to make them easier to understand visually. For example, the advice providing unit uses the generation AI to provide advice such as, "Display success stories in graphs and charts to improve self-esteem." In this way, advice to improve self-evaluation can be provided by visualizing past successful experiences to make them easier to understand visually.

[0081] The analysis unit can use the emotion estimation function to predict in advance whether a user will experience a decline in self-esteem in certain situations and provide preventative advice. For example, the analysis unit uses the emotion estimation function to analyze in real time whether a user will experience a decline in self-esteem in certain situations. For example, the analysis unit can use the generation AI to detect nervousness before a presentation. The analysis unit can also use the generation AI to detect anxiety before an exam. For example, the generation AI can analyze a user's heart rate and facial expressions to detect signs of a decline in self-esteem. The advice providing unit provides preventative advice. For example, the advice providing unit can use the generation AI to provide advice such as, "Take a deep breath and relax before a presentation." The advice providing unit can also use the generation AI to provide advice such as, "Take a short break to relax before an exam." The advice providing unit can also use the generation AI to provide advice such as, "Try relaxation techniques before your self-esteem declines." This makes it possible to predict a decline in self-esteem in certain situations and provide preventative advice.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The mental state collection unit collects the user's mental state and performance data. For example, the mental state collection unit may use a wearable device that measures the user's heart rate and stress level. It may also use an application that records the user's mood score and work efficiency, or a sensor that measures reaction time and concentration. Step 2: The analysis unit analyzes the collected data. For example, the analysis unit may use generative AI to analyze the data and evaluate the user's mental state. It may also analyze performance data and generate advice to improve the user's performance. It may also analyze fluctuations in the user's mental state and identify trends. Step 3: The advice provider provides personalized positive advice based on the analysis results. For example, the generative AI can provide specific advice such as "Take a deep breath and relax" or encouraging words such as "You did a good job today. You'll do better next time." It can also provide advice such as "You've had many successes in the past. Use that experience to continue working with confidence."

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a mental state collection unit that collects mental state and performance data of a user; an analysis unit that analyzes the data collected by the mental state collection unit; an advice providing unit that provides personalized positive advice based on the results of the analysis by the analysis unit; A system characterized by:

2. The advice providing unit Tracking the user's past mental state and performance data over time, analyzing trends to predict future mental state, and providing preventative advice 2. The system of claim 1.

3. The advice providing unit Providing mental advice to improve performance in fields other than sports 2. The system of claim 1.

4. The advice providing unit Analyze the user's consultation history and provide more personalized advice based on the content of past consultations.

2. The system of claim 1.

5. The analysis unit Analyze the user's emotions in real time and provide optimal positive advice based on those emotions.

2. The system of claim 1.

6. The analysis unit Detecting stress and anxiety felt by the user in specific situations in advance and providing preventative advice 2. The system of claim 1.

7. The analysis unit Analyze the user's emotional state in real time and provide encouragement and advice at the optimal time.

2. The system of claim 1.

8. The analysis unit To detect in advance a decrease in self-esteem felt by the user in a specific situation and provide preventive advice in response to the decrease 2. The system of claim 1.

Images