system

Abstract

The system according to this embodiment aims to diagnose the user's mental state and provide positive suggestions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a wearable device. The analysis unit analyzes the data collected by the data collection unit and diagnoses the user's mental state. The suggestion unit makes positive suggestions based on the diagnosis results obtained by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the early detection and prevention of mental disorders have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to diagnose the mental state of a user and make positive suggestions. <000..029>

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data from a wearable terminal. The analysis unit analyzes the data collected by the collection unit and diagnoses the mental state of the user. The suggestion unit makes positive suggestions based on the diagnosis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can diagnose the user's mental state and make positive suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​agent system specializing in mental care according to an embodiment of the present invention is a system that diagnoses symptoms of mental illness based on data collected from a wearable device and makes positive suggestions via voice. This system collects data such as heart rate, sleep duration, and steps taken from the wearable device, and the AI ​​analyzes the collected data to diagnose the user's mental state. For example, it determines whether the user is experiencing stress from fluctuations in heart rate or a decrease in sleep duration. Furthermore, based on the diagnosis results, the AI ​​makes positive suggestions to the user via voice. For example, it may suggest, "Let's take a short break and relax today," or "Let's do some light exercise to change your mood." Through this mechanism, the user can receive mental care in their daily life, which contributes to the prevention of mental illness. In addition, the AI ​​continuously monitors the user's mental state and adjusts the suggestions as needed. This allows the user to always be aware of their mental state and receive appropriate care. Furthermore, the AI ​​can make more accurate suggestions by referring to a vast amount of data and performing individual diagnoses. For example, it analyzes the user's mental state trends from past data and learns what kind of suggestions are effective in specific situations. In this way, the AI ​​can provide the user with optimal mental care. The ultimate goal is to use multimodal AI to reduce the number of mental illnesses worldwide and improve people's well-being. This will enable a specialized AI agent system for mental healthcare to efficiently diagnose users' mental states and provide positive suggestions, thereby preventing mental illness.

[0029] The AI ​​agent system specializing in mental care according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a wearable device. The data collection unit collects data such as heart rate, sleep duration, and steps taken. For example, the data collection unit uses a heart rate sensor built into the wearable device to measure heart rate. For example, the data collection unit uses an accelerometer built into the wearable device to measure sleep duration. For example, the data collection unit uses a pedometer built into the wearable device to measure steps taken. The analysis unit analyzes the data collected by the data collection unit and diagnoses the user's mental state. For example, the analysis unit analyzes fluctuations in heart rate to determine whether the user is experiencing stress. For example, the analysis unit analyzes a decrease in sleep duration to determine whether the user is fatigued. For example, the analysis unit analyzes a decrease in steps taken to determine whether the user is not getting enough exercise. The suggestion unit makes positive suggestions based on the diagnostic results obtained by the analysis unit. The suggestion function might, for example, suggest to the user, "Let's take a short break and relax today." The suggestion function might also suggest to the user, "Let's do some light exercise to change your mood." The suggestion function might also suggest to the user, "Let's get enough sleep." In this way, the AI ​​agent system specializing in mental care according to the embodiment can efficiently diagnose the user's mental state and make positive suggestions, thereby enabling the prevention of mental illness.

[0030] The data collection unit collects data from wearable devices. For example, it collects data such as heart rate, sleep duration, and step count. Specifically, to measure heart rate, it uses a heart rate sensor built into the wearable device. The heart rate sensor uses optical or electrical sensors to measure the user's heart rate in real time. Optical sensors measure heart rate by shining light onto the skin and detecting the reflected light to detect changes in blood flow. Electrical sensors, on the other hand, measure heart rate by passing a weak electric current through the skin and detecting changes in the resulting electrical signal. To measure sleep duration, the data collection unit uses an accelerometer built into the wearable device. The accelerometer detects the user's body movements and evaluates sleep quality and duration by analyzing the amount of movement during sleep and specific movement patterns. Furthermore, to measure step count, the data collection unit uses a pedometer built into the wearable device. The pedometer combines an accelerometer and a gyroscope to detect the user's walking motion and count steps. This allows the data collection unit to understand the user's daily activity levels and exercise habits. The collected data is transmitted wirelessly from the wearable device to a central database and updated in real time. This enables the data collection unit to efficiently collect diverse data on the user's mental state, making it available for use by the analysis and proposal units.

[0031] The analysis unit analyzes the data collected by the data collection unit to diagnose the user's mental state. The analysis uses AI to process data in real time and understand the user's mental state. Specifically, it analyzes heart rate variability to determine if the user is experiencing stress. The AI ​​learns heart rate variability patterns and detects signs of stress when there are abnormal fluctuations compared to the normal heart rate. It also analyzes sleep duration to determine if the user is fatigued. The AI ​​detects signs of fatigue when sleep duration is significantly reduced compared to past sleep data. Furthermore, it analyzes step count to determine if the user is not getting enough exercise. The AI ​​detects signs of inactivity when step count is reduced compared to the user's past exercise data. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's mental state in real time. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term fluctuations and trends in mental state. For example, based on past stress data, it can predict increases or decreases in stress during specific periods or situations and develop future mental care strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor mental states in real time but also to provide long-term mental care and anomaly detection, thereby improving the overall reliability and safety of the system.

[0032] The suggestion department makes positive suggestions based on the diagnostic results obtained by the analysis department. Specifically, it suggests to the user, "Let's take a short break and relax today." If the suggestion department determines that the user's stress level is high, it will suggest specific ways to relax. For example, it will suggest relaxation methods such as deep breathing, meditation, and light stretching, and guide the user so that they can put them into practice. It will also suggest to the user, "Let's do some light exercise to change your mood." If the suggestion department determines that the user is not getting enough exercise, it will suggest specific ways to encourage moderate exercise. For example, it will suggest light exercises such as walking, jogging, and yoga, and support the user so that they can put them into practice without difficulty. Furthermore, it will suggest to the user, "Let's make sure to get enough sleep." If the suggestion department determines that the user is not getting enough sleep, it will suggest specific ways to improve the quality of sleep. For example, it will explain relaxation methods before going to bed, how to create a suitable sleep environment, and the importance of regular lifestyle habits, and advise the user so that they can put them into practice. In this way, the suggestion department proposes specific actions to improve the user's mental state and supports the user so that they can experience positive changes. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can revise proposals or add new ones based on feedback from users who have received them. The proposal department can also provide customized proposals tailored to users' preferences and lifestyles. This allows the proposal department to provide individually optimized proposals to users, maximizing the effectiveness of mental care.

[0033] The data collection unit can collect data such as heart rate, sleep duration, and steps taken. For example, to measure heart rate, the data collection unit uses a heart rate sensor built into the wearable device. For example, to measure sleep duration, the data collection unit uses an accelerometer built into the wearable device. For example, to measure steps taken, the data collection unit uses a pedometer built into the wearable device. By collecting data such as the user's heart rate, sleep duration, and steps taken, it becomes possible to diagnose their mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the wearable device into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze the collected data and determine whether the user is experiencing stress. For example, the analysis unit can analyze fluctuations in heart rate to determine if the user is experiencing stress. For example, the analysis unit can analyze a decrease in sleep duration to determine if the user is fatigued. For example, the analysis unit can analyze a decrease in steps taken to determine if the user is not getting enough exercise. In this way, by analyzing the collected data, it is possible to determine whether the user is experiencing stress. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the stress determination.

[0035] The suggestion unit can provide positive suggestions to the user via voice based on the diagnostic results. For example, the suggestion unit might suggest to the user, "Let's take a short break and relax today." Or, "Let's do some light exercise to change your mood." Or, "Let's get enough sleep." By providing positive suggestions via voice based on the diagnostic results, the system can provide mental care for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the diagnostic results into a generating AI and have the generating AI generate positive suggestions.

[0036] The suggestion unit can continuously monitor the user's mental state and adjust the suggestions as needed. For example, the suggestion unit can periodically check the user's mental state and suggest relaxation methods if stress levels are high. For example, if the user's mental state is stable, the suggestion unit can suggest maintaining the status quo. For example, if the user's mental state is deteriorating, the suggestion unit can suggest consulting a professional. This allows for more appropriate mental care by continuously monitoring the user's mental state and adjusting the suggestions as needed. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's mental state data into a generating AI and have the generating AI adjust the suggestions.

[0037] The analysis unit can refer to vast amounts of data and perform individual diagnoses. For example, the analysis unit can analyze trends in a user's mental state from past data and learn what kind of suggestions are effective in specific situations. For example, the analysis unit can refer to data from other users and make effective suggestions to users with similar mental states. For example, the analysis unit can use vast amounts of data to diagnose a user's mental state more accurately. This improves the accuracy of individual diagnoses by referring to vast amounts of data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vast amounts of data into a generating AI and have the generating AI perform individual diagnoses.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection timing. For example, the data collection unit can identify periods in the user's past when they were under high stress and concentrate data collection during those times. For example, the data collection unit can predict the most effective collection timing from the user's past data collection history and optimize the collection schedule. For example, the data collection unit can adjust the timing of data collection based on the user's daily rhythm and acquire data in a natural way. This allows the optimal collection timing to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection timing.

[0039] The data collection unit can adjust the types of data it collects based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will prioritize collecting heart rate and step count data. If the user is sleeping, the data collection unit will collect data on sleep quality and duration. If the user is working, the data collection unit will collect data on stress levels and concentration levels. By adjusting the types of data collected based on the user's current activity status and environment, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity status and environment data into a generating AI and have the generating AI adjust the types of data to be collected.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at home, the data collection unit prioritizes the collection of data related to relaxation. If the user is at work, the data collection unit prioritizes the collection of data related to stress levels and concentration. If the user is out, the data collection unit prioritizes the collection of data related to exercise and heart rate. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user is experiencing stress on social media, the data collection unit can collect data on their heart rate and stress level at that time. For example, if the user is relaxing on social media, the data collection unit can collect data on the quality and duration of their sleep at that time. For example, if the user is experiencing anxiety on social media, the data collection unit can collect data on their breathing patterns and heart rate variability at that time. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can predict the current mental state by referring to past data during analysis. For example, the analysis unit can refer to the user's past heart rate data to predict the current stress level. For example, the analysis unit can refer to the user's past sleep data to predict the current sleep quality. For example, the analysis unit can refer to the user's past exercise data to predict the current mental state. In this way, the current mental state can be predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform a prediction of the current mental state.

[0043] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply a stress level analysis method to heart rate data. For example, the analysis unit can apply a sleep quality analysis method to sleep data. For example, the analysis unit can apply a mental state analysis method to exercise data. By applying different analysis methods to each data category, more appropriate analysis becomes possible. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis methods for each data category into a generating AI and have the generating AI perform the analysis.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data to understand the latest mental state. For example, the analysis unit may refer to past data to analyze long-term changes in mental state. For example, the analysis unit may prioritize the analysis of data before and after a specific event to understand the impact of the event. By determining the priority of analysis based on the data collection timing, more appropriate analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationship between heart rate data and stress levels. For example, the analysis unit can adjust the order of analysis based on the relationship between sleep data and mental state. For example, the analysis unit can adjust the order of analysis based on the relationship between exercise data and mental state. By adjusting the order of analysis based on the relationships between the data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0046] The suggestion unit can adjust the level of detail in its suggestions based on the importance of mental care. For example, if the user is under high stress, the suggestion unit will provide detailed mental care suggestions. If the user is relaxed, the suggestion unit will provide concise mental care suggestions. If the user is feeling anxious, the suggestion unit will provide detailed suggestions that offer reassurance. By adjusting the level of detail in suggestions based on the importance of mental care, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of mental care into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0047] The proposal unit can select the most suitable proposal by referring to the user's past responses when making a proposal. For example, the proposal unit may prioritize proposals that the user has previously accepted favorably. For example, the proposal unit may avoid proposals that the user has previously rejected when making new proposals. For example, the proposal unit may analyze the user's past responses and select the most effective proposal. In this way, the optimal proposal can be selected by referring to the user's past responses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input the user's past response data into a generating AI and have the generating AI select the optimal proposal.

[0048] The suggestion unit can make optimal suggestions by considering the user's current activity status. For example, if the user is exercising, the suggestion unit will suggest ways to relax after exercise. For example, if the user is working, the suggestion unit will suggest taking a short break. For example, if the user is resting, the suggestion unit will suggest relaxing activities. This allows for more appropriate suggestions by considering the user's current activity status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current activity status data into a generating AI and have the generating AI generate optimal suggestions.

[0049] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, if the user is feeling stressed on social media, the suggestion unit can suggest ways to relax. For example, if the user is feeling relaxed on social media, the suggestion unit can suggest active activities. For example, if the user is feeling anxious on social media, the suggestion unit can make suggestions that provide reassurance. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate relevant suggestions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect user lifestyle data and provide it to the analysis unit. For example, it can collect user dietary information and calorie intake, allowing the analysis unit to analyze its relationship to mental state. It can also collect user alcohol and smoking frequency, allowing the analysis unit to analyze its relationship to stress levels. Furthermore, it can collect user hobbies and relaxation methods, which the analysis unit can use to suggest mental care. In this way, collecting user lifestyle data enables more comprehensive mental care.

[0052] The data collection unit can collect user activity data and provide it to the analysis unit. For example, it can collect the user's exercise volume and type, allowing the analysis unit to analyze its relationship with their mental state. It can also collect the user's work progress and working hours, allowing the analysis unit to analyze its relationship with their stress levels. Furthermore, it can collect the user's rest time and relaxation methods, which the analysis unit can use to suggest mental care. In this way, collecting user activity data enables more comprehensive mental care.

[0053] The analysis unit can predict the user's current mental state by referring to the user's past mental state data. For example, it can predict the user's current stress level by referring to the user's past heart rate data. It can predict the user's current sleep quality by referring to the user's past sleep data. It can predict the user's current mental state by referring to the user's past exercise data. In this way, the current mental state can be predicted by referring to past data.

[0054] The proposal department can select the most suitable proposal by referring to the user's past responses. For example, it can prioritize proposals that the user has previously accepted favorably. It can also avoid proposals that the user has previously rejected when making new proposals. By analyzing the user's past responses, it can select the most effective proposal. In this way, the optimal proposal can be selected by referring to the user's past responses.

[0055] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location. For example, if the user is at home, it can prioritize the collection of data related to their relaxation state. If the user is at work, it can prioritize the collection of data related to their stress level and concentration level. If the user is out and about, it can prioritize the collection of data related to their activity level and heart rate. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0056] The analysis unit can apply different analysis methods to each data category. For example, a method for analyzing stress levels can be applied to heart rate data. A method for analyzing sleep quality can be applied to sleep data. A method for analyzing mental state can be applied to exercise data. By applying different analysis methods to each data category, more appropriate analysis becomes possible.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects data from the wearable device. The data collection unit collects data such as heart rate, sleep duration, and steps taken. To measure heart rate, the data collection unit uses the heart rate sensor built into the wearable device. To measure sleep duration, the data collection unit uses the accelerometer built into the wearable device. To measure steps taken, the data collection unit uses the pedometer built into the wearable device. Step 2: The analysis unit analyzes the data collected by the data collection unit to diagnose the user's mental state. The analysis unit analyzes heart rate variability to determine if the user is experiencing stress. The analysis unit analyzes the decrease in sleep duration to determine if the user is fatigued. The analysis unit analyzes the decrease in step count to determine if the user is not getting enough exercise. Step 3: The suggestion department makes positive suggestions based on the diagnostic results obtained by the analysis department. The suggestion department suggests to the user, "Let's take a short break and relax today." The suggestion department suggests to the user, "Let's do some light exercise to change your mood." The suggestion department suggests to the user, "Let's get enough sleep."

[0059] (Example of form 2) The AI ​​agent system specializing in mental care according to an embodiment of the present invention is a system that diagnoses symptoms of mental illness based on data collected from a wearable device and makes positive suggestions via voice. This system collects data such as heart rate, sleep duration, and steps taken from the wearable device, and the AI ​​analyzes the collected data to diagnose the user's mental state. For example, it determines whether the user is experiencing stress from fluctuations in heart rate or a decrease in sleep duration. Furthermore, based on the diagnosis results, the AI ​​makes positive suggestions to the user via voice. For example, it may suggest, "Let's take a short break and relax today," or "Let's do some light exercise to change your mood." Through this mechanism, the user can receive mental care in their daily life, which contributes to the prevention of mental illness. In addition, the AI ​​continuously monitors the user's mental state and adjusts the suggestions as needed. This allows the user to always be aware of their mental state and receive appropriate care. Furthermore, the AI ​​can make more accurate suggestions by referring to a vast amount of data and performing individual diagnoses. For example, it analyzes the user's mental state trends from past data and learns what kind of suggestions are effective in specific situations. In this way, the AI ​​can provide the user with optimal mental care. The ultimate goal is to use multimodal AI to reduce the number of mental illnesses worldwide and improve people's well-being. This will enable a specialized AI agent system for mental healthcare to efficiently diagnose users' mental states and provide positive suggestions, thereby preventing mental illness.

[0060] The AI ​​agent system specializing in mental care according to this embodiment comprises a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a wearable device. The data collection unit collects data such as heart rate, sleep duration, and steps taken. For example, the data collection unit uses a heart rate sensor built into the wearable device to measure heart rate. For example, the data collection unit uses an accelerometer built into the wearable device to measure sleep duration. For example, the data collection unit uses a pedometer built into the wearable device to measure steps taken. The analysis unit analyzes the data collected by the data collection unit and diagnoses the user's mental state. For example, the analysis unit analyzes fluctuations in heart rate to determine whether the user is experiencing stress. For example, the analysis unit analyzes a decrease in sleep duration to determine whether the user is fatigued. For example, the analysis unit analyzes a decrease in steps taken to determine whether the user is not getting enough exercise. The suggestion unit makes positive suggestions based on the diagnostic results obtained by the analysis unit. The suggestion function might, for example, suggest to the user, "Let's take a short break and relax today." The suggestion function might also suggest to the user, "Let's do some light exercise to change your mood." The suggestion function might also suggest to the user, "Let's get enough sleep." In this way, the AI ​​agent system specializing in mental care according to the embodiment can efficiently diagnose the user's mental state and make positive suggestions, thereby enabling the prevention of mental illness.

[0061] The data collection unit collects data from wearable devices. For example, it collects data such as heart rate, sleep duration, and step count. Specifically, to measure heart rate, it uses a heart rate sensor built into the wearable device. The heart rate sensor uses optical or electrical sensors to measure the user's heart rate in real time. Optical sensors measure heart rate by shining light onto the skin and detecting the reflected light to detect changes in blood flow. Electrical sensors, on the other hand, measure heart rate by passing a weak electric current through the skin and detecting changes in the resulting electrical signal. To measure sleep duration, the data collection unit uses an accelerometer built into the wearable device. The accelerometer detects the user's body movements and evaluates sleep quality and duration by analyzing the amount of movement during sleep and specific movement patterns. Furthermore, to measure step count, the data collection unit uses a pedometer built into the wearable device. The pedometer combines an accelerometer and a gyroscope to detect the user's walking motion and count steps. This allows the data collection unit to understand the user's daily activity levels and exercise habits. The collected data is transmitted wirelessly from the wearable device to a central database and updated in real time. This enables the data collection unit to efficiently collect diverse data on the user's mental state, making it available for use by the analysis and proposal units.

[0062] The analysis unit analyzes the data collected by the data collection unit to diagnose the user's mental state. The analysis uses AI to process data in real time and understand the user's mental state. Specifically, it analyzes heart rate variability to determine if the user is experiencing stress. The AI ​​learns heart rate variability patterns and detects signs of stress when there are abnormal fluctuations compared to the normal heart rate. It also analyzes sleep duration to determine if the user is fatigued. The AI ​​detects signs of fatigue when sleep duration is significantly reduced compared to past sleep data. Furthermore, it analyzes step count to determine if the user is not getting enough exercise. The AI ​​detects signs of inactivity when step count is reduced compared to the user's past exercise data. This allows the analysis unit to quickly and accurately analyze collected data and understand the user's mental state in real time. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term fluctuations and trends in mental state. For example, based on past stress data, it can predict increases or decreases in stress during specific periods or situations and develop future mental care strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor mental states in real time but also to provide long-term mental care and anomaly detection, thereby improving the overall reliability and safety of the system.

[0063] The suggestion department makes positive suggestions based on the diagnostic results obtained by the analysis department. Specifically, it suggests to the user, "Let's take a short break and relax today." If the suggestion department determines that the user's stress level is high, it will suggest specific ways to relax. For example, it will suggest relaxation methods such as deep breathing, meditation, and light stretching, and guide the user so that they can put them into practice. It will also suggest to the user, "Let's do some light exercise to change your mood." If the suggestion department determines that the user is not getting enough exercise, it will suggest specific ways to encourage moderate exercise. For example, it will suggest light exercises such as walking, jogging, and yoga, and support the user so that they can put them into practice without difficulty. Furthermore, it will suggest to the user, "Let's make sure to get enough sleep." If the suggestion department determines that the user is not getting enough sleep, it will suggest specific ways to improve the quality of sleep. For example, it will explain relaxation methods before going to bed, how to create a suitable sleep environment, and the importance of regular lifestyle habits, and advise the user so that they can put them into practice. In this way, the suggestion department proposes specific actions to improve the user's mental state and supports the user so that they can experience positive changes. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can revise proposals or add new ones based on feedback from users who have received them. The proposal department can also provide customized proposals tailored to users' preferences and lifestyles. This allows the proposal department to provide individually optimized proposals to users, maximizing the effectiveness of mental care.

[0064] The data collection unit can collect data such as heart rate, sleep duration, and steps taken. For example, to measure heart rate, the data collection unit uses a heart rate sensor built into the wearable device. For example, to measure sleep duration, the data collection unit uses an accelerometer built into the wearable device. For example, to measure steps taken, the data collection unit uses a pedometer built into the wearable device. By collecting data such as the user's heart rate, sleep duration, and steps taken, it becomes possible to diagnose their mental state. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the wearable device into a generating AI and have the generating AI perform data analysis.

[0065] The analysis unit can analyze the collected data and determine whether the user is experiencing stress. For example, the analysis unit can analyze fluctuations in heart rate to determine if the user is experiencing stress. For example, the analysis unit can analyze a decrease in sleep duration to determine if the user is fatigued. For example, the analysis unit can analyze a decrease in steps taken to determine if the user is not getting enough exercise. In this way, by analyzing the collected data, it is possible to determine whether the user is experiencing stress. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the stress determination.

[0066] The suggestion unit can provide positive suggestions to the user via voice based on the diagnostic results. For example, the suggestion unit might suggest to the user, "Let's take a short break and relax today." Or, "Let's do some light exercise to change your mood." Or, "Let's get enough sleep." By providing positive suggestions via voice based on the diagnostic results, the system can provide mental care for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the diagnostic results into a generating AI and have the generating AI generate positive suggestions.

[0067] The suggestion unit can continuously monitor the user's mental state and adjust the suggestions as needed. For example, the suggestion unit can periodically check the user's mental state and suggest relaxation methods if stress levels are high. For example, if the user's mental state is stable, the suggestion unit can suggest maintaining the status quo. For example, if the user's mental state is deteriorating, the suggestion unit can suggest consulting a professional. This allows for more appropriate mental care by continuously monitoring the user's mental state and adjusting the suggestions as needed. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's mental state data into a generating AI and have the generating AI adjust the suggestions.

[0068] The analysis unit can refer to vast amounts of data and perform individual diagnoses. For example, the analysis unit can analyze trends in a user's mental state from past data and learn what kind of suggestions are effective in specific situations. For example, the analysis unit can refer to data from other users and make effective suggestions to users with similar mental states. For example, the analysis unit can use vast amounts of data to diagnose a user's mental state more accurately. This improves the accuracy of individual diagnoses by referring to vast amounts of data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input vast amounts of data into a generating AI and have the generating AI perform individual diagnoses.

[0069] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the frequency of data collection to obtain more detailed information. For example, if the user is relaxed, the data collection unit decreases the frequency of data collection to reduce the burden. For example, if the user's emotions are unstable, the data collection unit dynamically adjusts the collection frequency to acquire data at the appropriate time. This allows for more appropriate data collection by adjusting the frequency of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the data collection frequency.

[0070] The data collection unit can analyze the user's past data collection history and select the optimal collection timing. For example, the data collection unit can identify periods in the user's past when they were under high stress and concentrate data collection during those times. For example, the data collection unit can predict the most effective collection timing from the user's past data collection history and optimize the collection schedule. For example, the data collection unit can adjust the timing of data collection based on the user's daily rhythm and acquire data in a natural way. This allows the optimal collection timing to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection timing.

[0071] The data collection unit can adjust the types of data it collects based on the user's current activity status and environment. For example, if the user is exercising, the data collection unit will prioritize collecting heart rate and step count data. If the user is sleeping, the data collection unit will collect data on sleep quality and duration. If the user is working, the data collection unit will collect data on stress levels and concentration levels. By adjusting the types of data collected based on the user's current activity status and environment, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity status and environment data into a generating AI and have the generating AI adjust the types of data to be collected.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting heart rate and stress level data. For example, if the user is relaxed, the data collection unit will prioritize collecting sleep quality and duration data. For example, if the user is anxious, the data collection unit will prioritize collecting breathing patterns and heart rate variability data. This allows for more appropriate data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data collection.

[0073] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is at home, the data collection unit prioritizes the collection of data related to relaxation. If the user is at work, the data collection unit prioritizes the collection of data related to stress levels and concentration. If the user is out, the data collection unit prioritizes the collection of data related to exercise and heart rate. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0074] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user is experiencing stress on social media, the data collection unit can collect data on their heart rate and stress level at that time. For example, if the user is relaxing on social media, the data collection unit can collect data on the quality and duration of their sleep at that time. For example, if the user is experiencing anxiety on social media, the data collection unit can collect data on their breathing patterns and heart rate variability at that time. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit enhances the stress level analysis algorithm. For example, if the user is relaxed, the analysis unit enhances the sleep quality and duration analysis algorithm. For example, if the user is anxious, the analysis unit enhances the breathing pattern and heart rate variability analysis algorithm. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0076] The analysis unit can predict the current mental state by referring to past data during analysis. For example, the analysis unit can refer to the user's past heart rate data to predict the current stress level. For example, the analysis unit can refer to the user's past sleep data to predict the current sleep quality. For example, the analysis unit can refer to the user's past exercise data to predict the current mental state. In this way, the current mental state can be predicted by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform a prediction of the current mental state.

[0077] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit can apply a stress level analysis method to heart rate data. For example, the analysis unit can apply a sleep quality analysis method to sleep data. For example, the analysis unit can apply a mental state analysis method to exercise data. By applying different analysis methods to each data category, more appropriate analysis becomes possible. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis methods for each data category into a generating AI and have the generating AI perform the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is anxious, the analysis unit provides a display method that provides a sense of security. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0079] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data to understand the latest mental state. For example, the analysis unit may refer to past data to analyze long-term changes in mental state. For example, the analysis unit may prioritize the analysis of data before and after a specific event to understand the impact of the event. By determining the priority of analysis based on the data collection timing, more appropriate analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0080] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can adjust the order of analysis based on the relationship between heart rate data and stress levels. For example, the analysis unit can adjust the order of analysis based on the relationship between sleep data and mental state. For example, the analysis unit can adjust the order of analysis based on the relationship between exercise data and mental state. By adjusting the order of analysis based on the relationships between the data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is stressed, the suggestion unit will make suggestions in gentle language. If the user is relaxed, the suggestion unit will make suggestions in cheerful language. If the user is anxious, the suggestion unit will make suggestions in reassuring language. By adjusting the way suggestions are expressed based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are expressed.

[0082] The suggestion unit can adjust the level of detail in its suggestions based on the importance of mental care. For example, if the user is under high stress, the suggestion unit will provide detailed mental care suggestions. If the user is relaxed, the suggestion unit will provide concise mental care suggestions. If the user is feeling anxious, the suggestion unit will provide detailed suggestions that offer reassurance. By adjusting the level of detail in suggestions based on the importance of mental care, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of mental care into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0083] The proposal unit can select the most suitable proposal by referring to the user's past responses when making a proposal. For example, the proposal unit may prioritize proposals that the user has previously accepted favorably. For example, the proposal unit may avoid proposals that the user has previously rejected when making new proposals. For example, the proposal unit may analyze the user's past responses and select the most effective proposal. In this way, the optimal proposal can be selected by referring to the user's past responses. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may input the user's past response data into a generating AI and have the generating AI select the optimal proposal.

[0084] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit will make suggestions at a time when the user is feeling relaxed. For example, if the user is relaxed, the suggestion unit will make suggestions at a time when the user is feeling active. For example, if the user is feeling anxious, the suggestion unit will make suggestions at a time when the user feels reassured. By adjusting the timing of suggestions based on the user's emotions, it becomes possible to make suggestions at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0085] The suggestion unit can make optimal suggestions by considering the user's current activity status. For example, if the user is exercising, the suggestion unit will suggest ways to relax after exercise. For example, if the user is working, the suggestion unit will suggest taking a short break. For example, if the user is resting, the suggestion unit will suggest relaxing activities. This allows for more appropriate suggestions by considering the user's current activity status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's current activity status data into a generating AI and have the generating AI generate optimal suggestions.

[0086] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, if the user is feeling stressed on social media, the suggestion unit can suggest ways to relax. For example, if the user is feeling relaxed on social media, the suggestion unit can suggest active activities. For example, if the user is feeling anxious on social media, the suggestion unit can make suggestions that provide reassurance. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI generate relevant suggestions.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on those estimated emotions. For example, if the user is stressed, the reliability of the analysis results can be evaluated low, and a re-analysis can be performed. If the user is relaxed, the reliability of the analysis results can be evaluated high, and the results can be provided directly to the proposal unit. If the user is anxious, the reliability of the analysis results can be evaluated moderately, and additional data can be collected. This allows for more accurate mental care by evaluating the reliability of the analysis results based on the user's emotions.

[0089] The data collection unit can collect user lifestyle data and provide it to the analysis unit. For example, it can collect user dietary information and calorie intake, allowing the analysis unit to analyze its relationship to mental state. It can also collect user alcohol and smoking frequency, allowing the analysis unit to analyze its relationship to stress levels. Furthermore, it can collect user hobbies and relaxation methods, which the analysis unit can use to suggest mental care. In this way, collecting user lifestyle data enables more comprehensive mental care.

[0090] The suggestion function can estimate the user's emotions and adjust the frequency of suggestions based on those estimates. For example, if the user is stressed, the frequency of suggestions can be increased to provide more frequent mental care suggestions. If the user is relaxed, the frequency of suggestions can be decreased to reduce the burden. If the user is anxious, the frequency of suggestions can be adjusted to a moderate level, and suggestions can be provided at appropriate times. In this way, by adjusting the frequency of suggestions based on the user's emotions, more appropriate mental care becomes possible.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is feeling anxious, a display method that provides a sense of security can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible.

[0092] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those estimates. For example, if the user is stressed, the suggestion can be presented in gentle language. If the user is relaxed, the suggestion can be presented in cheerful language. If the user is anxious, the suggestion can be presented in reassuring language. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made.

[0093] The data collection unit can collect user activity data and provide it to the analysis unit. For example, it can collect the user's exercise volume and type, allowing the analysis unit to analyze its relationship with their mental state. It can also collect the user's work progress and working hours, allowing the analysis unit to analyze its relationship with their stress levels. Furthermore, it can collect the user's rest time and relaxation methods, which the analysis unit can use to suggest mental care. In this way, collecting user activity data enables more comprehensive mental care.

[0094] The analysis unit can predict the user's current mental state by referring to the user's past mental state data. For example, it can predict the user's current stress level by referring to the user's past heart rate data. It can predict the user's current sleep quality by referring to the user's past sleep data. It can predict the user's current mental state by referring to the user's past exercise data. In this way, the current mental state can be predicted by referring to past data.

[0095] The proposal department can select the most suitable proposal by referring to the user's past responses. For example, it can prioritize proposals that the user has previously accepted favorably. It can also avoid proposals that the user has previously rejected when making new proposals. By analyzing the user's past responses, it can select the most effective proposal. In this way, the optimal proposal can be selected by referring to the user's past responses.

[0096] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location. For example, if the user is at home, it can prioritize the collection of data related to their relaxation state. If the user is at work, it can prioritize the collection of data related to their stress level and concentration level. If the user is out and about, it can prioritize the collection of data related to their activity level and heart rate. This allows for more appropriate data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0097] The analysis unit can apply different analysis methods to each data category. For example, a method for analyzing stress levels can be applied to heart rate data. A method for analyzing sleep quality can be applied to sleep data. A method for analyzing mental state can be applied to exercise data. By applying different analysis methods to each data category, more appropriate analysis becomes possible.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects data from the wearable device. The data collection unit collects data such as heart rate, sleep duration, and steps taken. To measure heart rate, the data collection unit uses the heart rate sensor built into the wearable device. To measure sleep duration, the data collection unit uses the accelerometer built into the wearable device. To measure steps taken, the data collection unit uses the pedometer built into the wearable device. Step 2: The analysis unit analyzes the data collected by the data collection unit to diagnose the user's mental state. The analysis unit analyzes heart rate variability to determine if the user is experiencing stress. The analysis unit analyzes the decrease in sleep duration to determine if the user is fatigued. The analysis unit analyzes the decrease in step count to determine if the user is not getting enough exercise. Step 3: The suggestion department makes positive suggestions based on the diagnostic results obtained by the analysis department. The suggestion department suggests to the user, "Let's take a short break and relax today." The suggestion department suggests to the user, "Let's do some light exercise to change your mood." The suggestion department suggests to the user, "Let's get enough sleep."

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the heart rate sensor, accelerometer, and pedometer of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to diagnose the user's mental state. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates positive suggestions based on the diagnosis results, which are provided to the user through the speaker of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the heart rate sensor, accelerometer, and pedometer of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to diagnose the user's mental state. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates positive suggestions based on the diagnosis results, which are provided to the user through the speaker of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the heart rate sensor, accelerometer, and pedometer of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to diagnose the user's mental state. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates positive suggestions based on the diagnosis results, which are provided to the user through the speaker of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the heart rate sensor, accelerometer, and pedometer of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to diagnose the user's mental state. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates positive suggestions based on the diagnosis results, which are provided to the user through the speaker of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A data collection unit that collects data from wearable devices, An analysis unit analyzes the data collected by the aforementioned collection unit and diagnoses the user's mental state, The system includes a proposal unit that makes positive suggestions based on the diagnostic results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as heart rate, sleep duration, and steps taken. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine whether the user is experiencing stress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the diagnostic results, the system provides positive suggestions to the user via voice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We continuously monitor the user's mental state and adjust the suggestions as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We refer to vast amounts of data and perform individual diagnoses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history to select the optimal collection timing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, adjust the types of data collected based on the user's current activity and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past data is referenced to predict the current mental state. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of mental health care. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, the optimal proposal content is selected by referring to the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we will consider the user's current activity status to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from wearable devices, An analysis unit analyzes the data collected by the aforementioned collection unit and diagnoses the user's mental state, The system includes a proposal unit that makes positive suggestions based on the diagnostic results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as heart rate, sleep duration, and steps taken. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine whether the user is experiencing stress. The system according to feature 1.

4. The aforementioned proposal section is, Based on the diagnostic results, the system provides positive suggestions to the user via voice. The system according to feature 1.

5. The aforementioned proposal section is, We continuously monitor the user's mental state and adjust the suggestions as needed. The system according to feature 1.

6. The aforementioned analysis unit, Referencing vast amounts of data to perform individual diagnoses The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history to select the optimal collection timing. The system according to feature 1.

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