System

Abstract

An object of the system according to the embodiment is to allow a smartphone user to acquire a healthy digital habit.SOLUTION: A system includes a monitoring unit and a proposal unit. The monitoring unit monitors a smartphone use time of a user in real time. The suggestion unit suggests a healthy digital habit on the basis of the smartphone usage time data acquired by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, excessive smartphone usage can have a negative impact on lifestyle habits.

[0005] The system of the embodiment aims to help smartphone users develop healthy digital habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit and a suggestion unit. The monitoring unit monitors a user's smartphone usage time in real time. The suggestion unit suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment enables smartphone users to develop healthy digital habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention prevents "smartphone addiction" caused by excessive smartphone use and supports users in developing healthier digital habits. This system uses AI-based behavioral change techniques, which enable users to prevent excessive smartphone use and develop healthier digital habits.

[0029] The system according to the embodiment includes a monitoring unit and a suggestion unit. The monitoring unit monitors a user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage status. The monitoring unit can also record the amount of smartphone usage at each time of day. The monitoring unit analyzes the usage time data and identifies the user's usage pattern. The suggestion unit suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit provides specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit can also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit can make individually customized suggestions based on the user's usage pattern. As a result, the system according to the embodiment monitors a user's smartphone usage time in real time and suggests healthy digital habits, thereby preventing smartphone addiction.

[0030] The monitoring unit can analyze the user's lifestyle and activity patterns and suggest optimal smartphone usage time. For example, the monitoring unit uses AI to analyze the user's smartphone usage data and identify lifestyle and activity patterns. For example, it suggests optimal smartphone usage time based on data such as wake-up time, bedtime, and meal times. The monitoring unit can also analyze data such as the user's exercise volume and distance traveled to identify activity patterns. For example, if the user's exercise volume is low, it will suggest reducing smartphone usage time. This makes it possible to promote healthy digital habits by suggesting optimal smartphone usage time based on the user's lifestyle and activity patterns.

[0031] The monitoring unit analyzes not only the amount of time spent using smartphones, but also the types and content of apps being used, making it possible to identify highly addictive apps. For example, the monitoring unit uses AI to analyze smartphone usage data and identify the types and content of apps being used. For example, it records detailed usage time for social media and game apps. The monitoring unit can also identify highly addictive apps based on the frequency and duration of app use. For example, it determines that apps that are used for long periods of time or frequently are highly addictive. By identifying highly addictive apps, it is possible to effectively prevent smartphone addiction in users.

[0032] The monitoring unit can monitor overall digital device usage time, including devices other than smartphones. For example, the monitoring unit can use AI to collect usage data from devices other than smartphones (tablets, PCs, etc.) and monitor overall digital device usage time. For example, it can integrate and record usage time for each device. The monitoring unit can also record detailed usage time for each device and simultaneous usage time. For example, it can identify the time when a smartphone and a tablet are used simultaneously. This allows for effective management of a user's digital device addiction by monitoring overall digital device usage time, including devices other than smartphones.

[0033] The monitoring unit can share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, the monitoring unit can use AI to share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, it can enable parents to check their children's smartphone usage time in real time. The monitoring unit can also work with educational institutions to manage students' smartphone usage time. For example, teachers can monitor students' smartphone usage time and provide appropriate guidance. By sharing monitoring data with families and educational institutions, they can jointly manage screen time and prevent smartphone addiction.

[0034] The suggestion unit can analyze the user's lifestyle data and provide comprehensive health management. For example, the suggestion unit uses AI to analyze the user's lifestyle data (such as sleep time and exercise amount) and provide comprehensive health management. For example, it can suggest optimal sleep times based on sleep data. The suggestion unit can also suggest activities to increase exercise volume based on exercise data. For example, if the amount of exercise is low, it can suggest walking or stretching. In this way, the user's health can be maintained by analyzing the user's lifestyle data and providing comprehensive health management.

[0035] The suggestion unit can customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, the suggestion unit uses AI to customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, suggestions are made based on the user's favorite exercises and meals. The suggestion unit can also make effective suggestions based on the user's past behavioral history. For example, advice that was effective in the past can be provided again. This makes it easier for users to practice healthy digital habits by providing advice customized to the user's preferences.

[0036] The suggestion unit can provide a mechanism for suggesting healthy digital habits for the entire family or group and practicing them together. For example, the suggestion unit provides a mechanism for suggesting healthy digital habits for the entire family or group and practicing them together. For example, limiting smartphone usage time for the entire family. The suggestion unit can also set goals and check progress for the group. For example, it can manage the total usage time for the entire group and aim to achieve the goal. This allows the entire family or group to practice healthy digital habits and maintain their health together.

[0037] The suggestion unit can gamify the suggestions, allowing users to develop healthy habits while having fun. For example, the suggestion unit gamifies the suggestions of healthy digital habits using AI, allowing users to practice them while having fun. For example, it can suggest exercise and rest in a game format. The suggestion unit can also increase user motivation by setting a point system, leveling up, and rewards. For example, points can be earned by exercising, and rewards can be obtained when a certain number of points are reached. In this way, gamifying the suggestions allows users to develop healthy habits while having fun.

[0038] The suggestion unit can customize the content of the reminder based on the user's past behavioral history and provide a more effective message. For example, the suggestion unit uses AI to analyze the user's past behavioral history and customize the content of the reminder. For example, it can reuse messages that have been effective in the past. The suggestion unit can also generate effective reminders based on the user's behavioral patterns. For example, it can send an effective message at a specific time period. In this way, by customizing the content of the reminder based on the user's past behavioral history, it is possible to provide a more effective message.

[0039] The suggestion unit can dynamically adjust the frequency and content of reminders based on user feedback. For example, the suggestion unit uses AI to analyze user feedback and dynamically adjust the frequency and content of reminders. For example, if the user finds reminders annoying, the frequency can be reduced. The suggestion unit can also change the content of reminders based on feedback data. For example, it can prioritize sending messages that the user prefers. This makes it possible to provide more effective reminders by dynamically adjusting the frequency and content of reminders based on user feedback.

[0040] The suggestion unit can also link reminders to other devices, such as voice assistants and smartwatches. For example, the suggestion unit builds a system in which AI links reminders to other devices, such as voice assistants and smartwatches. For example, a reminder is notified from a smart speaker. The suggestion unit can also display reminders on a smartwatch. For example, a reminder to exercise is displayed on a smartwatch. In this way, by linking reminders to other devices, the user can receive reminders wherever they are.

[0041] The suggestion unit can provide a mechanism for sharing the contents of the reminder with family and friends, and for jointly supporting behavior change. For example, the suggestion unit provides a mechanism for AI to share the contents of the reminder with family and friends, and for jointly supporting behavior change. For example, all family members receive the reminder. The suggestion unit can also share reminders with friends, and work together to achieve goals. For example, a reminder can be set to exercise together with a friend. In this way, by sharing the contents of the reminder with family and friends, behavior change can be jointly supported.

[0042] The suggestion unit can customize the usage restriction settings to suit the user's lifestyle and activity patterns. For example, the suggestion unit uses AI to analyze the user's lifestyle and activity patterns and customize the usage restriction settings. For example, it may restrict smartphone use during bedtime. The suggestion unit can also adjust usage restrictions based on data such as the user's exercise volume and distance traveled. For example, it may tighten usage restrictions if the user exercises less. This allows the suggestion unit to provide more effective usage restrictions by customizing the usage restriction settings to suit the user's lifestyle and activity patterns.

[0043] The suggestion unit can also apply usage restrictions to devices other than smartphones, enabling comprehensive digital device management. For example, the suggestion unit constructs a system in which AI applies usage restrictions to devices other than smartphones (tablets, PCs, etc.) and performs comprehensive digital device management. For example, the usage time of each device can be integrated and managed. The suggestion unit can also set usage restrictions for each device and perform comprehensive management. For example, the usage time of smartphones and tablets can be limited combined. This allows comprehensive digital device management by applying usage restrictions to devices other than smartphones.

[0044] The proposal unit can build a system in which usage limit settings are shared with families and educational institutions and managed jointly. For example, the proposal unit builds a system in which AI shares usage limit settings with families and educational institutions and manages them jointly. For example, it allows parents to check their children's smartphone usage limits in real time. The proposal unit can also work with educational institutions to manage smartphone usage limits for students. For example, teachers can monitor students' smartphone usage limits and provide appropriate guidance. By sharing usage limit settings with families and educational institutions, it is possible to manage them jointly and prevent smartphone addiction.

[0045] The suggestion unit can integrate the evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the suggestion unit uses AI to integrate the smartphone addiction evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the comprehensive evaluation is performed based on sleep data and exercise data. The suggestion unit can also integrate the user's dietary data and stress data to perform a comprehensive health evaluation. For example, a health score is calculated taking into account the content of the diet and stress level. In this way, a comprehensive health evaluation can be performed by integrating the evaluation results with the user's lifestyle data.

[0046] The proposal unit can build a system in which the smartphone addiction assessment results are shared with families and educational institutions, and the addiction levels are jointly managed. For example, the proposal unit builds a system in which AI shares the smartphone addiction assessment results with families and educational institutions, and the addiction levels are jointly managed. For example, it allows parents to check their children's smartphone addiction levels in real time. The proposal unit can also work with educational institutions to manage students' smartphone addiction levels. For example, teachers can monitor students' smartphone addiction levels and provide appropriate guidance. In this way, by sharing the smartphone addiction assessment results with families and educational institutions, the addiction levels can be jointly managed and smartphone addiction can be prevented.

[0047] The suggestion unit can gamify the assessment results, allowing the user to reduce their addiction while having fun. For example, the suggestion unit uses AI to gamify the smartphone addiction assessment results, allowing the user to reduce their addiction while having fun. For example, it sets missions and challenges to reduce the addiction. The suggestion unit can also set a point system, level ups, rewards, etc. to increase the user's motivation. For example, points can be earned by reducing the addiction, and a reward can be obtained when a certain number of points are reached. In this way, gamifying the assessment results allows the user to reduce their addiction while having fun.

[0048] The suggestion unit can customize the usage restriction settings to suit the user's lifestyle and activity patterns. For example, the suggestion unit uses AI to analyze the user's lifestyle and activity patterns and customize the usage restriction settings. For example, it may restrict smartphone use during bedtime. The suggestion unit can also adjust usage restrictions based on data such as the user's exercise volume and distance traveled. For example, it may tighten usage restrictions if the user exercises less. This allows the suggestion unit to provide more effective usage restrictions by customizing the usage restriction settings to suit the user's lifestyle and activity patterns.

[0049] The suggestion unit can also apply usage restrictions to devices other than smartphones, enabling comprehensive digital device management. For example, the suggestion unit constructs a system in which AI applies usage restrictions to devices other than smartphones (tablets, PCs, etc.) and performs comprehensive digital device management. For example, the usage time of each device can be integrated and managed. The suggestion unit can also set usage restrictions for each device and perform comprehensive management. For example, the usage time of smartphones and tablets can be limited combined. This allows comprehensive digital device management by applying usage restrictions to devices other than smartphones.

[0050] The proposal unit can build a system in which usage limit settings are shared with families and educational institutions and managed jointly. For example, the proposal unit builds a system in which AI shares usage limit settings with families and educational institutions and manages them jointly. For example, it allows parents to check their children's smartphone usage limits in real time. The proposal unit can also work with educational institutions to manage smartphone usage limits for students. For example, teachers can monitor students' smartphone usage limits and provide appropriate guidance. By sharing usage limit settings with families and educational institutions, it is possible to manage them jointly and prevent smartphone addiction.

[0051] The suggestion unit can integrate the evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the suggestion unit uses AI to integrate the smartphone addiction evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the comprehensive evaluation is performed based on sleep data and exercise data. The suggestion unit can also integrate the user's dietary data and stress data to perform a comprehensive health evaluation. For example, a health score is calculated taking into account the content of the diet and stress level. In this way, a comprehensive health evaluation can be performed by integrating the evaluation results with the user's lifestyle data.

[0052] The proposal unit can build a system in which the smartphone addiction assessment results are shared with families and educational institutions, and the addiction levels are jointly managed. For example, the proposal unit builds a system in which AI shares the smartphone addiction assessment results with families and educational institutions, and the addiction levels are jointly managed. For example, it allows parents to check their children's smartphone addiction levels in real time. The proposal unit can also work with educational institutions to manage students' smartphone addiction levels. For example, teachers can monitor students' smartphone addiction levels and provide appropriate guidance. In this way, by sharing the smartphone addiction assessment results with families and educational institutions, the addiction levels can be jointly managed and smartphone addiction can be prevented.

[0053] The suggestion unit can gamify the assessment results, allowing the user to reduce their addiction while having fun. For example, the suggestion unit uses AI to gamify the smartphone addiction assessment results, allowing the user to reduce their addiction while having fun. For example, it sets missions and challenges to reduce the addiction. The suggestion unit can also set a point system, level ups, rewards, etc. to increase the user's motivation. For example, points can be earned by reducing the addiction, and a reward can be obtained when a certain number of points are reached. In this way, gamifying the assessment results allows the user to reduce their addiction while having fun.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The monitoring unit monitors the user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage status. The monitoring unit can also record how much smartphone usage occurs during each time period of the day. The monitoring unit then analyzes the usage time data to identify the user's usage pattern. The suggestion unit then suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit provides specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit can also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit can provide individually customized suggestions based on the user's usage pattern. As a result, the system according to the embodiment can prevent smartphone addiction by monitoring the user's smartphone usage time in real time and suggesting healthy digital habits.

[0056] The monitoring unit can analyze the user's lifestyle and activity patterns and suggest optimal smartphone usage time. For example, the monitoring unit uses AI to analyze the user's smartphone usage data and identify lifestyle and activity patterns. For example, it suggests optimal smartphone usage time based on data such as wake-up time, bedtime, and meal times. The monitoring unit can also analyze data such as the user's exercise volume and distance traveled to identify activity patterns. For example, if the user's exercise volume is low, it will suggest reducing smartphone usage time. This makes it possible to promote healthy digital habits by suggesting optimal smartphone usage time based on the user's lifestyle and activity patterns.

[0057] The monitoring unit analyzes not only the amount of time spent using smartphones, but also the types and content of apps being used, making it possible to identify highly addictive apps. For example, the monitoring unit uses AI to analyze smartphone usage data and identify the types and content of apps being used. For example, it records detailed usage time for social media and game apps. The monitoring unit can also identify highly addictive apps based on the frequency and duration of app use. For example, it determines that apps that are used for long periods of time or frequently are highly addictive. By identifying highly addictive apps, it is possible to effectively prevent smartphone addiction in users.

[0058] The monitoring unit can monitor overall digital device usage time, including devices other than smartphones. For example, the monitoring unit can use AI to collect usage data from devices other than smartphones (tablets, PCs, etc.) and monitor overall digital device usage time. For example, it can integrate and record usage time for each device. The monitoring unit can also record detailed usage time for each device and simultaneous usage time. For example, it can identify the time when a smartphone and a tablet are used simultaneously. This allows for effective management of a user's digital device addiction by monitoring overall digital device usage time, including devices other than smartphones.

[0059] The monitoring unit can share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, the monitoring unit can use AI to share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, it can enable parents to check their children's smartphone usage time in real time. The monitoring unit can also work with educational institutions to manage students' smartphone usage time. For example, teachers can monitor students' smartphone usage time and provide appropriate guidance. By sharing monitoring data with families and educational institutions, they can jointly manage screen time and prevent smartphone addiction.

[0060] The suggestion unit can analyze the user's lifestyle data and provide comprehensive health management. For example, the suggestion unit uses AI to analyze the user's lifestyle data (such as sleep time and exercise amount) and provide comprehensive health management. For example, it can suggest optimal sleep times based on sleep data. The suggestion unit can also suggest activities to increase exercise volume based on exercise data. For example, if the amount of exercise is low, it can suggest walking or stretching. In this way, the user's health can be maintained by analyzing the user's lifestyle data and providing comprehensive health management.

[0061] The suggestion unit can customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, the suggestion unit uses AI to customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, suggestions are made based on the user's favorite exercises and meals. The suggestion unit can also make effective suggestions based on the user's past behavioral history. For example, advice that was effective in the past can be provided again. This makes it easier for users to practice healthy digital habits by providing advice customized to the user's preferences.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The monitoring unit monitors the user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage. The monitoring unit can also record how much smartphone usage occurs during each time period of the day. Furthermore, the monitoring unit analyzes the usage time data and identifies the user's usage patterns. Step 2: The suggestion unit suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit may provide specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit may also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit may provide individually customized suggestions based on the user's usage patterns.

[0064] (Example 2) A system according to an embodiment of the present invention prevents "smartphone addiction" caused by excessive smartphone use and supports users in developing healthier digital habits. This system uses AI-based behavioral change techniques, which enable users to prevent excessive smartphone use and develop healthier digital habits.

[0065] The system according to the embodiment includes a monitoring unit and a suggestion unit. The monitoring unit monitors a user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage status. The monitoring unit can also record the amount of smartphone usage at each time of day. The monitoring unit analyzes the usage time data and identifies the user's usage pattern. The suggestion unit suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit provides specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit can also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit can make individually customized suggestions based on the user's usage pattern. As a result, the system according to the embodiment monitors a user's smartphone usage time in real time and suggests healthy digital habits, thereby preventing smartphone addiction.

[0066] The monitoring unit can estimate the user's emotional state and adjust smartphone usage time according to changes in emotion. For example, the monitoring unit uses AI to analyze the user's facial expressions and voice to estimate the user's emotional state in real time. For example, it can analyze the user's emotions using a camera or microphone and limit smartphone usage time if stress or fatigue increases. The monitoring unit can also calculate an emotion score based on the emotion estimation data and adjust usage time according to changes in emotion. For example, if the emotion score is high, usage time is shortened, and if it is low, usage time is extended. In this way, stress and fatigue can be reduced by adjusting smartphone usage time according to the user's emotional state.

[0067] The monitoring unit can analyze the user's lifestyle and activity patterns and suggest optimal smartphone usage time. For example, the monitoring unit uses AI to analyze the user's smartphone usage data and identify lifestyle and activity patterns. For example, it suggests optimal smartphone usage time based on data such as wake-up time, bedtime, and meal times. The monitoring unit can also analyze data such as the user's exercise volume and distance traveled to identify activity patterns. For example, if the user's exercise volume is low, it will suggest reducing smartphone usage time. This makes it possible to promote healthy digital habits by suggesting optimal smartphone usage time based on the user's lifestyle and activity patterns.

[0068] The monitoring unit analyzes not only the amount of time spent using smartphones, but also the types and content of apps being used, making it possible to identify highly addictive apps. For example, the monitoring unit uses AI to analyze smartphone usage data and identify the types and content of apps being used. For example, it records detailed usage time for social media and game apps. The monitoring unit can also identify highly addictive apps based on the frequency and duration of app use. For example, it determines that apps that are used for long periods of time or frequently are highly addictive. By identifying highly addictive apps, it is possible to effectively prevent smartphone addiction in users.

[0069] The monitoring unit can monitor overall digital device usage time, including devices other than smartphones. For example, the monitoring unit can use AI to collect usage data from devices other than smartphones (tablets, PCs, etc.) and monitor overall digital device usage time. For example, it can integrate and record usage time for each device. The monitoring unit can also record detailed usage time for each device and simultaneous usage time. For example, it can identify the time when a smartphone and a tablet are used simultaneously. This allows for effective management of a user's digital device addiction by monitoring overall digital device usage time, including devices other than smartphones.

[0070] The monitoring unit can share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, the monitoring unit can use AI to share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, it can enable parents to check their children's smartphone usage time in real time. The monitoring unit can also work with educational institutions to manage students' smartphone usage time. For example, teachers can monitor students' smartphone usage time and provide appropriate guidance. By sharing monitoring data with families and educational institutions, they can jointly manage screen time and prevent smartphone addiction.

[0071] The monitoring unit can use the emotion estimation function to identify times when the user is feeling stressed and restrict smartphone use during those times. The monitoring unit, for example, uses AI to identify times when the user is feeling stressed, for example, by analyzing facial expressions and voice to measure stress levels. The monitoring unit can also restrict smartphone use during times when the user is feeling stressed. For example, restricting smartphone use during times when stress levels are high. This makes it possible to reduce stress and promote healthy digital habits by restricting smartphone use during times when the user is feeling stressed.

[0072] The suggestion unit can estimate the user's emotional state and suggest healthy digital habits according to the emotion. For example, the suggestion unit uses AI to estimate the user's emotional state and suggest healthy digital habits according to the emotion. For example, if stress is high, it suggests taking a break to relax. The suggestion unit can also calculate an emotion score based on the emotion estimation data and make suggestions according to the emotion. For example, if the emotion score is high, it suggests an activity for relaxation, and if the emotion score is low, it suggests an activity to improve concentration. In this way, by suggesting healthy digital habits according to the user's emotional state, it is possible to reduce the user's stress and fatigue.

[0073] The suggestion unit can analyze the user's lifestyle data and provide comprehensive health management. For example, the suggestion unit uses AI to analyze the user's lifestyle data (such as sleep time and exercise amount) and provide comprehensive health management. For example, it can suggest optimal sleep times based on sleep data. The suggestion unit can also suggest activities to increase exercise volume based on exercise data. For example, if the amount of exercise is low, it can suggest walking or stretching. In this way, the user's health can be maintained by analyzing the user's lifestyle data and providing comprehensive health management.

[0074] The suggestion unit can customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, the suggestion unit uses AI to customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, suggestions are made based on the user's favorite exercises and meals. The suggestion unit can also make effective suggestions based on the user's past behavioral history. For example, advice that was effective in the past can be provided again. This makes it easier for users to practice healthy digital habits by providing advice customized to the user's preferences.

[0075] The suggestion unit can provide a mechanism for suggesting healthy digital habits for the entire family or group and practicing them together. For example, the suggestion unit provides a mechanism for suggesting healthy digital habits for the entire family or group and practicing them together. For example, limiting smartphone usage time for the entire family. The suggestion unit can also set goals and check progress for the group. For example, it can manage the total usage time for the entire group and aim to achieve the goal. This allows the entire family or group to practice healthy digital habits and maintain their health together.

[0076] The suggestion unit can gamify the suggestions, allowing users to develop healthy habits while having fun. For example, the suggestion unit gamifies the suggestions of healthy digital habits using AI, allowing users to practice them while having fun. For example, it can suggest exercise and rest in a game format. The suggestion unit can also increase user motivation by setting a point system, leveling up, and rewards. For example, points can be earned by exercising, and rewards can be obtained when a certain number of points are reached. In this way, gamifying the suggestions allows users to develop healthy habits while having fun.

[0077] The suggestion unit can use the emotion estimation function to preferentially present suggestions that evoke the most positive emotions in the user. For example, the suggestion unit uses the emotion estimation function by AI to preferentially present suggestions that evoke the most positive emotions in the user. For example, suggestions with high emotion scores are preferentially displayed. The suggestion unit can also identify suggestions that evoke positive emotions based on user feedback and preferentially present them. For example, it can re-present suggestions for which the user has given positive feedback in the past. This can increase the user's motivation by preferentially presenting suggestions that evoke the most positive emotions in the user.

[0078] The suggestion unit can adjust the timing of reminders based on the user's emotional state and send notifications at the optimal timing. For example, the suggestion unit uses AI to analyze the user's emotional state and adjust the timing of reminders. For example, if stress is high, the reminder is delayed. The suggestion unit can also calculate the optimal timing of reminders based on emotion estimation data and send notifications. For example, a reminder is sent during a time period when the emotion score is low. In this way, by adjusting the timing of reminders based on the user's emotional state, notifications can be sent at the optimal timing.

[0079] The suggestion unit can customize the content of the reminder based on the user's past behavioral history and provide a more effective message. For example, the suggestion unit uses AI to analyze the user's past behavioral history and customize the content of the reminder. For example, it can reuse messages that have been effective in the past. The suggestion unit can also generate effective reminders based on the user's behavioral patterns. For example, it can send an effective message at a specific time period. In this way, by customizing the content of the reminder based on the user's past behavioral history, it is possible to provide a more effective message.

[0080] The suggestion unit can dynamically adjust the frequency and content of reminders based on user feedback. For example, the suggestion unit uses AI to analyze user feedback and dynamically adjust the frequency and content of reminders. For example, if the user finds reminders annoying, the frequency can be reduced. The suggestion unit can also change the content of reminders based on feedback data. For example, it can prioritize sending messages that the user prefers. This makes it possible to provide more effective reminders by dynamically adjusting the frequency and content of reminders based on user feedback.

[0081] The suggestion unit can also link reminders to other devices, such as voice assistants and smartwatches. For example, the suggestion unit builds a system in which AI links reminders to other devices, such as voice assistants and smartwatches. For example, a reminder is notified from a smart speaker. The suggestion unit can also display reminders on a smartwatch. For example, a reminder to exercise is displayed on a smartwatch. In this way, by linking reminders to other devices, the user can receive reminders wherever they are.

[0082] The suggestion unit can provide a mechanism for sharing the contents of the reminder with family and friends, and for jointly supporting behavior change. For example, the suggestion unit provides a mechanism for AI to share the contents of the reminder with family and friends, and for jointly supporting behavior change. For example, all family members receive the reminder. The suggestion unit can also share reminders with friends, and work together to achieve goals. For example, a reminder can be set to exercise together with a friend. In this way, by sharing the contents of the reminder with family and friends, behavior change can be jointly supported.

[0083] The suggestion unit can use the emotion estimation function to prioritize sending reminders that evoke the most positive emotions in the user. For example, the suggestion unit uses the emotion estimation function by AI to prioritize sending reminders that evoke the most positive emotions in the user. For example, reminders with high emotion scores are displayed preferentially. The suggestion unit can also identify reminders that evoke positive emotions based on user feedback and send them preferentially. For example, it resends reminders for which the user has given positive feedback in the past. This can increase the user's motivation by prioritized sending reminders that evoke the most positive emotions in the user.

[0084] The suggestion unit can dynamically adjust the usage limit settings based on the user's emotional state to reduce stress. For example, the suggestion unit uses AI to analyze the user's emotional state and dynamically adjust the usage limit settings. For example, the usage limit is relaxed when stress is high. The suggestion unit can also calculate optimal usage limit settings based on emotion estimation data to reduce stress. For example, the usage limit is tightened when the emotion score is low. In this way, stress can be reduced by dynamically adjusting the usage limit settings based on the user's emotional state.

[0085] The suggestion unit can customize the usage restriction settings to suit the user's lifestyle and activity patterns. For example, the suggestion unit uses AI to analyze the user's lifestyle and activity patterns and customize the usage restriction settings. For example, it may restrict smartphone use during bedtime. The suggestion unit can also adjust usage restrictions based on data such as the user's exercise volume and distance traveled. For example, it may tighten usage restrictions if the user exercises less. This allows the suggestion unit to provide more effective usage restrictions by customizing the usage restriction settings to suit the user's lifestyle and activity patterns.

[0086] The suggestion unit can also apply usage restrictions to devices other than smartphones, enabling comprehensive digital device management. For example, the suggestion unit constructs a system in which AI applies usage restrictions to devices other than smartphones (tablets, PCs, etc.) and performs comprehensive digital device management. For example, the usage time of each device can be integrated and managed. The suggestion unit can also set usage restrictions for each device and perform comprehensive management. For example, the usage time of smartphones and tablets can be limited combined. This allows comprehensive digital device management by applying usage restrictions to devices other than smartphones.

[0087] The proposal unit can build a system in which usage limit settings are shared with families and educational institutions and managed jointly. For example, the proposal unit builds a system in which AI shares usage limit settings with families and educational institutions and manages them jointly. For example, it allows parents to check their children's smartphone usage limits in real time. The proposal unit can also work with educational institutions to manage smartphone usage limits for students. For example, teachers can monitor students' smartphone usage limits and provide appropriate guidance. By sharing usage limit settings with families and educational institutions, it is possible to manage them jointly and prevent smartphone addiction.

[0088] The suggestion unit can use the emotion estimation function to prioritize the use limits that evoke the most positive emotions in the user. For example, the suggestion unit uses the emotion estimation function by AI to prioritize the use limits that evoke the most positive emotions in the user. For example, the suggestion unit prioritizes the use limits with high emotion scores. The suggestion unit can also identify use limits that evoke positive emotions based on user feedback and prioritize them. For example, the suggestion unit re-sets a use limit for which the user has given positive feedback in the past. This can increase the user's motivation by prioritizing the use limits that evoke the most positive emotions in the user.

[0089] The suggestion unit can also evaluate the emotional level of dependence by taking into account the user's emotional state when assessing smartphone addiction. For example, the suggestion unit uses AI to analyze the user's emotional state and consider the emotional level of dependence when assessing smartphone addiction. For example, if stress or anxiety is high, the suggestion unit can assess the level of dependence as high. The suggestion unit can also calculate the emotional level of dependence based on emotion estimation data and reflect this in the assessment. For example, if the emotion score is high, the suggestion unit can determine that the level of dependence is high. This allows for a more accurate assessment of smartphone addiction by taking the user's emotional state into account.

[0090] The suggestion unit can integrate the evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the suggestion unit uses AI to integrate the smartphone addiction evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the comprehensive evaluation is performed based on sleep data and exercise data. The suggestion unit can also integrate the user's dietary data and stress data to perform a comprehensive health evaluation. For example, a health score is calculated taking into account the content of the diet and stress level. In this way, a comprehensive health evaluation can be performed by integrating the evaluation results with the user's lifestyle data.

[0091] The proposal unit can build a system in which the smartphone addiction assessment results are shared with families and educational institutions, and the addiction levels are jointly managed. For example, the proposal unit builds a system in which AI shares the smartphone addiction assessment results with families and educational institutions, and the addiction levels are jointly managed. For example, it allows parents to check their children's smartphone addiction levels in real time. The proposal unit can also work with educational institutions to manage students' smartphone addiction levels. For example, teachers can monitor students' smartphone addiction levels and provide appropriate guidance. In this way, by sharing the smartphone addiction assessment results with families and educational institutions, the addiction levels can be jointly managed and smartphone addiction can be prevented.

[0092] The suggestion unit can gamify the assessment results, allowing the user to reduce their addiction while having fun. For example, the suggestion unit uses AI to gamify the smartphone addiction assessment results, allowing the user to reduce their addiction while having fun. For example, it sets missions and challenges to reduce the addiction. The suggestion unit can also set a point system, level ups, rewards, etc. to increase the user's motivation. For example, points can be earned by reducing the addiction, and a reward can be obtained when a certain number of points are reached. In this way, gamifying the assessment results allows the user to reduce their addiction while having fun.

[0093] The suggestion unit can use the emotion estimation function to preferentially provide feedback that evokes the most positive emotion for the user. For example, the suggestion unit uses the emotion estimation function by AI to preferentially provide feedback that evokes the most positive emotion for the user. For example, feedback with a high emotion score can be preferentially displayed. The suggestion unit can also identify feedback that evokes positive emotion based on the user's feedback and provide it preferentially. For example, it can provide content for which the user has given positive feedback in the past again. In this way, the user's motivation can be increased by preferentially providing feedback that evokes the most positive emotion for the user using the emotion estimation function.

[0094] The suggestion unit can dynamically adjust the usage limit settings based on the user's emotional state to reduce stress. For example, the suggestion unit uses AI to analyze the user's emotional state and dynamically adjust the usage limit settings. For example, the usage limit is relaxed when stress is high. The suggestion unit can also calculate optimal usage limit settings based on emotion estimation data to reduce stress. For example, the usage limit is tightened when the emotion score is low. In this way, stress can be reduced by dynamically adjusting the usage limit settings based on the user's emotional state.

[0095] The suggestion unit can customize the usage restriction settings to suit the user's lifestyle and activity patterns. For example, the suggestion unit uses AI to analyze the user's lifestyle and activity patterns and customize the usage restriction settings. For example, it may restrict smartphone use during bedtime. The suggestion unit can also adjust usage restrictions based on data such as the user's exercise volume and distance traveled. For example, it may tighten usage restrictions if the user exercises less. This allows the suggestion unit to provide more effective usage restrictions by customizing the usage restriction settings to suit the user's lifestyle and activity patterns.

[0096] The suggestion unit can also apply usage restrictions to devices other than smartphones, enabling comprehensive digital device management. For example, the suggestion unit constructs a system in which AI applies usage restrictions to devices other than smartphones (tablets, PCs, etc.) and performs comprehensive digital device management. For example, the usage time of each device can be integrated and managed. The suggestion unit can also set usage restrictions for each device and perform comprehensive management. For example, the usage time of smartphones and tablets can be limited combined. This allows comprehensive digital device management by applying usage restrictions to devices other than smartphones.

[0097] The proposal unit can build a system in which usage limit settings are shared with families and educational institutions and managed jointly. For example, the proposal unit builds a system in which AI shares usage limit settings with families and educational institutions and manages them jointly. For example, it allows parents to check their children's smartphone usage limits in real time. The proposal unit can also work with educational institutions to manage smartphone usage limits for students. For example, teachers can monitor students' smartphone usage limits and provide appropriate guidance. By sharing usage limit settings with families and educational institutions, it is possible to manage them jointly and prevent smartphone addiction.

[0098] The suggestion unit can use the emotion estimation function to prioritize the use limits that evoke the most positive emotions in the user. For example, the suggestion unit uses the emotion estimation function by AI to prioritize the use limits that evoke the most positive emotions in the user. For example, the suggestion unit prioritizes the use limits with high emotion scores. The suggestion unit can also identify use limits that evoke positive emotions based on user feedback and prioritize them. For example, the suggestion unit re-sets a use limit for which the user has given positive feedback in the past. This can increase the user's motivation by prioritizing the use limits that evoke the most positive emotions in the user.

[0099] The suggestion unit can also evaluate the emotional level of dependence by taking into account the user's emotional state when assessing smartphone addiction. For example, the suggestion unit uses AI to analyze the user's emotional state and consider the emotional level of dependence when assessing smartphone addiction. For example, if stress or anxiety is high, the suggestion unit can assess the level of dependence as high. The suggestion unit can also calculate the emotional level of dependence based on emotion estimation data and reflect this in the assessment. For example, if the emotion score is high, the suggestion unit can determine that the level of dependence is high. This allows for a more accurate assessment of smartphone addiction by taking the user's emotional state into account.

[0100] The suggestion unit can integrate the evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the suggestion unit uses AI to integrate the smartphone addiction evaluation results with the user's lifestyle data to perform a comprehensive health evaluation. For example, the comprehensive evaluation is performed based on sleep data and exercise data. The suggestion unit can also integrate the user's dietary data and stress data to perform a comprehensive health evaluation. For example, a health score is calculated taking into account the content of the diet and stress level. In this way, a comprehensive health evaluation can be performed by integrating the evaluation results with the user's lifestyle data.

[0101] The proposal unit can build a system in which the smartphone addiction assessment results are shared with families and educational institutions, and the addiction levels are jointly managed. For example, the proposal unit builds a system in which AI shares the smartphone addiction assessment results with families and educational institutions, and the addiction levels are jointly managed. For example, it allows parents to check their children's smartphone addiction levels in real time. The proposal unit can also work with educational institutions to manage students' smartphone addiction levels. For example, teachers can monitor students' smartphone addiction levels and provide appropriate guidance. In this way, by sharing the smartphone addiction assessment results with families and educational institutions, the addiction levels can be jointly managed and smartphone addiction can be prevented.

[0102] The suggestion unit can gamify the assessment results, allowing the user to reduce their addiction while having fun. For example, the suggestion unit uses AI to gamify the smartphone addiction assessment results, allowing the user to reduce their addiction while having fun. For example, it sets missions and challenges to reduce the addiction. The suggestion unit can also set a point system, level ups, rewards, etc. to increase the user's motivation. For example, points can be earned by reducing the addiction, and a reward can be obtained when a certain number of points are reached. In this way, gamifying the assessment results allows the user to reduce their addiction while having fun.

[0103] The suggestion unit can use the emotion estimation function to preferentially provide feedback that evokes the most positive emotion for the user. For example, the suggestion unit uses the emotion estimation function by AI to preferentially provide feedback that evokes the most positive emotion for the user. For example, feedback with a high emotion score can be preferentially displayed. The suggestion unit can also identify feedback that evokes positive emotion based on the user's feedback and provide it preferentially. For example, it can provide content for which the user has given positive feedback in the past again. In this way, the user's motivation can be increased by preferentially providing feedback that evokes the most positive emotion for the user using the emotion estimation function.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The monitoring unit monitors the user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage status. The monitoring unit can also record how much smartphone usage occurs during each time period of the day. The monitoring unit then analyzes the usage time data to identify the user's usage pattern. The suggestion unit then suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit provides specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit can also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit can provide individually customized suggestions based on the user's usage pattern. As a result, the system according to the embodiment can prevent smartphone addiction by monitoring the user's smartphone usage time in real time and suggesting healthy digital habits.

[0106] The monitoring unit can estimate the user's emotional state and adjust smartphone usage time according to changes in emotion. For example, the monitoring unit uses AI to analyze the user's facial expressions and voice to estimate the user's emotional state in real time. For example, it can analyze the user's emotions using a camera or microphone and limit smartphone usage time if stress or fatigue increases. The monitoring unit can also calculate an emotion score based on the emotion estimation data and adjust usage time according to changes in emotion. For example, if the emotion score is high, usage time is shortened, and if it is low, usage time is extended. In this way, stress and fatigue can be reduced by adjusting smartphone usage time according to the user's emotional state.

[0107] The monitoring unit can analyze the user's lifestyle and activity patterns and suggest optimal smartphone usage time. For example, the monitoring unit uses AI to analyze the user's smartphone usage data and identify lifestyle and activity patterns. For example, it suggests optimal smartphone usage time based on data such as wake-up time, bedtime, and meal times. The monitoring unit can also analyze data such as the user's exercise volume and distance traveled to identify activity patterns. For example, if the user's exercise volume is low, it will suggest reducing smartphone usage time. This makes it possible to promote healthy digital habits by suggesting optimal smartphone usage time based on the user's lifestyle and activity patterns.

[0108] The monitoring unit analyzes not only the amount of time spent using smartphones, but also the types and content of apps being used, making it possible to identify highly addictive apps. For example, the monitoring unit uses AI to analyze smartphone usage data and identify the types and content of apps being used. For example, it records detailed usage time for social media and game apps. The monitoring unit can also identify highly addictive apps based on the frequency and duration of app use. For example, it determines that apps that are used for long periods of time or frequently are highly addictive. By identifying highly addictive apps, it is possible to effectively prevent smartphone addiction in users.

[0109] The monitoring unit can monitor overall digital device usage time, including devices other than smartphones. For example, the monitoring unit can use AI to collect usage data from devices other than smartphones (tablets, PCs, etc.) and monitor overall digital device usage time. For example, it can integrate and record usage time for each device. The monitoring unit can also record detailed usage time for each device and simultaneous usage time. For example, it can identify the time when a smartphone and a tablet are used simultaneously. This allows for effective management of a user's digital device addiction by monitoring overall digital device usage time, including devices other than smartphones.

[0110] The monitoring unit can share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, the monitoring unit can use AI to share monitoring data with families and educational institutions to build a system for jointly managing screen time. For example, it can enable parents to check their children's smartphone usage time in real time. The monitoring unit can also work with educational institutions to manage students' smartphone usage time. For example, teachers can monitor students' smartphone usage time and provide appropriate guidance. By sharing monitoring data with families and educational institutions, they can jointly manage screen time and prevent smartphone addiction.

[0111] The monitoring unit can use the emotion estimation function to identify times when the user is feeling stressed and restrict smartphone use during those times. The monitoring unit, for example, uses AI to identify times when the user is feeling stressed, for example, by analyzing facial expressions and voice to measure stress levels. The monitoring unit can also restrict smartphone use during times when the user is feeling stressed. For example, restricting smartphone use during times when stress levels are high. This makes it possible to reduce stress and promote healthy digital habits by restricting smartphone use during times when the user is feeling stressed.

[0112] The suggestion unit can estimate the user's emotional state and suggest healthy digital habits according to the emotion. For example, the suggestion unit uses AI to estimate the user's emotional state and suggest healthy digital habits according to the emotion. For example, if stress is high, it suggests taking a break to relax. The suggestion unit can also calculate an emotion score based on the emotion estimation data and make suggestions according to the emotion. For example, if the emotion score is high, it suggests an activity for relaxation, and if the emotion score is low, it suggests an activity to improve concentration. In this way, by suggesting healthy digital habits according to the user's emotional state, it is possible to reduce the user's stress and fatigue.

[0113] The suggestion unit can analyze the user's lifestyle data and provide comprehensive health management. For example, the suggestion unit uses AI to analyze the user's lifestyle data (such as sleep time and exercise amount) and provide comprehensive health management. For example, it can suggest optimal sleep times based on sleep data. The suggestion unit can also suggest activities to increase exercise volume based on exercise data. For example, if the amount of exercise is low, it can suggest walking or stretching. In this way, the user's health can be maintained by analyzing the user's lifestyle data and providing comprehensive health management.

[0114] The suggestion unit can customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, the suggestion unit uses AI to customize the suggestion content to match the user's preferences and provide advice that is easier to put into practice. For example, suggestions are made based on the user's favorite exercises and meals. The suggestion unit can also make effective suggestions based on the user's past behavioral history. For example, advice that was effective in the past can be provided again. This makes it easier for users to practice healthy digital habits by providing advice customized to the user's preferences.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The monitoring unit monitors the user's smartphone usage time in real time. For example, the monitoring unit records the smartphone usage time and app usage. The monitoring unit can also record how much smartphone usage occurs during each time period of the day. Furthermore, the monitoring unit analyzes the usage time data and identifies the user's usage patterns. Step 2: The suggestion unit suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. For example, the suggestion unit may provide specific advice such as "take a five-minute break every hour" or "avoid using your smartphone for one hour before bed." The suggestion unit may also generate optimal suggestions based on the user's smartphone usage data and information on healthy digital habits. Furthermore, the suggestion unit may provide individually customized suggestions based on the user's usage patterns.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

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

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0161] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. A monitoring unit that monitors the user's smartphone usage time in real time; a suggestion unit that suggests healthy digital habits based on the smartphone usage time data acquired by the monitoring unit. A system characterized by:

2. The monitoring unit The emotional state of the user is estimated, and the smartphone usage time is adjusted according to the change in the emotional state.

2. The system of claim 1.

3. The monitoring unit Monitor overall digital device usage time, including devices other than smartphones 2. The system of claim 1.

4. The proposal unit Estimating the emotional state of the user and suggesting the healthy digital habits according to the emotion.

2. The system of claim 1.

5. The proposal unit The timing of the reminder is adjusted based on the user's emotional state, and notifications are sent at optimal times.

2. The system of claim 1.

6. The proposal unit Dynamically adjust usage limit settings based on the user's emotional state to reduce stress 2. The system of claim 1.

7. The proposal unit In assessing the smartphone addiction level, the emotional state of the user is taken into consideration, and the emotional addiction level is also assessed.

2. The system of claim 1.

8. The proposal unit Using an emotion estimation function, the feedback that the user feels the most positive about is given preferentially.

2. The system of claim 1.

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