system

Abstract

The system according to the embodiment aims to quantify the fatigue level of an individual and support appropriate health management. [Solution] The system according to the embodiment comprises an input unit, a counting unit, a data collection unit, an analysis unit, and an advice unit. The input unit inputs basic information about an individual. The counting unit automatically counts browsing time on a PC or smartphone. The data collection unit inputs or counts daily body temperature and sleep time. The analysis unit quantifies daily fatigue levels based on data collected by the input unit, counting unit, and data collection unit. The advice unit evaluates the individual's physical condition each morning and provides advice based on the fatigue level data quantified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately quantify an individual's fatigue level and manage their physical condition appropriately.

[0005] The system according to the embodiment aims to quantify the fatigue level of an individual and support appropriate health management. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a counting unit, a data collection unit, an analysis unit, and an advice unit. The input unit inputs basic information about an individual. The counting unit automatically counts browsing time on a PC or smartphone. The data collection unit inputs or counts daily body temperature and sleep time. The analysis unit quantifies daily fatigue levels based on data collected by the input unit, counting unit, and data collection unit. The advice unit evaluates the individual's physical condition each morning and provides advice based on the fatigue level data quantified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quantify the fatigue level of an individual and support appropriate health management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 health management system according to an embodiment of the present invention inputs basic personal information, automatically counts the time spent browsing a PC or smartphone, and inputs or counts daily body temperature and sleep time. This health management system automatically counts the time spent browsing a PC or smartphone. Furthermore, the system inputs or counts daily body temperature and sleep time to quantify the individual's daily fatigue. Once a certain amount of data has been accumulated, the AI ​​provides advice on the individual's health condition each morning, suggests work leave, dietary therapy tailored to the level of fatigue, and suggests medical visits to manage the individual's health. For example, the individual's basic information (age, height, and weight) is input. Next, the system automatically counts the time spent browsing a PC or smartphone. This allows the individual to understand their daily digital device usage. Furthermore, the individual's daily body temperature and sleep time are input or counted. For example, a thermometer or smartwatch can be used to record body temperature and sleep time. This allows the individual's daily health status to be understood in detail. Based on this data, the AI ​​quantifies daily fatigue. For example, the fatigue value increases if the individual spends a lot of time using digital devices or sleeps less. Conversely, if you get enough sleep or spend less time using digital devices, your fatigue level will be lower. Once a certain amount of data has been accumulated, the AI ​​will provide advice on your physical condition each morning. For example, if your fatigue level is high, it will suggest taking time off work. It will also suggest a diet tailored to your level of fatigue. For example, it will advise you to eat a diet rich in vitamins and minerals. It will also suggest medical visits as needed to manage your health. This system allows for a detailed understanding of an individual's health condition and allows for appropriate health management. For example, reducing the amount of time spent using digital devices can reduce fatigue and maintain health. Furthermore, following an appropriate diet and visiting the hospital can improve your physical condition and increase work efficiency. In this way, the health management system can provide a detailed understanding of an individual's health condition and allow for appropriate health management.

[0029] A health management system according to an embodiment includes an input unit, a counting unit, a data collection unit, an analysis unit, and an advice unit. The input unit inputs basic information about an individual. Examples of basic information about an individual include, but are not limited to, name, age, gender, and address. The input unit provides an interface through which a user manually inputs information. The input unit can also provide an auto-complete function based on the user's past input history. For example, the input unit provides an auto-complete function to reduce the effort required for input based on information previously input by the user. The counting unit automatically counts browsing time on a PC or smartphone. Examples of browsing time include, but are not limited to, browser active time and app usage time. For example, the counting unit measures browser active time and collects data. The counting unit can also measure app usage time and collect data. Furthermore, the counting unit can analyze device usage patterns and select an optimal counting method. For example, the counting unit analyzes the usage patterns of devices frequently used by the user and suggests an optimal counting method. The data collection unit inputs or counts daily body temperature and sleep time. The body temperature may include, but is not limited to, the measurement time, the measurement site, the type of thermometer used, and the like. The data collection unit may, for example, measure the body temperature using a thermometer and collect data. The data collection unit may also use a smartwatch to record sleep time and collect data. The data collection unit may also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit may delay the timing of data collection and collect data while the user is relaxed. The analysis unit quantifies the daily fatigue level based on the data collected by the input unit, the counting unit, and the data collection unit. The fatigue level may include, but is not limited to, a scoring system, an algorithm used, and the like. The analysis unit may quantify the fatigue level based on, for example, the time spent using a digital device and the amount of sleep. The analysis unit may also optimize the analysis algorithm by referring to past data.Furthermore, the analysis unit can apply different analysis methods depending on the type of data. For example, the analysis unit applies time series analysis to body temperature data to extract fluctuation patterns. The advice unit evaluates the physical condition of the individual each morning and provides advice based on the fatigue level data quantified by the analysis unit. Examples of the evaluation and advice include, but are not limited to, a health score and recommended actions. For example, the advice unit can suggest taking time off work if the fatigue level is high. The advice unit can also suggest a dietary therapy tailored to the level of fatigue. Furthermore, the advice unit can suggest visiting a doctor as needed. As a result, the physical condition management system according to the embodiment can grasp the individual's health condition in detail and provide appropriate physical condition management.

[0030] The advice unit includes a vacation suggestion unit that suggests vacation from work when the fatigue level is high. The vacation suggestion unit, for example, suggests vacation from work when the fatigue level is high. For example, the vacation suggestion unit advises the user to take a vacation when the user's fatigue level exceeds a certain standard. The vacation suggestion unit can also estimate the user's emotions and adjust the vacation suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the vacation suggestion unit suggests a relaxing vacation. Furthermore, the vacation suggestion unit can provide optimal suggestions by referring to the user's past vacation history. For example, the vacation suggestion unit suggests an optimal vacation based on patterns of vacations the user has enjoyed in the past. In this way, the user's health can be maintained by suggesting an appropriate vacation when the fatigue level is high.

[0031] The advice unit includes a diet suggestion unit that suggests a diet tailored to the level of fatigue. The diet suggestion unit, for example, suggests a diet tailored to the level of fatigue. For example, if the user's level of fatigue is high, the diet suggestion unit advises the user to eat a diet rich in vitamins and minerals. The diet suggestion unit can also estimate the user's emotions and adjust the method of suggesting meals based on the estimated user's emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest meals that will help the user relax. Furthermore, the meal suggestion unit can provide optimal suggestions by referring to the user's past meal history. For example, the meal suggestion unit suggests optimal meals based on meals that the user has liked to eat in the past. This makes it possible to support the user's health by suggesting a diet tailored to the level of fatigue.

[0032] The advice unit includes a hospital visit suggestion unit that recommends a hospital visit as needed. The hospital visit suggestion unit, for example, recommends a hospital visit as needed. For example, if the user's health condition worsens, the hospital visit suggestion unit advises the user to see a doctor. The hospital visit suggestion unit can also estimate the user's emotions and adjust the method of suggesting a hospital visit based on the estimated user's emotions. For example, if the user is feeling stressed, the hospital visit suggestion unit suggests a hospital visit method that will help the user relax. Furthermore, the hospital visit suggestion unit can also provide optimal suggestions by referring to the user's past hospital visit history. For example, the hospital visit suggestion unit suggests the optimal hospital to visit based on the hospital the user has visited in the past. This makes it possible to support the user's health management by suggesting hospital visits as needed.

[0033] The data collection unit records body temperature and sleep duration using a thermometer or a smartwatch. The data collection unit, for example, measures body temperature using a thermometer and collects data. For example, the data collection unit measures body temperature every morning using a thermometer and records the data. The data collection unit can also record sleep duration using a smartwatch. For example, the data collection unit uses a smartwatch to record sleep duration every night and collect data. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit delays the timing of data collection and collects data when the user is relaxed. This makes it possible to collect accurate body temperature and sleep duration data by using a thermometer or a smartwatch.

[0034] The analysis unit quantifies the fatigue level based on the time spent using the digital device and the sleep time. The analysis unit quantifies the fatigue level based on, for example, the time spent using the digital device and the sleep time. For example, the analysis unit increases the fatigue level numerical value if the digital device is used for a long time or the sleep time is short. Furthermore, the analysis unit decreases the fatigue level numerical value if the user has had sufficient sleep or the digital device is used for a short time. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to past data. For example, the analysis unit applies an optimal analysis algorithm based on the user's past health data. This allows the user's fatigue level to be accurately evaluated by quantifying fatigue based on the time spent using the digital device and the sleep time.

[0035] The input unit can analyze the user's past input history and select an appropriate input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the input unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. Furthermore, the input unit can provide an auto-completion function to reduce the effort of input based on information the user has input in the past. For example, the input unit can provide an auto-completion function to reduce the effort of input based on information the user has input in the past. In this way, the optimal input method can be provided to the user by analyzing the past input history.

[0036] The input unit can filter the input content based on the user's current living situation and areas of interest when inputting information. For example, if the user is interested in health, the input unit can prioritize displaying health-related input items. For example, if the user is interested in health, the input unit can prioritize displaying health-related input items. The input unit can also prioritize displaying work-related input items when the user is concentrating on work. For example, if the user is concentrating on work, the input unit can also prioritize displaying work-related input items. Furthermore, the input unit can also prioritize displaying input items related to hobbies and entertainment when the user is relaxing. For example, the input unit can also prioritize displaying input items related to hobbies and entertainment when the user is relaxing. This enables efficient information collection by providing input content according to the user's living situation and areas of interest.

[0037] The input unit can, at the time of input, prioritize input of highly relevant information taking into consideration the user's geographical location information. For example, when the user is at home, the input unit prioritizes input of information related to the home. For example, when the user is at home, the input unit prioritizes input of information related to the home. Furthermore, when the user is at work, the input unit can also prioritize input of information related to the workplace. For example, when the user is at work, the input unit can also prioritize input of information related to the workplace. Furthermore, when the user is traveling, the input unit can also prioritize input of information related to the travel destination. For example, when the user is traveling, the input unit can also prioritize input of information related to the travel destination. This enables efficient information input by providing highly relevant information based on the user's geographical location information.

[0038] The input unit can analyze the user's social media activity at the time of input and input related information. The input unit can suggest related input items based on, for example, information shared by the user on social media. For example, the input unit can suggest related input items based on information shared by the user on social media. The input unit can also suggest related input items based on information about accounts the user follows on social media. For example, the input unit can suggest related input items based on information about accounts the user follows on social media. Furthermore, the input unit can also suggest related input items based on information about groups the user participates in on social media. For example, the input unit can suggest related input items based on information about groups the user participates in on social media. This makes it possible to input information according to the user's interests by providing related information based on social media activity.

[0039] The counting unit can analyze the device usage pattern and select the optimal counting method when counting. For example, the counting unit analyzes the usage pattern of a device that the user frequently uses and proposes the optimal counting method. For example, the counting unit analyzes the usage pattern of a device that the user frequently uses and proposes the optimal counting method. The counting unit can also propose a method for performing counting during a specific time period based on the user's device usage pattern. For example, the counting unit can also propose a method for performing counting during a specific time period based on the user's device usage pattern. Furthermore, the counting unit can provide an automatic counting function to reduce the effort of counting based on the user's device usage pattern. For example, the counting unit can provide an automatic counting function to reduce the effort of counting based on the user's device usage pattern. This enables efficient data collection by providing the optimal counting method based on the device usage pattern.

[0040] The counting unit can apply different counting algorithms depending on the type of device when counting. For example, when using a smartphone, the counting unit performs counting based on screen time. For example, when using a smartphone, the counting unit performs counting based on screen time. Furthermore, when using a PC, the counting unit can also perform counting based on the keyboard and mouse operation time. For example, when using a PC, the counting unit can also perform counting based on the keyboard and mouse operation time. Furthermore, when using a tablet, the counting unit can also perform counting based on the frequency of touch operations. For example, when using a tablet, the counting unit can also perform counting based on the frequency of touch operations. This makes it possible to collect accurate data by applying a counting algorithm depending on the type of device.

[0041] The counting unit can take into account the geographical location information of the device when counting. For example, when the user is at home, the counting unit counts the device usage time at home. For example, when the user is at home, the counting unit counts the device usage time at home. Furthermore, when the user is at work, the counting unit can also count the device usage time at work. For example, when the user is at work, the counting unit can also count the device usage time at work. Furthermore, when the user is traveling, the counting unit can also count the device usage time at a travel destination. For example, when the user is traveling, the counting unit can also count the device usage time at a travel destination. This makes it possible to collect accurate data by counting based on geographical location information.

[0042] The counting unit can improve the accuracy of counting by referring to the device usage history when counting. The counting unit improves the accuracy of counting, for example, based on the user's past device usage history. For example, the counting unit improves the accuracy of counting based on the user's past device usage history. The counting unit can also suggest a method of counting for a specific time period based on the user's device usage history. For example, the counting unit can also suggest a method of counting for a specific time period based on the user's device usage history. Furthermore, the counting unit can analyze the user's device usage history and suggest the most efficient counting method. For example, the counting unit can analyze the user's device usage history and suggest the most efficient counting method. This improves the accuracy of counting based on the device usage history, making it possible to collect accurate data.

[0043] When collecting data, the data collection unit can analyze the user's past health data and select the optimal collection method. The data collection unit, for example, suggests the optimal data collection method based on the user's past health data. For example, the data collection unit suggests the optimal data collection method based on the user's past health data. The data collection unit can also suggest a method for collecting data at a specific time period based on the user's past health data. For example, the data collection unit can also suggest a method for collecting data at a specific time period based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and suggest the most efficient data collection method. For example, the data collection unit can analyze the user's past health data and suggest the most efficient data collection method. In this way, the optimal data collection method can be provided by analyzing the past health data.

[0044] The data collection unit can apply different collection algorithms depending on the type of device when collecting data. For example, when a smartwatch is used, the data collection unit collects data based on heart rate and sleep data. For example, when a smartwatch is used, the data collection unit collects data based on heart rate and sleep data. Furthermore, when a thermometer is used, the data collection unit can also collect data based on body temperature data. For example, when a thermometer is used, the data collection unit can also collect data based on body temperature data. Furthermore, when a smartphone is used, the data collection unit can also collect data based on app usage data. For example, when a smartphone is used, the data collection unit can also collect data based on app usage data. This enables accurate data collection by applying a collection algorithm depending on the type of device.

[0045] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the data collection unit prioritizes collecting health data at home. For example, when the user is at home, the data collection unit prioritizes collecting health data at home. Furthermore, when the user is at work, the data collection unit can prioritize collecting health data at work. For example, when the user is at work, the data collection unit can prioritize collecting health data at work. Furthermore, when the user is traveling, the data collection unit can prioritize collecting health data at the travel destination. For example, when the user is traveling, the data collection unit can prioritize collecting health data at the travel destination. This enables efficient data collection by collecting highly relevant data based on geographical location information.

[0046] The data collection unit can analyze the user's social media activities and collect related data during data collection. The data collection unit, for example, collects related data based on health information shared by the user on social media. For example, the data collection unit collects related data based on health information shared by the user on social media. The data collection unit can also collect related data based on information about health-related accounts the user follows on social media. For example, the data collection unit can collect related data based on information about health-related accounts the user follows on social media. Furthermore, the data collection unit can collect related data based on information about health-related groups the user joins on social media. For example, the data collection unit can collect related data based on information about health-related groups the user joins on social media. In this way, collecting related data based on social media activities makes it possible to collect data according to the user's interests.

[0047] During analysis, the analysis unit can optimize the analysis algorithm by referring to past data. The analysis unit, for example, applies an optimal analysis algorithm based on the user's past health data. For example, the analysis unit applies an optimal analysis algorithm based on the user's past health data. The analysis unit can also extract a specific pattern from the user's past data and optimize the analysis algorithm. For example, the analysis unit can extract a specific pattern from the user's past data and optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past data and apply the most efficient analysis algorithm. For example, the analysis unit can analyze the user's past data and apply the most efficient analysis algorithm. In this way, by referring to past data, it is possible to provide an optimal analysis algorithm.

[0048] During analysis, the analysis unit can apply different analysis methods depending on the type of data. For example, the analysis unit applies time series analysis to body temperature data to extract a fluctuation pattern. For example, the analysis unit applies time series analysis to body temperature data to extract a fluctuation pattern. The analysis unit can also apply a clustering method to sleep data to evaluate sleep quality. For example, the analysis unit can apply a clustering method to sleep data to evaluate sleep quality. Furthermore, the analysis unit can apply regression analysis to device usage data to clarify the relationship between usage time and fatigue level. For example, the analysis unit can apply regression analysis to device usage data to clarify the relationship between usage time and fatigue level. This enables accurate analysis by applying the optimal analysis method depending on the type of data.

[0049] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the latest data and provides real-time results. For example, the analysis unit prioritizes analysis of the latest data and provides real-time results. The analysis unit can also determine the priority of analysis based on data collected periodically. For example, the analysis unit can also determine the priority of analysis based on data collected periodically. Furthermore, the analysis unit can prioritize analysis of data submitted by a user within a specific period. For example, the analysis unit can prioritize analysis of data submitted by a user within a specific period. In this way, by determining the priority of analysis based on the time of data submission, it is possible to provide real-time results.

[0050] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis based on, for example, the relevance between body temperature data and sleep data. For example, the analysis unit adjusts the order of analysis based on the relevance between body temperature data and sleep data. The analysis unit can also adjust the order of analysis based on the relevance between device usage data and fatigue level data. For example, the analysis unit can adjust the order of analysis based on the relevance between device usage data and fatigue level data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the overall health data. For example, the analysis unit can adjust the order of analysis based on the relevance of the overall health data. In this way, adjusting the order of analysis based on the relevance of data enables efficient analysis.

[0051] When providing advice, the advice unit may adjust the level of detail of the advice based on the fatigue numerical value. For example, when the fatigue numerical value is high, the advice unit provides detailed advice. For example, when the fatigue numerical value is high, the advice unit provides detailed advice. Furthermore, when the fatigue numerical value is medium, the advice unit may provide advice with a moderate level of detail. For example, when the fatigue numerical value is medium, the advice unit may provide advice with a moderate level of detail. Furthermore, when the fatigue numerical value is low, the advice unit may provide concise advice. For example, when the fatigue numerical value is low, the advice unit may provide concise advice. In this way, by adjusting the level of detail of the advice based on the fatigue numerical value, more appropriate advice can be provided.

[0052] When providing advice, the advice unit can apply different advice algorithms depending on the category of fatigue. For example, in the case of physical fatigue, the advice unit provides advice to rest and stretch. For example, in the case of physical fatigue, the advice unit provides advice to rest and stretch. Furthermore, in the case of mental fatigue, the advice unit can provide advice to relax and meditate. For example, in the case of mental fatigue, the advice unit can provide advice to relax and meditate. Furthermore, in the case of digital fatigue, the advice unit can provide advice to reduce the amount of time spent using devices. For example, in the case of digital fatigue, the advice unit can provide advice to reduce the amount of time spent using devices. This makes it possible to provide more appropriate advice by applying advice algorithms according to the category of fatigue.

[0053] When providing advice, the advice unit can determine the priority of advice based on the time of submission of the fatigue information. The advice unit, for example, provides advice preferentially based on the latest fatigue data. For example, the advice unit provides advice preferentially based on the latest fatigue data. The advice unit can also determine the priority of advice based on fatigue data collected periodically. For example, the advice unit can also determine the priority of advice based on fatigue data collected periodically. Furthermore, the advice unit can also determine the priority of advice based on fatigue data submitted by the user during a specific period. For example, the advice unit can also determine the priority of advice based on fatigue data submitted by the user during a specific period. In this way, by determining the priority of advice based on the time of submission of the fatigue information, more appropriate advice can be provided.

[0054] When giving advice, the advice unit can adjust the order of advice based on the relevance of fatigue. The advice unit adjusts the order of advice based on, for example, the relevance between physical fatigue and mental fatigue. For example, the advice unit adjusts the order of advice based on the relevance between physical fatigue and mental fatigue. The advice unit can also adjust the order of advice based on the relevance between digital fatigue and lack of sleep. For example, the advice unit can also adjust the order of advice based on the relevance between digital fatigue and lack of sleep. Furthermore, the advice unit can adjust the order of advice based on the relevance of the overall health data. For example, the advice unit can adjust the order of advice based on the relevance of the overall health data. In this way, adjusting the order of advice based on the relevance of fatigue enables more appropriate advice to be given.

[0055] When suggesting a vacation, the vacation suggestion unit can provide the optimal suggestion by referring to the user's past vacation history. The vacation suggestion unit, for example, suggests the optimal vacation based on patterns of vacations the user has enjoyed in the past. For example, the vacation suggestion unit suggests the optimal vacation based on patterns of vacations the user has enjoyed in the past. The vacation suggestion unit can also suggest vacations suitable for a particular season based on the user's past vacation history. For example, the vacation suggestion unit can also suggest vacations suitable for a particular season based on the user's past vacation history. Furthermore, the vacation suggestion unit can analyze the user's past vacation history and suggest the most refreshing vacation. For example, the vacation suggestion unit can analyze the user's past vacation history and suggest the most refreshing vacation. In this way, by referring to the past vacation history, it is possible to suggest the optimal vacation to the user.

[0056] When proposing a vacation, the vacation suggestion unit can provide an optimal suggestion taking into consideration the user's geographical location information. For example, when the user is at home, the vacation suggestion unit suggests a vacation in a location close to the user's home. For example, when the user is at home, the vacation suggestion unit suggests a vacation in a location close to the user's home. Furthermore, when the user is at work, the vacation suggestion unit can also suggest a vacation in a location that is easily accessible from the user's workplace. For example, when the user is at work, the vacation suggestion unit can also suggest a vacation in a location that is easily accessible from the user's workplace. Furthermore, when the user is traveling, the vacation suggestion unit can also suggest a vacation that can be enjoyed at the user's travel destination. For example, when the user is traveling, the vacation suggestion unit can also suggest a vacation that can be enjoyed at the user's travel destination. This allows for more appropriate vacation suggestions to be made by providing optimal vacation suggestions based on the geographical location information.

[0057] When suggesting a meal, the meal suggestion unit can provide optimal suggestions by referring to the user's past meal history. The meal suggestion unit, for example, suggests optimal meals based on meals that the user has liked to eat in the past. For example, the meal suggestion unit suggests optimal meals based on meals that the user has liked to eat in the past. The meal suggestion unit can also suggest meals that supplement specific nutrients based on the user's past meal history. For example, the meal suggestion unit can also suggest meals that supplement specific nutrients based on the user's past meal history. Furthermore, the meal suggestion unit can analyze the user's past meal history and suggest the healthiest meal. For example, the meal suggestion unit can analyze the user's past meal history and suggest the healthiest meal. In this way, optimal meal suggestions can be made to the user by referring to the past meal history.

[0058] When suggesting a meal, the meal suggestion unit can provide optimal suggestions by taking into consideration the user's geographical location information. For example, when the user is at home, the meal suggestion unit suggests meals that can be easily prepared at home. For example, when the user is at home, the meal suggestion unit suggests meals that can be easily prepared at home. Furthermore, when the user is at work, the meal suggestion unit can also suggest meals that can be easily eaten at work. For example, when the user is at work, the meal suggestion unit can also suggest meals that can be easily eaten at work. Furthermore, when the user is traveling, the meal suggestion unit can also suggest meals that can be enjoyed at the travel destination. For example, when the user is traveling, the meal suggestion unit can also suggest meals that can be enjoyed at the travel destination. This enables more appropriate meal suggestions to be made by providing optimal meal suggestions based on the geographical location information.

[0059] When suggesting a hospital visit, the hospital visit suggestion unit can provide an optimal suggestion by referring to the user's past hospital visit history. The hospital visit suggestion unit, for example, suggests an optimal hospital visit destination based on hospitals the user has visited in the past. For example, the hospital visit suggestion unit suggests an optimal hospital visit destination based on hospitals the user has visited in the past. The hospital visit suggestion unit can also suggest a hospital visit destination suitable for a specific symptom based on the user's past hospital visit history. For example, the hospital visit suggestion unit can also suggest a hospital visit destination suitable for a specific symptom based on the user's past hospital visit history. Furthermore, the hospital visit suggestion unit can analyze the user's past hospital visit history and suggest the most effective hospital visit destination. For example, the hospital visit suggestion unit can analyze the user's past hospital visit history and suggest the most effective hospital visit destination. In this way, by referring to the past hospital visit history, it is possible to make an optimal hospital visit suggestion for the user.

[0060] When suggesting a hospital visit, the hospital visit suggestion unit can provide an optimal suggestion taking into consideration the user's geographical location information. For example, when the user is at home, the hospital visit suggestion unit suggests a hospital close to the user's home. For example, when the user is at home, the hospital visit suggestion unit suggests a hospital close to the user's home. Furthermore, when the user is at work, the hospital visit suggestion unit can also suggest a hospital that is easily accessible from the user's workplace. For example, when the user is at work, the hospital visit suggestion unit can also suggest a hospital that is easily accessible from the user's workplace. Furthermore, when the user is traveling, the hospital visit suggestion unit can also suggest a hospital that is available at the user's travel destination. For example, when the user is traveling, the hospital visit suggestion unit can also suggest a hospital that is available at the user's travel destination. This allows for more appropriate hospital visit suggestions to be made by providing an optimal hospital visit suggestion based on the geographical location information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The health management system may further include a fluid management unit that manages the user's fluid intake. The fluid management unit, for example, records the amount of fluid consumed by the user and encourages appropriate fluid intake. The fluid management unit may also suggest an optimal fluid intake amount based on the user's activity level and environmental conditions. For example, it may recommend more fluid intake after exercise or in a hot environment. Furthermore, the fluid management unit may refer to the user's past fluid intake data to provide an optimal fluid intake plan. This allows the user to manage their fluid intake and maintain an appropriate fluid balance.

[0063] The health management system may further include a posture monitoring unit that monitors the user's posture. The posture monitoring unit may monitor the user's posture in real time using, for example, a smart device, and encourage posture improvement. The posture monitoring unit may also suggest optimal posture improvement exercises based on the user's activity data. For example, it may recommend stretching or light exercise after a long period of desk work. Furthermore, the posture monitoring unit may refer to the user's past posture data and provide an optimal posture improvement plan. This may improve the user's posture and support a healthy lifestyle.

[0064] The health management system may further include a respiratory data collection unit that collects the user's respiratory data. The respiratory data collection unit may, for example, use a smart device to record the user's breathing patterns and evaluate the quality of breathing. The respiratory data collection unit may also suggest optimal breathing exercises based on the user's activity level and emotional state. For example, if the user is feeling stressed, the respiratory data collection unit may recommend deep breathing or meditation. Furthermore, the respiratory data collection unit may refer to the user's past respiratory data to provide an optimal breathing improvement plan. This may improve the user's breathing and support their overall health.

[0065] The health management system may further include an environmental data collection unit that collects environmental data about the user. The environmental data collection unit records environmental data, such as temperature, humidity, and air quality, and evaluates factors that affect the user's health. The environmental data collection unit can also make optimal suggestions for improving the environment based on the user's activity data and emotional state. For example, if the room temperature is high, it can suggest cooling methods. Furthermore, the environmental data collection unit can refer to the user's past environmental data to provide an optimal environmental management plan. This can optimize the user's environment and support a healthy lifestyle.

[0066] The health management system may further include a goal setting unit that sets health goals for the user and evaluates the degree of achievement. The goal setting unit, for example, records health goals set by the user (weight loss, increased exercise, etc.) and tracks progress. The goal setting unit may also evaluate the degree of achievement and provide feedback based on the user's activity data and health data. For example, if the user is close to achieving a goal, it may send an encouraging message. Furthermore, the goal setting unit may refer to the user's past goal achievement data to provide an optimal goal setting plan. This may provide support for the user to achieve their health goals.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The input unit inputs basic information about a person. Basic information about a person includes name, age, gender, address, etc. The input unit provides an interface for users to manually input information, and can also provide an auto-completion function based on past input history. Step 2: The counting unit automatically counts browsing time on your PC or smartphone. Browsing time includes active browser time and app usage time. The counting unit measures and collects data on active browser time and app usage time. It can also analyze device usage patterns and select the optimal counting method. Step 3: The data collection unit inputs or counts daily body temperature and sleep time. Body temperature information includes the time of measurement, the measurement site, and the type of thermometer used. The data collection unit records body temperature and sleep time using a thermometer or smartwatch to collect data. It can also estimate the user's emotions and adjust the timing of data collection. Step 4: The analysis unit quantifies the daily fatigue level based on the data collected by the input unit, counting unit, and data collection unit. The analysis unit quantifies the fatigue level based on the amount of time spent using digital devices and the amount of sleep, and can also optimize the analysis algorithm by referring to past data. Different analysis methods can also be applied depending on the type of data. Step 5: The advice unit evaluates the employee's physical condition each morning based on the fatigue level data quantified by the analysis unit and provides advice. If the fatigue level is high, the advice unit can suggest taking time off work, or suggest dietary therapy or medical treatment tailored to the employee's fatigue level.

[0069] (Example 2) A health management system according to an embodiment of the present invention inputs basic personal information, automatically counts the time spent browsing a PC or smartphone, and inputs or counts daily body temperature and sleep time. This health management system automatically counts the time spent browsing a PC or smartphone. Furthermore, the system inputs or counts daily body temperature and sleep time to quantify the individual's daily fatigue. Once a certain amount of data has been accumulated, the AI ​​provides advice on the individual's health condition each morning, suggests work leave, dietary therapy tailored to the level of fatigue, and suggests medical visits to manage the individual's health. For example, the individual's basic information (age, height, and weight) is input. Next, the system automatically counts the time spent browsing a PC or smartphone. This allows the individual to understand their daily digital device usage. Furthermore, the individual's daily body temperature and sleep time are input or counted. For example, a thermometer or smartwatch can be used to record body temperature and sleep time. This allows the individual's daily health status to be understood in detail. Based on this data, the AI ​​quantifies daily fatigue. For example, the fatigue value increases if the individual spends a lot of time using digital devices or sleeps less. Conversely, if you get enough sleep or spend less time using digital devices, your fatigue level will be lower. Once a certain amount of data has been accumulated, the AI ​​will provide advice on your physical condition each morning. For example, if your fatigue level is high, it will suggest taking time off work. It will also suggest a diet tailored to your level of fatigue. For example, it will advise you to eat a diet rich in vitamins and minerals. It will also suggest medical visits as needed to manage your health. This system allows for a detailed understanding of an individual's health condition and allows for appropriate health management. For example, reducing the amount of time spent using digital devices can reduce fatigue and maintain health. Furthermore, following an appropriate diet and visiting the hospital can improve your physical condition and increase work efficiency. In this way, the health management system can provide a detailed understanding of an individual's health condition and allow for appropriate health management.

[0070] A health management system according to an embodiment includes an input unit, a counting unit, a data collection unit, an analysis unit, and an advice unit. The input unit inputs basic information about an individual. Examples of basic information about an individual include, but are not limited to, name, age, gender, and address. The input unit provides an interface through which a user manually inputs information. The input unit can also provide an auto-complete function based on the user's past input history. For example, the input unit provides an auto-complete function to reduce the effort required for input based on information previously input by the user. The counting unit automatically counts browsing time on a PC or smartphone. Examples of browsing time include, but are not limited to, browser active time and app usage time. For example, the counting unit measures browser active time and collects data. The counting unit can also measure app usage time and collect data. Furthermore, the counting unit can analyze device usage patterns and select an optimal counting method. For example, the counting unit analyzes the usage patterns of devices frequently used by the user and suggests an optimal counting method. The data collection unit inputs or counts daily body temperature and sleep time. The body temperature may include, but is not limited to, the measurement time, the measurement site, the type of thermometer used, and the like. The data collection unit may, for example, measure the body temperature using a thermometer and collect data. The data collection unit may also use a smartwatch to record sleep time and collect data. The data collection unit may also estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit may delay the timing of data collection and collect data while the user is relaxed. The analysis unit quantifies the daily fatigue level based on the data collected by the input unit, the counting unit, and the data collection unit. The fatigue level may include, but is not limited to, a scoring system, an algorithm used, and the like. The analysis unit may quantify the fatigue level based on, for example, the time spent using a digital device and the amount of sleep. The analysis unit may also optimize the analysis algorithm by referring to past data.Furthermore, the analysis unit can apply different analysis methods depending on the type of data. For example, the analysis unit applies time series analysis to body temperature data to extract fluctuation patterns. The advice unit evaluates the physical condition of the individual each morning and provides advice based on the fatigue level data quantified by the analysis unit. Examples of the evaluation and advice include, but are not limited to, a health score and recommended actions. For example, the advice unit can suggest taking time off work if the fatigue level is high. The advice unit can also suggest a dietary therapy tailored to the level of fatigue. Furthermore, the advice unit can suggest visiting a doctor as needed. As a result, the physical condition management system according to the embodiment can grasp the individual's health condition in detail and provide appropriate physical condition management.

[0071] The advice unit includes a vacation suggestion unit that suggests vacation from work when the fatigue level is high. The vacation suggestion unit, for example, suggests vacation from work when the fatigue level is high. For example, the vacation suggestion unit advises the user to take a vacation when the user's fatigue level exceeds a certain standard. The vacation suggestion unit can also estimate the user's emotions and adjust the vacation suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the vacation suggestion unit suggests a relaxing vacation. Furthermore, the vacation suggestion unit can provide optimal suggestions by referring to the user's past vacation history. For example, the vacation suggestion unit suggests an optimal vacation based on patterns of vacations the user has enjoyed in the past. In this way, the user's health can be maintained by suggesting an appropriate vacation when the fatigue level is high.

[0072] The advice unit includes a diet suggestion unit that suggests a diet tailored to the level of fatigue. The diet suggestion unit, for example, suggests a diet tailored to the level of fatigue. For example, if the user's level of fatigue is high, the diet suggestion unit advises the user to eat a diet rich in vitamins and minerals. The diet suggestion unit can also estimate the user's emotions and adjust the method of suggesting meals based on the estimated user's emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest meals that will help the user relax. Furthermore, the meal suggestion unit can provide optimal suggestions by referring to the user's past meal history. For example, the meal suggestion unit suggests optimal meals based on meals that the user has liked to eat in the past. This makes it possible to support the user's health by suggesting a diet tailored to the level of fatigue.

[0073] The advice unit includes a hospital visit suggestion unit that recommends a hospital visit as needed. The hospital visit suggestion unit, for example, recommends a hospital visit as needed. For example, if the user's health condition worsens, the hospital visit suggestion unit advises the user to see a doctor. The hospital visit suggestion unit can also estimate the user's emotions and adjust the method of suggesting a hospital visit based on the estimated user's emotions. For example, if the user is feeling stressed, the hospital visit suggestion unit suggests a hospital visit method that will help the user relax. Furthermore, the hospital visit suggestion unit can also provide optimal suggestions by referring to the user's past hospital visit history. For example, the hospital visit suggestion unit suggests the optimal hospital to visit based on the hospital the user has visited in the past. This makes it possible to support the user's health management by suggesting hospital visits as needed.

[0074] The data collection unit records body temperature and sleep duration using a thermometer or a smartwatch. The data collection unit, for example, measures body temperature using a thermometer and collects data. For example, the data collection unit measures body temperature every morning using a thermometer and records the data. The data collection unit can also record sleep duration using a smartwatch. For example, the data collection unit uses a smartwatch to record sleep duration every night and collect data. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the data collection unit delays the timing of data collection and collects data when the user is relaxed. This makes it possible to collect accurate body temperature and sleep duration data by using a thermometer or a smartwatch.

[0075] The analysis unit quantifies the fatigue level based on the time spent using the digital device and the sleep time. The analysis unit quantifies the fatigue level based on, for example, the time spent using the digital device and the sleep time. For example, the analysis unit increases the fatigue level numerical value if the digital device is used for a long time or the sleep time is short. Furthermore, the analysis unit decreases the fatigue level numerical value if the user has had sufficient sleep or the digital device is used for a short time. Furthermore, the analysis unit can also optimize the analysis algorithm by referring to past data. For example, the analysis unit applies an optimal analysis algorithm based on the user's past health data. This allows the user's fatigue level to be accurately evaluated by quantifying fatigue based on the time spent using the digital device and the sleep time.

[0076] The input unit can estimate the user's emotions and adjust the input timing based on the estimated user's emotions. For example, when the user is feeling stressed, the input unit delays the timing of the input and encourages the user to input in a relaxed state. For example, when the user is feeling stressed, the input unit delays the timing of the input and encourages the user to input in a relaxed state. The input unit can also advance the timing of the input when the user is relaxed, to efficiently collect information. For example, when the user is relaxed, the input unit can advance the timing of the input and efficiently collect information. Furthermore, when the user is in a hurry, the input unit can optimize the timing of the input so that the user can input information quickly. For example, when the user is in a hurry, the input unit can optimize the timing of the input so that the user can input information quickly. This allows more appropriate input by adjusting the timing of the input according to the user's emotions.

[0077] The input unit can analyze the user's past input history and select an appropriate input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, the input unit can predict and suggest an input method to be used in a specific time period based on the user's past input history. Furthermore, the input unit can provide an auto-completion function to reduce the effort of input based on information the user has input in the past. For example, the input unit can provide an auto-completion function to reduce the effort of input based on information the user has input in the past. In this way, the optimal input method can be provided to the user by analyzing the past input history.

[0078] The input unit can filter the input content based on the user's current living situation and areas of interest when inputting information. For example, if the user is interested in health, the input unit can prioritize displaying health-related input items. For example, if the user is interested in health, the input unit can prioritize displaying health-related input items. The input unit can also prioritize displaying work-related input items when the user is concentrating on work. For example, if the user is concentrating on work, the input unit can also prioritize displaying work-related input items. Furthermore, the input unit can also prioritize displaying input items related to hobbies and entertainment when the user is relaxing. For example, the input unit can also prioritize displaying input items related to hobbies and entertainment when the user is relaxing. This enables efficient information collection by providing input content according to the user's living situation and areas of interest.

[0079] The input unit can estimate the user's emotions and determine the priority of input information based on the estimated user's emotions. For example, when the user is feeling stressed, the input unit prioritizes input of important information. For example, when the user is feeling stressed, the input unit prioritizes input of important information. Furthermore, when the user is relaxed, the input unit can prioritize input of detailed information. For example, when the user is relaxed, the input unit can prioritize input of detailed information. Furthermore, when the user is in a hurry, the input unit can prioritize input of the minimum necessary information. For example, when the user is in a hurry, the input unit can prioritize input of the minimum necessary information. In this way, by determining the priority of information according to the user's emotions, it is possible to preferentially input important information.

[0080] The input unit can, at the time of input, prioritize input of highly relevant information taking into consideration the user's geographical location information. For example, when the user is at home, the input unit prioritizes input of information related to the home. For example, when the user is at home, the input unit prioritizes input of information related to the home. Furthermore, when the user is at work, the input unit can also prioritize input of information related to the workplace. For example, when the user is at work, the input unit can also prioritize input of information related to the workplace. Furthermore, when the user is traveling, the input unit can also prioritize input of information related to the travel destination. For example, when the user is traveling, the input unit can also prioritize input of information related to the travel destination. This enables efficient information input by providing highly relevant information based on the user's geographical location information.

[0081] The input unit can analyze the user's social media activity at the time of input and input related information. The input unit can suggest related input items based on, for example, information shared by the user on social media. For example, the input unit can suggest related input items based on information shared by the user on social media. The input unit can also suggest related input items based on information about accounts the user follows on social media. For example, the input unit can suggest related input items based on information about accounts the user follows on social media. Furthermore, the input unit can also suggest related input items based on information about groups the user participates in on social media. For example, the input unit can suggest related input items based on information about groups the user participates in on social media. This makes it possible to input information according to the user's interests by providing related information based on social media activity.

[0082] The counting unit can estimate the user's emotions and adjust the counting frequency based on the estimated user's emotions. For example, when the user is feeling stressed, the counting unit reduces the counting frequency and performs counting in a relaxed state. For example, when the user is feeling stressed, the counting unit reduces the counting frequency and performs counting in a relaxed state. Furthermore, when the user is relaxed, the counting unit can increase the counting frequency and collect detailed data. For example, when the user is relaxed, the counting unit can increase the counting frequency and collect detailed data. Furthermore, when the user is in a hurry, the counting unit can optimize the counting frequency and collect data quickly. For example, when the user is in a hurry, the counting unit can optimize the counting frequency and collect data quickly. This allows more appropriate data collection by adjusting the counting frequency according to the user's emotions.

[0083] The counting unit can analyze the device usage pattern and select the optimal counting method when counting. For example, the counting unit analyzes the usage pattern of a device that the user frequently uses and proposes the optimal counting method. For example, the counting unit analyzes the usage pattern of a device that the user frequently uses and proposes the optimal counting method. The counting unit can also propose a method for performing counting during a specific time period based on the user's device usage pattern. For example, the counting unit can also propose a method for performing counting during a specific time period based on the user's device usage pattern. Furthermore, the counting unit can provide an automatic counting function to reduce the effort of counting based on the user's device usage pattern. For example, the counting unit can provide an automatic counting function to reduce the effort of counting based on the user's device usage pattern. This enables efficient data collection by providing the optimal counting method based on the device usage pattern.

[0084] The counting unit can apply different counting algorithms depending on the type of device when counting. For example, when using a smartphone, the counting unit performs counting based on screen time. For example, when using a smartphone, the counting unit performs counting based on screen time. Furthermore, when using a PC, the counting unit can also perform counting based on the keyboard and mouse operation time. For example, when using a PC, the counting unit can also perform counting based on the keyboard and mouse operation time. Furthermore, when using a tablet, the counting unit can also perform counting based on the frequency of touch operations. For example, when using a tablet, the counting unit can also perform counting based on the frequency of touch operations. This makes it possible to collect accurate data by applying a counting algorithm depending on the type of device.

[0085] The counting unit can estimate the user's emotions and adjust the display method of the counting results based on the estimated user's emotions. For example, when the user is nervous, the counting unit provides a simple, highly visible display method. For example, when the user is nervous, the counting unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the counting unit can also provide a display method including detailed information. For example, when the user is relaxed, the counting unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the counting unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the counting unit can also provide a display method that focuses on the main points. In this way, by providing a display method according to the user's emotions, it is possible to display a highly visible counting result.

[0086] The counting unit can take into account the geographical location information of the device when counting. For example, when the user is at home, the counting unit counts the device usage time at home. For example, when the user is at home, the counting unit counts the device usage time at home. Furthermore, when the user is at work, the counting unit can also count the device usage time at work. For example, when the user is at work, the counting unit can also count the device usage time at work. Furthermore, when the user is traveling, the counting unit can also count the device usage time at a travel destination. For example, when the user is traveling, the counting unit can also count the device usage time at a travel destination. This makes it possible to collect accurate data by counting based on geographical location information.

[0087] The counting unit can improve the accuracy of counting by referring to the device usage history when counting. The counting unit improves the accuracy of counting, for example, based on the user's past device usage history. For example, the counting unit improves the accuracy of counting based on the user's past device usage history. The counting unit can also suggest a method of counting for a specific time period based on the user's device usage history. For example, the counting unit can also suggest a method of counting for a specific time period based on the user's device usage history. Furthermore, the counting unit can analyze the user's device usage history and suggest the most efficient counting method. For example, the counting unit can analyze the user's device usage history and suggest the most efficient counting method. This improves the accuracy of counting based on the device usage history, making it possible to collect accurate data.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the data collection unit delays the timing of data collection and collects data in a relaxed state. For example, when the user is feeling stressed, the data collection unit delays the timing of data collection and collects data in a relaxed state. Furthermore, when the user is relaxed, the data collection unit can also advance the timing of data collection to efficiently collect data. For example, when the user is relaxed, the data collection unit can also advance the timing of data collection to efficiently collect data. Furthermore, when the user is in a hurry, the data collection unit can optimize the timing of data collection to quickly collect data. For example, when the user is in a hurry, the data collection unit can optimize the timing of data collection to quickly collect data. This allows more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0089] When collecting data, the data collection unit can analyze the user's past health data and select the optimal collection method. The data collection unit, for example, suggests the optimal data collection method based on the user's past health data. For example, the data collection unit suggests the optimal data collection method based on the user's past health data. The data collection unit can also suggest a method for collecting data at a specific time period based on the user's past health data. For example, the data collection unit can also suggest a method for collecting data at a specific time period based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and suggest the most efficient data collection method. For example, the data collection unit can analyze the user's past health data and suggest the most efficient data collection method. In this way, the optimal data collection method can be provided by analyzing the past health data.

[0090] The data collection unit can apply different collection algorithms depending on the type of device when collecting data. For example, when a smartwatch is used, the data collection unit collects data based on heart rate and sleep data. For example, when a smartwatch is used, the data collection unit collects data based on heart rate and sleep data. Furthermore, when a thermometer is used, the data collection unit can also collect data based on body temperature data. For example, when a thermometer is used, the data collection unit can also collect data based on body temperature data. Furthermore, when a smartphone is used, the data collection unit can also collect data based on app usage data. For example, when a smartphone is used, the data collection unit can also collect data based on app usage data. This enables accurate data collection by applying a collection algorithm depending on the type of device.

[0091] The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the data collection unit prioritizes collecting important health data. For example, when the user is feeling stressed, the data collection unit prioritizes collecting important health data. The data collection unit can also prioritize collecting detailed health data when the user is relaxed. For example, when the user is relaxed, the data collection unit can also prioritize collecting detailed health data. Furthermore, when the user is in a hurry, the data collection unit can also prioritize collecting the minimum necessary health data. For example, when the user is in a hurry, the data collection unit can also prioritize collecting the minimum necessary health data. In this way, by determining the priority of data according to the user's emotions, important health data can be collected preferentially.

[0092] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the data collection unit prioritizes collecting health data at home. For example, when the user is at home, the data collection unit prioritizes collecting health data at home. Furthermore, when the user is at work, the data collection unit can prioritize collecting health data at work. For example, when the user is at work, the data collection unit can prioritize collecting health data at work. Furthermore, when the user is traveling, the data collection unit can prioritize collecting health data at the travel destination. For example, when the user is traveling, the data collection unit can prioritize collecting health data at the travel destination. This enables efficient data collection by collecting highly relevant data based on geographical location information.

[0093] The data collection unit can analyze the user's social media activities and collect related data during data collection. The data collection unit, for example, collects related data based on health information shared by the user on social media. For example, the data collection unit collects related data based on health information shared by the user on social media. The data collection unit can also collect related data based on information about health-related accounts the user follows on social media. For example, the data collection unit can collect related data based on information about health-related accounts the user follows on social media. Furthermore, the data collection unit can collect related data based on information about health-related groups the user joins on social media. For example, the data collection unit can collect related data based on information about health-related groups the user joins on social media. In this way, collecting related data based on social media activities makes it possible to collect data according to the user's interests.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit applies a simple analysis method and quickly provides results. For example, when the user is feeling stressed, the analysis unit applies a simple analysis method and quickly provides results. Furthermore, when the user is relaxed, the analysis unit can apply a detailed analysis method and quickly provide results with high accuracy. For example, when the user is relaxed, the analysis unit can apply a detailed analysis method and quickly provide results with high accuracy. Furthermore, when the user is in a hurry, the analysis unit can apply a quick analysis method and quickly provide results. For example, when the user is in a hurry, the analysis unit can apply a quick analysis method and quickly provide results. This enables more appropriate analysis by adjusting the analysis method according to the user's emotions.

[0095] During analysis, the analysis unit can optimize the analysis algorithm by referring to past data. The analysis unit, for example, applies an optimal analysis algorithm based on the user's past health data. For example, the analysis unit applies an optimal analysis algorithm based on the user's past health data. The analysis unit can also extract a specific pattern from the user's past data and optimize the analysis algorithm. For example, the analysis unit can extract a specific pattern from the user's past data and optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past data and apply the most efficient analysis algorithm. For example, the analysis unit can analyze the user's past data and apply the most efficient analysis algorithm. In this way, by referring to past data, it is possible to provide an optimal analysis algorithm.

[0096] During analysis, the analysis unit can apply different analysis methods depending on the type of data. For example, the analysis unit applies time series analysis to body temperature data to extract a fluctuation pattern. For example, the analysis unit applies time series analysis to body temperature data to extract a fluctuation pattern. The analysis unit can also apply a clustering method to sleep data to evaluate sleep quality. For example, the analysis unit can apply a clustering method to sleep data to evaluate sleep quality. Furthermore, the analysis unit can apply regression analysis to device usage data to clarify the relationship between usage time and fatigue level. For example, the analysis unit can apply regression analysis to device usage data to clarify the relationship between usage time and fatigue level. This enables accurate analysis by applying the optimal analysis method depending on the type of data.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple, highly visible display method. For example, when the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. For example, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by providing a display method according to the user's emotions, it is possible to display analysis results with high visibility.

[0098] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. For example, the analysis unit prioritizes analysis of the latest data and provides real-time results. For example, the analysis unit prioritizes analysis of the latest data and provides real-time results. The analysis unit can also determine the priority of analysis based on data collected periodically. For example, the analysis unit can also determine the priority of analysis based on data collected periodically. Furthermore, the analysis unit can prioritize analysis of data submitted by a user within a specific period. For example, the analysis unit can prioritize analysis of data submitted by a user within a specific period. In this way, by determining the priority of analysis based on the time of data submission, it is possible to provide real-time results.

[0099] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. The analysis unit adjusts the order of analysis based on, for example, the relevance between body temperature data and sleep data. For example, the analysis unit adjusts the order of analysis based on the relevance between body temperature data and sleep data. The analysis unit can also adjust the order of analysis based on the relevance between device usage data and fatigue level data. For example, the analysis unit can adjust the order of analysis based on the relevance between device usage data and fatigue level data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the overall health data. For example, the analysis unit can adjust the order of analysis based on the relevance of the overall health data. In this way, adjusting the order of analysis based on the relevance of data enables efficient analysis.

[0100] The advice unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit provides advice in gentle words. For example, if the user is feeling stressed, the advice unit provides advice in gentle words. Furthermore, if the user is relaxed, the advice unit can provide advice including detailed explanations. For example, if the user is relaxed, the advice unit can provide advice including detailed explanations. Furthermore, if the user is in a hurry, the advice unit can provide advice that is concise and to the point. For example, if the user is in a hurry, the advice unit can provide advice that is concise and to the point. This makes it possible to provide more appropriate advice by providing an expression method that corresponds to the user's emotions.

[0101] When providing advice, the advice unit may adjust the level of detail of the advice based on the fatigue numerical value. For example, when the fatigue numerical value is high, the advice unit provides detailed advice. For example, when the fatigue numerical value is high, the advice unit provides detailed advice. Furthermore, when the fatigue numerical value is medium, the advice unit may provide advice with a moderate level of detail. For example, when the fatigue numerical value is medium, the advice unit may provide advice with a moderate level of detail. Furthermore, when the fatigue numerical value is low, the advice unit may provide concise advice. For example, when the fatigue numerical value is low, the advice unit may provide concise advice. In this way, by adjusting the level of detail of the advice based on the fatigue numerical value, more appropriate advice can be provided.

[0102] When providing advice, the advice unit can apply different advice algorithms depending on the category of fatigue. For example, in the case of physical fatigue, the advice unit provides advice to rest and stretch. For example, in the case of physical fatigue, the advice unit provides advice to rest and stretch. Furthermore, in the case of mental fatigue, the advice unit can provide advice to relax and meditate. For example, in the case of mental fatigue, the advice unit can provide advice to relax and meditate. Furthermore, in the case of digital fatigue, the advice unit can provide advice to reduce the amount of time spent using devices. For example, in the case of digital fatigue, the advice unit can provide advice to reduce the amount of time spent using devices. This makes it possible to provide more appropriate advice by applying advice algorithms according to the category of fatigue.

[0103] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit provides short and to the point advice. For example, if the user is feeling stressed, the advice unit provides short and to the point advice. Furthermore, if the user is relaxed, the advice unit can provide longer advice including detailed explanations. For example, if the user is relaxed, the advice unit can provide longer advice including detailed explanations. Furthermore, if the user is in a hurry, the advice unit can provide concise and quick advice. For example, if the user is in a hurry, the advice unit can provide concise and quick advice. This allows for more appropriate advice by providing the length of advice according to the user's emotions.

[0104] When providing advice, the advice unit can determine the priority of advice based on the time of submission of the fatigue information. The advice unit, for example, provides advice preferentially based on the latest fatigue data. For example, the advice unit provides advice preferentially based on the latest fatigue data. The advice unit can also determine the priority of advice based on fatigue data collected periodically. For example, the advice unit can also determine the priority of advice based on fatigue data collected periodically. Furthermore, the advice unit can also determine the priority of advice based on fatigue data submitted by the user during a specific period. For example, the advice unit can also determine the priority of advice based on fatigue data submitted by the user during a specific period. In this way, by determining the priority of advice based on the time of submission of the fatigue information, more appropriate advice can be provided.

[0105] When giving advice, the advice unit can adjust the order of advice based on the relevance of fatigue. The advice unit adjusts the order of advice based on, for example, the relevance between physical fatigue and mental fatigue. For example, the advice unit adjusts the order of advice based on the relevance between physical fatigue and mental fatigue. The advice unit can also adjust the order of advice based on the relevance between digital fatigue and lack of sleep. For example, the advice unit can also adjust the order of advice based on the relevance between digital fatigue and lack of sleep. Furthermore, the advice unit can adjust the order of advice based on the relevance of the overall health data. For example, the advice unit can adjust the order of advice based on the relevance of the overall health data. In this way, adjusting the order of advice based on the relevance of fatigue enables more appropriate advice to be given.

[0106] The vacation suggestion unit can estimate the user's emotions and adjust the vacation suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the vacation suggestion unit suggests a relaxing vacation. For example, if the user is feeling stressed, the vacation suggestion unit suggests a relaxing vacation. The vacation suggestion unit can also suggest an active vacation if the user is relaxed. For example, if the user is relaxing, the vacation suggestion unit can also suggest an active vacation. Furthermore, if the user is in a hurry, the vacation suggestion unit can also suggest a vacation that will allow the user to refresh in a short period of time. For example, if the user is in a hurry, the vacation suggestion unit can also suggest a vacation that will allow the user to refresh in a short period of time. This allows for more appropriate vacation suggestions by providing a vacation suggestion method that corresponds to the user's emotions.

[0107] When suggesting a vacation, the vacation suggestion unit can provide the optimal suggestion by referring to the user's past vacation history. The vacation suggestion unit, for example, suggests the optimal vacation based on patterns of vacations the user has enjoyed in the past. For example, the vacation suggestion unit suggests the optimal vacation based on patterns of vacations the user has enjoyed in the past. The vacation suggestion unit can also suggest vacations suitable for a particular season based on the user's past vacation history. For example, the vacation suggestion unit can also suggest vacations suitable for a particular season based on the user's past vacation history. Furthermore, the vacation suggestion unit can analyze the user's past vacation history and suggest the most refreshing vacation. For example, the vacation suggestion unit can analyze the user's past vacation history and suggest the most refreshing vacation. In this way, by referring to the past vacation history, it is possible to suggest the optimal vacation to the user.

[0108] The vacation suggestion unit can estimate the user's emotions and determine the priority of vacation suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the vacation suggestion unit prioritizes suggesting a relaxing vacation. For example, if the user is feeling stressed, the vacation suggestion unit prioritizes suggesting a relaxing vacation. Furthermore, if the user is feeling relaxed, the vacation suggestion unit can also prioritize suggesting an active vacation. For example, if the user is feeling relaxed, the vacation suggestion unit can also prioritize suggesting an active vacation. Furthermore, if the user is in a hurry, the vacation suggestion unit can also prioritize suggesting a vacation that allows for short-term refreshing. For example, if the user is in a hurry, the vacation suggestion unit can also prioritize suggesting a vacation that allows for short-term refreshing. This allows for more appropriate vacation suggestions by providing a priority order of vacation suggestions according to the user's emotions.

[0109] When proposing a vacation, the vacation suggestion unit can provide an optimal suggestion taking into consideration the user's geographical location information. For example, when the user is at home, the vacation suggestion unit suggests a vacation in a location close to the user's home. For example, when the user is at home, the vacation suggestion unit suggests a vacation in a location close to the user's home. Furthermore, when the user is at work, the vacation suggestion unit can also suggest a vacation in a location that is easily accessible from the user's workplace. For example, when the user is at work, the vacation suggestion unit can also suggest a vacation in a location that is easily accessible from the user's workplace. Furthermore, when the user is traveling, the vacation suggestion unit can also suggest a vacation that can be enjoyed at the user's travel destination. For example, when the user is traveling, the vacation suggestion unit can also suggest a vacation that can be enjoyed at the user's travel destination. This allows for more appropriate vacation suggestions to be made by providing optimal vacation suggestions based on the geographical location information.

[0110] The meal suggestion unit can estimate the user's emotions and adjust the method of suggesting meals based on the estimated user's emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest a meal that will help the user relax. For example, if the user is feeling stressed, the meal suggestion unit can suggest a meal that will help the user relax. Furthermore, the meal suggestion unit can also suggest a nutritionally balanced meal if the user is relaxed. For example, if the user is relaxed, the meal suggestion unit can also suggest a nutritionally balanced meal. Furthermore, if the user is in a hurry, the meal suggestion unit can also suggest a meal that is easy to prepare. For example, if the user is in a hurry, the meal suggestion unit can also suggest a meal that is easy to prepare. This provides a method of suggesting meals according to the user's emotions, making it possible to make more appropriate meal suggestions.

[0111] When suggesting a meal, the meal suggestion unit can provide optimal suggestions by referring to the user's past meal history. The meal suggestion unit, for example, suggests optimal meals based on meals that the user has liked to eat in the past. For example, the meal suggestion unit suggests optimal meals based on meals that the user has liked to eat in the past. The meal suggestion unit can also suggest meals that supplement specific nutrients based on the user's past meal history. For example, the meal suggestion unit can also suggest meals that supplement specific nutrients based on the user's past meal history. Furthermore, the meal suggestion unit can analyze the user's past meal history and suggest the healthiest meal. For example, the meal suggestion unit can analyze the user's past meal history and suggest the healthiest meal. In this way, optimal meal suggestions can be made to the user by referring to the past meal history.

[0112] The meal suggestion unit can estimate the user's emotions and determine the priority of meal suggestions based on the estimated user's emotions. For example, when the user is feeling stressed, the meal suggestion unit prioritizes suggesting meals that are relaxing. For example, when the user is feeling stressed, the meal suggestion unit prioritizes suggesting meals that are relaxing. Furthermore, when the user is relaxed, the meal suggestion unit can also prioritize suggesting nutritionally balanced meals. For example, when the user is relaxed, the meal suggestion unit can also prioritize suggesting nutritionally balanced meals. Furthermore, when the user is in a hurry, the meal suggestion unit can also prioritize suggesting meals that are easy to prepare. For example, when the user is in a hurry, the meal suggestion unit can also prioritize suggesting meals that are easy to prepare. This allows for more appropriate meal suggestions by providing a priority order of meal suggestions according to the user's emotions.

[0113] When suggesting a meal, the meal suggestion unit can provide optimal suggestions by taking into consideration the user's geographical location information. For example, when the user is at home, the meal suggestion unit suggests meals that can be easily prepared at home. For example, when the user is at home, the meal suggestion unit suggests meals that can be easily prepared at home. Furthermore, when the user is at work, the meal suggestion unit can also suggest meals that can be easily eaten at work. For example, when the user is at work, the meal suggestion unit can also suggest meals that can be easily eaten at work. Furthermore, when the user is traveling, the meal suggestion unit can also suggest meals that can be enjoyed at the travel destination. For example, when the user is traveling, the meal suggestion unit can also suggest meals that can be enjoyed at the travel destination. This enables more appropriate meal suggestions to be made by providing optimal meal suggestions based on the geographical location information.

[0114] The hospital visit suggestion unit can estimate the user's emotions and adjust the hospital visit suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the hospital visit suggestion unit suggests a hospital visit method that will allow the user to relax. For example, if the user is feeling stressed, the hospital visit suggestion unit suggests a hospital visit method that will allow the user to relax. Furthermore, the hospital visit suggestion unit can also suggest a detailed hospital visit method when the user is relaxed. For example, if the user is relaxed, the hospital visit suggestion unit can also suggest a detailed hospital visit method. Furthermore, if the user is in a hurry, the hospital visit suggestion unit can also suggest a method that will allow the user to visit the hospital quickly. For example, if the user is in a hurry, the hospital visit suggestion unit can also suggest a method that will allow the user to visit the hospital quickly. This makes it possible to provide a hospital visit suggestion method that corresponds to the user's emotions, thereby making it possible to make more appropriate hospital visit suggestions.

[0115] When suggesting a hospital visit, the hospital visit suggestion unit can provide an optimal suggestion by referring to the user's past hospital visit history. The hospital visit suggestion unit, for example, suggests an optimal hospital visit destination based on hospitals the user has visited in the past. For example, the hospital visit suggestion unit suggests an optimal hospital visit destination based on hospitals the user has visited in the past. The hospital visit suggestion unit can also suggest a hospital visit destination suitable for a specific symptom based on the user's past hospital visit history. For example, the hospital visit suggestion unit can also suggest a hospital visit destination suitable for a specific symptom based on the user's past hospital visit history. Furthermore, the hospital visit suggestion unit can analyze the user's past hospital visit history and suggest the most effective hospital visit destination. For example, the hospital visit suggestion unit can analyze the user's past hospital visit history and suggest the most effective hospital visit destination. In this way, by referring to the past hospital visit history, it is possible to make an optimal hospital visit suggestion for the user.

[0116] The hospital visit suggestion unit can estimate the user's emotions and determine the priority of hospital visit suggestions based on the estimated user's emotions. For example, when the user is feeling stressed, the hospital visit suggestion unit prioritizes suggesting hospital destinations where the user can relax. For example, when the user is feeling stressed, the hospital visit suggestion unit prioritizes suggesting hospital destinations where the user can relax. Furthermore, when the user is relaxed, the hospital visit suggestion unit can also prioritize suggesting detailed hospital visit destinations. For example, when the user is relaxed, the hospital visit suggestion unit can also prioritize suggesting detailed hospital visit destinations. Furthermore, when the user is in a hurry, the hospital visit suggestion unit can also prioritize suggesting hospital destinations where the user can quickly visit. For example, when the user is in a hurry, the hospital visit suggestion unit can also prioritize suggesting hospital destinations where the user can quickly visit. This allows for more appropriate hospital visit suggestions to be made by providing a priority order of hospital visit suggestions according to the user's emotions.

[0117] When suggesting a hospital visit, the hospital visit suggestion unit can provide an optimal suggestion taking into consideration the user's geographical location information. For example, when the user is at home, the hospital visit suggestion unit suggests a hospital close to the user's home. For example, when the user is at home, the hospital visit suggestion unit suggests a hospital close to the user's home. Furthermore, when the user is at work, the hospital visit suggestion unit can also suggest a hospital that is easily accessible from the user's workplace. For example, when the user is at work, the hospital visit suggestion unit can also suggest a hospital that is easily accessible from the user's workplace. Furthermore, when the user is traveling, the hospital visit suggestion unit can also suggest a hospital that is available at the user's travel destination. For example, when the user is traveling, the hospital visit suggestion unit can also suggest a hospital that is available at the user's travel destination. This allows for more appropriate hospital visit suggestions to be made by providing an optimal hospital visit suggestion based on the geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, counting unit, data collection unit, analysis unit, and advice unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the smart device 14 and provides an interface through which the user manually inputs information. The counting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically counts the browsing time on a PC or smartphone. The data collection unit records body temperature and sleep time using, for example, the camera 42 of the smart device 14 or a smartwatch. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and quantifies daily fatigue levels based on collected data. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the user's physical condition each morning based on the quantified fatigue levels and provides advice. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, counting unit, data collection unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214 and provides an interface through which the user manually inputs information. The counting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically counts the browsing time on a PC or smartphone. The data collection unit records body temperature and sleep time using, for example, the camera 42 of the smart glasses 214 or a smartwatch. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quantifies daily fatigue levels based on collected data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the user's physical condition each morning based on the quantified fatigue levels and provides advice. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, counting unit, data collection unit, analysis unit, and advice unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface through which the user manually inputs information. The counting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically counts the browsing time on a PC or smartphone. The data collection unit records body temperature and sleep time using, for example, the camera 42 of the headset terminal 314 or a smartwatch. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and quantifies daily fatigue levels based on collected data. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the user's physical condition each morning based on the quantified fatigue levels and provides advice. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, counting unit, data collection unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414 and provides an interface through which the user manually inputs information. The counting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically counts the browsing time on a PC or smartphone. The data collection unit records body temperature and sleep time using, for example, the camera 42 of the robot 414 or a smartwatch. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quantifies daily fatigue levels based on collected data. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the user's physical condition each morning based on the quantified fatigue levels and provides advice.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The health management system may further include an exercise data collection unit that collects the user's exercise data. The exercise data collection unit may use, for example, a smartwatch or fitness tracker to collect exercise data such as the user's steps, heart rate, and calories burned. The exercise data collection unit may also analyze the user's exercise patterns and propose an optimal exercise plan. For example, the exercise data collection unit may propose an effective exercise plan based on the user's past exercise history. Furthermore, the exercise data collection unit may estimate the user's emotions and adjust the timing of exercise based on the estimated emotions. For example, if the user is feeling stressed, it may suggest an exercise that will help them relax. In this way, health management can be supported by collecting the user's exercise data and proposing an appropriate exercise plan.

[0120] The advice unit may further include a hobby suggestion unit that suggests a refreshment method based on the user's hobbies and interests. The hobby suggestion unit suggests a refreshment method based on, for example, hobbies and interests that the user has enjoyed in the past. The hobby suggestion unit may also estimate the user's emotions and adjust the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the hobby suggestion unit may suggest a hobby that will help them relax. Furthermore, the hobby suggestion unit may refer to the user's past hobby history to provide an optimal refreshment method. This makes it possible to support effective stress relief by suggesting a refreshment method based on the user's hobbies and interests.

[0121] The advice unit may further include a sleep environment suggestion unit that makes suggestions to improve the user's sleep environment. The sleep environment suggestion unit may, for example, suggest optimal bedding, room temperature, lighting, etc. based on the user's sleep data. The sleep environment suggestion unit may also estimate the user's emotions and adjust the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the sleep environment suggestion unit may suggest relaxing bedding and lighting. Furthermore, the sleep environment suggestion unit may refer to the user's past sleep data to provide an optimal sleep environment. This may improve the user's sleep environment and support high-quality sleep.

[0122] The advice unit may further include a mental health suggestion unit that makes suggestions to support the user's mental health. The mental health suggestion unit may make suggestions such as relaxation, meditation, or counseling based on the user's emotional data, for example. The mental health suggestion unit may also estimate the user's emotions and adjust the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the mental health suggestion unit may suggest relaxation or meditation. Furthermore, the mental health suggestion unit may refer to the user's past mental health data to provide the optimal support method. This may support the user's mental health, thereby enhancing overall health management.

[0123] The data collection unit may further include a meal data collection unit that collects the user's meal data. The meal data collection unit, for example, records the contents and calories of meals consumed by the user. The meal data collection unit may also estimate the user's emotions and adjust meal timing based on the estimated emotions. For example, if the user is feeling stressed, the meal data collection unit may suggest meals that will help the user relax. Furthermore, the meal data collection unit may also refer to the user's past meal data to provide an optimal meal plan. In this way, health management can be supported by collecting the user's meal data and suggesting an appropriate meal plan.

[0124] The health management system may further include a fluid management unit that manages the user's fluid intake. The fluid management unit, for example, records the amount of fluid consumed by the user and encourages appropriate fluid intake. The fluid management unit may also suggest an optimal fluid intake amount based on the user's activity level and environmental conditions. For example, it may recommend more fluid intake after exercise or in a hot environment. Furthermore, the fluid management unit may refer to the user's past fluid intake data to provide an optimal fluid intake plan. This allows the user to manage their fluid intake and maintain an appropriate fluid balance.

[0125] The health management system may further include a posture monitoring unit that monitors the user's posture. The posture monitoring unit may monitor the user's posture in real time using, for example, a smart device, and encourage posture improvement. The posture monitoring unit may also suggest optimal posture improvement exercises based on the user's activity data. For example, it may recommend stretching or light exercise after a long period of desk work. Furthermore, the posture monitoring unit may refer to the user's past posture data and provide an optimal posture improvement plan. This may improve the user's posture and support a healthy lifestyle.

[0126] The health management system may further include a respiratory data collection unit that collects the user's respiratory data. The respiratory data collection unit may, for example, use a smart device to record the user's breathing patterns and evaluate the quality of breathing. The respiratory data collection unit may also suggest optimal breathing exercises based on the user's activity level and emotional state. For example, if the user is feeling stressed, the respiratory data collection unit may recommend deep breathing or meditation. Furthermore, the respiratory data collection unit may refer to the user's past respiratory data to provide an optimal breathing improvement plan. This may improve the user's breathing and support their overall health.

[0127] The health management system may further include an environmental data collection unit that collects environmental data about the user. The environmental data collection unit records environmental data, such as temperature, humidity, and air quality, and evaluates factors that affect the user's health. The environmental data collection unit can also make optimal suggestions for improving the environment based on the user's activity data and emotional state. For example, if the room temperature is high, it can suggest cooling methods. Furthermore, the environmental data collection unit can refer to the user's past environmental data to provide an optimal environmental management plan. This can optimize the user's environment and support a healthy lifestyle.

[0128] The health management system may further include a goal setting unit that sets health goals for the user and evaluates the degree of achievement. The goal setting unit, for example, records health goals set by the user (weight loss, increased exercise, etc.) and tracks progress. The goal setting unit may also evaluate the degree of achievement and provide feedback based on the user's activity data and health data. For example, if the user is close to achieving a goal, it may send an encouraging message. Furthermore, the goal setting unit may refer to the user's past goal achievement data to provide an optimal goal setting plan. This may provide support for the user to achieve their health goals.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The input unit inputs basic information about a person. Basic information about a person includes name, age, gender, address, etc. The input unit provides an interface for users to manually input information, and can also provide an auto-completion function based on past input history. Step 2: The counting unit automatically counts browsing time on your PC or smartphone. Browsing time includes active browser time and app usage time. The counting unit measures and collects data on active browser time and app usage time. It can also analyze device usage patterns and select the optimal counting method. Step 3: The data collection unit inputs or counts daily body temperature and sleep time. Body temperature information includes the time of measurement, the measurement site, and the type of thermometer used. The data collection unit records body temperature and sleep time using a thermometer or smartwatch to collect data. It can also estimate the user's emotions and adjust the timing of data collection. Step 4: The analysis unit quantifies the daily fatigue level based on the data collected by the input unit, counting unit, and data collection unit. The analysis unit quantifies the fatigue level based on the amount of time spent using digital devices and the amount of sleep, and can also optimize the analysis algorithm by referring to past data. Different analysis methods can also be applied depending on the type of data. Step 5: The advice unit evaluates the employee's physical condition each morning based on the fatigue level data quantified by the analysis unit and provides advice. If the fatigue level is high, the advice unit can suggest taking time off work, or suggest dietary therapy or medical treatment tailored to the employee's fatigue level.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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. an input section for inputting basic personal information; A counting section that automatically counts the browsing time on a PC or smartphone, a data collection unit for inputting or counting daily body temperature and sleep time; an analysis unit that quantifies a daily fatigue level based on the data collected by the input unit, the counting unit, and the data collection unit; and an advice unit that evaluates the physical condition of the user every morning based on the fatigue level data quantified by the analysis unit and gives advice. A system characterized by:

2. The advice unit Equipped with a leave suggestion unit that suggests time off work when fatigue levels are high The system of claim 1 .

3. The advice unit Equipped with a meal suggestion department that suggests dietary therapy according to the level of fatigue The system of claim 1 .

4. The advice unit Equipped with a hospital visit suggestion department that recommends hospital visits as needed The system of claim 1 .

5. The data collection unit Use a thermometer or smartwatch to record your temperature and sleep duration The system of claim 1 .

6. The analysis unit Quantifying fatigue levels based on time spent using digital devices and sleep duration The system of claim 1 .

7. The input unit Estimate the user's emotions and adjust the input timing based on the estimated user emotions. The system of claim 1 .

8. The input unit Analyze the user's past input history and select the appropriate input method The system of claim 1 .

9. The input unit As you type, filter your input based on your current life situation and interests The system of claim 1 .

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