system

Abstract

The system according to the embodiment aims to grasp the situation of family members living apart in real time. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires data. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a message based on the data analyzed by the analysis unit. The provision unit provides the message generated by the generation 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] With conventional technology, it was difficult to grasp the real-time status of family members living far away.

[0005] The system according to the embodiment aims to grasp the situation of family members living apart in real time. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires data. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a message based on the data analyzed by the analysis unit. The provision unit provides the message generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the situation of family members living apart in real time. [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 smart band service system for parents and children according to an embodiment of the present invention is provided as a subscription model for approximately 500 yen per month. This system provides one smart band each to the parent and child, collecting data such as activity level, sleep, and heart rate, and periodically collecting questionnaire response data. This data is analyzed by a dedicated generation AI, and a message interpreting the parent's (or child's) status is provided via a dedicated application. This service allows users to learn about and be aware of the status of their parents (or children) who live far away. For example, the smart band collects data such as activity level, sleep, and heart rate in real time. Examples include the number of steps, exercise time, sleep quality and duration, and heart rate. This data is sent from the smart band to a dedicated application. Next, questionnaires are periodically sent via the application, and the user answers questions about their physical condition, mood, lifestyle habits, etc. This questionnaire data is also collected in the application. A dedicated generation AI analyzes the collected activity level data, sleep data, heart rate data, and questionnaire response data. Based on this data, the generation AI interprets the parent's (or child's) status and provides it as a message within the dedicated application. For example, if a parent's activity level decreases or a child's sleep quality deteriorates, a warning message is sent. This service allows for real-time monitoring of the health and lifestyle of parents (or children) living apart, enabling prompt provision of necessary support. For example, if a parent's heart rate is abnormally high or a child's sleep time is extremely short, early detection allows for appropriate response. The AI ​​also performs trend analysis based on past data to support long-term health management. For example, if a parent's activity level gradually decreases or a child's sleep quality declines over a certain period of time, identifying trends allows preventative measures to be taken. Thus, the present invention is a service that utilizes a smart band and AI to monitor the health and lifestyle of parents and children in real time and provide appropriate support, thereby deepening the bond between parents and children living apart.This allows the parent-child smart band service system to grasp the health and living conditions of parents and children in real time and provide appropriate support.

[0029] A smart band service system for parents and children according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires data from smart bands provided to each parent and child. The acquired data includes, for example, activity level, sleep, and heart rate. For example, the acquisition unit acquires the number of steps and exercise time from the smart band. The acquisition unit can also acquire sleep quality and duration from the smart band. The acquisition unit can also acquire heart rate from the smart band. For example, the acquisition unit acquires data in real time from the smart band and transmits it to a dedicated application. The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes activity level data to understand the parent's (or child's) exercise status. The analysis unit can also analyze sleep data to evaluate the parent's (or child's) sleep quality. The analysis unit can also analyze heart rate data and monitor fluctuations in the parent's (or child's) heart rate. For example, the analysis unit performs analysis based on the acquired data using statistical analysis or machine learning algorithms. The generation unit generates a message based on the data analyzed by the analysis unit. For example, the generation unit generates a warning message when the parent's activity level is decreasing. The generation unit can also generate a message including suggestions for improvement when the child's sleep quality is declining. Furthermore, the generation unit can generate a message recommending a medical examination when the child's heart rate is abnormally high. For example, the generation unit generates an appropriate message based on the acquired data using a generation AI. The provision unit provides the message generated by the generation unit. The provision unit displays the message through, for example, a dedicated application. The provision unit can also send the message via email or push notification. Furthermore, the provision unit can provide the message as a voice message. For example, the provision unit provides a means for quickly delivering the generated message to the user. As a result, the parent-child smart band service system according to the embodiment can grasp the health and living conditions of parents and children in real time and provide appropriate support.

[0030] The parent-child smart band service system includes a collection unit that collects questionnaire data. The collection unit periodically sends questionnaires and collects responses regarding the user's physical condition, mood, lifestyle, etc. For example, the collection unit may send the questionnaire through an application, allowing the user to input responses. The collection unit may also aggregate the questionnaire response data in a dedicated application. For example, the collection unit may send a questionnaire including questions regarding the user's physical condition. The collection unit may also send a questionnaire including questions regarding the user's mood and stress level. For example, the collection unit may send a questionnaire including questions regarding the user's lifestyle. This allows the collection unit to collect detailed information regarding the user's physical condition, mood, and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the questionnaire response data into a generation AI and have the generation AI analyze the response data. This allows more detailed information to be obtained by collecting the questionnaire data.

[0031] The parent-child smart band service system includes an analysis unit that performs trend analysis. The analysis unit performs trend analysis based on the acquired data to understand long-term changes in the parent's (or child's) health condition and living situation. For example, the analysis unit analyzes the trend of the parent's activity level based on past activity data. The analysis unit can also analyze the trend of the child's sleep quality based on past sleep data. The analysis unit can also analyze the trend of the parent's heart rate based on past heart rate data. For example, the analysis unit understands trends using time series analysis or regression analysis. This allows the analysis unit to understand long-term changes in the parent's (or child's) health condition and living situation and take preventive measures. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the acquired data into a generation AI and have the generation AI perform trend analysis. This enables long-term health management through trend analysis.

[0032] The parent-child smart band service system includes a detection unit that detects abnormalities. The detection unit detects abnormalities based on acquired data and issues a warning if there is an abnormality in the parent's (or child's) health condition or living situation. For example, the detection unit detects an abnormality if the parent's heart rate is abnormally high. The detection unit can also detect an abnormality if the child's sleep time is extremely short. The detection unit can also detect an abnormality if the parent's activity level drops sharply. For example, the detection unit sets a threshold and detects data exceeding the threshold as an abnormality. This allows the detection unit to respond quickly if there is an abnormality in the parent's (or child's) health condition or living situation. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input acquired data to a generation AI and have the generation AI perform anomaly detection. This allows for early response by detecting an abnormality.

[0033] The acquisition unit can acquire activity level, sleep, and heart rate data from the smart band. The acquisition unit, for example, acquires step counts and exercise time from the smart band. For example, the acquisition unit acquires step count data from the smart band to grasp the user's activity level. The acquisition unit can also acquire exercise time data from the smart band to grasp the user's exercise status. The acquisition unit can also acquire calorie consumption data from the smart band to grasp the user's energy consumption. The acquisition unit, for example, acquires sleep quality and sleep time from the smart band. For example, the acquisition unit acquires sleep time data from the smart band to grasp the user's sleep status. The acquisition unit can also acquire sleep quality data from the smart band to evaluate the user's sleep quality. The acquisition unit can also acquire sleep stage data from the smart band to grasp the user's sleep pattern. The acquisition unit, for example, acquires heart rate data from the smart band. For example, the acquisition unit acquires heart rate data from the smart band to monitor the user's heart rate fluctuations. The acquisition unit can also acquire heart rate variability data from the smart band to evaluate the user's stress level. Furthermore, the acquisition unit can acquire resting heart rate data from the smart band to understand the user's health condition. This makes it possible to understand the user's health condition in real time by acquiring data from the smart band. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the data acquired from the smart band into the generation AI and have the generation AI analyze the data.

[0034] The analysis unit can analyze the acquired data and interpret the status of the parent or child. For example, the analysis unit analyzes the acquired activity data to understand the parent's (or child's) exercise status. For example, the analysis unit analyzes changes in the parent's exercise level based on the activity data. The analysis unit can also analyze the child's exercise pattern and evaluate their exercise habits. Furthermore, the analysis unit can analyze the parent's energy expenditure based on the activity data and evaluate their health condition. For example, the analysis unit analyzes the acquired sleep data and evaluates the parent's (or child's) sleep quality. For example, the analysis unit analyzes the parent's sleep pattern based on sleep duration data. The analysis unit can also evaluate the child's sleep quality and identify areas for improvement. Furthermore, the analysis unit can analyze the depth of the parent's sleep based on sleep stage data and evaluate their health condition. For example, the analysis unit analyzes the acquired heart rate data and monitors fluctuations in the parent's (or child's) heart rate. For example, the analysis unit analyzes the parent's stress level based on the heart rate data. The analysis unit can also analyze the child's heart rate variability and evaluate their health condition. Furthermore, the analysis unit can evaluate the parent's health condition based on the resting heart rate data. This allows the parent's (or child's) condition to be accurately understood through data analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the acquired data into a generation AI and have the generation AI analyze the data.

[0035] The acquisition unit can analyze the user's past data acquisition history and select an appropriate acquisition method. The acquisition unit can set the optimal acquisition timing based on, for example, time periods during which the user frequently acquired data in the past. For example, the acquisition unit can analyze the user's past data acquisition history and identify the most effective time periods for data acquisition. The acquisition unit can also preferentially suggest a data acquisition method (manual, automatic, etc.) that the user has previously preferred. For example, the acquisition unit can suggest manual acquisition to a user who prefers manual data acquisition based on the user's past data acquisition history. Furthermore, the acquisition unit can predict and suggest the optimal acquisition method for a specific situation based on the user's past data acquisition history. For example, if the user often acquires data while exercising, the acquisition unit can preferentially suggest data acquisition during exercise. In this way, the optimal data acquisition method can be selected by analyzing the past data acquisition history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past data acquisition history into a generation AI and cause the generation AI to select the optimal acquisition method.

[0036] When acquiring data, the acquisition unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the acquisition unit prioritizes acquiring heart rate and activity data. For example, the acquisition unit prioritizes acquiring heart rate data and activity data based on the user's health interests. Furthermore, if the user is interested in sleep, the acquisition unit can also acquire detailed sleep data. For example, the acquisition unit prioritizes acquiring data on sleep time and sleep quality based on the user's interest in sleep. Furthermore, if the user is interested in stress management, the acquisition unit can prioritize acquiring data related to stress levels. For example, the acquisition unit prioritizes acquiring heart rate variability data and stress level data based on the user's interest in stress management. This allows more relevant data to be acquired by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data related to the user's lifestyle and areas of interest to the generation AI and cause the generation AI to filter the data.

[0037] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, when the user is exercising, the acquisition unit prioritizes acquiring activity amount and heart rate data. For example, the acquisition unit prioritizes acquiring activity amount data and heart rate data during exercise based on the user's geographical location information. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring sleep data and heart rate data when relaxing. For example, the acquisition unit prioritizes acquiring sleep data at home and heart rate data when relaxing based on the user's geographical location information. Furthermore, when the user is at work, the acquisition unit can prioritize acquiring data related to stress level and concentration level. For example, the acquisition unit prioritizes acquiring stress level data and concentration level data at work based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into the generation AI and cause the generation AI to acquire highly relevant data.

[0038] The acquisition unit can analyze the user's social media activity when acquiring data and acquire related data. For example, if the user is feeling stressed because of social media, the acquisition unit prioritizes acquiring heart rate and stress level data. For example, the acquisition unit prioritizes acquiring heart rate data and stress level data when the user's stress level is high based on the user's social media activity. The acquisition unit can also prioritize acquiring activity level and sleep data when the user is relaxing on social media. For example, the acquisition unit prioritizes acquiring activity level data and sleep data in a relaxed state based on the user's social media activity. Furthermore, the acquisition unit can also prioritize acquiring exercise data when the user posts about exercise on social media. For example, the acquisition unit prioritizes acquiring exercise-related data based on the user's social media activity. This allows related data to be acquired by analyzing social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, analyzes data with high importance (such as heart rate and stress level) in detail. For example, the analysis unit analyzes heart rate data and stress level data in detail to evaluate the user's health condition. The analysis unit can also analyze data with low importance (such as activity level and sleep data) in a simplified manner. For example, the analysis unit analyzes activity level data and sleep data in a simplified manner to grasp the user's basic health condition. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis when a detailed analysis is necessary based on the importance of the data, and performs a simplified analysis when a simplified analysis is sufficient. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit analyzes heart rate variability based on the heart rate data and evaluates the user's stress level. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit analyzes sleep stages based on the sleep data and evaluates the user's sleep quality. The analysis unit can also apply an exercise intensity analysis algorithm to activity data. For example, the analysis unit analyzes exercise intensity based on the activity data and evaluates the user's exercise status. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was acquired. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit evaluates the user's current health condition based on the most recent data. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit identifies trends based on past data and performs analysis by comparing the past data with the current data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was acquired. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was acquired, and analyzes past data complementary to the past data. In this way, by determining the analysis priority based on the time when the data was acquired, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was acquired to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of heart rate data based on the relevance between heart rate data and activity data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of sleep data based on the relevance between sleep data and activity data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] When generating a message, the generation unit can adjust the level of detail of the message based on the importance of the data. For example, the generation unit writes a detailed message based on data of high importance. For example, the generation unit writes a detailed message based on heart rate data or stress level data to provide specific advice to the user. The generation unit can also write a concise message based on data of low importance. For example, the generation unit writes a concise message based on activity data or sleep data to provide basic information to the user. Furthermore, the generation unit can dynamically adjust the level of detail of the message depending on the importance of the data. For example, the generation unit generates a detailed message when a detailed message is necessary based on the importance of the data, and generates a concise message when a concise message is sufficient based on the importance of the data. In this way, by adjusting the level of detail of the message based on the importance of the data, it is possible to write a detailed message based on important data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the message.

[0044] When generating a message, the generation unit can apply different message generation algorithms depending on the data category. For example, the generation unit can include health management advice in a message based on heart rate data. For example, the generation unit can evaluate the user's health condition based on the heart rate data and provide appropriate health management advice. The generation unit can also include suggestions for improving sleep quality in a message based on sleep data. For example, the generation unit can evaluate the user's sleep quality based on the sleep data and provide specific suggestions for improvement. The generation unit can also include exercise recommendations and cautions in a message based on activity data. For example, the generation unit can evaluate the user's exercise status based on the activity data and provide appropriate exercise recommendations and cautions. This allows for the generation of more appropriate messages by applying different message generation algorithms depending on the data category. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate message generation algorithm.

[0045] When generating messages, the generation unit can determine the priority of messages based on the time when the data was acquired. For example, the generation unit prioritizes generating messages based on the most recent data. For example, the generation unit evaluates the user's current health condition based on the most recent data and generates an appropriate message. The generation unit can also generate messages based on current data while referring to past data. For example, the generation unit identifies trends based on past data and compares the past data to generate a message. Furthermore, the generation unit can dynamically adjust the priority of messages depending on the time when the data was acquired. For example, the generation unit prioritizes reflecting the most recent data in messages based on the time when the data was acquired and includes past data as a supplement to the messages. In this way, by determining the priority of messages based on the time when the data was acquired, messages based on the most recent data can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the data was acquired into the generation AI and have the generation AI determine the priority of messages.

[0046] When generating messages, the generation unit can adjust the order of messages based on the relevance of the data. For example, the generation unit prioritizes generating messages based on highly relevant data. For example, the generation unit prioritizes generating messages based on heart rate data based on the relevance between heart rate data and activity data. The generation unit can also postpone generating messages based on less relevant data. For example, the generation unit postpones generating messages based on sleep data based on the relevance between sleep data and activity data. Furthermore, the generation unit can dynamically adjust the order of messages according to the relevance of the data. For example, the generation unit prioritizes generating messages based on highly relevant data and postpones messages based on less relevant data based on the relevance of the data. In this way, by adjusting the order of messages based on the relevance of the data, messages based on highly relevant data can be preferentially generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of the messages.

[0047] When providing a message, the providing unit can select the optimal delivery method by referring to the user's past message reception history. For example, the providing unit prioritizes providing a message format that the user has previously preferred. For example, the providing unit identifies a preferred message format based on the user's past message reception history and provides the message in that format. The providing unit can also predict and suggest the optimal delivery timing based on the user's past message reception history. For example, the providing unit identifies the most effective delivery timing based on the user's past message reception history and provides the message at that timing. Furthermore, the providing unit can analyze the user's past message reception history and select the most effective delivery method. For example, the providing unit identifies the most effective delivery method based on the user's past message reception history and provides the message using that method. In this way, the optimal delivery method can be selected by referring to the user's past message reception history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past message reception history into a generation AI and cause the generation AI to select the optimal delivery method.

[0048] The providing unit can customize the means of providing a message based on the user's current living situation when providing the message. For example, when the user is exercising, the providing unit prioritizes providing a voice message. For example, the providing unit provides a voice message while exercising based on the user's current living situation. The providing unit can also provide a detailed text message when the user is relaxed. For example, when the providing unit detects that the user is relaxed, it provides a text message including detailed data and its interpretation. Furthermore, the providing unit can also provide a concise notification message when the user is in a hurry. For example, when the providing unit detects that the user is in a hurry, it provides a concise and to-the-point notification message. This allows the provision of a more appropriate message by customizing the means of providing based on the user's current living situation. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's living situation data into a generation AI and cause the generation AI to customize the means of providing.

[0049] When providing a message, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, when the user is at home, the providing unit provides a detailed text message. For example, the providing unit provides a detailed text message when the user is at home based on the user's geographical location information. The providing unit can also preferentially provide a voice message when the user is out. For example, the providing unit provides a voice message when the user is out based on the user's geographical location information. Furthermore, the providing unit can also provide a concise notification message when the user is at work. For example, the providing unit provides a concise notification message when the user is at work based on the user's geographical location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0050] When providing a message, the providing unit can analyze the user's social media activity and suggest a means of delivery. For example, if the user is active on social media, the providing unit provides the message via social media. For example, the providing unit provides the message via social media if the user is active based on the user's social media activity. The providing unit can also provide a private message if the user is feeling stressed on social media. For example, the providing unit provides a private message if the user is feeling stressed based on the user's social media activity. Furthermore, the providing unit can also provide a message including detailed information if the user is relaxing on social media. For example, the providing unit provides a message including detailed information if the user is relaxing based on the user's social media activity. This makes it possible to suggest the optimal means of delivery by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of delivery.

[0051] When collecting survey responses, the collection unit can analyze the user's past response history and select the optimal collection method. For example, the collection unit can prioritize providing a survey format that the user has previously preferred. For example, the collection unit can identify a preferred survey format based on the user's past response history and provide the survey in that format. The collection unit can also predict and suggest the optimal collection timing based on the user's past response history. For example, the collection unit can identify the most effective collection timing based on the user's past response history and provide the survey at that timing. Furthermore, the collection unit can analyze the user's past response history and select the most effective collection method. For example, the collection unit can identify the most effective collection method based on the user's past response history and provide the survey using that method. In this way, the optimal collection method can be selected by analyzing the user's past response history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the user's past response history into a generation AI and cause the generation AI to select the optimal collection method.

[0052] When collecting surveys, the collection unit can prioritize collecting highly relevant surveys by taking into account the user's geographical location information. For example, when the user is at home, the collection unit collects detailed surveys. For example, the collection unit collects detailed surveys when the user is at home based on the user's geographical location information. The collection unit can also prioritize collecting brief surveys when the user is out. For example, the collection unit collects brief surveys when the user is out based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting surveys related to the workplace when the user is at work. For example, the collection unit prioritizes collecting surveys related to the workplace based on the user's geographical location information when the user is at work. In this way, highly relevant surveys can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant surveys.

[0053] During trend analysis, the analysis unit can predict a current trend by referring to past data. The analysis unit, for example, predicts a current activity trend based on past activity data. For example, the analysis unit predicts changes in current activity based on past activity data and evaluates the user's exercise status. The analysis unit can also predict a current sleep trend based on past sleep data. For example, the analysis unit predicts changes in current sleep quality based on past sleep data and evaluates the user's sleep status. The analysis unit can also predict a current heart rate trend based on past heart rate data. For example, the analysis unit predicts current heart rate fluctuations based on past heart rate data and evaluates the user's health status. By referring to past data, the current trend can be predicted more accurately. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to predict a current trend.

[0054] During trend analysis, the analysis unit can analyze changes in the trend based on the time of data acquisition. The analysis unit, for example, analyzes changes in the current trend based on the latest data. For example, the analysis unit analyzes changes in the current activity level based on the latest data and evaluates the user's exercise status. The analysis unit can also analyze changes in the current trend while referring to past data. For example, the analysis unit analyzes changes in the current sleep quality based on past sleep data and evaluates the user's sleep status. Furthermore, the analysis unit can dynamically analyze changes in the trend depending on the time of data acquisition. For example, the analysis unit analyzes changes in the trend based on the latest heart rate data based on the time of data acquisition and evaluates the user's health status. In this way, the latest trend can be identified by analyzing changes in the trend based on the time of data acquisition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data acquisition to the generation AI and cause the generation AI to analyze changes in the trend.

[0055] The detection unit can improve the accuracy of anomaly detection by taking into account the correlation between data. The detection unit, for example, detects anomalies by combining heart rate and activity data. For example, the detection unit detects anomalies and evaluates the user's health status based on the correlation between heart rate data and activity data. The detection unit can also detect anomalies by combining sleep data and heart rate data. For example, the detection unit detects anomalies and evaluates the user's sleep status based on the correlation between sleep data and heart rate data. The detection unit can also detect anomalies by combining activity data and sleep data. For example, the detection unit detects anomalies and evaluates the user's exercise status and sleep status based on the correlation between activity data and sleep data. This allows the accuracy of anomaly detection to be improved by taking into account the correlation between data. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the correlation between data to a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0056] The detection unit can perform anomaly detection while taking into account the geographical distribution of data. For example, when a user is at home, the detection unit detects anomalies based on data around the user's home. For example, the detection unit detects anomalies using data around the user's home based on the user's geographical location information. Furthermore, when a user is out, the detection unit can also detect anomalies based on data from the user's location outside the home. For example, the detection unit detects anomalies using data from the user's location outside the home based on the user's geographical location information. Furthermore, when a user is at work, the detection unit can also detect anomalies based on data from the user's location outside the home. For example, the detection unit detects anomalies using data from the user's location outside the home based on the user's geographical location information. In this way, by taking the geographical distribution of data into account, more accurate anomaly detection is possible. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical location information to a generation AI and cause the generation AI to perform anomaly detection.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The parent-child smart band service system may further include a meal acquisition unit that acquires the user's meal data. The meal acquisition unit may acquire the meal contents and calorie intake, for example, through an application that allows the user to record meals. For example, the meal acquisition unit may allow the user to take a photo of the meal and analyze the photo to identify the meal contents. The meal acquisition unit may also allow the user to manually input the meal contents. Furthermore, the meal acquisition unit may also automatically acquire the meal contents by scanning the menu when the user eats out. In this way, by acquiring the meal data, the user's nutritional status can be understood and health management can be supported.

[0059] The parent-child smart band service system may further include an exercise acquisition unit that acquires the user's exercise data. The exercise acquisition unit acquires, for example, the type of exercise performed by the user, the time, and calories burned. For example, if the user runs, the exercise acquisition unit acquires the distance, time, and calories burned. In addition, if the user trains at the gym, the exercise acquisition unit can also acquire the details, time, and calories burned. Furthermore, if the user performs exercises such as yoga or Pilates, the exercise acquisition unit can also acquire the details, time, and calories burned. In this way, by acquiring exercise data, the user's exercise status can be understood and health management can be supported.

[0060] The parent-child smart band service system may further include an environment acquisition unit that acquires the user's sleep environment data. The environment acquisition unit acquires, for example, the temperature, humidity, and lighting brightness of the user's bedroom. For example, the environment acquisition unit acquires temperature data of the user's bedroom and evaluates whether the temperature is appropriate. The environment acquisition unit may also acquire humidity data of the user's bedroom and evaluate whether the humidity is appropriate. Furthermore, the environment acquisition unit may also acquire lighting data of the user's bedroom and evaluate whether the brightness is appropriate. In this way, by acquiring the sleep environment data, advice for improving the user's sleep quality can be provided.

[0061] The parent-child smart band service system may further include an activity acquisition unit that acquires user activity data. The activity acquisition unit acquires, for example, the user's daily activities and travel distance. For example, the activity acquisition unit acquires the user's walking distance and the number of times the user has climbed and descended stairs. The activity acquisition unit may also acquire the user's bicycle travel distance and time. The activity acquisition unit may also acquire the user's car travel distance and time. By acquiring activity data, the user's daily activity status can be grasped and health management can be supported.

[0062] The parent-child smart band service system may further include a hobby acquisition unit that acquires data related to the user's hobbies and interests. The hobby acquisition unit acquires, for example, activities related to the user's hobbies and interests. For example, the hobby acquisition unit acquires the amount of time the user spent reading and the titles of books read. The hobby acquisition unit may also acquire the amount of time the user spent watching movies and the titles of the movies watched. Furthermore, the hobby acquisition unit may also acquire the amount of time the user spent playing sports and the types of sports played. In this way, by acquiring data related to hobbies and interests, the user's lifestyle can be understood and more personalized support can be provided.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The acquisition unit acquires data from the smart bands provided to the parent and child. The acquired data includes activity level, sleep, heart rate, etc. For example, the acquisition unit acquires the number of steps, exercise time, sleep quality and duration, and heart rate from the smart band in real time and sends them to a dedicated application. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the activity data to understand the parent's (or child's) exercise status. The analysis unit also analyzes the sleep data to evaluate the parent's (or child's) sleep quality, and analyzes the heart rate data to monitor heart rate fluctuations. For example, the analysis unit performs analysis based on the acquired data using statistical analysis or machine learning algorithms. Step 3: The generation unit generates messages based on the data analyzed by the analysis unit. The generation unit generates a warning message if the parent's activity level is decreasing, and generates a message containing suggestions for improvement if the child's sleep quality is declining. Also, if the heart rate is abnormally high, the generation unit generates a message recommending a medical examination. For example, the generation unit uses a generation AI to generate appropriate messages based on the acquired data. Step 4: The providing unit provides the message generated by the generating unit. The providing unit displays the message through a dedicated application, sends the message via email or push notification, or can provide the message as a voice message. For example, the providing unit provides a means for quickly delivering the generated message to the user.

[0065] (Example 2) A smart band service system for parents and children according to an embodiment of the present invention is provided as a subscription model for approximately 500 yen per month. This system provides one smart band each to the parent and child, collecting data such as activity level, sleep, and heart rate, and periodically collecting questionnaire response data. This data is analyzed by a dedicated generation AI, and a message interpreting the parent's (or child's) status is provided via a dedicated application. This service allows users to learn about and be aware of the status of their parents (or children) who live far away. For example, the smart band collects data such as activity level, sleep, and heart rate in real time. Examples include the number of steps, exercise time, sleep quality and duration, and heart rate. This data is sent from the smart band to a dedicated application. Next, questionnaires are periodically sent via the application, and the user answers questions about their physical condition, mood, lifestyle habits, etc. This questionnaire data is also collected in the application. A dedicated generation AI analyzes the collected activity level data, sleep data, heart rate data, and questionnaire response data. Based on this data, the generation AI interprets the parent's (or child's) status and provides it as a message within the dedicated application. For example, if a parent's activity level decreases or a child's sleep quality deteriorates, a warning message is sent. This service allows for real-time monitoring of the health and lifestyle of parents (or children) living apart, enabling prompt provision of necessary support. For example, if a parent's heart rate is abnormally high or a child's sleep time is extremely short, early detection allows for appropriate response. The AI ​​also performs trend analysis based on past data to support long-term health management. For example, if a parent's activity level gradually decreases or a child's sleep quality declines over a certain period of time, identifying trends allows preventative measures to be taken. Thus, the present invention is a service that utilizes a smart band and AI to monitor the health and lifestyle of parents and children in real time and provide appropriate support, thereby deepening the bond between parents and children living apart.This allows the parent-child smart band service system to grasp the health and living conditions of parents and children in real time and provide appropriate support.

[0066] A smart band service system for parents and children according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires data from smart bands provided to each parent and child. The acquired data includes, for example, activity level, sleep, and heart rate. For example, the acquisition unit acquires the number of steps and exercise time from the smart band. The acquisition unit can also acquire sleep quality and duration from the smart band. The acquisition unit can also acquire heart rate from the smart band. For example, the acquisition unit acquires data in real time from the smart band and transmits it to a dedicated application. The analysis unit analyzes the data acquired by the acquisition unit. For example, the analysis unit analyzes activity level data to understand the parent's (or child's) exercise status. The analysis unit can also analyze sleep data to evaluate the parent's (or child's) sleep quality. The analysis unit can also analyze heart rate data and monitor fluctuations in the parent's (or child's) heart rate. For example, the analysis unit performs analysis based on the acquired data using statistical analysis or machine learning algorithms. The generation unit generates a message based on the data analyzed by the analysis unit. For example, the generation unit generates a warning message when the parent's activity level is decreasing. The generation unit can also generate a message including suggestions for improvement when the child's sleep quality is declining. Furthermore, the generation unit can generate a message recommending a medical examination when the child's heart rate is abnormally high. For example, the generation unit generates an appropriate message based on the acquired data using a generation AI. The provision unit provides the message generated by the generation unit. The provision unit displays the message through, for example, a dedicated application. The provision unit can also send the message via email or push notification. Furthermore, the provision unit can provide the message as a voice message. For example, the provision unit provides a means for quickly delivering the generated message to the user. As a result, the parent-child smart band service system according to the embodiment can grasp the health and living conditions of parents and children in real time and provide appropriate support.

[0067] The parent-child smart band service system includes a collection unit that collects questionnaire data. The collection unit periodically sends questionnaires and collects responses regarding the user's physical condition, mood, lifestyle, etc. For example, the collection unit may send the questionnaire through an application, allowing the user to input responses. The collection unit may also aggregate the questionnaire response data in a dedicated application. For example, the collection unit may send a questionnaire including questions regarding the user's physical condition. The collection unit may also send a questionnaire including questions regarding the user's mood and stress level. For example, the collection unit may send a questionnaire including questions regarding the user's lifestyle. This allows the collection unit to collect detailed information regarding the user's physical condition, mood, and lifestyle. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the questionnaire response data into a generation AI and have the generation AI analyze the response data. This allows more detailed information to be obtained by collecting the questionnaire data.

[0068] The parent-child smart band service system includes an analysis unit that performs trend analysis. The analysis unit performs trend analysis based on the acquired data to understand long-term changes in the parent's (or child's) health condition and living situation. For example, the analysis unit analyzes the trend of the parent's activity level based on past activity data. The analysis unit can also analyze the trend of the child's sleep quality based on past sleep data. The analysis unit can also analyze the trend of the parent's heart rate based on past heart rate data. For example, the analysis unit understands trends using time series analysis or regression analysis. This allows the analysis unit to understand long-term changes in the parent's (or child's) health condition and living situation and take preventive measures. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the acquired data into a generation AI and have the generation AI perform trend analysis. This enables long-term health management through trend analysis.

[0069] The parent-child smart band service system includes a detection unit that detects abnormalities. The detection unit detects abnormalities based on acquired data and issues a warning if there is an abnormality in the parent's (or child's) health condition or living situation. For example, the detection unit detects an abnormality if the parent's heart rate is abnormally high. The detection unit can also detect an abnormality if the child's sleep time is extremely short. The detection unit can also detect an abnormality if the parent's activity level drops sharply. For example, the detection unit sets a threshold and detects data exceeding the threshold as an abnormality. This allows the detection unit to take early action if there is an abnormality in the parent's (or child's) health condition or living situation. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input acquired data to a generation AI and have the generation AI perform anomaly detection. This allows for early action by detecting an abnormality.

[0070] The acquisition unit can acquire activity level, sleep, and heart rate data from the smart band. The acquisition unit, for example, acquires step counts and exercise time from the smart band. For example, the acquisition unit acquires step count data from the smart band to grasp the user's activity level. The acquisition unit can also acquire exercise time data from the smart band to grasp the user's exercise status. The acquisition unit can also acquire calorie consumption data from the smart band to grasp the user's energy consumption. The acquisition unit, for example, acquires sleep quality and sleep time from the smart band. For example, the acquisition unit acquires sleep time data from the smart band to grasp the user's sleep status. The acquisition unit can also acquire sleep quality data from the smart band to evaluate the user's sleep quality. The acquisition unit can also acquire sleep stage data from the smart band to grasp the user's sleep pattern. The acquisition unit, for example, acquires heart rate data from the smart band. For example, the acquisition unit acquires heart rate data from the smart band to monitor the user's heart rate fluctuations. The acquisition unit can also acquire heart rate variability data from the smart band to evaluate the user's stress level. Furthermore, the acquisition unit can acquire resting heart rate data from the smart band to understand the user's health condition. This makes it possible to understand the user's health condition in real time by acquiring data from the smart band. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the data acquired from the smart band into the generation AI and have the generation AI analyze the data.

[0071] The analysis unit can analyze the acquired data and interpret the status of the parent or child. For example, the analysis unit analyzes the acquired activity data to understand the parent's (or child's) exercise status. For example, the analysis unit analyzes changes in the parent's exercise level based on the activity data. The analysis unit can also analyze the child's exercise pattern and evaluate their exercise habits. Furthermore, the analysis unit can analyze the parent's energy expenditure based on the activity data and evaluate their health condition. For example, the analysis unit analyzes the acquired sleep data and evaluates the parent's (or child's) sleep quality. For example, the analysis unit analyzes the parent's sleep pattern based on sleep duration data. The analysis unit can also evaluate the child's sleep quality and identify areas for improvement. Furthermore, the analysis unit can analyze the depth of the parent's sleep based on sleep stage data and evaluate their health condition. For example, the analysis unit analyzes the acquired heart rate data and monitors fluctuations in the parent's (or child's) heart rate. For example, the analysis unit analyzes the parent's stress level based on the heart rate data. The analysis unit can also analyze the child's heart rate variability and evaluate their health condition. Furthermore, the analysis unit can evaluate the parent's health condition based on the resting heart rate data. This allows the parent's (or child's) condition to be accurately understood through data analysis. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the acquired data into a generation AI and have the generation AI analyze the data.

[0072] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit increases the frequency of data acquisition and collects detailed data. For example, when the user's stress level is high, the acquisition unit frequently acquires heart rate and stress level data. Furthermore, when the user is relaxed, the acquisition unit can reduce the frequency of data acquisition and collect the minimum amount of data necessary. For example, when the acquisition unit detects that the user is relaxed, it reduces the frequency of activity level and sleep data acquisition. Furthermore, when the user is in a hurry, the acquisition unit can temporarily stop data acquisition and acquire the data collectively later. For example, when the acquisition unit detects that the user is in a hurry, it temporarily stops data acquisition and acquires the data collectively later. This allows for more appropriate data collection by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0073] The acquisition unit can analyze the user's past data acquisition history and select an appropriate acquisition method. The acquisition unit can set the optimal acquisition timing based on, for example, time periods during which the user frequently acquired data in the past. For example, the acquisition unit can analyze the user's past data acquisition history and identify the most effective time periods for data acquisition. The acquisition unit can also preferentially suggest a data acquisition method (manual, automatic, etc.) that the user has previously preferred. For example, the acquisition unit can suggest manual acquisition to a user who prefers manual data acquisition based on the user's past data acquisition history. Furthermore, the acquisition unit can predict and suggest the optimal acquisition method for a specific situation based on the user's past data acquisition history. For example, if the user often acquires data while exercising, the acquisition unit can preferentially suggest data acquisition during exercise. In this way, the optimal data acquisition method can be selected by analyzing the past data acquisition history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past data acquisition history into a generation AI and cause the generation AI to select the optimal acquisition method.

[0074] When acquiring data, the acquisition unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the acquisition unit prioritizes acquiring heart rate and activity data. For example, the acquisition unit prioritizes acquiring heart rate data and activity data based on the user's health interests. Furthermore, if the user is interested in sleep, the acquisition unit can also acquire detailed sleep data. For example, the acquisition unit prioritizes acquiring data on sleep time and sleep quality based on the user's interest in sleep. Furthermore, if the user is interested in stress management, the acquisition unit can prioritize acquiring data related to stress levels. For example, the acquisition unit prioritizes acquiring heart rate variability data and stress level data based on the user's interest in stress management. This allows more relevant data to be acquired by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data related to the user's lifestyle and areas of interest to the generation AI and cause the generation AI to filter the data.

[0075] The acquisition unit can estimate the user's emotions and determine the priority of data to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring heart rate and stress level data. For example, when the user's stress level is high, the acquisition unit prioritizes acquiring heart rate data and stress level data. The acquisition unit can also prioritize acquiring activity level and sleep data when the user is relaxed. For example, when the acquisition unit detects that the user is relaxed, it prioritizes acquiring activity level data and sleep data. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring only the most important data. For example, when the acquisition unit detects that the user is in a hurry, it prioritizes acquiring only the most important data. In this way, by determining the priority of data based on the user's emotions, important data can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0076] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location information. For example, when the user is exercising, the acquisition unit prioritizes acquiring activity amount and heart rate data. For example, the acquisition unit prioritizes acquiring activity amount data and heart rate data during exercise based on the user's geographical location information. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring sleep data and heart rate data when relaxing. For example, the acquisition unit prioritizes acquiring sleep data at home and heart rate data when relaxing based on the user's geographical location information. Furthermore, when the user is at work, the acquisition unit can prioritize acquiring data related to stress level and concentration level. For example, the acquisition unit prioritizes acquiring stress level data and concentration level data at work based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information into the generation AI and cause the generation AI to acquire highly relevant data.

[0077] The acquisition unit can analyze the user's social media activity when acquiring data and acquire related data. For example, if the user is feeling stressed because of social media, the acquisition unit prioritizes acquiring heart rate and stress level data. For example, the acquisition unit prioritizes acquiring heart rate data and stress level data when the user's stress level is high based on the user's social media activity. The acquisition unit can also prioritize acquiring activity level and sleep data when the user is relaxing on social media. For example, the acquisition unit prioritizes acquiring activity level data and sleep data in a relaxed state based on the user's social media activity. Furthermore, the acquisition unit can also prioritize acquiring exercise data when the user posts about exercise on social media. For example, the acquisition unit prioritizes acquiring exercise-related data based on the user's social media activity. This allows related data to be acquired by analyzing social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides simple, easy-to-understand analysis results. For example, if the user's stress level is high, the analysis unit displays concise, to-the-point analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the analysis unit detects that the user is relaxed, it displays analysis results including detailed data and their interpretations. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are concise and include only important information. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, analyzes data with high importance (such as heart rate and stress level) in detail. For example, the analysis unit analyzes heart rate data and stress level data in detail to evaluate the user's health condition. The analysis unit can also analyze data with low importance (such as activity level and sleep data) in a simplified manner. For example, the analysis unit analyzes activity level data and sleep data in a simplified manner to grasp the user's basic health condition. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis when a detailed analysis is necessary based on the importance of the data, and performs a simplified analysis when a simplified analysis is sufficient. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit analyzes heart rate variability based on the heart rate data and evaluates the user's stress level. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit analyzes sleep stages based on the sleep data and evaluates the user's sleep quality. The analysis unit can also apply an exercise intensity analysis algorithm to activity data. For example, the analysis unit analyzes exercise intensity based on the activity data and evaluates the user's exercise status. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a short and concise analysis result. For example, if the user's stress level is high, the analysis unit displays a concise analysis result containing only important information. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit detects that the user is relaxed, it displays an analysis result containing detailed data and its interpretation. Furthermore, the analysis unit can provide a concise analysis result if the user is in a hurry. For example, if the analysis unit detects that the user is in a hurry, it displays a concise and concise analysis result. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] During analysis, the analysis unit can determine the analysis priority based on the time when the data was acquired. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit evaluates the user's current health condition based on the most recent data. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit identifies trends based on past data and performs analysis by comparing the past data with the current data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was acquired. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was acquired, and analyzes past data complementary to the past data. In this way, by determining the analysis priority based on the time when the data was acquired, the most recent data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was acquired to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of heart rate data based on the relevance between heart rate data and activity data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of sleep data based on the relevance between sleep data and activity data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way a message is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a message using gentle language. For example, if the user's stress level is high, the generation unit generates a message using gentle language that provides a sense of security. The generation unit can also generate a message containing detailed information if the user is relaxed. For example, if the generation unit detects that the user is relaxed, it generates a message containing detailed data and its interpretation. Furthermore, if the user is in a hurry, the generation unit can generate a concise and to-the-point message. For example, if the generation unit detects that the user is in a hurry, it generates a concise message containing only important information. This allows the user to provide a more appropriate message by adjusting the way the message is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] When generating a message, the generation unit can adjust the level of detail of the message based on the importance of the data. For example, the generation unit writes a detailed message based on data of high importance. For example, the generation unit writes a detailed message based on heart rate data or stress level data to provide specific advice to the user. The generation unit can also write a concise message based on data of low importance. For example, the generation unit writes a concise message based on activity data or sleep data to provide basic information to the user. Furthermore, the generation unit can dynamically adjust the level of detail of the message depending on the importance of the data. For example, the generation unit generates a detailed message when a detailed message is necessary based on the importance of the data, and generates a concise message when a concise message is sufficient based on the importance of the data. In this way, by adjusting the level of detail of the message based on the importance of the data, it is possible to write a detailed message based on important data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the message.

[0086] When generating a message, the generation unit can apply different message generation algorithms depending on the data category. For example, the generation unit can include health management advice in a message based on heart rate data. For example, the generation unit can evaluate the user's health condition based on the heart rate data and provide appropriate health management advice. The generation unit can also include suggestions for improving sleep quality in a message based on sleep data. For example, the generation unit can evaluate the user's sleep quality based on the sleep data and provide specific suggestions for improvement. The generation unit can also include exercise recommendations and cautions in a message based on activity data. For example, the generation unit can evaluate the user's exercise status based on the activity data and provide appropriate exercise recommendations and cautions. This allows for the generation of more appropriate messages by applying different message generation algorithms depending on the data category. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate message generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of the message based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a short, to-the-point message. For example, if the user's stress level is high, the generation unit generates a concise message containing only important information. The generation unit can also generate a longer message containing detailed information if the user is relaxed. For example, if the generation unit detects that the user is relaxed, it generates a message containing detailed data and its interpretation. Furthermore, the generation unit can also generate a concise message if the user is in a hurry. For example, if the generation unit detects that the user is in a hurry, it generates a concise, to-the-point message. This allows the user to provide a more appropriate message by adjusting the length of the message based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0088] When generating messages, the generation unit can determine the priority of messages based on the time when the data was acquired. For example, the generation unit prioritizes generating messages based on the most recent data. For example, the generation unit evaluates the user's current health condition based on the most recent data and generates an appropriate message. The generation unit can also generate messages based on current data while referring to past data. For example, the generation unit identifies trends based on past data and compares the past data to generate a message. Furthermore, the generation unit can dynamically adjust the priority of messages depending on the time when the data was acquired. For example, the generation unit prioritizes reflecting the most recent data in messages based on the time when the data was acquired and includes past data as a supplement to the messages. In this way, by determining the priority of messages based on the time when the data was acquired, messages based on the most recent data can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the time when the data was acquired into the generation AI and have the generation AI determine the priority of messages.

[0089] When generating messages, the generation unit can adjust the order of messages based on the relevance of the data. For example, the generation unit prioritizes generating messages based on highly relevant data. For example, the generation unit prioritizes generating messages based on heart rate data based on the relevance between heart rate data and activity data. The generation unit can also postpone generating messages based on less relevant data. For example, the generation unit postpones generating messages based on sleep data based on the relevance between sleep data and activity data. Furthermore, the generation unit can dynamically adjust the order of messages according to the relevance of the data. For example, the generation unit prioritizes generating messages based on highly relevant data and postpones messages based on less relevant data based on the relevance of the data. In this way, by adjusting the order of messages based on the relevance of the data, messages based on highly relevant data can be preferentially generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of the messages.

[0090] The providing unit can estimate the user's emotions and adjust the message delivery method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a message using gentle language. For example, if the user's stress level is high, the providing unit can provide a message using gentle language that gives a sense of security. The providing unit can also provide a message containing detailed information if the user is relaxed. For example, if the providing unit detects that the user is relaxed, it can provide a message containing detailed data and its interpretation. Furthermore, if the user is in a hurry, the providing unit can provide a concise and to-the-point message. For example, if the providing unit detects that the user is in a hurry, it can provide a concise message containing only important information. This allows the delivery of a more appropriate message by adjusting the message delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When providing a message, the providing unit can select the optimal delivery method by referring to the user's past message reception history. For example, the providing unit prioritizes providing a message format that the user has previously preferred. For example, the providing unit identifies a preferred message format based on the user's past message reception history and provides the message in that format. The providing unit can also predict and suggest the optimal delivery timing based on the user's past message reception history. For example, the providing unit identifies the most effective delivery timing based on the user's past message reception history and provides the message at that timing. Furthermore, the providing unit can analyze the user's past message reception history and select the most effective delivery method. For example, the providing unit identifies the most effective delivery method based on the user's past message reception history and provides the message using that method. In this way, the optimal delivery method can be selected by referring to the user's past message reception history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past message reception history into a generation AI and cause the generation AI to select the optimal delivery method.

[0092] The providing unit can customize the means of providing a message based on the user's current living situation when providing the message. For example, when the user is exercising, the providing unit prioritizes providing a voice message. For example, the providing unit provides a voice message while exercising based on the user's current living situation. The providing unit can also provide a detailed text message when the user is relaxed. For example, when the providing unit detects that the user is relaxed, it provides a text message including detailed data and its interpretation. Furthermore, the providing unit can also provide a concise notification message when the user is in a hurry. For example, when the providing unit detects that the user is in a hurry, it provides a concise and to-the-point notification message. This allows the provision of a more appropriate message by customizing the means of providing based on the user's current living situation. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's living situation data into a generation AI and cause the generation AI to customize the means of providing.

[0093] The providing unit can estimate the user's emotions and determine the priority of message delivery based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing important messages. For example, if the user's stress level is high, the providing unit can prioritize providing messages containing important information. The providing unit can also prioritize providing messages containing detailed information if the user is relaxed. For example, if the providing unit detects that the user is relaxed, it can prioritize providing messages containing detailed data and their interpretation. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise and to-the-point messages. For example, if the providing unit detects that the user is in a hurry, it can prioritize providing messages containing only concise and important information. In this way, by determining the priority of message delivery based on the user's emotions, important messages can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to estimate the emotion.

[0094] When providing a message, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, when the user is at home, the providing unit provides a detailed text message. For example, the providing unit provides a detailed text message when the user is at home based on the user's geographical location information. The providing unit can also preferentially provide a voice message when the user is out. For example, the providing unit provides a voice message when the user is out based on the user's geographical location information. Furthermore, the providing unit can also provide a concise notification message when the user is at work. For example, the providing unit provides a concise notification message when the user is at work based on the user's geographical location information. This makes it possible to select the optimal delivery method by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0095] When providing a message, the providing unit can analyze the user's social media activity and suggest a means of delivery. For example, if the user is active on social media, the providing unit provides the message via social media. For example, the providing unit provides the message via social media if the user is active based on the user's social media activity. The providing unit can also provide a private message if the user is feeling stressed on social media. For example, the providing unit provides a private message if the user is feeling stressed based on the user's social media activity. Furthermore, the providing unit can also provide a message including detailed information if the user is relaxing on social media. For example, the providing unit provides a message including detailed information if the user is relaxing based on the user's social media activity. This makes it possible to suggest the optimal means of delivery by analyzing social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of delivery.

[0096] The collection unit can estimate the user's emotions and adjust the timing of collecting surveys based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of collecting surveys to reduce the burden on the user. For example, if the user's stress level is high, the collection unit reduces the frequency of collecting surveys to reduce the burden on the user. The collection unit can also increase the frequency of collecting surveys to collect more detailed information if the user is relaxed. For example, if the collection unit detects that the user is relaxed, it increases the frequency of collecting surveys to collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can temporarily stop collecting surveys and collect them all at once later. For example, if the collection unit detects that the user is in a hurry, it temporarily stops collecting surveys and collects them all at once later. This allows the timing of collecting surveys to be adjusted based on the user's emotions, thereby allowing the surveys to be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0097] When collecting survey responses, the collection unit can analyze the user's past response history and select the optimal collection method. For example, the collection unit can prioritize providing a survey format that the user has previously preferred. For example, the collection unit can identify a preferred survey format based on the user's past response history and provide the survey in that format. The collection unit can also predict and suggest the optimal collection timing based on the user's past response history. For example, the collection unit can identify the most effective collection timing based on the user's past response history and provide the survey at that timing. Furthermore, the collection unit can analyze the user's past response history and select the most effective collection method. For example, the collection unit can identify the most effective collection method based on the user's past response history and provide the survey using that method. In this way, the optimal collection method can be selected by analyzing the user's past response history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the user's past response history into a generation AI and cause the generation AI to select the optimal collection method.

[0098] The collection unit can estimate the user's emotions and determine the priority of questionnaires to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important questionnaires. For example, when the user's stress level is high, the collection unit prioritizes collecting questionnaires containing important information. The collection unit can also prioritize collecting questionnaires containing detailed information when the user is relaxed. For example, when the collection unit detects that the user is relaxed, the collection unit prioritizes collecting questionnaires containing detailed data and their interpretations. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting concise and to-the-point questionnaires. For example, when the collection unit detects that the user is in a hurry, the collection unit prioritizes collecting questionnaires containing concise and important information only. In this way, by determining the priority of questionnaires based on the user's emotions, important questionnaires can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0099] When collecting surveys, the collection unit can prioritize collecting highly relevant surveys by taking into account the user's geographical location information. For example, when the user is at home, the collection unit collects detailed surveys. For example, the collection unit collects detailed surveys when the user is at home based on the user's geographical location information. The collection unit can also prioritize collecting brief surveys when the user is out. For example, the collection unit collects brief surveys when the user is out based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting surveys related to the workplace when the user is at work. For example, the collection unit prioritizes collecting surveys related to the workplace based on the user's geographical location information when the user is at work. In this way, highly relevant surveys can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant surveys.

[0100] The analysis unit can estimate the user's emotions and adjust the trend analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple and easy-to-understand trend analysis. For example, if the user's stress level is high, the analysis unit provides a concise and to-the-point trend analysis. The analysis unit can also provide a detailed trend analysis if the user is relaxed. For example, if the analysis unit detects that the user is relaxed, it provides a trend analysis including detailed data and its interpretation. Furthermore, if the user is in a hurry, the analysis unit can provide a trend analysis that is concise and includes only important information. This allows the trend analysis method to be adjusted based on the user's emotions, thereby providing a more appropriate trend analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0101] During trend analysis, the analysis unit can predict a current trend by referring to past data. The analysis unit, for example, predicts a current activity trend based on past activity data. For example, the analysis unit predicts changes in current activity based on past activity data and evaluates the user's exercise status. The analysis unit can also predict a current sleep trend based on past sleep data. For example, the analysis unit predicts changes in current sleep quality based on past sleep data and evaluates the user's sleep status. The analysis unit can also predict a current heart rate trend based on past heart rate data. For example, the analysis unit predicts current heart rate fluctuations based on past heart rate data and evaluates the user's health status. By referring to past data, the current trend can be predicted more accurately. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to predict a current trend.

[0102] The analysis unit can estimate the user's emotions and adjust the importance of trends based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing important trends. For example, if the user's stress level is high, the analysis unit prioritizes analyzing trends containing important information. The analysis unit can also prioritize analyzing detailed trends if the user is relaxed. For example, if the analysis unit detects that the user is relaxed, it prioritizes analyzing trends containing detailed data and their interpretations. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing concise trends. For example, if the analysis unit detects that the user is in a hurry, it prioritizes analyzing trends containing only concise and important information. This allows the priority analysis of important trends by adjusting the importance of trends based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0103] During trend analysis, the analysis unit can analyze changes in the trend based on the time of data acquisition. The analysis unit, for example, analyzes changes in the current trend based on the latest data. For example, the analysis unit analyzes changes in the current activity level based on the latest data and evaluates the user's exercise status. The analysis unit can also analyze changes in the current trend while referring to past data. For example, the analysis unit analyzes changes in the current sleep quality based on past sleep data and evaluates the user's sleep status. Furthermore, the analysis unit can dynamically analyze changes in the trend depending on the time of data acquisition. For example, the analysis unit analyzes changes in the trend based on the latest heart rate data based on the time of data acquisition and evaluates the user's health status. In this way, the latest trend can be identified by analyzing changes in the trend based on the time of data acquisition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data acquisition to the generation AI and cause the generation AI to analyze changes in the trend.

[0104] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit sets stricter anomaly detection criteria. For example, if the user's stress level is high, the detection unit sets stricter anomaly detection criteria to detect anomalies early. The detection unit can also set lenient anomaly detection criteria when the user is relaxed. For example, if the detection unit detects that the user is relaxed, the detection unit sets lenient anomaly detection criteria to reduce the frequency of anomaly detection. Furthermore, the detection unit can temporarily relax the anomaly detection criteria when the user is in a hurry. For example, if the detection unit detects that the user is in a hurry, the detection unit temporarily relaxes the anomaly detection criteria to reduce the user's burden. This enables more appropriate anomaly detection by adjusting the anomaly detection criteria based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0105] The detection unit can improve the accuracy of anomaly detection by taking into account the correlation between data. The detection unit, for example, detects anomalies by combining heart rate and activity data. For example, the detection unit detects anomalies and evaluates the user's health status based on the correlation between heart rate data and activity data. The detection unit can also detect anomalies by combining sleep data and heart rate data. For example, the detection unit detects anomalies and evaluates the user's sleep status based on the correlation between sleep data and heart rate data. The detection unit can also detect anomalies by combining activity data and sleep data. For example, the detection unit detects anomalies and evaluates the user's exercise status and sleep status based on the correlation between activity data and sleep data. This allows the accuracy of anomaly detection to be improved by taking into account the correlation between data. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the correlation between data to a generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0106] The detection unit can estimate the user's emotions and adjust the display order of anomaly detection results based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit prioritizes displaying important anomalies. For example, if the user's stress level is high, the detection unit prioritizes displaying anomalies containing important information. The detection unit can also prioritize displaying detailed anomalies when the user is relaxed. For example, if the detection unit detects that the user is relaxed, the detection unit prioritizes displaying anomalies containing detailed data and their interpretations. Furthermore, if the user is in a hurry, the detection unit can prioritize displaying concise anomalies. For example, if the detection unit detects that the user is in a hurry, the detection unit prioritizes displaying anomalies containing only concise and important information. In this way, by adjusting the display order of anomaly detection results based on the user's emotions, important anomalies can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0107] The detection unit can perform anomaly detection while taking into account the geographical distribution of data. For example, when a user is at home, the detection unit detects anomalies based on data around the user's home. For example, the detection unit detects anomalies using data around the user's home based on the user's geographical location information. Furthermore, when a user is out, the detection unit can also detect anomalies based on data from the user's location outside the home. For example, the detection unit detects anomalies using data from the user's location outside the home based on the user's geographical location information. Furthermore, when a user is at work, the detection unit can also detect anomalies based on data from the user's location outside the home. For example, the detection unit detects anomalies using data from the user's location outside the home based on the user's geographical location information. In this way, by taking the geographical distribution of data into account, more accurate anomaly detection is possible. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical location information to a generation AI and cause the generation AI to perform anomaly detection. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, generation unit, provision unit, collection unit, analysis unit, and detection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires data such as activity level, sleep, and heart rate using a sensor in the smart device 14 and transmits the data to the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 in the data processing device 12 and generates a message based on the analysis result. The provision unit is realized by the control unit 46A in the smart device 14 and provides the generated message to the user. The collection unit sends a questionnaire through an application in the smart device 14 and collects response data. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and performs trend analysis. The detection unit is realized by the specific processing unit 290 in the data processing device 12 and detects anomalies. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, collection unit, analysis unit, and detection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires data such as activity level, sleep, and heart rate using a sensor in the smart glasses 214 and transmits the data to the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 in the data processing device 12 and generates a message based on the analysis result. The provision unit is realized by the control unit 46A in the smart glasses 214 and provides the generated message to the user. The collection unit sends a questionnaire through an application in the smart glasses 214 and collects response data. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and performs trend analysis. The detection unit is realized by the specific processing unit 290 in the data processing device 12 and detects anomalies. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, collection unit, analysis unit, and detection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires data such as activity level, sleep, and heart rate using a sensor of the headset type terminal 314 and transmits the data to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a message based on the analysis result. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated message to the user. The collection unit sends a questionnaire via an application of the headset type terminal 314 and collects response data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs trend analysis. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects anomalies. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, collection unit, analysis unit, and detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires data such as activity level, sleep, and heart rate using sensors in the robot 414 and transmits the data to the data processing device 12. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 in the data processing device 12 and generates a message based on the analysis result. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated message to the user. The collection unit sends a questionnaire through an application in the robot 414 and collects response data. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and performs trend analysis. The detection unit is realized by the specific processing unit 290 in the data processing device 12 and detects anomalies.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The parent-child smart band service system may further include a meal acquisition unit that acquires the user's meal data. The meal acquisition unit may acquire the meal contents and calorie intake, for example, through an application that allows the user to record meals. For example, the meal acquisition unit may allow the user to take a photo of the meal and analyze the photo to identify the meal contents. The meal acquisition unit may also allow the user to manually input the meal contents. Furthermore, the meal acquisition unit may also automatically acquire the meal contents by scanning the menu when the user eats out. In this way, by acquiring the meal data, the user's nutritional status can be understood and health management can be supported.

[0110] The parent-child smart band service system can further estimate the user's emotions and customize the content of the questionnaire based on the estimated emotions. For example, if the user is feeling stressed, the collection unit increases the number of questions related to stress and collects detailed information. For example, if the user's stress level is high, the collection unit adds questions related to the causes of stress and how to deal with it. Furthermore, if the user is relaxed, the collection unit can increase the number of questions related to relaxation and understand the user's relaxation methods. For example, if the collection unit detects that the user is in a relaxed state, it adds questions related to relaxation methods and their effects. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting concise questions. For example, if the collection unit detects that the user is in a hurry, it provides concise and to-the-point questions. In this way, more appropriate information can be collected by customizing the content of the questionnaire based on the user's emotions.

[0111] The parent-child smart band service system may further include an exercise acquisition unit that acquires the user's exercise data. The exercise acquisition unit acquires, for example, the type of exercise performed by the user, the time, and calories burned. For example, if the user runs, the exercise acquisition unit acquires the distance, time, and calories burned. In addition, if the user trains at the gym, the exercise acquisition unit can also acquire the details, time, and calories burned. Furthermore, if the user performs exercises such as yoga or Pilates, the exercise acquisition unit can also acquire the details, time, and calories burned. In this way, by acquiring exercise data, the user's exercise status can be understood and health management can be supported.

[0112] The parent-child smart band service system can further estimate the user's emotions and adjust the notification method for anomaly detection based on the estimated emotions. For example, if the user is feeling stressed, the detection unit notifies the user of an abnormality using gentle language. For example, if the user's stress level is high, the detection unit issues an abnormality notification using gentle language that provides a sense of security. Furthermore, if the user is relaxed, the detection unit can also issue an abnormality notification including detailed information. For example, if the detection unit detects that the user is in a relaxed state, the detection unit issues an abnormality notification including detailed data and its interpretation. Furthermore, if the user is in a hurry, the detection unit can issue an abnormality notification that is concise and to the point. For example, if the detection unit detects that the user is in a hurry, the detection unit issues an abnormality notification that is concise and includes only important information. This allows for more appropriate abnormality notification by adjusting the notification method for anomaly detection based on the user's emotions.

[0113] The parent-child smart band service system may further include an environment acquisition unit that acquires the user's sleep environment data. The environment acquisition unit acquires, for example, the temperature, humidity, and lighting brightness of the user's bedroom. For example, the environment acquisition unit acquires temperature data of the user's bedroom and evaluates whether the temperature is appropriate. The environment acquisition unit may also acquire humidity data of the user's bedroom and evaluate whether the humidity is appropriate. Furthermore, the environment acquisition unit may also acquire lighting data of the user's bedroom and evaluate whether the brightness is appropriate. In this way, by acquiring the sleep environment data, advice for improving the user's sleep quality can be provided.

[0114] The parent-child smart band service system can further estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user's stress level is high, the analysis unit displays concise and to-the-point analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the analysis unit detects that the user is relaxed, it displays analysis results including detailed data and its interpretation. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are concise and include only important information. This allows the system to provide more appropriate analysis results by adjusting the display method of the analysis results based on the user's emotions.

[0115] The parent-child smart band service system may further include an activity acquisition unit that acquires user activity data. The activity acquisition unit acquires, for example, the user's daily activities and travel distance. For example, the activity acquisition unit acquires the user's walking distance and the number of times the user has climbed and descended stairs. The activity acquisition unit may also acquire the user's bicycle travel distance and time. The activity acquisition unit may also acquire the user's car travel distance and time. By acquiring activity data, the user's daily activity status can be grasped and health management can be supported.

[0116] The parent-child smart band service system can further estimate the user's emotions and customize the content of the message based on the estimated emotions. For example, the generation unit generates an encouraging message when the user is feeling stressed. For example, the generation unit generates an encouraging or comforting message when the user's stress level is high. The generation unit can also generate a message containing detailed information when the user is relaxed. For example, the generation unit generates a message containing detailed data and its interpretation when it detects that the user is relaxed. Furthermore, the generation unit can generate a concise and to-the-point message when the user is in a hurry. For example, the generation unit generates a concise message containing only important information when it detects that the user is in a hurry. This allows the user to provide more appropriate messages by customizing the content of the message based on the user's emotions.

[0117] The parent-child smart band service system may further include a hobby acquisition unit that acquires data related to the user's hobbies and interests. The hobby acquisition unit acquires, for example, activities related to the user's hobbies and interests. For example, the hobby acquisition unit acquires the amount of time the user spent reading and the titles of books read. The hobby acquisition unit may also acquire the amount of time the user spent watching movies and the titles of the movies watched. Furthermore, the hobby acquisition unit may also acquire the amount of time the user spent playing sports and the types of sports played. In this way, by acquiring data related to hobbies and interests, the user's lifestyle can be understood and more personalized support can be provided.

[0118] The parent-child smart band service system can further estimate the user's emotions and adjust the timing of message delivery based on the estimated emotions. For example, the delivery unit delays the delivery of messages when the user is feeling stressed. For example, the delivery unit delays the delivery of messages when the user's stress level is high to reduce the burden on the user. The delivery unit can also speed up the delivery of messages when the user is relaxed. For example, the delivery unit speeds up the delivery of messages to provide more detailed information when it detects that the user is relaxed. Furthermore, the delivery unit can temporarily stop the delivery of messages when the user is in a hurry and provide them all at once later. For example, when it detects that the user is in a hurry, the delivery unit temporarily stops the delivery of messages and provides them all at once later. In this way, by adjusting the timing of message delivery based on the user's emotions, messages can be delivered at more appropriate times.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The acquisition unit acquires data from the smart bands provided to the parent and child. The acquired data includes activity level, sleep, heart rate, etc. For example, the acquisition unit acquires the number of steps, exercise time, sleep quality and duration, and heart rate from the smart band in real time and sends them to a dedicated application. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the activity data to understand the parent's (or child's) exercise status. The analysis unit also analyzes the sleep data to evaluate the parent's (or child's) sleep quality, and analyzes the heart rate data to monitor heart rate fluctuations. For example, the analysis unit performs analysis based on the acquired data using statistical analysis or machine learning algorithms. Step 3: The generation unit generates messages based on the data analyzed by the analysis unit. The generation unit generates a warning message if the parent's activity level is decreasing, and generates a message containing suggestions for improvement if the child's sleep quality is declining. Also, if the heart rate is abnormally high, the generation unit generates a message recommending a medical examination. For example, the generation unit uses a generation AI to generate appropriate messages based on the acquired data. Step 4: The providing unit provides the message generated by the generating unit. The providing unit displays the message through a dedicated application, sends the message via email or push notification, or can provide the message as a voice message. For example, the providing unit provides a means for quickly delivering the generated message to the user.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 acquisition unit for acquiring data; an analysis unit that analyzes the data acquired by the acquisition unit; a generation unit that generates a message based on the data analyzed by the analysis unit; a providing unit that provides the message generated by the generating unit; Equipped with A system characterized by:

2. Equipped with a collection unit for collecting survey data 2. The system of claim 1.

3. Equipped with an analysis department that performs trend analysis 2. The system of claim 1.

4. Equipped with a detection unit that detects abnormalities 2. The system of claim 1.

5. The acquisition unit Get activity, sleep, and heart rate data from your smart band 2. The system of claim 1.

6. The analysis unit Parse the retrieved data and interpret the parent or child situation 2. The system of claim 1.

7. The acquisition unit Estimate user emotions and adjust data acquisition timing based on the estimated user emotions 2. The system of claim 1.

8. The acquisition unit Analyze the user's past data acquisition history and select the appropriate acquisition method 2. The system of claim 1.

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