system

Abstract

The system according to the embodiment aims to send notifications encouraging health based on the user's work situation. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on PC operating time and whether a sensing chair, chat, or email tool is being used. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The notification unit sends a message encouraging the user to stay healthy based on the evaluation results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide timely notifications encouraging health promotion based on the user's work situation, and there is room for improvement.

[0005] The system according to the embodiment aims to send notifications encouraging health based on the user's work situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on the operating time of a PC and whether a sensing chair, chat, or email tool is used. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The notification unit sends a message encouraging the user to take care of their health based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can send health promotion notifications based on the user's work status. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health promotion system according to an embodiment of the present invention analyzes work progress and sends health promotion messages to users. This health promotion system assesses the user's health status by collecting data on PC operating time, whether a sensing chair is used, and whether chat or email tools are used, and analyzing the data using AI. For example, if a user sits for long periods of time or uses a PC for more than a certain period of time, the AI ​​sends health promotion messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" This mechanism helps users maintain a healthy work style. First, the system collects data on PC operating time, whether a sensing chair is used, and whether chat or email tools are used. Each data item is collected in detail and used as basic data for AI analysis. For example, the PC operating time records the amount of time the user uses the PC, and the sensing chair measures the amount of time the user spends sitting. The use of chat or email tools provides data to understand the extent of the user's communication. Next, the AI ​​analyzes the collected data. The AI ​​evaluates the user's health status based on the collected data. For example, if the user sits for long periods of time or uses a PC for more than a certain period of time, the AI ​​evaluates the user's health risks and suggests appropriate actions. Furthermore, based on the analysis results, the system sends messages encouraging health to users. For example, specific messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" are sent to support the user's health. This system allows users to maintain a healthy working style. This allows the health promotion system to automatically analyze the user's work situation and send health promotion messages at appropriate times.

[0029] A health promotion system according to an embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on the operating time of a PC and whether or not a sensing chair, chat tool, or email tool is being used. The operating time of a PC is recorded, for example, by recording the time the PC is turned on and the time during which active operations are being performed. The sensing chair measures the amount of time a user spends sitting using, for example, a seat pressure sensor or a posture detection sensor. Whether or not a chat tool or email tool is being used is determined, for example, based on the tool's login status and message sending / receiving history. The analysis unit analyzes the data collected by the collection unit to evaluate the user's health condition. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm. The analysis unit evaluates the user's sitting time, work time, communication frequency, and other factors based on the collected data to determine health risks. The notification unit sends a health promotion message to the user based on the evaluation results obtained by the analysis unit. The notification unit sends specific messages, such as "Why don't you take a break?" or "Stand up and get some exercise!" The notification unit can send messages at appropriate times depending on the user's health condition. As a result, the health promotion system according to the embodiment can automatically analyze the user's work situation and send health promotion messages at appropriate times.

[0030] The collection unit can collect the operating time of the PC. The operating time of the PC includes, for example, the time the PC is turned on and the time when active operations are being performed. The collection unit, for example, uses software that records the operating time of the PC to record the time when the user uses the PC in detail. The collection unit can also monitor the operating time of the PC in real time to understand the user's working time. For example, the collection unit records the time when the PC is turned on and measures the time when active operations are being performed. In this way, by collecting the operating time of the PC, the user's working time can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the operating time data of the PC to a generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect sensing chair data. The sensing chair data includes, for example, the amount of time the user spends sitting, measured using a seat pressure sensor and a posture detection sensor. The collection unit, for example, uses sensors mounted on the sensing chair to record the amount of time the user spends sitting in detail. The collection unit can also monitor the sensing chair data in real time to determine the amount of time the user spends sitting. For example, the collection unit measures the amount of time the user spends sitting using a seat pressure sensor and detects the user's posture using a posture detection sensor. In this way, by collecting the sensing chair data, the amount of time the user spends sitting can be determined. 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 can input the sensing chair data into a generation AI and have the generation AI analyze the data.

[0032] The collection unit can collect information on whether a chat or email tool is being used. Information on whether a chat or email tool is being used includes, for example, the login status of the tool and message sending and receiving history. The collection unit, for example, monitors the login status of the chat tool or email tool to understand the extent to which the user is communicating. The collection unit can also analyze the message sending and receiving history to evaluate the frequency of the user's communication. For example, the collection unit records the login status of the chat tool and analyzes the message sending and receiving history. By collecting information on whether a chat or email tool is being used, the user's communication status can be understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage data of the chat or email tool into a generation AI and have the generation AI analyze the data.

[0033] The analysis unit can evaluate the user's health condition based on the collected data. For example, the analysis unit evaluates the user's sedentary time, work time, communication frequency, etc. based on the collected data. The analysis is performed using, for example, statistical analysis of data or machine learning algorithms. The analysis unit can determine the user's health risk based on the collected data and suggest appropriate actions. For example, the analysis unit can evaluate the user's health risk and suggest appropriate actions if the user has been sitting for a long time or has used a PC for more than a certain period of time. In this way, by evaluating the user's health condition based on the collected data, appropriate health promotion notifications can be sent. 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 collected data into a generation AI and have the generation AI analyze the data.

[0034] The notification unit can send a health promotion message to the user, such as "Why don't you take a break?" or "Stand up and exercise!" based on the evaluation results. The notification unit can send a specific message to the user, such as "Why don't you take a break?" or "Stand up and exercise!" based on the evaluation results. The notification unit can send a message at an appropriate time depending on the user's health condition. For example, the notification unit can send a message to the user encouraging them to take a break if the user has been sitting for a long time or has been using a PC for more than a certain period of time. This supports the user's healthy working style by sending a health promotion message based on the evaluation results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the evaluation results into a generation AI and cause the generation AI to generate an appropriate message.

[0035] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, if the user has collected data frequently in the past, the collection unit can collect data at a similar frequency. Also, if the user has collected data during a specific time period in the past, the collection unit can also collect data according to that time period. Furthermore, the collection unit can select the most efficient collection method from the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into a generation AI and have the generation AI select the optimal collection method.

[0036] The collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, if the user is in a meeting, the collection unit can collect only data related to the meeting. Also, if the user is concentrating on a specific project, the collection unit can collect only data related to the project. Furthermore, the collection unit can collect only highly relevant data based on the user's areas of interest. In this way, highly relevant data can be collected by filtering data based on the user's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's work situation and areas of interest to a generation AI and have the generation AI perform data filtering.

[0037] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, when the user is in the office, the collection unit prioritizes collecting data within the office. Also, when the user is at home, the collection unit can prioritize collecting data within the home. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting data at the business trip destination. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. 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 select highly relevant data.

[0038] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit can analyze the user's social media activity and collect related data when collecting data. For example, if the user posts about health on social media, the collection unit can collect health-related data. Also, if the user posts about work on social media, the collection unit can collect work-related data. Furthermore, if the user posts about a specific event on social media, the collection unit can collect data related to the event. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis 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 can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm that evaluates health risks to health data. The analysis unit can also apply an algorithm that evaluates business efficiency to business data. Furthermore, the analysis unit can apply an algorithm that evaluates communication quality to communication data. In this way, by applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can 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] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can determine the analysis priority according to the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The notification unit can select an appropriate notification method by analyzing the user's past reaction history at the time of notification. For example, the notification unit can select the optimal notification method by analyzing the user's past reaction history at the time of notification. For example, the notification unit can prioritize the use of notification methods that the user has previously received favorably. The notification unit can also avoid notification methods that the user has previously ignored. Furthermore, the notification unit can select the optimal notification method based on the user's past reaction history. In this way, by selecting the optimal notification method based on the user's past reaction history, it is possible to provide effective notifications to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past reaction history into a generation AI and have the generation AI select the optimal notification method.

[0044] The notification unit can customize the timing of the notification based on the user's current work status when notifying the user. For example, the notification unit customizes the timing of the notification based on the user's current work status when notifying the user. For example, if the user is in a meeting, the notification unit can notify the user after the meeting ends. Furthermore, if the user is concentrating, the notification unit can notify the user when the user has finished their work. Furthermore, the notification unit can notify the user at the optimal timing depending on the user's work status. In this way, by customizing the timing of the notification depending on the user's work status, the notification can be made at an appropriate timing that does not interfere with the user's work. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's work status data into the generation AI and cause the generation AI to customize the timing of the notification.

[0045] The notification unit can select an appropriate notification method by taking into account the user's geographical location information at the time of notification. For example, the notification unit selects an appropriate notification method by taking into account the user's geographical location information at the time of notification. For example, when the user is in the office, the notification unit selects an appropriate notification method for the office. Furthermore, when the user is at home, the notification unit can also select an appropriate notification method for the home. Furthermore, when the user is on a business trip, the notification unit can also select an appropriate notification method for the business trip destination. In this way, an appropriate notification method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information to the generation AI and cause the generation AI to select an appropriate notification method.

[0046] The notification unit can customize the content of the notification by analyzing the user's social media activity at the time of notification. For example, the notification unit can customize the content of the notification by analyzing the user's social media activity at the time of notification. For example, if the user posts about health on social media, the notification unit can provide a health-related notification. Furthermore, if the user posts about work on social media, the notification unit can provide a work-related notification. Furthermore, if the user posts about a specific event on social media, the notification unit can provide a notification related to the event. In this way, by analyzing the user's social media activity, it is possible to provide relevant notification content. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content of the notification.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The health promotion system can also collect the user's dietary data and have the analysis unit evaluate the nutritional balance. For example, the user takes a photo of their meal, and the collection unit acquires the image data. The analysis unit uses image recognition technology to analyze the meal contents and evaluate the balance of nutrients. Furthermore, based on the analysis results, the notification unit can send specific advice such as, "You're lacking in vitamins. Eat some fruit!" This can also support the user's dietary habits.

[0049] The health promotion system can also collect the user's sleep data and use the analysis unit to evaluate the quality of the sleep. For example, the collection unit obtains sleep data from the user's smartwatch or smartphone. The analysis unit analyzes the depth of sleep and the number of interruptions to evaluate the user's sleep quality. Based on the analysis results, the notification unit can send advice such as, "It seems you slept lightly last night. Try going to bed earlier tonight." This can help improve the user's sleep habits.

[0050] The health promotion system can also collect the user's exercise data and use the analysis unit to evaluate the amount of exercise. For example, the collection unit obtains exercise data from the user's smartwatch or fitness tracker. The analysis unit analyzes the number of steps taken and calories burned to evaluate the user's amount of exercise. Based on the analysis results, the notification unit can send specific advice such as, "You haven't exercised enough today. Walk 1,000 more steps!" This can support the user's exercise habits.

[0051] The health promotion system can also collect the user's stress level and use the analysis unit to manage stress. For example, the collection unit acquires data from a device that measures the user's heart rate and electrodermal activity. The analysis unit analyzes this data and evaluates the user's stress level. Based on the analysis results, the notification unit can send advice such as "Your stress is increasing. Take a deep breath and relax." This can support the user's stress management.

[0052] The health promotion system can also collect the user's water intake and use the analysis unit to evaluate the need for hydration. For example, the collection unit acquires data from an application that records the amount of water the user drinks. The analysis unit analyzes this data and evaluates the user's water intake. Based on the analysis results, the notification unit can send advice such as "You're not hydrated enough. Drink a glass of water." This can support the user's hydration.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects information on the PC's operating time, the sensing chair, and whether chat or email tools are being used. The PC's operating time is recorded, for example, by recording the time the PC is turned on and the time that active operations are being performed. The sensing chair measures the amount of time the user is sitting using, for example, a seat pressure sensor or posture detection sensor. The use of chat or email tools is determined, for example, based on the tool's login status and message sending / receiving history. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The analysis is performed, for example, using statistical analysis of the data or machine learning algorithms. Based on the collected data, the analysis unit evaluates the user's sedentary time, work time, communication frequency, etc., and determines health risks. Step 3: The notification unit sends a message encouraging the user to improve their health based on the evaluation results obtained by the analysis unit. For example, the notification unit sends specific messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" The notification unit can send messages at appropriate times depending on the user's health condition.

[0055] (Example 2) A health promotion system according to an embodiment of the present invention analyzes work progress and sends health promotion messages to users. This health promotion system assesses the user's health status by collecting data on PC operating time, whether a sensing chair is used, and whether chat or email tools are used, and analyzing the data using AI. For example, if a user sits for long periods of time or uses a PC for more than a certain period of time, the AI ​​sends health promotion messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" This mechanism helps users maintain a healthy work style. First, the system collects data on PC operating time, whether a sensing chair is used, and whether chat or email tools are used. Each data item is collected in detail and used as basic data for AI analysis. For example, the PC operating time records the amount of time the user uses the PC, and the sensing chair measures the amount of time the user spends sitting. The use of chat or email tools provides data to understand the extent of the user's communication. Next, the AI ​​analyzes the collected data. The AI ​​evaluates the user's health status based on the collected data. For example, if the user sits for long periods of time or uses a PC for more than a certain period of time, the AI ​​evaluates the user's health risks and suggests appropriate actions. Furthermore, based on the analysis results, the system sends messages encouraging health to users. For example, specific messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" are sent to support the user's health. This system allows users to maintain a healthy working style. This allows the health promotion system to automatically analyze the user's work situation and send health promotion messages at appropriate times.

[0056] A health promotion system according to an embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects information on the operating time of a PC and whether or not a sensing chair, chat tool, or email tool is being used. The operating time of a PC is recorded, for example, by recording the time the PC is turned on and the time during which active operations are being performed. The sensing chair measures the amount of time a user spends sitting using, for example, a seat pressure sensor or a posture detection sensor. Whether or not a chat tool or email tool is being used is determined, for example, based on the tool's login status and message sending / receiving history. The analysis unit analyzes the data collected by the collection unit to evaluate the user's health condition. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm. The analysis unit evaluates the user's sitting time, work time, communication frequency, and other factors based on the collected data to determine health risks. The notification unit sends a health promotion message to the user based on the evaluation results obtained by the analysis unit. The notification unit sends specific messages, such as "Why don't you take a break?" or "Stand up and get some exercise!" The notification unit can send messages at appropriate times depending on the user's health condition. As a result, the health promotion system according to the embodiment can automatically analyze the user's work situation and send health promotion messages at appropriate times.

[0057] The collection unit can collect the operating time of the PC. The operating time of the PC includes, for example, the time the PC is turned on and the time when active operations are being performed. The collection unit, for example, uses software that records the operating time of the PC to record the time when the user uses the PC in detail. The collection unit can also monitor the operating time of the PC in real time to understand the user's working time. For example, the collection unit records the time when the PC is turned on and measures the time when active operations are being performed. In this way, by collecting the operating time of the PC, the user's working time can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the operating time data of the PC to a generation AI and have the generation AI analyze the data.

[0058] The collection unit can collect sensing chair data. The sensing chair data includes, for example, the amount of time the user spends sitting, measured using a seat pressure sensor and a posture detection sensor. The collection unit, for example, uses sensors mounted on the sensing chair to record the amount of time the user spends sitting in detail. The collection unit can also monitor the sensing chair data in real time to determine the amount of time the user spends sitting. For example, the collection unit measures the amount of time the user spends sitting using a seat pressure sensor and detects the user's posture using a posture detection sensor. In this way, by collecting the sensing chair data, the amount of time the user spends sitting can be determined. 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 can input the sensing chair data into a generation AI and have the generation AI analyze the data.

[0059] The collection unit can collect information on whether a chat or email tool is being used. Information on whether a chat or email tool is being used includes, for example, the login status of the tool and message sending and receiving history. The collection unit, for example, monitors the login status of the chat tool or email tool to understand the extent to which the user is communicating. The collection unit can also analyze the message sending and receiving history to evaluate the frequency of the user's communication. For example, the collection unit records the login status of the chat tool and analyzes the message sending and receiving history. By collecting information on whether a chat or email tool is being used, the user's communication status can be understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input usage data of the chat or email tool into a generation AI and have the generation AI analyze the data.

[0060] The analysis unit can evaluate the user's health condition based on the collected data. For example, the analysis unit evaluates the user's sedentary time, work time, communication frequency, etc. based on the collected data. The analysis is performed using, for example, statistical analysis of data or machine learning algorithms. The analysis unit can determine the user's health risk based on the collected data and suggest appropriate actions. For example, the analysis unit can evaluate the user's health risk and suggest appropriate actions if the user has been sitting for a long time or has used a PC for more than a certain period of time. In this way, by evaluating the user's health condition based on the collected data, appropriate health promotion notifications can be sent. 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 collected data into a generation AI and have the generation AI analyze the data.

[0061] The notification unit can send a health promotion message to the user, such as "Why don't you take a break?" or "Stand up and exercise!" based on the evaluation results. The notification unit can send a specific message to the user, such as "Why don't you take a break?" or "Stand up and exercise!" based on the evaluation results. The notification unit can send a message at an appropriate time depending on the user's health condition. For example, the notification unit can send a message to the user encouraging them to take a break if the user has been sitting for a long time or has been using a PC for more than a certain period of time. This supports the user's healthy working style by sending a health promotion message based on the evaluation results. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the evaluation results into a generation AI and cause the generation AI to generate an appropriate message.

[0062] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, when the user is feeling stressed, the collection unit reduces the collection frequency to reduce the user's burden. The collection unit can also increase the collection frequency to acquire more detailed data when the user is relaxed. Furthermore, when the user is concentrating, the collection unit can adjust the collection timing to avoid interfering with the user's work. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0063] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, if the user has collected data frequently in the past, the collection unit can collect data at a similar frequency. Also, if the user has collected data during a specific time period in the past, the collection unit can also collect data according to that time period. Furthermore, the collection unit can select the most efficient collection method from the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past data collection history into a generation AI and have the generation AI select the optimal collection method.

[0064] The collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, if the user is in a meeting, the collection unit can collect only data related to the meeting. Also, if the user is concentrating on a specific project, the collection unit can collect only data related to the project. Furthermore, the collection unit can collect only highly relevant data based on the user's areas of interest. In this way, highly relevant data can be collected by filtering data based on the user's work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's work situation and areas of interest to a generation AI and have the generation AI perform data filtering.

[0065] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting health-related data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting work-related data. Furthermore, when the user is concentrating, the collection unit can prioritize collecting work efficiency data. In this way, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0066] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, when the user is in the office, the collection unit prioritizes collecting data within the office. Also, when the user is at home, the collection unit can prioritize collecting data within the home. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting data at the business trip destination. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. 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 select highly relevant data.

[0067] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit can analyze the user's social media activity and collect related data when collecting data. For example, if the user posts about health on social media, the collection unit can collect health-related data. Also, if the user posts about work on social media, the collection unit can collect work-related data. Furthermore, if the user posts about a specific event on social media, the collection unit can collect data related to the event. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is concentrating, the analysis unit can also provide analysis results related to work efficiency. By adjusting the presentation method of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient analysis 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.

[0070] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm that evaluates health risks to health data. The analysis unit can also apply an algorithm that evaluates business efficiency to business data. Furthermore, the analysis unit can apply an algorithm that evaluates communication quality to communication data. In this way, by applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can 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.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result related to work efficiency. By adjusting the length of the analysis based on the user's emotions, an analysis result of an appropriate length for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can determine the analysis priority according to the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance 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 relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0074] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user emotion. The notification unit, for example, estimates the user's emotion and adjusts the notification expression method based on the estimated emotion. For example, if the user is feeling stressed, the notification unit can provide a notification in gentle language. Furthermore, if the user is relaxed, the notification unit can provide a notification in friendly language. Furthermore, if the user is concentrating, the notification unit can provide a notification that is concise and to the point. In this way, by adjusting the notification expression method based on the user's emotion, it is possible to provide a notification that is easy for the user to accept. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The notification unit can select an appropriate notification method by analyzing the user's past reaction history at the time of notification. For example, the notification unit can select the optimal notification method by analyzing the user's past reaction history at the time of notification. For example, the notification unit can prioritize the use of notification methods that the user has previously received favorably. The notification unit can also avoid notification methods that the user has previously ignored. Furthermore, the notification unit can select the optimal notification method based on the user's past reaction history. In this way, by selecting the optimal notification method based on the user's past reaction history, it is possible to provide effective notifications to the user. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past reaction history into a generation AI and have the generation AI select the optimal notification method.

[0076] The notification unit can customize the timing of the notification based on the user's current work status when notifying the user. For example, the notification unit customizes the timing of the notification based on the user's current work status when notifying the user. For example, if the user is in a meeting, the notification unit can notify the user after the meeting ends. Furthermore, if the user is concentrating, the notification unit can notify the user when the user has finished their work. Furthermore, the notification unit can notify the user at the optimal timing depending on the user's work status. In this way, by customizing the timing of the notification depending on the user's work status, the notification can be made at an appropriate timing that does not interfere with the user's work. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's work status data into the generation AI and cause the generation AI to customize the timing of the notification.

[0077] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. The notification unit, for example, estimates the user's emotions and determines the priority of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can prioritize health-related notifications. Furthermore, if the user is relaxed, the notification unit can prioritize work-related notifications. Furthermore, if the user is concentrating, the notification unit can prioritize work efficiency-related notifications. By determining the priority of notifications based on the user's emotions, important notifications 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] The notification unit can select an appropriate notification method by taking into account the user's geographical location information at the time of notification. For example, the notification unit selects an appropriate notification method by taking into account the user's geographical location information at the time of notification. For example, when the user is in the office, the notification unit selects an appropriate notification method for the office. Furthermore, when the user is at home, the notification unit can also select an appropriate notification method for the home. Furthermore, when the user is on a business trip, the notification unit can also select an appropriate notification method for the business trip destination. In this way, an appropriate notification method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information to the generation AI and cause the generation AI to select an appropriate notification method.

[0079] The notification unit can customize the content of the notification by analyzing the user's social media activity at the time of notification. For example, the notification unit can customize the content of the notification by analyzing the user's social media activity at the time of notification. For example, if the user posts about health on social media, the notification unit can provide a health-related notification. Furthermore, if the user posts about work on social media, the notification unit can provide a work-related notification. Furthermore, if the user posts about a specific event on social media, the notification unit can provide a notification related to the event. In this way, by analyzing the user's social media activity, it is possible to provide relevant notification content. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content of the notification. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the sensors and communication I / F 44 of the smart device 14 to collect data such as PC operating time and sensing chair data, and records whether chat or email tools are used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition based on the collected data. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and sends a message encouraging the user to stay healthy. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the PC's operating time and sensing chair using the sensors and communication I / F 44 of the smart glasses 214, and records whether chat or email tools are used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition based on the collected data. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends a message encouraging the user to stay healthy. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit uses the sensors and communication I / F 44 of the headset type terminal 314 to collect data on the PC's operating time and sensing chair, and records whether chat or email tools are used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition based on the collected data. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sends a message encouraging the user to stay healthy. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the sensors and communication I / F 44 of the robot 414 to collect data on the PC's operating time and sensing chair, and records whether chat or email tools are used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition based on the collected data. The notification unit is realized, for example, by the control unit 46A of the robot 414, and sends a message encouraging the user to stay healthy.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The health promotion system can also collect the user's dietary data and have the analysis unit evaluate the nutritional balance. For example, the user takes a photo of their meal, and the collection unit acquires the image data. The analysis unit uses image recognition technology to analyze the meal contents and evaluate the balance of nutrients. Furthermore, based on the analysis results, the notification unit can send specific advice such as, "You're lacking in vitamins. Eat some fruit!" This can also support the user's dietary habits.

[0082] The health promotion system can also collect the user's sleep data and use the analysis unit to evaluate the quality of the sleep. For example, the collection unit obtains sleep data from the user's smartwatch or smartphone. The analysis unit analyzes the depth of sleep and the number of interruptions to evaluate the user's sleep quality. Based on the analysis results, the notification unit can send advice such as, "It seems you slept lightly last night. Try going to bed earlier tonight." This can help improve the user's sleep habits.

[0083] The health promotion system can also collect the user's exercise data and use the analysis unit to evaluate the amount of exercise. For example, the collection unit obtains exercise data from the user's smartwatch or fitness tracker. The analysis unit analyzes the number of steps taken and calories burned to evaluate the user's amount of exercise. Based on the analysis results, the notification unit can send specific advice such as, "You haven't exercised enough today. Walk 1,000 more steps!" This can support the user's exercise habits.

[0084] The health promotion system can also collect the user's stress level and use the analysis unit to manage stress. For example, the collection unit acquires data from a device that measures the user's heart rate and electrodermal activity. The analysis unit analyzes this data and evaluates the user's stress level. Based on the analysis results, the notification unit can send advice such as "Your stress is increasing. Take a deep breath and relax." This can support the user's stress management.

[0085] The health promotion system can also collect the user's water intake and use the analysis unit to evaluate the need for hydration. For example, the collection unit acquires data from an application that records the amount of water the user drinks. The analysis unit analyzes this data and evaluates the user's water intake. Based on the analysis results, the notification unit can send advice such as "You're not hydrated enough. Drink a glass of water." This can support the user's hydration.

[0086] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is concentrating, the analysis unit can also provide analysis results related to work efficiency. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0087] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can reduce the frequency of notifications to reduce the burden on the user. Also, if the user is relaxed, the notification unit can increase the frequency of notifications and provide detailed advice. Furthermore, if the user is concentrating, the notification unit can adjust the timing of notifications to avoid interrupting work. In this way, the burden on the user can be reduced by adjusting the timing of notifications based on the user's emotions.

[0088] The notification unit can estimate the user's emotions and customize the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide a notification in gentle language. If the user is relaxed, the notification unit can also provide a notification in friendly language. Furthermore, if the user is concentrating, the notification unit can provide a concise and to-the-point notification. In this way, by customizing the content of the notification based on the user's emotions, it is possible to provide a notification that is easy for the user to accept.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, health-related data can be analyzed with priority. Also, if the user is relaxed, work-related data can be analyzed with priority. Furthermore, if the user is concentrating, work efficiency data can be analyzed with priority. In this way, by determining the priority of analysis based on the user's emotions, important data can be analyzed with priority.

[0090] The notification unit can estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can provide the notification in gentle language. If the user is relaxed, the notification unit can also provide the notification in friendly language. Furthermore, if the user is concentrating, the notification can be concise and to the point. In this way, by adjusting the way the notification is expressed based on the user's emotions, it is possible to provide notifications that are easy for the user to accept.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection unit collects information on the PC's operating time, the sensing chair, and whether chat or email tools are being used. The PC's operating time is recorded, for example, by recording the time the PC is turned on and the time that active operations are being performed. The sensing chair measures the amount of time the user is sitting using, for example, a seat pressure sensor or posture detection sensor. The use of chat or email tools is determined, for example, based on the tool's login status and message sending / receiving history. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The analysis is performed, for example, using statistical analysis of the data or machine learning algorithms. Based on the collected data, the analysis unit evaluates the user's sedentary time, work time, communication frequency, etc., and determines health risks. Step 3: The notification unit sends a message encouraging the user to improve their health based on the evaluation results obtained by the analysis unit. For example, the notification unit sends specific messages such as "Why don't you take a break?" or "Get up and exercise for a bit!" The notification unit can send messages at appropriate times depending on the user's health condition.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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, in order to avoid confusion and to 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects information on PC operating time, sensing chairs, and whether chat or email tools are used; an analysis unit that analyzes the data collected by the collection unit and evaluates the health condition of the user; a notification unit that sends a message encouraging the user to stay healthy based on the evaluation result obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect PC uptime 2. The system of claim 1.

3. The collecting unit Collecting Sensing Chair Data 2. The system of claim 1.

4. The collecting unit Collect whether chat or email tools are used 2. The system of claim 1.

5. The analysis unit Evaluate the user's health status based on collected data 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions 2. The system of claim 1.

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

Images