System

Abstract

An object of a system according to an embodiment is to change a method of warning according to a degree of tension of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a warning unit. The collection unit collects biological data of a user. The analysis unit analyzes the biological data collected by the collection unit and estimates the degree of tension of the user. The warning unit changes a method of warning based on the degree of tension estimated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, excessive warnings can cause people to lose their composure and cloud their judgment.

[0005] The system according to the embodiment aims to change the warning method depending on the level of tension of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects biometric data of a user. The analysis unit analyzes the biometric data collected by the collection unit and estimates the user's level of tension. The warning unit changes the manner of warning based on the level of tension estimated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can change the way of warning depending on the level of tension of the user. [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) An evacuation assistance system according to an embodiment of the present invention estimates a user's level of tension based on biometric data and issues appropriate warnings. The evacuation assistance system collects and analyzes the user's biometric data, estimates the user's level of tension, and modifies the warning method to support the user in calmly taking evacuation actions. For example, the evacuation assistance system collects the user's biometric data. For example, sensors are used to collect data such as heart rate, blood pressure, and galvanic skin response in real time. The evacuation assistance system then analyzes the collected biometric data to estimate the user's level of tension. The collected data is then analyzed using AI to quantify the user's level of tension. For example, a sudden increase in heart rate can be determined to indicate high levels of tension. Next, the evacuation assistance system modifies the warning method when the level of tension is high. For example, if a warning causes a significant increase in blood pressure, the system may provide encouraging words or reduce the volume of the alert sound to help the user regain their composure. This allows the user to calmly take evacuation actions. The evacuation assistance system can also support the user in calmly taking evacuation actions during disasters such as earthquakes and fires. For example, the system can be used in evacuation drills to improve the quality of the user's evacuation actions. As a result, the evacuation support system allows users to calmly take evacuation actions and minimize damage from disasters. For example, in the event of a disaster such as an earthquake or fire, the system can support users in calmly taking evacuation actions. The system can also be used in evacuation drills, improving the quality of users' evacuation actions.

[0029] The evacuation assistance system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects biometric data of a user. The biometric data of the user includes, but is not limited to, heart rate, blood pressure, and electrodermal response, for example. The collection unit may use, for example, a heart rate sensor to measure the heart rate. The collection unit may also use a sphygmomanometer to measure blood pressure. The collection unit may also use a electrodermal response sensor to measure the electrodermal response. For example, the collection unit may measure the user's heart rate in real time using the heart rate sensor. The collection unit may also periodically measure the user's blood pressure using a sphygmomanometer. The collection unit may also continuously monitor the user's electrodermal response using the electrodermal response sensor. The analysis unit analyzes the biometric data collected by the collection unit and estimates the user's level of tension. For example, the analysis unit may analyze the collected data using AI and quantify the user's level of tension. For example, the analysis unit may analyze fluctuations in the heart rate to estimate the level of tension. The analysis unit can also analyze fluctuations in blood pressure to estimate the level of tension. The analysis unit can also analyze fluctuations in electrodermal response to estimate the level of tension. For example, the analysis unit determines that the level of tension is high when the heart rate increases rapidly. The analysis unit can also determine that the level of tension is high when the blood pressure increases rapidly. The analysis unit can also determine that the level of tension is high when the electrodermal response changes rapidly. The warning unit changes the manner of warning based on the level of tension estimated by the analysis unit. For example, the warning unit changes the manner of warning when the level of tension is high. For example, the warning unit can add words of encouragement when a warning causes a significant increase in blood pressure. The warning unit can also reduce the volume of the alert sound when a warning causes a significant increase in blood pressure. The warning unit can also reduce the frequency of warnings when the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure increases significantly. The warning unit can also reduce the volume of the alert sound when blood pressure increases significantly. The warning unit can also reduce the frequency of warnings when the level of tension is high. As a result, the evacuation support system according to the embodiment can estimate the level of tension based on the user's biological data and issue an appropriate warning, thereby promoting calm evacuation behavior.For example, an evacuation support system can collect and analyze a user's biometric data, estimate their level of tension, and change the way it warns them, thereby helping them to calmly take evacuation action.

[0030] The collection unit can collect biometric data such as heart rate, blood pressure, and electrodermal response. The collection unit, for example, uses a heart rate sensor to measure heart rate. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor. The collection unit can also use a sphygmomanometer to measure blood pressure. For example, the collection unit can periodically measure the user's blood pressure using the sphygmomanometer. The collection unit can also use a sphygmomanometer to measure electrodermal response. For example, the collection unit can continuously monitor the user's electrodermal response using the sphygmomanometer. This improves the accuracy of estimating the tension level by collecting various biometric data. 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 heart rate data acquired by the heart rate sensor to the generation AI and cause the generation AI to analyze the heart rate data.

[0031] The analysis unit can analyze the collected biometric data and quantify the user's level of tension. The analysis unit can analyze the collected data using, for example, AI and quantify the user's level of tension. For example, the analysis unit can analyze fluctuations in heart rate to estimate the level of tension. The analysis unit can also analyze fluctuations in blood pressure to estimate the level of tension. The analysis unit can also analyze fluctuations in electrodermal response to estimate the level of tension. For example, the analysis unit can determine that the level of tension is high if the heart rate increases rapidly. The analysis unit can also determine that the level of tension is high if the blood pressure increases rapidly. The analysis unit can also determine that the level of tension is high if the electrodermal response changes rapidly. By quantifying the level of tension, the warning method can be appropriately changed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected biometric data to a generation AI and have the generation AI quantify the level of tension.

[0032] The warning unit can change the manner of warning when the level of tension is above a certain level. The warning unit can change the manner of warning when, for example, the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure rises significantly due to a warning. The warning unit can also reduce the volume of the alert sound when blood pressure rises significantly due to a warning. The warning unit can also reduce the frequency of warnings when the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure rises significantly. The warning unit can also reduce the volume of the alert sound when blood pressure rises significantly. The warning unit can also reduce the frequency of warnings when the level of tension is high. In this way, by changing the manner of warning depending on the level of tension, it is possible to encourage the user to take calm evacuation action. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input tension level data to the generation AI and cause the generation AI to change the manner of warning.

[0033] The warning unit can provide words of encouragement if the warning causes a significant rise in blood pressure. For example, the warning unit can provide words of encouragement to the user in a voice, such as "Please calm down." The warning unit can also provide words of reassurance to the user in a voice, such as "It's okay." The warning unit can also provide words of encouragement to the user in a voice, such as "Please take a deep breath." In this way, by providing words of encouragement when blood pressure rises, the user can regain their composure. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input blood pressure data into a generation AI and cause the generation AI to generate words of encouragement.

[0034] The warning unit can lower the volume of the alert sound if the warning causes a significant rise in blood pressure. For example, the warning unit lowers the volume of the alert sound if the warning causes a significant rise in blood pressure. For example, the warning unit can automatically adjust the volume of the alert sound to warn the user in a gentler tone. The warning unit can also adjust the frequency of the alert sound to warn the user in a tone that is gentle on the ears. The warning unit can also shorten the duration of the alert sound to warn the user in a short time. In this way, by lowering the volume of the alert sound when blood pressure rises, the user can regain their composure. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input blood pressure data to a generation AI and cause the generation AI to adjust the alert sound.

[0035] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. The collection unit, for example, analyzes the user's past biometric data history and selects the optimal collection method. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency if abnormal fluctuations are detected. The collection unit can also concentrate collection during specific time periods based on the user's past blood pressure data. The collection unit can also intensify collection in stressful situations based on the user's past electrodermal response data. This improves the efficiency of data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the past biometric data history into the generation AI and have the generation AI select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current activity status and environment when collecting biometric data. For example, the collection unit performs filtering based on the user's current activity status and environment when collecting biometric data. For example, when the user is exercising, the collection unit filters out temporary increases in heart rate due to exercise. The collection unit can also prioritize collecting resting data when the user is stationary. The collection unit can also remove noise when the user is in a noisy environment to collect accurate data. This allows accurate data to be collected by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input activity status and environmental data to a generation AI and have the generation AI perform filtering.

[0037] The collection unit can select an appropriate collection means depending on the user's input method when collecting biometric data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting biometric data. For example, if the user uses voice input, the collection unit estimates the heart rate from the voice data. If the user uses text input, the collection unit can also estimate the stress level from the input content. If the user uses image input, the collection unit can also estimate the tension level from the facial expression. This enables efficient data collection by selecting the optimal collection means depending on the input method. 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 input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting biometric data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting biometric data, the collection unit prioritizes collecting heart rate and blood pressure data when the user is in a disaster-stricken area. Furthermore, the collection unit can prioritize collecting galvanic skin response data when the user is in an evacuation shelter. Furthermore, the collection unit can collect all biometric data equally when the user is at home. In this way, highly relevant data can be efficiently collected by taking into account the 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 geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting biometric data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting biometric data. For example, if the user posts on social media that they are feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. Furthermore, if the user posts that they are relaxed, the collection unit can also prioritize collecting galvanic skin response data. Furthermore, if the user posts about a disaster, the collection unit can collect all biometric data equally. This allows for efficient collection of related data by analyzing social media activity. 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 social media activity data to a generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting biometric data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting biometric data. For example, if the user has previously requested collection of heart rate data, the collection unit can prioritize collection of heart rate data. Furthermore, if the user has previously requested collection of blood pressure data, the collection unit can prioritize collection of blood pressure data. Furthermore, if the user has previously requested collection of electrodermal response data, the collection unit can prioritize collection of electrodermal response data. In this way, by reflecting past feedback, it is possible to provide the user with an optimal collection method. 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the biometric data during analysis. For example, the analysis unit performs a detailed analysis when heart rate data is important. The analysis unit can also perform a detailed analysis when blood pressure data is important. The analysis unit can also perform a detailed analysis when galvanic skin response data is important. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of biometric data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of biometric data during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress level analysis algorithm to electrodermal response data. In this way, by applying an analysis algorithm depending on the category, the accuracy of the analysis is improved. 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 biometric data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. The analysis unit can also improve the analysis accuracy of current electrodermal response data by referring to the user's past electrodermal response analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the collection time of the biological data during analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the biological data during analysis. For example, the analysis unit prioritizes analysis of recently collected heart rate data. The analysis unit can also prioritize analysis of recently collected blood pressure data. The analysis unit can also prioritize analysis of recently collected electrodermal response data. This enables efficient analysis by determining the priority based on the collection time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the biometric data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the biometric data during analysis. For example, the analysis unit simultaneously analyzes heart rate and blood pressure data when they are related. The analysis unit can also simultaneously analyze electrodermal response and heart rate data when they are related. The analysis unit can also simultaneously analyze blood pressure and electrodermal response data when they are related. This enables efficient analysis by adjusting the order of analysis based on the relevance. 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 relevance data of the biometric data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise. By adjusting the use of technical terminology according to the level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The warning unit can select an appropriate warning method by analyzing the user's past warning responses when issuing a warning. For example, the warning unit can select the optimal warning method by analyzing the user's past warning responses when issuing a warning. For example, if the user previously responded to a warning in a calm voice, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning containing encouraging words, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning in a normal voice, the warning unit can issue the warning in a similar manner. In this way, by analyzing the past warning responses, the optimal warning method for the user can be selected. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past warning response data into the generation AI and cause the generation AI to select the optimal warning method.

[0048] The warning unit can customize the warning method based on the user's current situation when issuing a warning. For example, the warning unit customizes the warning method based on the user's current situation when issuing a warning. For example, the warning unit prioritizes an audio warning when the user is exercising. The warning unit can also prioritize a visual warning when the user is stationary. The warning unit can also prioritize a vibration warning when the user is in a noisy environment. This enables effective warning by customizing the warning method based on the current situation. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input current situation data to a generation AI and cause the generation AI to customize the warning method.

[0049] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, the warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, if the user has provided feedback that audio warnings have been effective in the past, the warning unit can prioritize audio warnings. Furthermore, if the user has provided feedback that visual warnings have been effective in the past, the warning unit can prioritize visual warnings. Furthermore, if the user has provided feedback that vibration warnings have been effective in the past, the warning unit can prioritize vibration warnings. In this way, by reflecting feedback, it is possible to provide the optimal warning method for the user. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input feedback data to the generation AI and cause the generation AI to improve the warning method.

[0050] The warning unit can select an appropriate warning method by taking into account the user's geographical location information when issuing a warning. For example, the warning unit selects the optimal warning method by taking into account the user's geographical location information when issuing a warning. For example, the warning unit prioritizes audio warnings when the user is in a disaster-stricken area. The warning unit can also prioritize visual warnings when the user is in an evacuation shelter. The warning unit can also use all warning methods equally when the user is at home. In this way, the optimal warning method for the user can be selected by taking into account the geographical location information. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal warning method.

[0051] The warning unit may analyze the user's social media activity and suggest a warning method when issuing a warning. For example, the warning unit may analyze the user's social media activity and suggest a warning method when issuing a warning. For example, if the user posts on social media that they are feeling stressed, the warning unit may prioritize an audio warning. Furthermore, if the user posts that they are relaxing, the warning unit may prioritize a visual warning. Furthermore, if the user posts about a disaster, the warning unit may equally use all warning methods. In this way, by analyzing social media activity, the optimal warning method for the user can be suggested. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input social media activity data into a generation AI and have the generation AI suggest a warning method.

[0052] The warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, the warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, if the user has provided feedback that audio warnings were effective in the past, the warning unit can prioritize audio warnings. Furthermore, if the user has provided feedback that visual warnings were effective in the past, the warning unit can prioritize visual warnings. Furthermore, if the user has provided feedback that vibration warnings were effective in the past, the warning unit can prioritize vibration warnings. In this way, by reflecting past feedback, it is possible to provide the optimal warning method for the user. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input past feedback data into the generation AI and cause the generation AI to customize the warning method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency if abnormal fluctuations are detected. The collection unit can also concentrate collection during specific time periods based on the user's past blood pressure data. The collection unit can also strengthen collection in stressful situations by referring to the user's past electrodermal response data. This improves the efficiency of data collection by selecting the optimal collection method based on past data. 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 past biometric data history into the generation AI and have the generation AI select the optimal collection method.

[0055] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biometric data. For example, if heart rate data is important, the analysis unit can perform a detailed analysis. Also, if blood pressure data is important, the analysis unit can perform a detailed analysis. Also, if electrodermal response data is important, the analysis unit can perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0056] When issuing a warning, the warning unit can analyze the user's past warning responses and select an appropriate warning method. For example, if the user previously responded to a warning in a calm voice, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning containing encouraging words, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning in a normal voice, the warning unit can issue the warning in a similar manner. In this way, by analyzing past warning responses, the optimal warning method for the user can be selected. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past warning response data into the generation AI and have the generation AI select the optimal warning method.

[0057] When collecting biometric data, the collection unit can perform filtering based on the user's current activity status and environment. For example, if the user is exercising, the collection unit filters out temporary increases in heart rate due to exercise. Furthermore, if the user is stationary, the collection unit can also prioritize collecting resting data. Furthermore, if the user is in a noisy environment, the collection unit can also remove noise to collect accurate data. Thus, accurate data can be collected by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input activity status and environmental data into a generation AI and have the generation AI perform filtering.

[0058] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biometric data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress level analysis algorithm to electrodermal response data. By applying an analysis algorithm according to the category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the user's biometric data. The user's biometric data includes heart rate, blood pressure, and galvanic skin response. The collection unit collects this data using a heart rate sensor, a blood pressure monitor, and a galvanic skin response sensor. For example, the heart rate is measured in real time using the heart rate sensor, blood pressure is measured periodically using the sphygmomanometer, and galvanic skin response is continuously monitored using the galvanic skin response sensor. Step 2: The analysis unit analyzes the biometric data collected by the collection unit and estimates the user's level of tension. The analysis unit uses AI to analyze the data and quantify the level of tension based on fluctuations in heart rate, blood pressure, and electrodermal response. For example, if there is a sudden increase in heart rate or blood pressure, or if there is a sudden change in electrodermal response, it will determine that the level of tension is high. Step 3: The warning unit changes the warning method based on the level of tension estimated by the analysis unit. For example, if the level of tension is high and the warning significantly increases blood pressure, the unit can add words of encouragement, reduce the volume of the alert sound, or reduce the frequency of warnings.

[0061] (Example 2) An evacuation assistance system according to an embodiment of the present invention estimates a user's level of tension based on biometric data and issues appropriate warnings. The evacuation assistance system collects and analyzes the user's biometric data, estimates the user's level of tension, and modifies the warning method to support the user in calmly taking evacuation actions. For example, the evacuation assistance system collects the user's biometric data. For example, sensors are used to collect data such as heart rate, blood pressure, and galvanic skin response in real time. The evacuation assistance system then analyzes the collected biometric data to estimate the user's level of tension. The collected data is then analyzed using AI to quantify the user's level of tension. For example, a sudden increase in heart rate can be determined to indicate high levels of tension. Next, the evacuation assistance system modifies the warning method when the level of tension is high. For example, if a warning causes a significant increase in blood pressure, the system may provide encouraging words or reduce the volume of the alert sound to help the user regain their composure. This allows the user to calmly take evacuation actions. The evacuation assistance system can also support the user in calmly taking evacuation actions during disasters such as earthquakes and fires. For example, the system can be used in evacuation drills to improve the quality of the user's evacuation actions. As a result, the evacuation support system allows users to calmly take evacuation actions and minimize damage from disasters. For example, in the event of a disaster such as an earthquake or fire, the system can support users in calmly taking evacuation actions. The system can also be used in evacuation drills, improving the quality of users' evacuation actions.

[0062] The evacuation assistance system according to the embodiment includes a collection unit, an analysis unit, and a warning unit. The collection unit collects biometric data of a user. The biometric data of the user includes, but is not limited to, heart rate, blood pressure, and electrodermal response, for example. The collection unit may use, for example, a heart rate sensor to measure the heart rate. The collection unit may also use a sphygmomanometer to measure blood pressure. The collection unit may also use a electrodermal response sensor to measure the electrodermal response. For example, the collection unit may measure the user's heart rate in real time using the heart rate sensor. The collection unit may also periodically measure the user's blood pressure using a sphygmomanometer. The collection unit may also continuously monitor the user's electrodermal response using the electrodermal response sensor. The analysis unit analyzes the biometric data collected by the collection unit and estimates the user's level of tension. For example, the analysis unit may analyze the collected data using AI and quantify the user's level of tension. For example, the analysis unit may analyze fluctuations in the heart rate to estimate the level of tension. The analysis unit can also analyze fluctuations in blood pressure to estimate the level of tension. The analysis unit can also analyze fluctuations in electrodermal response to estimate the level of tension. For example, the analysis unit determines that the level of tension is high when the heart rate increases rapidly. The analysis unit can also determine that the level of tension is high when the blood pressure increases rapidly. The analysis unit can also determine that the level of tension is high when the electrodermal response changes rapidly. The warning unit changes the manner of warning based on the level of tension estimated by the analysis unit. For example, the warning unit changes the manner of warning when the level of tension is high. For example, the warning unit can add words of encouragement when a warning causes a significant increase in blood pressure. The warning unit can also reduce the volume of the alert sound when a warning causes a significant increase in blood pressure. The warning unit can also reduce the frequency of warnings when the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure increases significantly. The warning unit can also reduce the volume of the alert sound when blood pressure increases significantly. The warning unit can also reduce the frequency of warnings when the level of tension is high. As a result, the evacuation support system according to the embodiment can estimate the level of tension based on the user's biological data and issue an appropriate warning, thereby promoting calm evacuation behavior.For example, an evacuation support system can collect and analyze a user's biometric data, estimate their level of tension, and change the way it warns them, thereby helping them to calmly take evacuation action.

[0063] The collection unit can collect biometric data such as heart rate, blood pressure, and electrodermal response. The collection unit, for example, uses a heart rate sensor to measure heart rate. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor. The collection unit can also use a sphygmomanometer to measure blood pressure. For example, the collection unit can periodically measure the user's blood pressure using the sphygmomanometer. The collection unit can also use a sphygmomanometer to measure electrodermal response. For example, the collection unit can continuously monitor the user's electrodermal response using the sphygmomanometer. This improves the accuracy of estimating the tension level by collecting various biometric data. 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 heart rate data acquired by the heart rate sensor to the generation AI and cause the generation AI to analyze the heart rate data.

[0064] The analysis unit can analyze the collected biometric data and quantify the user's level of tension. The analysis unit can analyze the collected data using, for example, AI and quantify the user's level of tension. For example, the analysis unit can analyze fluctuations in heart rate to estimate the level of tension. The analysis unit can also analyze fluctuations in blood pressure to estimate the level of tension. The analysis unit can also analyze fluctuations in electrodermal response to estimate the level of tension. For example, the analysis unit can determine that the level of tension is high if the heart rate increases rapidly. The analysis unit can also determine that the level of tension is high if the blood pressure increases rapidly. The analysis unit can also determine that the level of tension is high if the electrodermal response changes rapidly. By quantifying the level of tension, the warning method can be appropriately changed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected biometric data to a generation AI and have the generation AI quantify the level of tension.

[0065] The warning unit can change the manner of warning when the level of tension is above a certain level. The warning unit can change the manner of warning when, for example, the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure rises significantly due to a warning. The warning unit can also reduce the volume of the alert sound when blood pressure rises significantly due to a warning. The warning unit can also reduce the frequency of warnings when the level of tension is high. For example, the warning unit can add words of encouragement when blood pressure rises significantly. The warning unit can also reduce the volume of the alert sound when blood pressure rises significantly. The warning unit can also reduce the frequency of warnings when the level of tension is high. In this way, by changing the manner of warning depending on the level of tension, it is possible to encourage the user to take calm evacuation action. Some or all of the above-mentioned processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input tension level data to the generation AI and cause the generation AI to change the manner of warning.

[0066] The warning unit can provide words of encouragement if the warning causes a significant rise in blood pressure. For example, the warning unit can provide words of encouragement to the user in a voice, such as "Please calm down." The warning unit can also provide words of reassurance to the user in a voice, such as "It's okay." The warning unit can also provide words of encouragement to the user in a voice, such as "Please take a deep breath." In this way, by providing words of encouragement when blood pressure rises, the user can regain their composure. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input blood pressure data into a generation AI and cause the generation AI to generate words of encouragement.

[0067] The warning unit can lower the volume of the alert sound if the warning causes a significant rise in blood pressure. For example, the warning unit lowers the volume of the alert sound if the warning causes a significant rise in blood pressure. For example, the warning unit can automatically adjust the volume of the alert sound to warn the user in a gentler tone. The warning unit can also adjust the frequency of the alert sound to warn the user in a tone that is gentle on the ears. The warning unit can also shorten the duration of the alert sound to warn the user in a short time. In this way, by lowering the volume of the alert sound when blood pressure rises, the user can regain their composure. Some or all of the above-mentioned processing in the warning unit may be performed using, or without, AI, for example. For example, the warning unit can input blood pressure data to a generation AI and cause the generation AI to adjust the alert sound.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting biometric data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting biometric data based on the estimated emotions. For example, when the user is nervous, the collection unit frequently collects heart rate and blood pressure data. Furthermore, when the user is relaxed, the collection unit can reduce the collection frequency and collect only necessary data. Furthermore, when the user is stressed, the collection unit can prioritize collection of galvanic skin response data. This allows for more accurate data collection by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input emotion data to the generation AI and have the generation AI adjust the collection timing.

[0069] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. The collection unit, for example, analyzes the user's past biometric data history and selects the optimal collection method. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency if abnormal fluctuations are detected. The collection unit can also concentrate collection during specific time periods based on the user's past blood pressure data. The collection unit can also intensify collection in stressful situations based on the user's past electrodermal response data. This improves the efficiency of data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the past biometric data history into the generation AI and have the generation AI select the optimal collection method.

[0070] The collection unit can perform filtering based on the user's current activity status and environment when collecting biometric data. For example, the collection unit performs filtering based on the user's current activity status and environment when collecting biometric data. For example, when the user is exercising, the collection unit filters out temporary increases in heart rate due to exercise. The collection unit can also prioritize collecting resting data when the user is stationary. The collection unit can also remove noise when the user is in a noisy environment to collect accurate data. This allows accurate data to be collected by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input activity status and environmental data to a generation AI and have the generation AI perform filtering.

[0071] The collection unit can select an appropriate collection means depending on the user's input method when collecting biometric data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting biometric data. For example, if the user uses voice input, the collection unit estimates the heart rate from the voice data. If the user uses text input, the collection unit can also estimate the stress level from the input content. If the user uses image input, the collection unit can also estimate the tension level from the facial expression. This enables efficient data collection by selecting the optimal collection means depending on the input method. 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 input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of the biometric 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 the biometric data to be collected based on the estimated emotions. For example, when the user is nervous, the collection unit prioritizes collecting heart rate and blood pressure data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting electrodermal response data. Furthermore, when the user is stressed, the collection unit can collect all biometric data equally. Thus, by prioritizing data based on 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input emotion data to the generation AI and have the generation AI determine the priority of the biometric data to be collected.

[0073] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting biometric data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting biometric data, the collection unit prioritizes collecting heart rate and blood pressure data when the user is in a disaster-stricken area. Furthermore, the collection unit can prioritize collecting galvanic skin response data when the user is in an evacuation shelter. Furthermore, the collection unit can collect all biometric data equally when the user is at home. In this way, highly relevant data can be efficiently collected by taking into account the 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 geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0074] The collection unit can analyze the user's social media activity and collect related data when collecting biometric data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting biometric data. For example, if the user posts on social media that they are feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. Furthermore, if the user posts that they are relaxed, the collection unit can also prioritize collecting galvanic skin response data. Furthermore, if the user posts about a disaster, the collection unit can collect all biometric data equally. This allows for efficient collection of related data by analyzing social media activity. 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 social media activity data to a generation AI and cause the generation AI to collect related data.

[0075] The collection unit can customize the collection method by reflecting the user's past feedback when collecting biometric data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting biometric data. For example, if the user has previously requested collection of heart rate data, the collection unit can prioritize collection of heart rate data. Furthermore, if the user has previously requested collection of blood pressure data, the collection unit can prioritize collection of blood pressure data. Furthermore, if the user has previously requested collection of electrodermal response data, the collection unit can prioritize collection of electrodermal response data. In this way, by reflecting past feedback, it is possible to provide the user with an optimal collection method. 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 past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit displays the analysis results in a simple and easy-to-understand format. Furthermore, if the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is stressed, the analysis unit can display the analysis results in a visually easy-to-understand graph format. By adjusting the presentation method of the analysis based on emotions, it is possible to provide analysis results that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 emotion data to the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the biometric data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the biometric data during analysis. For example, the analysis unit performs a detailed analysis when heart rate data is important. The analysis unit can also perform a detailed analysis when blood pressure data is important. The analysis unit can also perform a detailed analysis when galvanic skin response data is important. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of biometric data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of biometric data during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress level analysis algorithm to electrodermal response data. In this way, by applying an analysis algorithm depending on the category, the accuracy of the analysis is improved. 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 biometric data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. The analysis unit can also improve the analysis accuracy of current electrodermal response data by referring to the user's past electrodermal response analysis results. In this way, the accuracy of the current analysis is improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is nervous, 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. Furthermore, if the user is stressed, the analysis unit can provide a short, visually easy-to-understand analysis result. By adjusting the length of the analysis based on the emotions, it is possible to provide an appropriate analysis result for the user. The 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-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 emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0081] The analysis unit can determine the analysis priority based on the collection time of the biological data during analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the biological data during analysis. For example, the analysis unit prioritizes analysis of recently collected heart rate data. The analysis unit can also prioritize analysis of recently collected blood pressure data. The analysis unit can also prioritize analysis of recently collected electrodermal response data. This enables efficient analysis by determining the priority based on the collection time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the biometric data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the biometric data during analysis. For example, the analysis unit simultaneously analyzes heart rate and blood pressure data when they are related. The analysis unit can also simultaneously analyze electrodermal response and heart rate data when they are related. The analysis unit can also simultaneously analyze blood pressure and electrodermal response data when they are related. This enables efficient analysis by adjusting the order of analysis based on the relevance. 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 relevance data of the biometric data to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terminology according to the user's level of expertise. By adjusting the use of technical terminology according to the level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input expertise level data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0084] The warning unit can estimate the user's emotion and adjust the warning method based on the estimated user emotion. For example, the warning unit estimates the user's emotion and adjusts the warning method based on the estimated emotion. For example, if the user is nervous, the warning unit issues a warning in a calm voice. Also, if the user is relaxed, the warning unit can issue a warning in a normal voice. Also, if the user is stressed, the warning unit can issue a warning that includes words of encouragement. By adjusting the warning method based on the emotion, it is possible to provide an optimal warning for the user. 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 warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input emotion data to the generation AI and cause the generation AI to adjust the warning method.

[0085] The warning unit can select an appropriate warning method by analyzing the user's past warning responses when issuing a warning. For example, the warning unit can select the optimal warning method by analyzing the user's past warning responses when issuing a warning. For example, if the user previously responded to a warning in a calm voice, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning containing encouraging words, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning in a normal voice, the warning unit can issue the warning in a similar manner. In this way, by analyzing the past warning responses, the optimal warning method for the user can be selected. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past warning response data into the generation AI and cause the generation AI to select the optimal warning method.

[0086] The warning unit can customize the warning method based on the user's current situation when issuing a warning. For example, the warning unit customizes the warning method based on the user's current situation when issuing a warning. For example, the warning unit prioritizes an audio warning when the user is exercising. The warning unit can also prioritize a visual warning when the user is stationary. The warning unit can also prioritize a vibration warning when the user is in a noisy environment. This enables effective warning by customizing the warning method based on the current situation. Some or all of the above-mentioned processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input current situation data to a generation AI and cause the generation AI to customize the warning method.

[0087] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, the warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, if the user has provided feedback that audio warnings have been effective in the past, the warning unit can prioritize audio warnings. Furthermore, if the user has provided feedback that visual warnings have been effective in the past, the warning unit can prioritize visual warnings. Furthermore, if the user has provided feedback that vibration warnings have been effective in the past, the warning unit can prioritize vibration warnings. In this way, by reflecting feedback, it is possible to provide the optimal warning method for the user. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input feedback data to the generation AI and cause the generation AI to improve the warning method.

[0088] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated user emotions. The warning unit, for example, estimates the user's emotions and determines the priority of warnings based on the estimated emotions. For example, if the user is nervous, the warning unit can prioritize the most important warning. Also, if the user is relaxed, the warning unit can prioritize a normal warning. Also, if the user is stressed, the warning unit can prioritize a warning containing encouraging words. In this way, by determining the priority of warnings based on emotions, important warnings 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 warning unit may be performed using an AI, for example, or without an AI. For example, the warning unit can input emotion data to the generation AI and have the generation AI determine the priority of warnings.

[0089] The warning unit can select an appropriate warning method by taking into account the user's geographical location information when issuing a warning. For example, the warning unit selects the optimal warning method by taking into account the user's geographical location information when issuing a warning. For example, the warning unit prioritizes audio warnings when the user is in a disaster-stricken area. The warning unit can also prioritize visual warnings when the user is in an evacuation shelter. The warning unit can also use all warning methods equally when the user is at home. In this way, the optimal warning method for the user can be selected by taking into account the geographical location information. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal warning method.

[0090] The warning unit may analyze the user's social media activity and suggest a warning method when issuing a warning. For example, the warning unit may analyze the user's social media activity and suggest a warning method when issuing a warning. For example, if the user posts on social media that they are feeling stressed, the warning unit may prioritize an audio warning. Furthermore, if the user posts that they are relaxing, the warning unit may prioritize a visual warning. Furthermore, if the user posts about a disaster, the warning unit may equally use all warning methods. In this way, by analyzing social media activity, the optimal warning method for the user can be suggested. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may input social media activity data into a generation AI and have the generation AI suggest a warning method.

[0091] The warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, the warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, if the user has provided feedback that audio warnings were effective in the past, the warning unit can prioritize audio warnings. Furthermore, if the user has provided feedback that visual warnings were effective in the past, the warning unit can prioritize visual warnings. Furthermore, if the user has provided feedback that vibration warnings were effective in the past, the warning unit can prioritize vibration warnings. In this way, by reflecting past feedback, it is possible to provide the optimal warning method for the user. Some or all of the above-described processing in the warning unit may be performed using AI, for example, or may be performed without using AI. For example, the warning unit can input past feedback data into the generation AI and cause the generation AI to customize the warning method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and warning 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 can collect the user's biological data in real time using a heart rate sensor, a blood pressure monitor, and a skin electrodermal response sensor of the smart device 14. The analysis unit analyzes the biological data collected by the specific processing unit 290 of the data processing device 12 and estimates the user's level of tension. The warning unit can change the manner of warning using the control unit 46A of the smart device 14 based on the level of tension estimated by the analysis unit. For example, if the level of tension is high, the warning unit can output encouraging words using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and warning 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 can collect the user's biological data in real time using a heart rate sensor, a blood pressure monitor, or a skin electrodermal response sensor of the smart glasses 214. The analysis unit analyzes the biological data collected by the specific processing unit 290 of the data processing device 12 and estimates the user's level of tension. The warning unit can change the manner of warning using the control unit 46A of the smart glasses 214 based on the level of tension estimated by the analysis unit. For example, if the level of tension is high, the warning unit can output encouraging words using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and warning 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 can collect biometric data of the user in real time using a heart rate sensor, a blood pressure monitor, or a skin electrodermal response sensor of the headset type terminal 314. The analysis unit analyzes the biometric data collected by the specific processing unit 290 of the data processing device 12 and estimates the user's level of tension. The warning unit can change the manner of warning using the control unit 46A of the headset type terminal 314 based on the level of tension estimated by the analysis unit. For example, if the level of tension is high, the warning unit can output encouraging words using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and warning 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 can collect biometric data of the user in real time using a heart rate sensor, a blood pressure monitor, and a skin electrodermal response sensor of the robot 414. The analysis unit analyzes the biometric data collected by the specific processing unit 290 of the data processing device 12 and estimates the user's level of tension. The warning unit can change the manner of warning using the control unit 46A of the robot 414 based on the level of tension estimated by the analysis unit. For example, if the level of tension is high, the warning unit can output encouraging words using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing heart rate and blood pressure data. Also, if the user is relaxed, the analysis unit can prioritize analyzing electrodermal response data. Also, if the user is stressed, the analysis unit can analyze all biometric data equally. By determining the analysis priority based on emotions, important data can be prioritized for analysis. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotion data to the generation AI and have the generation AI determine the analysis priority.

[0094] The collection unit can analyze the user's past biometric data history and select an appropriate collection method. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency if abnormal fluctuations are detected. The collection unit can also concentrate collection during specific time periods based on the user's past blood pressure data. The collection unit can also strengthen collection in stressful situations by referring to the user's past electrodermal response data. This improves the efficiency of data collection by selecting the optimal collection method based on past data. 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 past biometric data history into the generation AI and have the generation AI select the optimal collection method.

[0095] The warning unit can estimate the user's emotions and adjust the warning method based on the estimated emotions. For example, if the user is nervous, the warning unit can issue a warning in a calm voice. Furthermore, if the user is relaxed, the warning unit can issue a warning in a normal voice. Furthermore, if the user is stressed, the warning unit can issue a warning that includes words of encouragement. By adjusting the warning method based on emotions, the optimal warning can be provided to the user. The 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 warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input emotion data to the generation AI and have the generation AI adjust the warning method.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biometric data. For example, if heart rate data is important, the analysis unit can perform a detailed analysis. Also, if blood pressure data is important, the analysis unit can perform a detailed analysis. Also, if electrodermal response data is important, the analysis unit can perform a detailed analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0097] The collection unit can estimate the user's emotions and adjust the timing of collecting biometric data based on the estimated emotions. For example, if the user is nervous, the collection unit can frequently collect heart rate and blood pressure data. Alternatively, if the user is relaxed, the collection unit can reduce the collection frequency and collect only necessary data. Alternatively, if the user is stressed, the collection unit can prioritize collecting galvanic skin response data. This allows for more accurate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input emotion data to the generation AI and have the generation AI adjust the collection timing.

[0098] When issuing a warning, the warning unit can analyze the user's past warning responses and select an appropriate warning method. For example, if the user previously responded to a warning in a calm voice, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning containing encouraging words, the warning unit can issue the warning in a similar manner. Furthermore, if the user previously responded to a warning in a normal voice, the warning unit can issue the warning in a similar manner. In this way, by analyzing past warning responses, the optimal warning method for the user can be selected. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input past warning response data into the generation AI and have the generation AI select the optimal warning method.

[0099] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can display the analysis results in a simple, easy-to-understand format. Furthermore, if the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is stressed, the analysis unit can display the analysis results in a visually easy-to-understand graph format. By adjusting the way the analysis is presented based on emotions, the analysis results can be provided in an easy-to-understand format. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0100] When collecting biometric data, the collection unit can perform filtering based on the user's current activity status and environment. For example, if the user is exercising, the collection unit filters out temporary increases in heart rate due to exercise. Furthermore, if the user is stationary, the collection unit can also prioritize collecting resting data. Furthermore, if the user is in a noisy environment, the collection unit can also remove noise to collect accurate data. Thus, accurate data can be collected by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input activity status and environmental data into a generation AI and have the generation AI perform filtering.

[0101] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biometric data. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress level analysis algorithm to electrodermal response data. By applying an analysis algorithm according to the category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input biometric data to a generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0102] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated emotions. For example, if the user is nervous, the warning unit can prioritize the most important warning. Also, if the user is relaxed, the warning unit can prioritize a normal warning. Also, if the user is stressed, the warning unit can prioritize a warning containing encouraging words. Thus, by determining the priority of warnings based on emotions, important warnings can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the warning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the warning unit can input emotion data to the generation AI and have the generation AI determine the priority of warnings.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects the user's biometric data. The user's biometric data includes heart rate, blood pressure, and galvanic skin response. The collection unit collects this data using a heart rate sensor, a blood pressure monitor, and a galvanic skin response sensor. For example, the heart rate is measured in real time using the heart rate sensor, blood pressure is measured periodically using the sphygmomanometer, and galvanic skin response is continuously monitored using the galvanic skin response sensor. Step 2: The analysis unit analyzes the biometric data collected by the collection unit and estimates the user's level of tension. The analysis unit uses AI to analyze the data and quantify the level of tension based on fluctuations in heart rate, blood pressure, and electrodermal response. For example, if there is a sudden increase in heart rate or blood pressure, or if there is a sudden change in electrodermal response, it will determine that the level of tension is high. Step 3: The warning unit changes the warning method based on the level of tension estimated by the analysis unit. For example, if the level of tension is high and the warning significantly increases blood pressure, the unit can add words of encouragement, reduce the volume of the alert sound, or reduce the frequency of warnings.

[0105] 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.

[0106] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 biometric data of a user; an analysis unit that analyzes the biological data collected by the collection unit and estimates a level of tension of the user; a warning unit that changes the manner of warning based on the degree of tension estimated by the analysis unit. A system characterized by:

2. The collecting unit Collects biometric data such as heart rate, blood pressure, and galvanic skin response 2. The system of claim 1.

3. The analysis unit Analyze the collected biometric data and quantify the user's level of tension 2. The system of claim 1.

4. The warning unit Change how you warn when tension is above a certain level 2. The system of claim 1.

5. The warning unit If the warning causes a significant rise in blood pressure, include words of encouragement.

2. The system of claim 1.

6. The warning unit If the warning causes a significant increase in blood pressure, the alert tone will be lowered.

2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of biometric data collection based on the estimated user emotions.

2. The system of claim 1.

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

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