system

Abstract

The system according to this embodiment aims to analyze biometric data collected from a smartwatch and provide appropriate notifications to the user. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a notification unit. The collection unit collects biometric data from a smartwatch. The analysis unit analyzes the biometric data collected by the collection unit. The notification unit notifies the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the amount of alcohol consumed and the health status in real time and to give appropriate feedback.

[0005] The system according to the embodiment aims to analyze the biological data collected from the smartwatch and give appropriate notifications to the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects biological data from the smartwatch. The analysis unit analyzes the biological data collected by the collection unit. The notification unit gives a notification to the user based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze biometric data collected from a smartwatch and provide appropriate notifications to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when three or more matters are expressed by connecting them with "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The alcohol management support system according to an embodiment of the present invention is a system that works in conjunction with a smartwatch to collect personal biometric data in real time and analyze it using a generative AI to provide personalized health management and safe driving support. The alcohol management support system uses a smartwatch to acquire biometric data such as heart rate, body temperature, and activity level in real time. This allows for the creation and analysis of an alcohol metabolism profile based on the individual's weight and constitution. Next, the collected data is analyzed using a generative AI. The generative AI provides the user with warnings in an interactive format according to the amount of alcohol consumed and their physical condition, and predicts and notifies them of the time it will take for the alcohol to leave their system based on their perceived time. This interactive approach allows the user to understand their drinking status in real time and use it to help with health management and safe driving. For example, when a user starts drinking, the smartwatch detects changes in heart rate and body temperature, and the generative AI analyzes this data to estimate the amount of alcohol consumed. The generative AI notifies the user, "Your current alcohol consumption is XX ml. It will take approximately XX hours for the alcohol to be completely metabolized." Furthermore, if the amount of alcohol consumed exceeds a certain standard, the generative AI provides a warning such as, "Your alcohol consumption is high. Please be careful about your health." Furthermore, the generating AI also provides a function to share drinking data so that partners can understand the user's drinking habits. This allows partners to support the user's health management. In this way, by utilizing smartwatches and generating AI, it is possible to support drinking management and promote a healthy lifestyle. As a result, the drinking management support system can understand the user's drinking habits in real time and use this information for health management and safe driving.

[0029] The alcohol management support system according to the embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects biometric data from a smartwatch. The data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time. The data collection unit measures heart rate using a heart rate sensor, for example. The data collection unit measures body temperature using a body temperature sensor, for example. The data collection unit measures activity level using an acceleration sensor, for example. The analysis unit analyzes the biometric data collected by the data collection unit. The analysis unit analyzes biometric data using a generative AI, for example. The analysis unit evaluates the user's physical condition by analyzing heart rate data, for example. The analysis unit evaluates the user's health status by analyzing body temperature data, for example. The analysis unit evaluates the user's exercise level by analyzing activity level data, for example. The notification unit notifies the user based on the analysis results obtained by the analysis unit. The notification unit sends a notification to a smartphone, for example. The notification unit issues an audio alert, for example. The notification unit issues a vibration alert, for example. This allows the alcohol management support system to collect, analyze, and notify users of their biometric data, enabling health management and safe driving support.

[0030] The data collection unit collects biometric data from the smartwatch. The unit acquires biometric data such as heart rate, body temperature, and activity level in real time. Specifically, it measures heart rate using a heart rate sensor, body temperature using a body temperature sensor, and activity level using an accelerometer. The heart rate sensor uses photoplethysmography (PPG) to detect blood flow and measure heart rate. The body temperature sensor uses an infrared sensor or thermistor to measure skin surface temperature. The accelerometer is used to detect the user's movements in three dimensions and evaluate activity level. These sensors are built into the smartwatch and, when worn on the user's wrist, can continuously collect data during daily life. The collected data is transmitted to a smartphone or cloud server via Bluetooth® or Wi-Fi for real-time monitoring. Furthermore, the data collection unit can use algorithms to integrate data from multiple sensors and remove noise to improve data accuracy. This allows the data collection unit to collect the user's biometric data with high accuracy and provide it to the analysis unit.

[0031] The analysis unit analyzes the biometric data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the biometric data. The generative AI utilizes deep learning technology to learn from large amounts of biometric data and accurately assess the user's health status and physical condition. Specifically, it analyzes heart rate data to assess the user's stress level and cardiac health, analyzes body temperature data to detect signs of fever or poor health, and analyzes activity level data to assess the user's exercise habits and energy expenditure. The generative AI integrates this data to build a model for evaluating the user's overall health status. For example, by combining heart rate variability patterns, changes in body temperature, and increases or decreases in activity levels, it can assess how the user is affected by alcohol consumption. Furthermore, the analysis unit can detect abnormal patterns and high-risk situations early by comparing them with past data and data from other users. This allows the analysis unit to monitor the user's health status in real time and respond quickly when necessary.

[0032] The notification unit notifies the user based on the analysis results obtained by the analysis unit. For example, the notification unit sends notifications to a smartphone. Through the smartphone application, it provides the user with information about their health status and physical condition and prompts them to take necessary actions. For example, if the heart rate is abnormally high or the body temperature is elevated, it will notify the user to take a rest. Also, if alcohol consumption is increasing health risks, it will warn the user to refrain from drinking. The notification unit can also issue voice alerts so that the user is immediately aware of them. Furthermore, by issuing vibration alerts, the smartwatch itself can draw the user's attention. This allows the user to receive important information about their health status in real time and take appropriate action. The notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can record how the user reacted to the notification and optimize the timing and content of the notification. In addition, the notification unit can reliably transmit information using multiple communication methods. This allows the notification unit to provide users with quick and reliable instructions, realizing health management and safe driving support.

[0033] The notification unit can alert the user in an interactive format according to their alcohol consumption and physical condition. For example, if the user is drinking too much, the notification unit might notify them with "You are drinking too much. Please take care of your health." For example, if the user is feeling unwell, the notification unit might notify them with "You seem to be feeling unwell. Please rest." The notification unit provides appropriate warnings based on the amount of alcohol consumed and the user's physical condition. This improves the user's health management through warnings tailored to their alcohol consumption and physical condition. Some or all of the above processing in the notification unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the notification unit inputs alcohol consumption and physical condition data into a generating AI, and the generating AI provides warnings in an interactive format.

[0034] The notification unit can predict and notify the time it will take for alcohol to leave the system based on perceived time. For example, the notification unit may notify, based on the elapsed time since the start of drinking, that "It will take approximately ○ hours for alcohol to be completely metabolized." For example, the notification unit may notify, based on the user's perceived time, that "It will take approximately ○ hours for alcohol to leave the system." For example, the notification unit may predict and notify the time it will take for alcohol to leave the system based on the amount of alcohol consumed and perceived time. This enables safe driving support by predicting and notifying the time it will take for alcohol to leave the system. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs perceived time data into a generation AI, and the generation AI predicts and notifies the time it will take for alcohol to leave the system.

[0035] The data collection unit can acquire biometric data such as heart rate, body temperature, and activity level in real time. For example, the data collection unit can acquire heart rate in real time using a heart rate sensor. For example, the data collection unit can acquire body temperature in real time using a body temperature sensor. For example, the data collection unit can acquire activity level in real time using an accelerometer. This enables accurate health management by acquiring biometric data in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs heart rate data into the AI, and the AI ​​analyzes the data in real time.

[0036] The analysis unit can create and analyze alcohol metabolism profiles based on individual weight and constitution. For example, the analysis unit can create an alcohol metabolism profile based on the user's weight data. For example, the analysis unit can create an alcohol metabolism profile based on the user's constitution data. For example, the analysis unit can create an alcohol metabolism profile by combining weight and constitution data. This enables personalized health management through analysis based on individual weight and constitution. Some or all of the above processing in the analysis unit may be performed using or without a generating AI. For example, the analysis unit inputs weight and constitution data into a generating AI, and the generating AI creates and analyzes an alcohol metabolism profile.

[0037] The notification unit can provide a function to share drinking data with a partner. For example, the notification unit can send the user's drinking data to the partner's smartphone. For example, the notification unit can store the drinking data in the cloud and make it accessible to the partner. For example, the notification unit can send the drinking data to the partner via email. This allows the partner to support the user's health management by sharing the drinking data. Some or all of the above processing in the notification unit may be performed using a generative AI, or not using a generative AI. For example, the notification unit inputs the drinking data into a generative AI, and the generative AI shares the data with the partner.

[0038] The data collection unit can analyze the user's past biometric data history and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data and acquire biometric data during the most stable time period. For example, the data collection unit can analyze the user's past body temperature data and acquire biometric data during the time period when the body temperature is stable. For example, the data collection unit can analyze the user's past activity level data and acquire biometric data during the time period when activity is low. By selecting the optimal acquisition method based on past data history, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs past biometric data history into a generating AI, and the generating AI selects the optimal acquisition method.

[0039] The data collection unit can filter biometric data based on the user's current activity status and environment when acquiring it. For example, if the user is exercising, the data collection unit will acquire biometric data after the exercise. For example, if the user is resting, the data collection unit will acquire biometric data during the rest period. For example, if the user is out, the data collection unit will acquire biometric data after they return home. This improves the accuracy of the data through filtering based on activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs current activity status and environment data into a generating AI, and the generating AI performs the filtering.

[0040] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring biometric data. For example, if the user is at high altitude, the data collection unit will prioritize the acquisition of oxygen saturation data. For example, if the user is in a cold region, the data collection unit will prioritize the acquisition of body temperature data. For example, if the user is in an urban area, the data collection unit will prioritize the acquisition of heart rate data. This allows for the priority acquisition of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI, and the generating AI will prioritize the acquisition of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity when acquiring biometric data and obtain relevant data. For example, if the user is stressed on social media, the data collection unit can acquire heart rate data. For example, if the user is relaxed on social media, the data collection unit can acquire body temperature data. For example, if the user is active on social media, the data collection unit can acquire activity level data. This allows for the efficient acquisition of relevant data by considering social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs social media activity data into a generating AI, and the generating AI acquires relevant data.

[0042] The analysis unit can optimize the analysis algorithm based on individual weight and constitution during analysis. For example, the analysis unit adjusts the alcohol metabolism rate based on the user's weight. For example, the analysis unit creates an alcohol metabolism profile based on the user's constitution. For example, the analysis unit selects the optimal analysis algorithm considering the user's weight and constitution. This improves the accuracy of the analysis by optimizing the analysis algorithm based on weight and constitution. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs weight and constitution data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs past analysis result data into a generating AI, and the generating AI improves the accuracy of the analysis.

[0044] The analysis unit can perform analysis while considering the user's lifestyle and dietary history. For example, the analysis unit adjusts the alcohol metabolism rate based on the user's dietary history. For example, the analysis unit optimizes the analysis algorithm by considering the user's lifestyle. For example, the analysis unit provides the optimal analysis result by considering the user's dietary history and lifestyle. As a result, the accuracy of the analysis result is improved by considering lifestyle and dietary history. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit inputs lifestyle and dietary history data into a generation AI, and the generation AI performs the analysis.

[0045] The analysis unit can adjust the level of detail of the analysis based on the user's health status during the analysis. For example, if the user is healthy, the analysis unit provides detailed analysis results. For example, if the user is unwell, the analysis unit provides concise analysis results. The analysis unit adjusts the level of detail of the analysis according to the user's health status. This ensures that appropriate analysis results are provided by adjusting the level of detail of the analysis according to the health status. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs health status data into a generating AI, and the generating AI adjusts the level of detail of the analysis.

[0046] The notification unit can customize the content of notifications according to the amount of alcohol consumed and the user's physical condition. For example, if the amount of alcohol consumed is high, the notification unit will issue a health warning. For example, if the user is feeling unwell, the notification unit will issue a notification encouraging rest. For example, the notification unit will provide the most appropriate notification content according to the amount of alcohol consumed and the user's physical condition. This allows the user to receive appropriate information by customizing the notification content according to the amount of alcohol consumed and the user's physical condition. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs alcohol consumption and physical condition data into the generation AI, and the generation AI customizes the notification content.

[0047] The notification unit can select the optimal notification timing by referring to the user's past drinking history when sending a notification. For example, the notification unit selects the optimal notification timing based on the user's past drinking history. For example, the notification unit refers to the user's past drinking history and sends a notification if the amount of alcohol consumed is high. For example, the notification unit analyzes the user's past drinking history and provides the optimal notification timing. This allows notifications to be sent at the optimal time by referring to past drinking history. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs past drinking history data into a generation AI, and the generation AI selects the optimal notification timing.

[0048] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is at home, the notification unit may send an audio notification. If the user is out, for example, the notification unit may send a vibration notification. If the user is driving, for example, the notification unit may send a visual notification. This allows the notification unit to notify the user in an appropriate manner by selecting a notification method that takes geographical location information into account. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs geographical location data into a generation AI, and the generation AI selects the optimal notification method.

[0049] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, if the user is feeling stressed on social media, the notification unit will send a notification encouraging relaxation. For example, if the user is relaxing on social media, the notification unit will send a general notification. For example, if the user is active on social media, the notification unit will send a notification about their activity. This allows the notification unit to provide the user with appropriate information by adjusting the notification content to take social media activity into account. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit inputs social media activity data into a generative AI, and the generative AI adjusts the notification content.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze the user's past biometric data history and select the optimal acquisition method. For example, it can analyze the user's past heart rate data and acquire biometric data during the most stable time period. It can analyze the user's past body temperature data and acquire biometric data during the time period when the body temperature is stable. It can analyze the user's past activity level data and acquire biometric data during the time period when activity is low. By selecting the optimal acquisition method based on past data history, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs past biometric data history into a generating AI, and the generating AI selects the optimal acquisition method.

[0052] The data collection unit can filter biometric data based on the user's current activity status and environment. For example, if the user is exercising, biometric data can be acquired after the exercise. If the user is resting, biometric data can be acquired during the rest period. If the user is out, biometric data can be acquired after returning home. This improves the accuracy of the data through filtering based on activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs current activity status and environmental data into a generating AI, which then performs the filtering.

[0053] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring biometric data. For example, if the user is at high altitude, oxygen saturation data can be prioritized. If the user is in a cold region, body temperature data can be prioritized. If the user is in an urban area, heart rate data can be prioritized. This allows for the priority acquisition of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs geographical location data into a generating AI, and the generating AI prioritizes the acquisition of highly relevant data.

[0054] The data collection unit can analyze the user's social media activity when acquiring biometric data and obtain relevant data. For example, if the user is stressed on social media, heart rate data can be acquired. If the user is relaxed on social media, body temperature data can be acquired. If the user is active on social media, activity level data can be acquired. This allows for efficient acquisition of relevant data by considering social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs social media activity data into a generating AI, and the generating AI acquires relevant data.

[0055] The analysis unit can perform analysis while considering the user's lifestyle and dietary history. For example, it can adjust the alcohol metabolism rate based on the user's dietary history. It can optimize the analysis algorithm by considering the user's lifestyle. It can provide optimal analysis results by considering the user's dietary history and lifestyle. As a result, the accuracy of the analysis results is improved by considering lifestyle and dietary history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs lifestyle and dietary history data into a generation AI, and the generation AI performs the analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects biometric data from the smartwatch. The data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time. The data collection unit measures heart rate using a heart rate sensor, body temperature using a body temperature sensor, and activity level using an accelerometer. Step 2: The analysis unit analyzes the biometric data collected by the collection unit. The analysis unit uses a generation AI to analyze the biometric data, analyzes heart rate data to assess the user's physical condition, analyzes body temperature data to assess the user's health status, and analyzes activity level data to assess the user's exercise level. Step 3: The notification unit notifies the user based on the analysis results obtained by the analysis unit. The notification unit sends a notification to the smartphone and issues an audio alert or vibration alert.

[0058] (Example of form 2) The alcohol management support system according to an embodiment of the present invention is a system that works in conjunction with a smartwatch to collect personal biometric data in real time and analyze it using a generative AI to provide personalized health management and safe driving support. The alcohol management support system uses a smartwatch to acquire biometric data such as heart rate, body temperature, and activity level in real time. This allows for the creation and analysis of an alcohol metabolism profile based on the individual's weight and constitution. Next, the collected data is analyzed using a generative AI. The generative AI provides the user with warnings in an interactive format according to the amount of alcohol consumed and their physical condition, and predicts and notifies them of the time it will take for the alcohol to leave their system based on their perceived time. This interactive approach allows the user to understand their drinking status in real time and use it to help with health management and safe driving. For example, when a user starts drinking, the smartwatch detects changes in heart rate and body temperature, and the generative AI analyzes this data to estimate the amount of alcohol consumed. The generative AI notifies the user, "Your current alcohol consumption is XX ml. It will take approximately XX hours for the alcohol to be completely metabolized." Furthermore, if the amount of alcohol consumed exceeds a certain standard, the generative AI provides a warning such as, "Your alcohol consumption is high. Please be careful about your health." Furthermore, the generating AI also provides a function to share drinking data so that partners can understand the user's drinking habits. This allows partners to support the user's health management. In this way, by utilizing smartwatches and generating AI, it is possible to support drinking management and promote a healthy lifestyle. As a result, the drinking management support system can understand the user's drinking habits in real time and use this information for health management and safe driving.

[0059] The alcohol management support system according to the embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects biometric data from a smartwatch. The data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time. The data collection unit measures heart rate using a heart rate sensor, for example. The data collection unit measures body temperature using a body temperature sensor, for example. The data collection unit measures activity level using an acceleration sensor, for example. The analysis unit analyzes the biometric data collected by the data collection unit. The analysis unit analyzes biometric data using a generative AI, for example. The analysis unit evaluates the user's physical condition by analyzing heart rate data, for example. The analysis unit evaluates the user's health status by analyzing body temperature data, for example. The analysis unit evaluates the user's exercise level by analyzing activity level data, for example. The notification unit notifies the user based on the analysis results obtained by the analysis unit. The notification unit sends a notification to a smartphone, for example. The notification unit issues an audio alert, for example. The notification unit issues a vibration alert, for example. This allows the alcohol management support system to collect, analyze, and notify users of their biometric data, enabling health management and safe driving support.

[0060] The data collection unit collects biometric data from the smartwatch. The unit acquires biometric data such as heart rate, body temperature, and activity level in real time. Specifically, it measures heart rate using a heart rate sensor, body temperature using a body temperature sensor, and activity level using an accelerometer. The heart rate sensor uses photoplethysmography (PPG) to detect blood flow and measure heart rate. The body temperature sensor uses an infrared sensor or thermistor to measure skin surface temperature. The accelerometer is used to detect the user's movement in three dimensions and evaluate activity level. These sensors are built into the smartwatch and, when worn on the user's wrist, can continuously collect data during daily life. The collected data is transmitted to a smartphone or cloud server via Bluetooth or Wi-Fi and monitored in real time. Furthermore, to improve data accuracy, the data collection unit can integrate data from multiple sensors and use algorithms to remove noise. This allows the data collection unit to collect the user's biometric data with high accuracy and provide it to the analysis unit.

[0061] The analysis unit analyzes the biometric data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the biometric data. The generative AI utilizes deep learning technology to learn from large amounts of biometric data and accurately assess the user's health status and physical condition. Specifically, it analyzes heart rate data to assess the user's stress level and cardiac health, analyzes body temperature data to detect signs of fever or poor health, and analyzes activity level data to assess the user's exercise habits and energy expenditure. The generative AI integrates this data to build a model for evaluating the user's overall health status. For example, by combining heart rate variability patterns, changes in body temperature, and increases or decreases in activity levels, it can assess how the user is affected by alcohol consumption. Furthermore, the analysis unit can detect abnormal patterns and high-risk situations early by comparing them with past data and data from other users. This allows the analysis unit to monitor the user's health status in real time and respond quickly when necessary.

[0062] The notification unit notifies the user based on the analysis results obtained by the analysis unit. For example, the notification unit sends notifications to a smartphone. Through the smartphone application, it provides the user with information about their health status and physical condition and prompts them to take necessary actions. For example, if the heart rate is abnormally high or the body temperature is elevated, it will notify the user to take a rest. Also, if alcohol consumption is increasing health risks, it will warn the user to refrain from drinking. The notification unit can also issue voice alerts so that the user is immediately aware of them. Furthermore, by issuing vibration alerts, the smartwatch itself can draw the user's attention. This allows the user to receive important information about their health status in real time and take appropriate action. The notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, it can record how the user reacted to the notification and optimize the timing and content of the notification. In addition, the notification unit can reliably transmit information using multiple communication methods. This allows the notification unit to provide users with quick and reliable instructions, realizing health management and safe driving support.

[0063] The notification unit can alert the user in an interactive format according to their alcohol consumption and physical condition. For example, if the user is drinking too much, the notification unit might notify them with "You are drinking too much. Please take care of your health." For example, if the user is feeling unwell, the notification unit might notify them with "You seem to be feeling unwell. Please rest." The notification unit provides appropriate warnings based on the amount of alcohol consumed and the user's physical condition. This improves the user's health management through warnings tailored to their alcohol consumption and physical condition. Some or all of the above processing in the notification unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the notification unit inputs alcohol consumption and physical condition data into a generating AI, and the generating AI provides warnings in an interactive format.

[0064] The notification unit can predict and notify the time it will take for alcohol to leave the system based on perceived time. For example, the notification unit may notify, based on the elapsed time since the start of drinking, that "It will take approximately ○ hours for alcohol to be completely metabolized." For example, the notification unit may notify, based on the user's perceived time, that "It will take approximately ○ hours for alcohol to leave the system." For example, the notification unit may predict and notify the time it will take for alcohol to leave the system based on the amount of alcohol consumed and perceived time. This enables safe driving support by predicting and notifying the time it will take for alcohol to leave the system. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs perceived time data into a generation AI, and the generation AI predicts and notifies the time it will take for alcohol to leave the system.

[0065] The data collection unit can acquire biometric data such as heart rate, body temperature, and activity level in real time. For example, the data collection unit can acquire heart rate in real time using a heart rate sensor. For example, the data collection unit can acquire body temperature in real time using a body temperature sensor. For example, the data collection unit can acquire activity level in real time using an accelerometer. This enables accurate health management by acquiring biometric data in real time. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs heart rate data into the AI, and the AI ​​analyzes the data in real time.

[0066] The analysis unit can create and analyze alcohol metabolism profiles based on individual weight and constitution. For example, the analysis unit can create an alcohol metabolism profile based on the user's weight data. For example, the analysis unit can create an alcohol metabolism profile based on the user's constitution data. For example, the analysis unit can create an alcohol metabolism profile by combining weight and constitution data. This enables personalized health management through analysis based on individual weight and constitution. Some or all of the above processing in the analysis unit may be performed using or without a generating AI. For example, the analysis unit inputs weight and constitution data into a generating AI, and the generating AI creates and analyzes an alcohol metabolism profile.

[0067] The notification unit can provide a function to share drinking data with a partner. For example, the notification unit can send the user's drinking data to the partner's smartphone. For example, the notification unit can store the drinking data in the cloud and make it accessible to the partner. For example, the notification unit can send the drinking data to the partner via email. This allows the partner to support the user's health management by sharing the drinking data. Some or all of the above processing in the notification unit may be performed using a generative AI, or not using a generative AI. For example, the notification unit inputs the drinking data into a generative AI, and the generative AI shares the data with the partner.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of biometric data acquisition based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay biometric data acquisition until the user is relaxed. For example, if the user is relaxed, the data collection unit may acquire biometric data periodically. For example, if the user is in a hurry, the data collection unit may acquire biometric data quickly. This allows for more accurate data collection by adjusting the data acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the data acquisition timing.

[0069] The data collection unit can analyze the user's past biometric data history and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data and acquire biometric data during the most stable time period. For example, the data collection unit can analyze the user's past body temperature data and acquire biometric data during the time period when the body temperature is stable. For example, the data collection unit can analyze the user's past activity level data and acquire biometric data during the time period when activity is low. By selecting the optimal acquisition method based on past data history, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs past biometric data history into a generating AI, and the generating AI selects the optimal acquisition method.

[0070] The data collection unit can filter biometric data based on the user's current activity status and environment when acquiring it. For example, if the user is exercising, the data collection unit will acquire biometric data after the exercise. For example, if the user is resting, the data collection unit will acquire biometric data during the rest period. For example, if the user is out, the data collection unit will acquire biometric data after they return home. This improves the accuracy of the data through filtering based on activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs current activity status and environment data into a generating AI, and the generating AI performs the filtering.

[0071] The data collection unit can estimate the user's emotions and determine the priority of biometric data to acquire based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes acquiring heart rate data. For example, if the user is relaxed, the data collection unit prioritizes acquiring body temperature data. For example, if the user is in a hurry, the data collection unit prioritizes acquiring activity level data. This allows for the priority acquisition of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI determines the data priority.

[0072] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring biometric data. For example, if the user is at high altitude, the data collection unit will prioritize the acquisition of oxygen saturation data. For example, if the user is in a cold region, the data collection unit will prioritize the acquisition of body temperature data. For example, if the user is in an urban area, the data collection unit will prioritize the acquisition of heart rate data. This allows for the priority acquisition of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI, and the generating AI will prioritize the acquisition of highly relevant data.

[0073] The data collection unit can analyze the user's social media activity when acquiring biometric data and obtain relevant data. For example, if the user is stressed on social media, the data collection unit can acquire heart rate data. For example, if the user is relaxed on social media, the data collection unit can acquire body temperature data. For example, if the user is active on social media, the data collection unit can acquire activity level data. This allows for the efficient acquisition of relevant data by considering social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs social media activity data into a generating AI, and the generating AI acquires relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. This allows for a deeper understanding of the analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the presentation of the analysis.

[0075] The analysis unit can optimize the analysis algorithm based on individual weight and constitution during analysis. For example, the analysis unit adjusts the alcohol metabolism rate based on the user's weight. For example, the analysis unit creates an alcohol metabolism profile based on the user's constitution. For example, the analysis unit selects the optimal analysis algorithm considering the user's weight and constitution. This improves the accuracy of the analysis by optimizing the analysis algorithm based on weight and constitution. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs weight and constitution data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0076] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs past analysis result data into a generating AI, and the generating AI improves the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes the analysis of heart rate data. For example, if the user is relaxed, the analysis unit prioritizes the analysis of body temperature data. For example, if the user is in a hurry, the analysis unit prioritizes the analysis of activity level data. This allows important data to be analyzed preferentially by prioritizing analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI determines the priority of analysis.

[0078] The analysis unit can perform analysis while considering the user's lifestyle and dietary history. For example, the analysis unit adjusts the alcohol metabolism rate based on the user's dietary history. For example, the analysis unit optimizes the analysis algorithm by considering the user's lifestyle. For example, the analysis unit provides the optimal analysis result by considering the user's dietary history and lifestyle. As a result, the accuracy of the analysis result is improved by considering lifestyle and dietary history. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit inputs lifestyle and dietary history data into a generation AI, and the generation AI performs the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the user's health status during the analysis. For example, if the user is healthy, the analysis unit provides detailed analysis results. For example, if the user is unwell, the analysis unit provides concise analysis results. The analysis unit adjusts the level of detail of the analysis according to the user's health status. This ensures that appropriate analysis results are provided by adjusting the level of detail of the analysis according to the health status. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs health status data into a generating AI, and the generating AI adjusts the level of detail of the analysis.

[0080] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated emotions. For example, if the user is tense, the notification unit will use calm language. If the user is relaxed, the notification unit will use cheerful language. If the user is in a hurry, the notification unit will use concise language. This allows for a deeper understanding of the notification content by adjusting the way notifications are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using the generative AI or not. For example, the notification unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the way notifications are expressed.

[0081] The notification unit can customize the content of notifications according to the amount of alcohol consumed and the user's physical condition. For example, if the amount of alcohol consumed is high, the notification unit will issue a health warning. For example, if the user is feeling unwell, the notification unit will issue a notification encouraging rest. For example, the notification unit will provide the most appropriate notification content according to the amount of alcohol consumed and the user's physical condition. This allows the user to receive appropriate information by customizing the notification content according to the amount of alcohol consumed and the user's physical condition. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs alcohol consumption and physical condition data into the generation AI, and the generation AI customizes the notification content.

[0082] The notification unit can select the optimal notification timing by referring to the user's past drinking history when sending a notification. For example, the notification unit selects the optimal notification timing based on the user's past drinking history. For example, the notification unit refers to the user's past drinking history and sends a notification if the amount of alcohol consumed is high. For example, the notification unit analyzes the user's past drinking history and provides the optimal notification timing. This allows notifications to be sent at the optimal time by referring to past drinking history. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs past drinking history data into a generation AI, and the generation AI selects the optimal notification timing.

[0083] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize health-related notifications. If the user is relaxed, the notification unit will prioritize general notifications. If the user is in a hurry, the notification unit will prioritize important notifications. This allows important notifications to be prioritized by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using a generative AI or not. For example, the notification unit inputs user emotion data into a generative AI, and the generative AI determines the priority of notifications.

[0084] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user is at home, the notification unit may send an audio notification. If the user is out, for example, the notification unit may send a vibration notification. If the user is driving, for example, the notification unit may send a visual notification. This allows the notification unit to notify the user in an appropriate manner by selecting a notification method that takes geographical location information into account. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit inputs geographical location data into a generation AI, and the generation AI selects the optimal notification method.

[0085] The notification unit can analyze the user's social media activity and adjust the notification content when sending a notification. For example, if the user is feeling stressed on social media, the notification unit will send a notification encouraging relaxation. For example, if the user is relaxing on social media, the notification unit will send a general notification. For example, if the user is active on social media, the notification unit will send a notification about their activity. This allows the notification unit to provide the user with appropriate information by adjusting the notification content to take social media activity into account. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit inputs social media activity data into a generative AI, and the generative AI adjusts the notification content.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. If the user is excited, visually stimulating analysis results can be provided. This allows for a deeper understanding of the analysis results by adjusting their presentation according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using generative AI or not.

[0088] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated emotions. For example, if the user is nervous, the notification can be expressed in a calm manner. If the user is relaxed, the notification can be expressed in a cheerful manner. If the user is in a hurry, the notification can be expressed in a concise manner. This allows for a deeper understanding of the notification content by adjusting the way notifications are expressed according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using generative AI or not.

[0089] The data collection unit can estimate the user's emotions and adjust the timing of biometric data acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition of biometric data can be delayed until the user is relaxed. If the user is relaxed, biometric data can be acquired regularly. If the user is in a hurry, biometric data can be acquired in a short amount of time. This allows for more accurate data collection by adjusting the data acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using generative AI or not.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis of heart rate data can be prioritized. If the user is relaxed, the analysis of body temperature data can be prioritized. If the user is in a hurry, the analysis of activity level data can be prioritized. This allows important data to be analyzed preferentially by prioritizing analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using generative AI or not using generative AI.

[0091] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, health-related notifications can be prioritized. If the user is relaxed, general notifications can be prioritized. If the user is in a hurry, important notifications can be prioritized. This allows important notifications to be prioritized by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the notification unit may be performed using generative AI or not using generative AI.

[0092] The data collection unit can analyze the user's past biometric data history and select the optimal acquisition method. For example, it can analyze the user's past heart rate data and acquire biometric data during the most stable time period. It can analyze the user's past body temperature data and acquire biometric data during the time period when the body temperature is stable. It can analyze the user's past activity level data and acquire biometric data during the time period when activity is low. By selecting the optimal acquisition method based on past data history, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs past biometric data history into a generating AI, and the generating AI selects the optimal acquisition method.

[0093] The data collection unit can filter biometric data based on the user's current activity status and environment. For example, if the user is exercising, biometric data can be acquired after the exercise. If the user is resting, biometric data can be acquired during the rest period. If the user is out, biometric data can be acquired after returning home. This improves the accuracy of the data through filtering based on activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs current activity status and environmental data into a generating AI, which then performs the filtering.

[0094] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring biometric data. For example, if the user is at high altitude, oxygen saturation data can be prioritized. If the user is in a cold region, body temperature data can be prioritized. If the user is in an urban area, heart rate data can be prioritized. This allows for the priority acquisition of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs geographical location data into a generating AI, and the generating AI prioritizes the acquisition of highly relevant data.

[0095] The data collection unit can analyze the user's social media activity when acquiring biometric data and obtain relevant data. For example, if the user is stressed on social media, heart rate data can be acquired. If the user is relaxed on social media, body temperature data can be acquired. If the user is active on social media, activity level data can be acquired. This allows for efficient acquisition of relevant data by considering social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs social media activity data into a generating AI, and the generating AI acquires relevant data.

[0096] The analysis unit can perform analysis while considering the user's lifestyle and dietary history. For example, it can adjust the alcohol metabolism rate based on the user's dietary history. It can optimize the analysis algorithm by considering the user's lifestyle. It can provide optimal analysis results by considering the user's dietary history and lifestyle. As a result, the accuracy of the analysis results is improved by considering lifestyle and dietary history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs lifestyle and dietary history data into a generation AI, and the generation AI performs the analysis.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects biometric data from the smartwatch. The data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time. The data collection unit measures heart rate using a heart rate sensor, body temperature using a body temperature sensor, and activity level using an accelerometer. Step 2: The analysis unit analyzes the biometric data collected by the collection unit. The analysis unit uses a generation AI to analyze the biometric data, analyzes heart rate data to assess the user's physical condition, analyzes body temperature data to assess the user's health status, and analyzes activity level data to assess the user's exercise level. Step 3: The notification unit notifies the user based on the analysis results obtained by the analysis unit. The notification unit sends a notification to the smartphone and issues an audio alert or vibration alert.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time using the heart rate sensor, body temperature sensor, and acceleration sensor of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected biometric data using a generation AI. The notification unit is implemented in the control unit 46A of the smart device 14 and sends notifications to a smartphone or issues voice alerts or vibration alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time using the heart rate sensor, body temperature sensor, and acceleration sensor of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected biometric data using a generation AI. The notification unit is implemented in the control unit 46A of the smart glasses 214 and sends notifications to a smartphone or issues voice alerts or vibration alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time using the heart rate sensor, body temperature sensor, and acceleration sensor of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected biometric data using a generation AI. The notification unit is implemented in the control unit 46A of the headset terminal 314 and sends notifications to a smartphone or issues voice alerts or vibration alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 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.

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

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit acquires biometric data such as heart rate, body temperature, and activity level in real time using the heart rate sensor, body temperature sensor, and acceleration sensor of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected biometric data using a generation AI. The notification unit is implemented in the control unit 46A of the robot 414 and sends notifications to a smartphone or issues voice alerts or vibration alerts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A collection unit that collects biometric data from a smartwatch, An analysis unit analyzes the biological data collected by the aforementioned collection unit, The system includes a notification unit that notifies the user based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned notification unit, The system provides users with interactive warnings based on their alcohol consumption and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, It predicts and notifies you of the time it will take for alcohol to leave your system based on your perceived time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It acquires biometric data such as heart rate, body temperature, and activity level in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Create and analyze an alcohol metabolism profile based on individual weight and body type. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Provides a feature to share drinking data with your partner. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of biometric data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past biometric data history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When acquiring biometric data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of biometric data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When acquiring biometric data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring biometric data, the system analyzes the user's social media activity and obtains relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized based on each individual's weight and body type. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past analysis results are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the user's lifestyle and dietary history are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized according to the amount of alcohol consumed and the user's physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending a notification, the system will refer to the user's past drinking history to select the optimal notification timing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and adjust the notification content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects biometric data from a smartwatch, An analysis unit analyzes the biological data collected by the aforementioned collection unit, The system includes a notification unit that notifies the user based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned notification unit, The system provides users with interactive warnings based on their alcohol consumption and physical condition. The system according to feature 1.

3. The aforementioned notification unit, It predicts and notifies you of the time it will take for alcohol to leave your system based on your perceived time. The system according to feature 1.

4. The aforementioned collection unit is It acquires biometric data such as heart rate, body temperature, and activity level in real time. The system according to feature 1.

5. The aforementioned analysis unit, Create and analyze an alcohol metabolism profile based on individual weight and body type. The system according to feature 1.

6. The aforementioned notification unit, Provides a feature to share drinking data with your partner. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of biometric data acquisition based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past biometric data history and select the optimal acquisition method. The system according to feature 1.

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