system

Abstract

The system according to the embodiment aims to provide support for users to make accurate decisions in emergencies. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects biometric information of a user and information about the surrounding environment. The analysis unit analyzes the current situation based on the information collected by the collection unit. The suggestion unit proposes appropriate advice or an action plan based on the analysis results obtained by the analysis unit. The provision unit provides the advice or action plan proposed by the suggestion unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient support for users to make accurate decisions in emergencies, and there is room for improvement.

[0005] The system according to the embodiment aims to provide support for users to make accurate decisions in emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects biometric information of the user and information on the surrounding environment. The analysis unit analyzes the current situation based on the information collected by the collection unit. The suggestion unit proposes appropriate advice or an action plan based on the analysis results obtained by the analysis unit. The provision unit provides the advice or action plan proposed by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide support for the user to make accurate decisions in an emergency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The RescueAI system according to an embodiment of the present invention provides accurate judgment and support in emergencies. This RescueAI system uses a wearable device to monitor the user's situation in real time, and a generating AI quickly proposes appropriate advice and action plans based on the situation. For example, the RescueAI system collects the user's biometric information and surrounding environmental information through the wearable device. Specifically, it can acquire data such as heart rate, body temperature, location information, ambient sound and light intensity, blood pressure, and oxygen saturation in real time. This allows for a detailed understanding of the user's current situation. The collected data is then sent to the generating AI. The generating AI analyzes the current situation based on past data and pre-trained models. For example, if the heart rate suddenly increases or the surrounding noise suddenly becomes louder, it determines that an emergency may have occurred. Based on the analysis results, the generating AI proposes appropriate advice and action plans to the user. For example, if the heart rate suddenly increases, the system may instruct the user to take deep breaths, and if the surrounding noise suddenly becomes louder, the system may suggest evacuating to a safe location. This allows the user to remain calm even in emergencies. Furthermore, the generative AI takes into account the user's past behavioral history and individual characteristics to provide more personalized advice. For example, it can suggest specific relaxation methods to a user who has previously experienced a panic attack. In this way, by combining a wearable device with generative AI, the RescueAI system provides accurate judgment and support in emergencies, ensuring the user's safety. This allows the RescueAI system to provide accurate judgment and support in emergencies.

[0029] The RescueAI system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects biometric information of a user and information about the surrounding environment. The biometric information includes, for example, heart rate, body temperature, and blood pressure. The environmental information includes, for example, temperature, humidity, and volume. The collection unit includes, for example, a heart rate sensor for measuring a heart rate. The heart rate sensor can measure the user's heart rate in real time and collect the data. The collection unit also includes a body temperature sensor for measuring body temperature. The body temperature sensor can measure the user's body temperature in real time and collect the data. The collection unit also includes a blood pressure sensor for measuring blood pressure. The blood pressure sensor can measure the user's blood pressure in real time and collect the data. The analysis unit analyzes the current situation based on the information collected by the collection unit. The analysis unit analyzes the current situation based on, for example, past data or a model learned in advance. For example, the analysis unit can determine whether the current heart rate is within a normal range based on past heart rate data. The analysis unit can also determine whether the user's current body temperature is within a normal range based on past body temperature data. The analysis unit can also determine whether the user's current blood pressure is within a normal range based on past blood pressure data. The suggestion unit proposes appropriate advice or an action plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit instructs the user to take deep breaths when the user's heart rate is rapidly increasing, and suggests evacuating to a safe place when the surrounding noise suddenly becomes louder. For example, the suggestion unit can instruct the user to "take deep breaths" when the user's heart rate is rapidly increasing. Furthermore, the suggestion unit can suggest the user to "evacuate to a safe place" when the surrounding noise suddenly becomes louder. Furthermore, the suggestion unit provides personalized advice taking into account the user's past behavioral history and individual characteristics. For example, the suggestion unit can suggest a specific relaxation method to a user who has experienced a panic attack in the past. The provision unit provides the user with the advice or action plan proposed by the suggestion unit. For example, the provision unit can send a notification to the user's smartphone.The providing unit can also send a notification to the user's wearable device by vibration or sound. Furthermore, the providing unit can provide advice and an action plan through a message displayed on the user's smartwatch. As a result, the RescueAI system according to the embodiment can collect and analyze the user's biometric information and surrounding environmental information, and provide appropriate advice and an action plan, thereby providing accurate judgment and support in an emergency.

[0030] The collection unit can collect data on heart rate, body temperature, location information, ambient sound or light intensity, blood pressure, and oxygen saturation. The collection unit includes, for example, a heart rate sensor for measuring heart rate. The heart rate sensor can measure the user's heart rate in real time and collect the data. The collection unit also includes a body temperature sensor for measuring body temperature. The body temperature sensor can measure the user's body temperature in real time and collect the data. The collection unit also includes a GPS device for acquiring location information. The GPS device can acquire the user's location information in real time and collect the data. The collection unit also includes a microphone for measuring ambient sound. The microphone can measure the intensity of ambient sound in real time and collect the data. The collection unit also includes a light sensor for measuring ambient light intensity. The light sensor can measure the intensity of ambient light in real time and collect the data. The collection unit also includes a blood pressure sensor for measuring blood pressure. The blood pressure sensor can measure the user's blood pressure in real time and collect the data. The collection unit also includes an oxygen sensor for measuring oxygen saturation. The oxygen sensor can measure the user's oxygen saturation level in real time and collect the data. This allows for the collection of various biological and environmental information, enabling a detailed understanding of the user's condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input heart rate data acquired by a heart rate sensor into the generation AI, which can then analyze the data and detect abnormalities.

[0031] The analysis unit can analyze the current situation based on past data and a previously trained model. For example, the analysis unit can determine whether the current heart rate is within a normal range based on past heart rate data. The analysis unit can also determine whether the current body temperature is within a normal range based on past body temperature data. The analysis unit can also determine whether the current blood pressure is within a normal range based on past blood pressure data. The analysis unit can predict the current situation using past data. For example, the analysis unit can predict whether the current heart rate is likely to increase suddenly using past heart rate data. The analysis unit can also predict whether the current body temperature is likely to increase suddenly using past body temperature data. The analysis unit can also predict whether the current blood pressure is likely to increase suddenly using past blood pressure data. This allows the current situation to be analyzed more accurately by utilizing past data and a learning model. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into a generation AI, which analyzes the data and predicts the current situation.

[0032] The suggestion unit can instruct the user to take a deep breath if the heart rate is rising rapidly, and can suggest evacuating to a safe place if the surrounding noise becomes louder. For example, the suggestion unit can instruct the user to "take a deep breath" if the heart rate is rising rapidly. Furthermore, the suggestion unit can suggest the user to "evacuate to a safe place" if the surrounding noise suddenly becomes louder. For example, the suggestion unit can instruct the user on how to take a deep breath if the heart rate is rising rapidly. For example, the suggestion unit can instruct the user to "take a slow, deep breath, inhale, and then exhale slowly." Furthermore, the suggestion unit can suggest the specific location of a safe place to the user if the surrounding noise suddenly becomes louder. For example, the suggestion unit can suggest the user to "evacuate to a nearby building." This allows the user's safety to be ensured by quickly suggesting appropriate actions in an emergency. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input heart rate data to a generation AI, which can analyze the data and generate appropriate advice.

[0033] The suggestion unit can provide personalized advice based on the user's past behavioral history or individual characteristics. For example, the suggestion unit can suggest a specific relaxation method to a user who has experienced a panic attack in the past. The suggestion unit can provide appropriate advice to the user based on the user's past behavioral history. For example, the suggestion unit can suggest, "Take a deep breath and relax" to a user who has experienced a panic attack in the past. The suggestion unit can also provide personalized advice taking into account the user's individual characteristics. For example, the suggestion unit can provide appropriate advice taking into account the user's age, gender, health condition, etc. For example, the suggestion unit can suggest a method for managing heart rate to a user who has suffered from heart disease in the past. This enables more effective support by providing advice based on the user's individual characteristics. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past behavioral history data into a generation AI, which can analyze the data and generate personalized advice.

[0034] The providing unit can provide the user with the proposed advice or action plan. The providing unit can, for example, send a notification to the user's smartphone. The providing unit can, for example, include an application that notifies the user's smartphone of the advice or action plan. For example, the providing unit can send a message such as "Take a deep breath" to the user's smartphone. The providing unit can also send a notification to the user's wearable device by vibration or sound. For example, the providing unit can display a message such as "Evacuate to a safe place" on the user's smartwatch. The providing unit can also provide the advice or action plan through a message displayed on the user's smartwatch. This can support emergency response by quickly providing the user with the proposed advice or action plan. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can notify the user's smartphone of the advice generated by the generation AI.

[0035] During collection, the collection unit can determine the priority of data to be collected based on the user's past health data. For example, if the user has had heart disease in the past, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the collection unit can prioritize collecting oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the collection unit can prioritize collecting heart rate variability and body temperature data. This allows important data to be collected preferentially by referencing the user's past health data. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the user's past health data into the generation AI, which can analyze the data and determine the priority of data to be collected.

[0036] The collection unit can adjust the frequency of data collection based on the user's current activity status during collection. For example, when the user is exercising, the collection unit can collect heart rate and oxygen saturation data at a high frequency. For example, when the user is resting, the collection unit can collect body temperature and blood pressure data at a low frequency. For example, when the user is working, the collection unit can collect stress level and heart rate variability data at a medium frequency. This allows appropriate data to be collected by adjusting the data collection frequency according to the user's activity status. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's activity status data into a generation AI, which can analyze the data and adjust the collection frequency.

[0037] At the time of collection, the collection unit can select the type of data to collect based on the user's geographical location information. For example, if the user is at high altitude, the collection unit can prioritize collecting oxygen saturation and heart rate data. For example, if the user is in an urban area, the collection unit can prioritize collecting ambient sound and light intensity data. For example, if the user is indoors, the collection unit can prioritize collecting body temperature and blood pressure data. This allows appropriate data to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information data into a generation AI, which then analyzes the data and selects the type of data to collect.

[0038] The collection unit can analyze the user's social media activity and collect relevant data during collection. For example, if the user posts on social media that they are feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user posts that they are relaxed, the collection unit can prioritize collecting body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the collection unit can prioritize collecting data on ambient sound and light intensity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can analyze the data and collect relevant data.

[0039] During analysis, the analysis unit can analyze the current situation more accurately based on past emergency data. For example, the analysis unit can refer to data on past heart attacks and analyze current heart rate and blood pressure data. For example, the analysis unit can refer to data on past panic attacks and analyze current respiratory rate and oxygen saturation data. For example, the analysis unit can refer to data on past traffic accidents and analyze current location information and ambient sound data. In this way, by referring to past emergency data, the current situation can be analyzed more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past emergency data into a generation AI, which then analyzes the data to more accurately analyze the current situation.

[0040] During analysis, the analysis unit can customize the analysis results based on the user's individual characteristics. For example, if the user has had heart disease in the past, the analysis unit can prioritize heart rate and blood pressure data during analysis. For example, if the user has had respiratory problems in the past, the analysis unit can prioritize oxygen saturation and respiration rate data during analysis. For example, if the user has had stress-related problems in the past, the analysis unit can prioritize heart rate variability and body temperature data during analysis. This allows for customizing the analysis results based on the user's individual characteristics to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's individual characteristic data into a generation AI, which then analyzes the data and customizes the analysis results.

[0041] During analysis, the analysis unit can provide analysis results based on the user's geographical location information. For example, if the user is at high altitude, the analysis unit can prioritize oxygen saturation and heart rate data during analysis. For example, if the user is in an urban area, the analysis unit can prioritize ambient sound and light intensity data during analysis. For example, if the user is indoors, the analysis unit can prioritize body temperature and blood pressure data during analysis. This allows for more appropriate analysis results to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information data into a generation AI, which then analyzes the data and provides analysis results.

[0042] During analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. For example, if the user posts on social media that they are feeling stressed, the analysis unit can focus on heart rate and blood pressure data in the analysis. For example, if the user posts that they are relaxed, the analysis unit can focus on body temperature and oxygen saturation data in the analysis. For example, if the user posts that they are feeling nervous, the analysis unit can focus on ambient sound and light intensity data in the analysis. In this way, by analyzing the user's social media activity, relevant analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into a generation AI, which then analyzes the data and provides relevant analysis results.

[0043] When making a suggestion, the suggestion unit can provide optimal suggestions based on the user's past behavioral history. For example, if the user has had heart disease in the past, the suggestion unit can make suggestions that emphasize heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the suggestion unit can make suggestions that emphasize oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the suggestion unit can make suggestions that emphasize heart rate variability and body temperature data. This makes it possible to provide more appropriate suggestions by referring to the user's past behavioral history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past behavioral history data into the generation AI, which can analyze the data and provide optimal suggestions.

[0044] When making a suggestion, the suggestion unit can customize the content of the suggestion based on the user's current situation. For example, if the user is exercising, the suggestion unit can make a suggestion that emphasizes heart rate and oxygen saturation data. For example, if the user is resting, the suggestion unit can make a suggestion that emphasizes body temperature and blood pressure data. For example, if the user is working, the suggestion unit can make a suggestion that emphasizes stress level and heart rate variability data. This allows the suggestion to be customized based on the user's current situation, thereby providing more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current situation data into the generation AI, which can analyze the data and customize the content of the suggestion.

[0045] When making a suggestion, the suggestion unit can provide optimal suggestions based on the user's geographical location information. For example, if the user is at high altitude, the suggestion unit can make suggestions that emphasize oxygen saturation and heart rate data. For example, if the user is in an urban area, the suggestion unit can make suggestions that emphasize ambient sound and light intensity data. For example, if the user is indoors, the suggestion unit can make suggestions that emphasize body temperature and blood pressure data. This makes it possible to provide more appropriate suggestions by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information data into the generation AI, which then analyzes the data and provides optimal suggestions.

[0046] When making a suggestion, the suggestion unit can analyze the user's social media activity and provide relevant suggestions. For example, if the user posts on social media that they are feeling stressed, the suggestion unit can provide suggestions that emphasize heart rate and blood pressure data. For example, if the user posts that they are relaxed, the suggestion unit can provide suggestions that emphasize body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the suggestion unit can provide suggestions that emphasize ambient sound and light intensity data. In this way, relevant suggestions can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's social media activity data into a generation AI, which can analyze the data and provide relevant suggestions.

[0047] The providing unit can provide optimal advice based on the user's past feedback at the time of providing. For example, if the user has had heart disease in the past, the providing unit can provide advice that emphasizes heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the providing unit can provide advice that emphasizes oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the providing unit can provide advice that emphasizes heart rate variability and body temperature data. This makes it possible to provide more appropriate advice by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can analyze the data and provide optimal advice.

[0048] The providing unit can customize the content of the advice based on the user's current situation when providing the advice. For example, when the user is exercising, the providing unit can provide advice that emphasizes heart rate and oxygen saturation data. For example, when the user is resting, the providing unit can provide advice that emphasizes body temperature and blood pressure data. For example, when the user is working, the providing unit can provide advice that emphasizes stress level and heart rate variability data. This allows the advice to be customized based on the user's current situation, making it possible to provide more appropriate advice. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input data on the user's current situation into the generation AI, which can analyze the data and customize the content of the advice.

[0049] The providing unit can provide optimal advice based on the user's geographical location information at the time of providing. For example, if the user is at high altitude, the providing unit can provide advice that emphasizes oxygen saturation and heart rate data. For example, if the user is in an urban area, the providing unit can provide advice that emphasizes ambient sound and light intensity data. For example, if the user is indoors, the providing unit can provide advice that emphasizes body temperature and blood pressure data. This makes it possible to provide more appropriate advice by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into a generation AI, which can analyze the data and provide optimal advice.

[0050] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing. For example, if the user posts on social media that they are feeling stressed, the providing unit can provide advice that emphasizes heart rate and blood pressure data. For example, if the user posts that they are relaxed, the providing unit can provide advice that emphasizes body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the providing unit can provide advice that emphasizes ambient sound and light intensity data. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can analyze the data and provide relevant advice.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The RescueAI system may further include a communication unit to ensure communication means in an emergency. For example, the communication unit may automatically send a notification to emergency contacts when the user is in an emergency. The communication unit may send a message including the user's location information and current situation. The communication unit may also include a button that the user can press manually to make an emergency call. This allows the user to quickly request help. Furthermore, the communication unit may analyze the user's voice in real time and automatically send a notification if it detects an emergency. This allows the system to detect an emergency and take appropriate action even when the user is unable to speak.

[0053] The RescueAI system can also include a history management unit for long-term monitoring of the user's health condition. The history management unit can store the user's past health data and periodically analyze it. For example, the history management unit can analyze long-term fluctuations in the user's heart rate and body temperature to detect abnormalities. The history management unit can also grasp trends in the user's health condition and provide preventative advice. This allows for long-term management of the user's health condition and early detection of abnormalities.

[0054] The RescueAI system can further include an activity tracking unit for monitoring the user's activity level. The activity tracking unit can record and analyze the user's amount of exercise and activity time. For example, the activity tracking unit can record the user's number of steps and calories burned and evaluate their daily activity level. The activity tracking unit can also understand the user's exercise habits and suggest appropriate exercise plans. This can support the user in maintaining their health.

[0055] The RescueAI system may further include a sleep analysis unit for monitoring the user's sleep state. The sleep analysis unit can record and analyze the user's sleep patterns. For example, the sleep analysis unit can evaluate the user's sleep time and sleep quality and provide advice for improvement. The sleep analysis unit can also monitor the user's heart rate and breathing rate while sleeping and detect abnormalities. This can improve the user's sleep state and maintain health.

[0056] The RescueAI system can further include a diet management unit for managing the user's dietary records. The diet management unit can record the user's dietary content and evaluate nutritional balance. For example, the diet management unit can record the user's calorie and nutrient intake and propose an appropriate meal plan. The diet management unit can also analyze the user's diet history and provide dietary improvements based on their health condition. This can improve the user's eating habits and maintain their health.

[0057] The RescueAI system may further include a stress analysis unit for monitoring the user's stress level. The stress analysis unit may analyze the user's heart rate variability and electrodermal activity to assess the user's stress level. For example, if the user's stress level is high, the stress analysis unit may suggest relaxation methods. The stress analysis unit may also monitor the fluctuations in the user's stress level over the long term and provide advice for stress management. This may help reduce the user's stress and maintain their health.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects the user's biometric information and surrounding environmental information. The biometric information includes heart rate, body temperature, blood pressure, etc., and the environmental information includes temperature, humidity, volume, etc. The collection unit is equipped with a heart rate sensor for measuring heart rate, a body temperature sensor for measuring body temperature, and a blood pressure sensor for measuring blood pressure, and collects this data in real time. Step 2: The analysis unit analyzes the current situation based on the information collected by the collection unit. Based on past data and pre-trained models, the analysis unit determines whether the current heart rate, body temperature, and blood pressure are within normal ranges. Step 3: The suggestion unit proposes appropriate advice and action plans based on the analysis results obtained by the analysis unit. For example, if your heart rate is rising rapidly, it will advise you to take deep breaths, or if surrounding noise suddenly increases, it will suggest evacuating to a safe place. It also provides personalized advice taking into account the user's past behavioral history and individual characteristics. Step 4: The providing unit provides the advice and action plan proposed by the suggesting unit to the user by sending a notification to the user's smartphone, sending a notification to the wearable device by vibration or sound, or providing the advice and action plan through a message displayed on the smartwatch.

[0060] (Example 2) The RescueAI system according to an embodiment of the present invention provides accurate judgment and support in emergencies. This RescueAI system uses a wearable device to monitor the user's situation in real time, and a generating AI quickly proposes appropriate advice and action plans based on the situation. For example, the RescueAI system collects the user's biometric information and surrounding environmental information through the wearable device. Specifically, it can acquire data such as heart rate, body temperature, location information, ambient sound and light intensity, blood pressure, and oxygen saturation in real time. This allows for a detailed understanding of the user's current situation. The collected data is then sent to the generating AI. The generating AI analyzes the current situation based on past data and pre-trained models. For example, if the heart rate suddenly increases or the surrounding noise suddenly becomes louder, it determines that an emergency may have occurred. Based on the analysis results, the generating AI proposes appropriate advice and action plans to the user. For example, if the heart rate suddenly increases, the system may instruct the user to take deep breaths, and if the surrounding noise suddenly becomes louder, the system may suggest evacuating to a safe location. This allows the user to remain calm even in emergencies. Furthermore, the generative AI takes into account the user's past behavioral history and individual characteristics to provide more personalized advice. For example, it can suggest specific relaxation methods to a user who has previously experienced a panic attack. In this way, by combining a wearable device with generative AI, the RescueAI system provides accurate judgment and support in emergencies, ensuring the user's safety. This allows the RescueAI system to provide accurate judgment and support in emergencies.

[0061] The RescueAI system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects biometric information of a user and information about the surrounding environment. The biometric information includes, for example, heart rate, body temperature, and blood pressure. The environmental information includes, for example, temperature, humidity, and volume. The collection unit includes, for example, a heart rate sensor for measuring a heart rate. The heart rate sensor can measure the user's heart rate in real time and collect the data. The collection unit also includes a body temperature sensor for measuring body temperature. The body temperature sensor can measure the user's body temperature in real time and collect the data. The collection unit also includes a blood pressure sensor for measuring blood pressure. The blood pressure sensor can measure the user's blood pressure in real time and collect the data. The analysis unit analyzes the current situation based on the information collected by the collection unit. The analysis unit analyzes the current situation based on, for example, past data or a model learned in advance. For example, the analysis unit can determine whether the current heart rate is within a normal range based on past heart rate data. The analysis unit can also determine whether the user's current body temperature is within a normal range based on past body temperature data. The analysis unit can also determine whether the user's current blood pressure is within a normal range based on past blood pressure data. The suggestion unit proposes appropriate advice or an action plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit instructs the user to take deep breaths when the user's heart rate is rapidly increasing, and suggests evacuating to a safe place when the surrounding noise suddenly becomes louder. For example, the suggestion unit can instruct the user to "take deep breaths" when the user's heart rate is rapidly increasing. Furthermore, the suggestion unit can suggest the user to "evacuate to a safe place" when the surrounding noise suddenly becomes louder. Furthermore, the suggestion unit provides personalized advice taking into account the user's past behavioral history and individual characteristics. For example, the suggestion unit can suggest a specific relaxation method to a user who has experienced a panic attack in the past. The provision unit provides the user with the advice or action plan proposed by the suggestion unit. For example, the provision unit can send a notification to the user's smartphone.The providing unit can also send a notification to the user's wearable device by vibration or sound. Furthermore, the providing unit can provide advice and an action plan through a message displayed on the user's smartwatch. As a result, the RescueAI system according to the embodiment can collect and analyze the user's biometric information and surrounding environmental information, and provide appropriate advice and an action plan, thereby providing accurate judgment and support in an emergency.

[0062] The collection unit can collect data on heart rate, body temperature, location information, ambient sound or light intensity, blood pressure, and oxygen saturation. The collection unit includes, for example, a heart rate sensor for measuring heart rate. The heart rate sensor can measure the user's heart rate in real time and collect the data. The collection unit also includes a body temperature sensor for measuring body temperature. The body temperature sensor can measure the user's body temperature in real time and collect the data. The collection unit also includes a GPS device for acquiring location information. The GPS device can acquire the user's location information in real time and collect the data. The collection unit also includes a microphone for measuring ambient sound. The microphone can measure the intensity of ambient sound in real time and collect the data. The collection unit also includes a light sensor for measuring ambient light intensity. The light sensor can measure the intensity of ambient light in real time and collect the data. The collection unit also includes a blood pressure sensor for measuring blood pressure. The blood pressure sensor can measure the user's blood pressure in real time and collect the data. The collection unit also includes an oxygen sensor for measuring oxygen saturation. The oxygen sensor can measure the user's oxygen saturation level in real time and collect the data. This allows for the collection of various biological and environmental information, enabling a detailed understanding of the user's condition. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input heart rate data acquired by a heart rate sensor into the generation AI, which can then analyze the data and detect abnormalities.

[0063] The analysis unit can analyze the current situation based on past data and a previously trained model. For example, the analysis unit can determine whether the current heart rate is within a normal range based on past heart rate data. The analysis unit can also determine whether the current body temperature is within a normal range based on past body temperature data. The analysis unit can also determine whether the current blood pressure is within a normal range based on past blood pressure data. The analysis unit can predict the current situation using past data. For example, the analysis unit can predict whether the current heart rate is likely to increase suddenly using past heart rate data. The analysis unit can also predict whether the current body temperature is likely to increase suddenly using past body temperature data. The analysis unit can also predict whether the current blood pressure is likely to increase suddenly using past blood pressure data. This allows the current situation to be analyzed more accurately by utilizing past data and a learning model. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past data into a generation AI, which analyzes the data and predicts the current situation.

[0064] The suggestion unit can instruct the user to take a deep breath if the heart rate is rising rapidly, and can suggest evacuating to a safe place if the surrounding noise becomes louder. For example, the suggestion unit can instruct the user to "take a deep breath" if the heart rate is rising rapidly. Furthermore, the suggestion unit can suggest the user to "evacuate to a safe place" if the surrounding noise suddenly becomes louder. For example, the suggestion unit can instruct the user on how to take a deep breath if the heart rate is rising rapidly. For example, the suggestion unit can instruct the user to "take a slow, deep breath, inhale, and then exhale slowly." Furthermore, the suggestion unit can suggest the specific location of a safe place to the user if the surrounding noise suddenly becomes louder. For example, the suggestion unit can suggest the user to "evacuate to a nearby building." This allows the user's safety to be ensured by quickly suggesting appropriate actions in an emergency. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input heart rate data to a generation AI, which can analyze the data and generate appropriate advice.

[0065] The suggestion unit can provide personalized advice based on the user's past behavioral history or individual characteristics. For example, the suggestion unit can suggest a specific relaxation method to a user who has experienced a panic attack in the past. The suggestion unit can provide appropriate advice to the user based on the user's past behavioral history. For example, the suggestion unit can suggest, "Take a deep breath and relax" to a user who has experienced a panic attack in the past. The suggestion unit can also provide personalized advice taking into account the user's individual characteristics. For example, the suggestion unit can provide appropriate advice taking into account the user's age, gender, health condition, etc. For example, the suggestion unit can suggest a method for managing heart rate to a user who has suffered from heart disease in the past. This enables more effective support by providing advice based on the user's individual characteristics. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past behavioral history data into a generation AI, which can analyze the data and generate personalized advice.

[0066] The providing unit can provide the user with the proposed advice or action plan. The providing unit can, for example, send a notification to the user's smartphone. The providing unit can, for example, include an application that notifies the user's smartphone of the advice or action plan. For example, the providing unit can send a message such as "Take a deep breath" to the user's smartphone. The providing unit can also send a notification to the user's wearable device by vibration or sound. For example, the providing unit can display a message such as "Evacuate to a safe place" on the user's smartwatch. The providing unit can also provide the advice or action plan through a message displayed on the user's smartwatch. This can support emergency response by quickly providing the user with the proposed advice or action plan. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can notify the user's smartphone of the advice generated by the generation AI.

[0067] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user is relaxed, the collection unit can prioritize collecting body temperature and oxygen saturation data. For example, if the user is tense, the collection unit can prioritize collecting data on the intensity of ambient sound and light. This allows more appropriate data to be collected by adjusting the type of data to be collected according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which then analyzes the data and adjusts the type of data to be collected.

[0068] During collection, the collection unit can determine the priority of data to be collected based on the user's past health data. For example, if the user has had heart disease in the past, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the collection unit can prioritize collecting oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the collection unit can prioritize collecting heart rate variability and body temperature data. This allows important data to be collected preferentially by referencing the user's past health data. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the user's past health data into the generation AI, which can analyze the data and determine the priority of data to be collected.

[0069] The collection unit can adjust the frequency of data collection based on the user's current activity status during collection. For example, when the user is exercising, the collection unit can collect heart rate and oxygen saturation data at a high frequency. For example, when the user is resting, the collection unit can collect body temperature and blood pressure data at a low frequency. For example, when the user is working, the collection unit can collect stress level and heart rate variability data at a medium frequency. This allows appropriate data to be collected by adjusting the data collection frequency according to the user's activity status. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's activity status data into a generation AI, which can analyze the data and adjust the collection frequency.

[0070] The collection unit can estimate the user's emotions and adjust the accuracy of the collected data based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can collect heart rate and blood pressure data with high accuracy. For example, when the user is relaxed, the collection unit can collect body temperature and oxygen saturation data with standard accuracy. For example, when the user is tense, the collection unit can collect data on the intensity of ambient sound and light with high accuracy. This allows more accurate data to be collected by adjusting the accuracy of the data according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which then analyzes the data and adjusts the accuracy of the data it collects.

[0071] At the time of collection, the collection unit can select the type of data to collect based on the user's geographical location information. For example, if the user is at high altitude, the collection unit can prioritize collecting oxygen saturation and heart rate data. For example, if the user is in an urban area, the collection unit can prioritize collecting ambient sound and light intensity data. For example, if the user is indoors, the collection unit can prioritize collecting body temperature and blood pressure data. This allows appropriate data to be collected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information data into a generation AI, which then analyzes the data and selects the type of data to collect.

[0072] The collection unit can analyze the user's social media activity and collect relevant data during collection. For example, if the user posts on social media that they are feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. For example, if the user posts that they are relaxed, the collection unit can prioritize collecting body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the collection unit can prioritize collecting data on ambient sound and light intensity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can analyze the data and collect relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize heart rate and blood pressure data for analysis. For example, if the user is relaxed, the analysis unit can prioritize body temperature and oxygen saturation data for analysis. For example, if the user is tense, the analysis unit can prioritize ambient sound and light intensity data for analysis. This allows for adjusting the analysis algorithm according to the user's emotions to obtain more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into a generation AI, which then analyzes the data and adjusts the analysis algorithm.

[0074] During analysis, the analysis unit can analyze the current situation more accurately based on past emergency data. For example, the analysis unit can refer to data on past heart attacks and analyze current heart rate and blood pressure data. For example, the analysis unit can refer to data on past panic attacks and analyze current respiratory rate and oxygen saturation data. For example, the analysis unit can refer to data on past traffic accidents and analyze current location information and ambient sound data. In this way, by referring to past emergency data, the current situation can be analyzed more accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past emergency data into a generation AI, which then analyzes the data to more accurately analyze the current situation.

[0075] During analysis, the analysis unit can customize the analysis results based on the user's individual characteristics. For example, if the user has had heart disease in the past, the analysis unit can prioritize heart rate and blood pressure data during analysis. For example, if the user has had respiratory problems in the past, the analysis unit can prioritize oxygen saturation and respiration rate data during analysis. For example, if the user has had stress-related problems in the past, the analysis unit can prioritize heart rate variability and body temperature data during analysis. This allows for customizing the analysis results based on the user's individual characteristics to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's individual characteristic data into a generation AI, which then analyzes the data and customizes the analysis results.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is tense, the analysis unit can provide a display method that focuses on the main points. This allows for a more highly visible display by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into a generation AI, which analyzes the data and adjusts the display method.

[0077] During analysis, the analysis unit can provide analysis results based on the user's geographical location information. For example, if the user is at high altitude, the analysis unit can prioritize oxygen saturation and heart rate data during analysis. For example, if the user is in an urban area, the analysis unit can prioritize ambient sound and light intensity data during analysis. For example, if the user is indoors, the analysis unit can prioritize body temperature and blood pressure data during analysis. This allows for more appropriate analysis results to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information data into a generation AI, which then analyzes the data and provides analysis results.

[0078] During analysis, the analysis unit can analyze the user's social media activity and provide relevant analysis results. For example, if the user posts on social media that they are feeling stressed, the analysis unit can focus on heart rate and blood pressure data in the analysis. For example, if the user posts that they are relaxed, the analysis unit can focus on body temperature and oxygen saturation data in the analysis. For example, if the user posts that they are feeling nervous, the analysis unit can focus on ambient sound and light intensity data in the analysis. In this way, by analyzing the user's social media activity, relevant analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into a generation AI, which then analyzes the data and provides relevant analysis results.

[0079] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. For example, if the user is nervous, the suggestion unit can make suggestions that focus on the main points. This allows for adjusting the way the suggestions are expressed according to the user's emotions, making it possible to provide more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input user emotion data into a generation AI, which can analyze the data and adjust the way the suggestions are expressed.

[0080] When making a suggestion, the suggestion unit can provide optimal suggestions based on the user's past behavioral history. For example, if the user has had heart disease in the past, the suggestion unit can make suggestions that emphasize heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the suggestion unit can make suggestions that emphasize oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the suggestion unit can make suggestions that emphasize heart rate variability and body temperature data. This makes it possible to provide more appropriate suggestions by referring to the user's past behavioral history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past behavioral history data into the generation AI, which can analyze the data and provide optimal suggestions.

[0081] When making a suggestion, the suggestion unit can customize the content of the suggestion based on the user's current situation. For example, if the user is exercising, the suggestion unit can make a suggestion that emphasizes heart rate and oxygen saturation data. For example, if the user is resting, the suggestion unit can make a suggestion that emphasizes body temperature and blood pressure data. For example, if the user is working, the suggestion unit can make a suggestion that emphasizes stress level and heart rate variability data. This allows the suggestion to be customized based on the user's current situation, thereby providing more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current situation data into the generation AI, which can analyze the data and customize the content of the suggestion.

[0082] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions that emphasize heart rate and blood pressure data. For example, if the user is relaxed, the suggestion unit can prioritize suggestions that emphasize body temperature and oxygen saturation data. For example, if the user is nervous, the suggestion unit can prioritize suggestions that emphasize ambient sound and light intensity data. This allows more appropriate suggestions to be provided by determining the priority of suggestions according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of suggestions.

[0083] When making a suggestion, the suggestion unit can provide optimal suggestions based on the user's geographical location information. For example, if the user is at high altitude, the suggestion unit can make suggestions that emphasize oxygen saturation and heart rate data. For example, if the user is in an urban area, the suggestion unit can make suggestions that emphasize ambient sound and light intensity data. For example, if the user is indoors, the suggestion unit can make suggestions that emphasize body temperature and blood pressure data. This makes it possible to provide more appropriate suggestions by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's geographical location information data into the generation AI, which then analyzes the data and provides optimal suggestions.

[0084] When making a suggestion, the suggestion unit can analyze the user's social media activity and provide relevant suggestions. For example, if the user posts on social media that they are feeling stressed, the suggestion unit can provide suggestions that emphasize heart rate and blood pressure data. For example, if the user posts that they are relaxed, the suggestion unit can provide suggestions that emphasize body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the suggestion unit can provide suggestions that emphasize ambient sound and light intensity data. In this way, relevant suggestions can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's social media activity data into a generation AI, which can analyze the data and provide relevant suggestions.

[0085] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide simple, highly visible advice. For example, if the user is relaxed, the providing unit can provide advice including detailed information. For example, if the user is nervous, the providing unit can provide advice that focuses on the main points. This allows more appropriate advice to be provided by adjusting the way in which advice is presented according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input user emotion data into the generation AI, which can analyze the data and adjust the way in which advice is presented.

[0086] The providing unit can provide optimal advice based on the user's past feedback at the time of providing. For example, if the user has had heart disease in the past, the providing unit can provide advice that emphasizes heart rate and blood pressure data. For example, if the user has had respiratory problems in the past, the providing unit can provide advice that emphasizes oxygen saturation and respiration rate data. For example, if the user has had stress-related problems in the past, the providing unit can provide advice that emphasizes heart rate variability and body temperature data. This makes it possible to provide more appropriate advice by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI, which can analyze the data and provide optimal advice.

[0087] The providing unit can customize the content of the advice based on the user's current situation when providing the advice. For example, when the user is exercising, the providing unit can provide advice that emphasizes heart rate and oxygen saturation data. For example, when the user is resting, the providing unit can provide advice that emphasizes body temperature and blood pressure data. For example, when the user is working, the providing unit can provide advice that emphasizes stress level and heart rate variability data. This allows the advice to be customized based on the user's current situation, making it possible to provide more appropriate advice. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input data on the user's current situation into the generation AI, which can analyze the data and customize the content of the advice.

[0088] The providing unit can estimate the user's emotions and determine the priority of advice to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize advice that emphasizes heart rate and blood pressure data. For example, if the user is relaxed, the providing unit can prioritize advice that emphasizes body temperature and oxygen saturation data. For example, if the user is tense, the providing unit can prioritize advice that emphasizes ambient sound and light intensity data. This allows more appropriate advice to be provided by determining the priority of advice according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can analyze the data and determine the priority of advice.

[0089] The providing unit can provide optimal advice based on the user's geographical location information at the time of providing. For example, if the user is at high altitude, the providing unit can provide advice that emphasizes oxygen saturation and heart rate data. For example, if the user is in an urban area, the providing unit can provide advice that emphasizes ambient sound and light intensity data. For example, if the user is indoors, the providing unit can provide advice that emphasizes body temperature and blood pressure data. This makes it possible to provide more appropriate advice by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into a generation AI, which can analyze the data and provide optimal advice.

[0090] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing. For example, if the user posts on social media that they are feeling stressed, the providing unit can provide advice that emphasizes heart rate and blood pressure data. For example, if the user posts that they are relaxed, the providing unit can provide advice that emphasizes body temperature and oxygen saturation data. For example, if the user posts that they are feeling nervous, the providing unit can provide advice that emphasizes ambient sound and light intensity data. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can analyze the data and provide relevant advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects biometric information of the user using a heart rate sensor or a body temperature sensor of the smart device 14 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice and action plans based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and notifies the user of the advice and action plans. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects biometric information of the user using a heart rate sensor or a body temperature sensor of the smart glasses 214 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice or an action plan based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the advice or action plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects biometric information of the user using a heart rate sensor or a body temperature sensor of the headset type terminal 314 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice and action plans based on the analysis results. The provision unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the advice and action plans. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects biometric information of the user using a heart rate sensor or a body temperature sensor of the robot 414 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice and an action plan based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and notifies the user of the advice and action plan.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The RescueAI system may further include a communication unit to ensure communication means in an emergency. For example, the communication unit may automatically send a notification to emergency contacts when the user is in an emergency. The communication unit may send a message including the user's location information and current situation. The communication unit may also include a button that the user can press manually to make an emergency call. This allows the user to quickly request help. Furthermore, the communication unit may analyze the user's voice in real time and automatically send a notification if it detects an emergency. This allows the system to detect an emergency and take appropriate action even when the user is unable to speak.

[0093] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting heart rate and blood pressure data. If the user is relaxed, the collection unit can prioritize collecting body temperature and oxygen saturation data. Furthermore, if the user is tense, the collection unit can prioritize collecting data on the intensity of ambient sound and light. In this way, by adjusting the type of data to be collected according to the user's emotions, more appropriate data can be collected.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis can prioritize heart rate and blood pressure data. If the user is relaxed, the analysis unit can prioritize body temperature and oxygen saturation data. Furthermore, if the user is tense, the analysis can prioritize ambient sound and light intensity data. This allows for more appropriate analysis results to be obtained by adjusting the analysis algorithm according to the user's emotions.

[0095] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is nervous, the suggestion unit can provide suggestions that focus on the main points. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided.

[0096] The providing unit can estimate the user's emotions and adjust the way in which advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, simple, highly visible advice can be provided. If the user is relaxed, the providing unit can provide advice that includes detailed information. Furthermore, if the user is nervous, advice that focuses on the main points can be provided. In this way, more appropriate advice can be provided by adjusting the way in which advice is presented according to the user's emotions.

[0097] The RescueAI system can also include a history management unit for long-term monitoring of the user's health condition. The history management unit can store the user's past health data and periodically analyze it. For example, the history management unit can analyze long-term fluctuations in the user's heart rate and body temperature to detect abnormalities. The history management unit can also grasp trends in the user's health condition and provide preventative advice. This allows for long-term management of the user's health condition and early detection of abnormalities.

[0098] The RescueAI system can further include an activity tracking unit for monitoring the user's activity level. The activity tracking unit can record and analyze the user's amount of exercise and activity time. For example, the activity tracking unit can record the user's number of steps and calories burned and evaluate their daily activity level. The activity tracking unit can also understand the user's exercise habits and suggest appropriate exercise plans. This can support the user in maintaining their health.

[0099] The RescueAI system may further include a sleep analysis unit for monitoring the user's sleep state. The sleep analysis unit can record and analyze the user's sleep patterns. For example, the sleep analysis unit can evaluate the user's sleep time and sleep quality and provide advice for improvement. The sleep analysis unit can also monitor the user's heart rate and breathing rate while sleeping and detect abnormalities. This can improve the user's sleep state and maintain health.

[0100] The RescueAI system can further include a diet management unit for managing the user's dietary records. The diet management unit can record the user's dietary content and evaluate nutritional balance. For example, the diet management unit can record the user's calorie and nutrient intake and propose an appropriate meal plan. The diet management unit can also analyze the user's diet history and provide dietary improvements based on their health condition. This can improve the user's eating habits and maintain their health.

[0101] The RescueAI system may further include a stress analysis unit for monitoring the user's stress level. The stress analysis unit may analyze the user's heart rate variability and electrodermal activity to assess the user's stress level. For example, if the user's stress level is high, the stress analysis unit may suggest relaxation methods. The stress analysis unit may also monitor the fluctuations in the user's stress level over the long term and provide advice for stress management. This may help reduce the user's stress and maintain their health.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects the user's biometric information and surrounding environmental information. The biometric information includes heart rate, body temperature, blood pressure, etc., and the environmental information includes temperature, humidity, volume, etc. The collection unit is equipped with a heart rate sensor for measuring heart rate, a body temperature sensor for measuring body temperature, and a blood pressure sensor for measuring blood pressure, and collects this data in real time. Step 2: The analysis unit analyzes the current situation based on the information collected by the collection unit. Based on past data and pre-trained models, the analysis unit determines whether the current heart rate, body temperature, and blood pressure are within normal ranges. Step 3: The suggestion unit proposes appropriate advice and action plans based on the analysis results obtained by the analysis unit. For example, if your heart rate is rising rapidly, it will advise you to take deep breaths, or if surrounding noise suddenly increases, it will suggest evacuating to a safe place. It also provides personalized advice taking into account the user's past behavioral history and individual characteristics. Step 4: The providing unit provides the advice and action plan proposed by the suggesting unit to the user by sending a notification to the user's smartphone, sending a notification to the wearable device by vibration or sound, or providing the advice and action plan through a message displayed on the smartwatch.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects biometric information and surrounding environmental information of a user; an analysis unit that analyzes a current situation based on the information collected by the collection unit; a suggestion unit that proposes appropriate advice or an action plan based on the analysis results obtained by the analysis unit; a providing unit that provides the user with the advice or action plan proposed by the suggesting unit; Equipped with A system characterized by:

2. The collecting unit Collects data on heart rate, body temperature, location, ambient sound or light intensity, blood pressure, and oxygen saturation 2. The system of claim 1.

3. The analysis unit Analyze the current situation based on past data and pre-trained models 2. The system of claim 1.

4. The proposal unit It advises deep breathing if your heart rate spikes and suggests evacuating to a safe location if surrounding noise increases.

2. The system of claim 1.

5. The proposal unit Providing personalized advice based on a user's past behavior or individual characteristics 2. The system of claim 1.

6. The providing unit Providing users with suggested advice and action plans 2. The system of claim 1.

7. The collecting unit Inferring user sentiment and adjusting the type of data collected based on the estimated user sentiment 2. The system of claim 1.

8. The collecting unit At the time of collection, prioritize data collection based on the user's past health data 2. The system of claim 1.

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