system

Abstract

The system according to the embodiment aims to improve the accuracy of incident detection while ensuring the privacy of users. [Solution] A system according to an embodiment includes a collection unit, a monitoring unit, a recognition unit, a determination unit, and a notification unit. The collection unit collects biometric information. The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. The recognition unit detects abnormalities detected by the monitoring unit and recognizes conversations based on the abnormalities. The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The notification unit notifies the user's family of the results of the incident determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately achieve both the accuracy of incident detection and the protection of privacy through remote monitoring, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the accuracy of incident detection while ensuring the privacy of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a monitoring unit, a recognition unit, a determination unit, and a notification unit. The collection unit collects biometric information. The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. The recognition unit detects abnormalities detected by the monitoring unit and recognizes conversations based on the abnormalities. The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The notification unit notifies the user's family of the results of the incident determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the accuracy of incident detection while ensuring the privacy of users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A monitoring system according to an embodiment of the present invention is designed to monitor elderly people in nursing homes or those living alone. This monitoring system combines a wearable device that collects biometric information with a communication robot equipped with an AI edge camera. It detects and notifies users of incidents such as safety checks, accidents, and abuse without transmitting the user's private information to the cloud. First, the wearable device collects the user's biometric information (e.g., heart rate, body temperature, activity level) in real time. This information is transmitted to the communication robot and analyzed in conjunction with the AI ​​edge camera. The AI ​​edge camera monitors the user's movements and facial expressions, and if an abnormality is detected, it recognizes it as a suspected incident. Next, a conversation recognition function using generative AI is activated. When the AI ​​edge camera detects a suspected incident, the communication robot engages in a dialogue with the user to confirm the authenticity of the incident. For example, if a user falls, the robot asks, "Are you okay?" and analyzes the user's response. The results of this dialogue are learned to improve the accuracy of the AI. Furthermore, in care facilities such as nursing homes, accidents and abuse can be identified based on conversations between the user and facility staff, images, and interviews with the user. This allows the user's family members to be notified quickly, enabling appropriate action to be taken. This system achieves highly accurate incident detection and notification while protecting the user's privacy. For example, if an elderly person living alone falls, the wearable device detects the abnormality and the communication robot confirms the situation through dialogue. This allows for a quick response and ensures the user's safety. This allows the monitoring system to achieve highly accurate incident detection and notification while protecting the user's privacy.

[0029] A monitoring system according to an embodiment includes a collection unit, a monitoring unit, a recognition unit, a determination unit, and a notification unit. The collection unit collects biometric information. The biometric information includes, but is not limited to, heart rate, body temperature, and activity level. For example, the collection unit measures the heart rate using a heart rate sensor. The collection unit can also measure body temperature using a thermometer. The collection unit can also measure activity level using an acceleration sensor. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor. The collection unit can also periodically measure the user's body temperature using a thermometer. The collection unit can also continuously measure the user's activity level using an acceleration sensor. The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. For example, the monitoring unit monitors the user's movements using an AI edge camera. The monitoring unit can also monitor the user's facial expressions using the AI ​​edge camera. For example, the monitoring unit monitors the user's walking movements using the AI ​​edge camera. The monitoring unit can also monitor the user's hand movements using the AI ​​edge camera. Furthermore, the monitoring unit can monitor the user's smiling or angry expressions using an AI edge camera. The recognition unit recognizes conversations using a generation AI based on abnormalities detected by the monitoring unit. The recognition unit can recognize the user's conversations using, for example, the generation AI. The recognition unit can also analyze the user's responses using the generation AI. For example, the recognition unit can analyze the user's response to the user's question, "Are you okay?" using the generation AI. The recognition unit can also analyze the user's response, "Help me!" using the generation AI. The recognition unit can also analyze the user's response, "It hurts!" using the generation AI. The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The determination unit can determine whether or not an incident has occurred based on, for example, the content of the user's response. The determination unit can also determine whether or not an incident has occurred based on additional sensor data. For example, the determination unit determines that an incident has not occurred based on the user's response, "I'm okay," to the user's question, "Are you okay?"The determination unit can also determine that an incident has occurred based on the user's response of "Help me." The determination unit can also determine that an incident has occurred based on the user's response of "It hurts." The notification unit notifies the user's family of the result of the incident determined by the determination unit. The notification unit can notify the user's family of the result of the incident using, for example, a text message. The notification unit can also notify the user's family of the result of the incident using a voice notification. For example, the notification unit can notify the user's family of the result of the incident using a text message. The notification unit can also notify the user's family of the result of the incident using a voice notification. The notification unit can also notify the user's family of the result of the incident using an email. This allows the monitoring system according to the embodiment to achieve highly accurate incident detection and notification while protecting the user's privacy.

[0030] The collection unit can collect biometric information such as heart rate, body temperature, and activity level. The collection unit, for example, measures the heart rate using a heart rate sensor. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor. The collection unit can also measure body temperature using a thermometer. For example, the collection unit can periodically measure the user's body temperature using the thermometer. Furthermore, the collection unit can also measure activity level using an acceleration sensor. For example, the collection unit can continuously measure the user's activity level using an acceleration sensor. This allows the collection unit to collect detailed biometric information of the user, enabling early detection of abnormalities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by the heart rate sensor into a generation AI to detect abnormalities in the heart rate.

[0031] The monitoring unit can monitor the user's movements or facial expressions. The monitoring unit can monitor the user's movements using, for example, an AI edge camera. For example, the monitoring unit can monitor the user's walking movements using an AI edge camera. The monitoring unit can also monitor the user's hand movements using an AI edge camera. The monitoring unit can also monitor the user's smiling or angry expressions using an AI edge camera. For example, the monitoring unit can monitor the user's smiling faces using an AI edge camera. The monitoring unit can also monitor the user's angry faces using an AI edge camera. This enables the monitoring unit to monitor the user's movements and facial expressions, thereby enabling early detection of abnormalities. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input video data acquired by the AI ​​edge camera into a generation AI to detect abnormal movements or facial expressions.

[0032] The recognition unit can recognize conversations using a generative AI. The recognition unit, for example, uses a generative AI to recognize a user's conversation. For example, the recognition unit can use a generative AI to analyze a response to a user's question, "Are you okay?". The recognition unit can also use a generative AI to analyze a user's response, "Help me!". The recognition unit can also use a generative AI to analyze a user's response, "It hurts!". In this way, the recognition unit improves the accuracy of conversation recognition by using a generative AI. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the recognition unit can input user conversation data into a generative AI and analyze the content of the conversation.

[0033] The determination unit can analyze conversations or images between the user and facility employees to determine whether an accident or abuse has occurred. The determination unit, for example, analyzes conversations between the user and facility employees. For example, the determination unit analyzes the content of the conversation between the user and facility employees to determine whether an accident or abuse has occurred. The determination unit can also analyze images of the user and facility employees. For example, the determination unit analyzes image data of the user and facility employees to determine whether an accident or abuse has occurred. In this way, the determination unit can accurately determine whether an accident or abuse has occurred by analyzing the conversations or images. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input conversation data between the user and facility employees into AI to determine whether an accident or abuse has occurred.

[0034] The notification unit can notify the user's family of the determination result. The notification unit can notify the user's family of the result of the incident, for example, by using a text message. For example, the notification unit can notify the user's family of the result of the incident by using a text message. The notification unit can also notify the user's family of the result of the incident by using a voice notification. For example, the notification unit can notify the user's family of the result of the incident by using a voice notification. The notification unit can also notify the user's family of the result of the incident by email. For example, the notification unit can notify the user's family of the result of the incident by email. This allows the notification unit to quickly notify the determination result, enabling an appropriate response. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the determination result into AI and select the optimal notification method.

[0035] The recognition unit can analyze the user's response using the generation AI and confirm the authenticity of the incident. The recognition unit, for example, uses the generation AI to analyze the user's response. For example, the recognition unit can use the generation AI to analyze the user's response to the question, "Are you okay?" The recognition unit can also use the generation AI to analyze the user's response, "Help me!" The recognition unit can also use the generation AI to analyze the user's response, "It hurts!" In this way, the recognition unit can use the generation AI to confirm the authenticity of the incident with high accuracy. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the recognition unit can input the user's response data into the generative AI and confirm the authenticity of the incident.

[0036] The determination unit can interview the user and determine whether an accident or abuse has occurred. The determination unit, for example, interviews the user. For example, the determination unit may ask the user, "Have you had any troubles recently?" The determination unit may also ask the user, "How is your relationship with the facility staff?" The determination unit may also ask the user, "Has there been any change in your health recently?" In this way, the determination unit can determine with high accuracy whether an accident or abuse has occurred through interviews with the user. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's interview data into AI and determine whether an accident or abuse has occurred.

[0037] The collection unit can analyze the user's past biometric data and select a collection method that enables early detection of abnormalities. The collection unit, for example, analyzes the user's past heart rate data. For example, the collection unit analyzes the user's past heart rate data to detect abnormal patterns early. The collection unit can also analyze the user's past body temperature data. For example, the collection unit analyzes the user's past body temperature data to detect abnormal fluctuations early. The collection unit can also analyze the user's past activity data. For example, the collection unit analyzes the user's past activity data to detect abnormal decreases early. This enables the collection unit to analyze past data and detect abnormalities early. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past biometric data into AI and select a collection method that enables early detection of abnormalities.

[0038] When collecting biometric information, the collection unit can dynamically change the type of data to be collected based on the user's current activity status. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data using a heart rate sensor and an acceleration sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is resting. For example, when the user is resting, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is sleeping. For example, when the user is sleeping, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. This allows the collection unit to change the type of data depending on the user's activity status, enabling more appropriate data collection. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user activity status data into the AI ​​and dynamically change the type of data to be collected.

[0039] When collecting biometric information, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting body temperature and heart rate data. For example, when the user is at home, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. The collection unit can also prioritize collecting activity level and heart rate data when the user is out. For example, when the user is out, the collection unit prioritizes collecting activity level and heart rate data using an acceleration sensor and a heart rate sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is in a hospital. For example, when the user is in a hospital, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. This enables the collection unit to collect more relevant data by taking geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the AI ​​and prioritize the collection of highly relevant data.

[0040] When collecting biometric information, the collection unit can analyze the user's social media activity and collect related biometric information. For example, if the user is feeling stressed because of social media, the collection unit prioritizes collecting heart rate and body temperature data. For example, if the user is feeling stressed because of social media, the collection unit prioritizes collecting heart rate and body temperature data using a heart rate sensor and a thermometer. The collection unit can also prioritize collecting activity level data if the user is relaxing because of social media. For example, if the user is relaxing because of social media, the collection unit prioritizes collecting activity level data using an acceleration sensor. Furthermore, the collection unit can also prioritize collecting heart rate data if the user is excited because of social media. For example, if the user is excited because of social media, the collection unit prioritizes collecting heart rate data using a heart rate sensor. This enables the collection unit to collect more relevant data by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's social media activity data into AI to collect related biometric information.

[0041] During monitoring, the monitoring unit can perform early detection of abnormalities based on the user's past motion or facial expression data. The monitoring unit, for example, refers to the user's past motion data to detect abnormal motions early. For example, the monitoring unit refers to the user's past motion data to detect abnormal motion patterns early. The monitoring unit can also refer to the user's past facial expression data to detect abnormal facial expressions early. For example, the monitoring unit refers to the user's past facial expression data to detect abnormal facial expression patterns early. Furthermore, the monitoring unit can combine the user's past motion and facial expression data to detect abnormal motions or facial expressions early. This enables the monitoring unit to detect abnormalities early by referring to past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past motion data and facial expression data into AI to perform early detection of abnormalities.

[0042] During monitoring, the monitoring unit can dynamically change the monitoring target based on the user's current living situation. For example, when the user is exercising, the monitoring unit prioritizes monitoring of movements. For example, when the user is exercising, the monitoring unit prioritizes monitoring of movements using an AI edge camera. The monitoring unit can also prioritize monitoring of facial expressions when the user is resting. For example, when the user is resting, the monitoring unit prioritizes monitoring of facial expressions using an AI edge camera. Furthermore, when the user is sleeping, the monitoring unit can combine monitoring of movements and facial expressions. For example, when the user is sleeping, the monitoring unit combines monitoring of movements and facial expressions using an AI edge camera. This allows the monitoring unit to change the monitoring target according to the living situation, enabling more appropriate monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the user's living situation data into AI and dynamically change the monitoring target.

[0043] During monitoring, the monitoring unit can prioritize monitoring highly relevant actions or facial expressions by taking into account the user's geographical location information. For example, when the user is at home, the monitoring unit prioritizes monitoring facial expressions. For example, when the user is at home, the monitoring unit prioritizes monitoring facial expressions using an AI edge camera. The monitoring unit can also prioritize monitoring actions when the user is out. For example, when the user is out, the monitoring unit prioritizes monitoring actions using an AI edge camera. Furthermore, the monitoring unit can combine monitoring of actions and facial expressions when the user is in a hospital. For example, when the user is in a hospital, the monitoring unit combines monitoring of actions and facial expressions using an AI edge camera. This enables the monitoring unit to perform more relevant monitoring by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into AI and prioritize monitoring highly relevant actions and facial expressions.

[0044] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related behaviors and facial expressions. For example, if the user is feeling stressed because of social media, the monitoring unit prioritizes behavior monitoring. For example, if the user is feeling stressed because of social media, the monitoring unit prioritizes behavior monitoring using an AI edge camera. The monitoring unit can also prioritize facial expression monitoring if the user is relaxing because of social media. For example, if the user is relaxing because of social media, the monitoring unit prioritizes facial expression monitoring using an AI edge camera. Furthermore, the monitoring unit can combine monitoring of behavior and facial expression if the user is excited because of social media. For example, if the user is excited because of social media, the monitoring unit combines monitoring of behavior and facial expression using an AI edge camera. This enables the monitoring unit to analyze social media activity and perform more relevant monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into AI and monitor related behaviors and facial expressions.

[0045] During recognition, the recognition unit can improve the accuracy of recognition based on the user's past conversation data. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific phrases. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific phrases. The recognition unit can also refer to the user's past conversation data to make it easier to recognize the pronunciation of specific words. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize the pronunciation of specific words. Furthermore, the recognition unit can also refer to the user's past conversation data to make it easier to recognize specific contexts. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific contexts. In this way, the recognition unit improves the accuracy of recognition by referring to the past conversation data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past conversation data into AI to improve the accuracy of recognition.

[0046] The recognition unit can dynamically change the content of the conversation to be recognized based on the user's current living situation during recognition. For example, when the user is exercising, the recognition unit prioritizes recognizing conversation related to exercise. For example, when the user is exercising, the recognition unit prioritizes recognizing conversation related to exercise. Furthermore, when the user is resting, the recognition unit can also prioritize recognizing conversation related to rest. For example, when the user is resting, the recognition unit prioritizes recognizing conversation related to rest. Furthermore, when the user is sleeping, the recognition unit can also prioritize recognizing conversation related to sleep. For example, when the user is sleeping, the recognition unit prioritizes recognizing conversation related to sleep. This allows the recognition unit to change the content of the conversation depending on the living situation, enabling more appropriate conversation recognition. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input the user's living situation data into AI and dynamically change the content of the conversation to be recognized.

[0047] During recognition, the recognition unit can prioritize recognizing highly relevant conversations by taking into account the user's geographical location information. For example, when the user is at home, the recognition unit prioritizes recognizing conversations related to the home. For example, when the user is at home, the recognition unit prioritizes recognizing conversations related to the home. Furthermore, when the user is out, the recognition unit can also prioritize recognizing conversations related to going out. For example, when the user is out, the recognition unit prioritizes recognizing conversations related to going out. Furthermore, when the user is in a hospital, the recognition unit can also prioritize recognizing conversations related to the hospital. For example, when the user is in a hospital, the recognition unit prioritizes recognizing conversations related to the hospital. This enables the recognition unit to recognize more relevant conversations by taking the geographical location information into account. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the user's geographical location information to AI and prioritize recognizing highly relevant conversations.

[0048] During recognition, the recognition unit can analyze the user's social media activity and recognize relevant conversations. For example, if the user feels stressed on social media, the recognition unit prioritizes recognizing conversations related to stress. For example, if the user feels stressed on social media, the recognition unit prioritizes recognizing conversations related to stress. Furthermore, if the user feels relaxed on social media, the recognition unit can prioritize recognizing conversations related to relaxation. For example, if the user feels relaxed on social media, the recognition unit prioritizes recognizing conversations related to relaxation. Furthermore, if the user feels excited on social media, the recognition unit can prioritize recognizing conversations related to excitement. For example, if the user feels excited on social media, the recognition unit prioritizes recognizing conversations related to excitement. This enables the recognition unit to recognize more relevant conversations by analyzing social media activity. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's social media activity data into AI to recognize relevant conversations.

[0049] When making a judgment, the judgment unit can improve the accuracy of the judgment based on the user's past incident data. The judgment unit, for example, references the user's past incident data to make it easier to recognize a specific pattern. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific pattern. The judgment unit can also reference the user's past incident data to make it easier to recognize a specific situation. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific situation. The judgment unit can also reference the user's past incident data to make it easier to recognize a specific behavior. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific behavior. As a result, the judgment unit improves the accuracy of the judgment by referring to the past incident data. Some or all of the above-described processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past incident data into AI to improve the accuracy of the judgment.

[0050] The determination unit can dynamically change the content of the incident to be determined based on the user's current living situation during the determination. For example, when the user is exercising, the determination unit determines a fall or excessive exercise as an incident. For example, when the user is exercising, the determination unit determines a fall or excessive exercise as an incident. The determination unit can also determine an abnormal heart rate or body temperature fluctuation as an incident when the user is resting. For example, when the user is resting, the determination unit determines an abnormal heart rate or body temperature fluctuation as an incident. The determination unit can also determine an abnormal movement or change in breathing as an incident when the user is sleeping. For example, when the user is sleeping, the determination unit determines an abnormal movement or change in breathing as an incident. This enables the determination unit to change the content of the incident depending on the living situation, thereby enabling more appropriate incident determination. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the user's living situation data into AI and dynamically change the content of the incident to be determined.

[0051] When making a determination, the determination unit can prioritize highly relevant incidents based on the user's geographical location information. For example, when the user is at home, the determination unit prioritizes determining incidents related to the home. For example, when the user is at home, the determination unit prioritizes determining incidents related to the home. Furthermore, when the user is out, the determination unit can also prioritize determining incidents related to going out. For example, when the user is out, the determination unit prioritizes determining incidents related to going out. Furthermore, when the user is in a hospital, the determination unit can also prioritize determining incidents related to the hospital. For example, when the user is in a hospital, the determination unit prioritizes determining incidents related to the hospital. This enables the determination unit to determine incidents with higher relevance by taking the geographical location information into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information to AI and prioritize highly relevant incidents.

[0052] At the time of determination, the determination unit can analyze the user's social media activity and determine a related incident. For example, if the user feels stressed because of social media, the determination unit prioritizes determining a stress-related incident. For example, if the user feels stressed because of social media, the determination unit prioritizes determining a stress-related incident. Furthermore, if the user is relaxing because of social media, the determination unit can also prioritize determining an incident related to relaxation. For example, if the user is relaxing because of social media, the determination unit prioritizes determining an incident related to relaxation. Furthermore, if the user is excited because of social media, the determination unit can also prioritize determining an incident related to excitement. For example, if the user is excited because of social media, the determination unit prioritizes determining an incident related to excitement. This enables the determination unit to determine a more relevant incident by analyzing social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media activity data into AI to determine a related incident.

[0053] The notification unit can select the optimal notification method based on the user's past notification history when sending a notification. For example, the notification unit prioritizes selecting a notification method (email, SMS, etc.) that the user has previously preferred. For example, the notification unit prioritizes selecting a notification method (email, SMS, etc.) that the user has previously preferred. The notification unit can also select the optimal notification method for a specific time period from the user's past notification history. For example, the notification unit selects the optimal notification method for a specific time period from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. For example, the notification unit analyzes the user's past notification history and selects the most effective notification method. In this way, the notification unit can select the optimal notification method by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI and select the optimal notification method.

[0054] The notification unit can dynamically change the timing of notification based on the user's current living situation when notifying. For example, if the user is exercising, the notification unit can notify after the user has finished exercising. For example, if the user is exercising, the notification unit can notify after the user has finished exercising. Furthermore, if the user is resting, the notification unit can notify after the user has finished resting. For example, if the user is resting, the notification unit can notify after the user has finished resting. Furthermore, if the user is sleeping, the notification unit can notify after the user has finished sleeping. For example, if the user is sleeping, the notification unit can notify after the user has finished sleeping. This allows the notification unit to change the timing of notification according to the living situation, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data into AI and dynamically change the timing of notification.

[0055] When providing a notification, the notification unit can prioritize notifications that are highly relevant based on the user's geographical location information. For example, when the user is at home, the notification unit prioritizes notifications related to the home. For example, when the user is at home, the notification unit prioritizes notifications related to the home. Furthermore, when the user is out, the notification unit can also prioritize notifications related to the user's outing. For example, when the user is out, the notification unit prioritizes notifications related to the user's outing. Furthermore, when the user is in a hospital, the notification unit can also prioritize notifications related to the hospital. For example, when the user is in a hospital, the notification unit prioritizes notifications related to the hospital. This enables the notification unit to provide more relevant notifications by taking the geographical location information into consideration. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information into AI and prioritize notifications that are highly relevant.

[0056] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, if the user is feeling stressed on social media, the notification unit can prioritize notifications related to stress. For example, if the user is feeling stressed on social media, the notification unit can prioritize notifications related to stress. Furthermore, if the user is relaxing on social media, the notification unit can prioritize notifications related to relaxation. For example, if the user is relaxing on social media, the notification unit can prioritize notifications related to relaxation. Furthermore, if the user is excited on social media, the notification unit can prioritize notifications related to excitement. For example, if the user is excited on social media, the notification unit can prioritize notifications related to excitement. This enables the notification unit to analyze social media activity and provide more relevant notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into AI and provide relevant notifications.

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

[0058] The collection unit can record the user's dietary content and analyze the nutritional balance. For example, when the user eats, the collection unit can capture the dietary content with a camera and identify nutrients using image analysis technology. The collection unit can also allow the user to input the dietary content by voice, and can analyze the voice data to evaluate the nutritional balance. Furthermore, the collection unit can accumulate the user's dietary history and compare it with past data to monitor fluctuations in nutritional balance. This allows the collection unit to provide detailed nutritional information to support the user's health management.

[0059] The monitoring unit can analyze the user's sleep patterns and evaluate the quality of their sleep. For example, the monitoring unit can monitor the user's movements and breathing patterns while they sleep using an AI edge camera to detect abnormal movements or changes in breathing. The monitoring unit can also collect fluctuations in the user's heart rate and body temperature to evaluate the depth and quality of their sleep. Furthermore, the monitoring unit can monitor the user's sleep environment (e.g., room temperature and humidity) and provide advice to provide an optimal sleeping environment. This allows the monitoring unit to provide detailed information to improve the user's sleep quality.

[0060] The determination unit can analyze the user's exercise habits and evaluate health risks. For example, the determination unit collects data on the user's activity level and detects insufficient or excessive exercise. The determination unit can also analyze fluctuations in the user's heart rate and body temperature to evaluate health risks during exercise. Furthermore, the determination unit can accumulate the user's exercise history and compare it with past data to monitor changes in exercise habits. This allows the determination unit to detect health risks in the user early and provide appropriate exercise advice.

[0061] The collection unit can use the user's geographical location information to assess region-specific health risks. For example, if the user is at high altitude, the collection unit can assess high altitude-specific health risks (e.g., hypoxia). If the user is in an urban area, the collection unit can also assess urban area-specific health risks (e.g., air pollution). Furthermore, if the user is traveling, the collection unit can also assess health risks at the travel destination (e.g., infectious disease outbreaks). This allows the collection unit to make more appropriate health risk assessments by taking the user's geographical location information into consideration.

[0062] The recognition unit can analyze the user's past conversation data and learn the user's language patterns. For example, the recognition unit can refer to the user's past conversation data to understand the frequency of use of specific words and phrases. The recognition unit can also analyze the user's past conversation data and learn how words are used in specific contexts. Furthermore, the recognition unit can learn the user's pronunciation characteristics based on the user's past conversation data. In this way, the recognition unit can improve the accuracy of conversation recognition by learning the user's language patterns.

[0063] The notification unit can analyze the user's past notification history and generate optimal notification content. For example, the notification unit can refer to notification content that the user has preferred in the past and generate similar content. The notification unit can also identify notification content that was effective in a particular situation from the user's past notification history and generate a new notification based on that content. Furthermore, the notification unit can analyze the user's past notification history and generate a notification based on the notification content that received the most positive response. This allows the notification unit to utilize the past notification history to provide more effective notification content.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects biological information. The biological information includes, for example, heart rate, body temperature, and activity level. The collection unit measures heart rate using a heart rate sensor, body temperature using a thermometer, and activity level using an acceleration sensor. Step 2: The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera. Step 3: The recognition unit uses the generation AI to recognize the conversation based on the anomaly detected by the monitoring unit. The recognition unit analyzes the user's conversation and responses. Step 4: The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The determination unit determines whether or not an incident has occurred based on the content of the user's response and additional sensor data. Step 5: The notification unit notifies the user's family of the result of the incident determined by the determination unit. The notification unit notifies the family of the result of the incident using a text message, a voice notification, or an email.

[0066] (Example 2) A monitoring system according to an embodiment of the present invention is designed to monitor elderly people in nursing homes or those living alone. This monitoring system combines a wearable device that collects biometric information with a communication robot equipped with an AI edge camera. It detects and notifies users of incidents such as safety checks, accidents, and abuse without transmitting the user's private information to the cloud. First, the wearable device collects the user's biometric information (e.g., heart rate, body temperature, activity level) in real time. This information is transmitted to the communication robot and analyzed in conjunction with the AI ​​edge camera. The AI ​​edge camera monitors the user's movements and facial expressions, and if an abnormality is detected, it recognizes it as a suspected incident. Next, a conversation recognition function using generative AI is activated. When the AI ​​edge camera detects a suspected incident, the communication robot engages in a dialogue with the user to confirm the authenticity of the incident. For example, if a user falls, the robot asks, "Are you okay?" and analyzes the user's response. The results of this dialogue are learned to improve the accuracy of the AI. Furthermore, in care facilities such as nursing homes, accidents and abuse can be identified based on conversations between the user and facility staff, images, and interviews with the user. This allows the user's family members to be notified quickly, enabling appropriate action to be taken. This system achieves highly accurate incident detection and notification while protecting the user's privacy. For example, if an elderly person living alone falls, the wearable device detects the abnormality and the communication robot confirms the situation through dialogue. This allows for a quick response and ensures the user's safety. This allows the monitoring system to achieve highly accurate incident detection and notification while protecting the user's privacy.

[0067] A monitoring system according to an embodiment includes a collection unit, a monitoring unit, a recognition unit, a determination unit, and a notification unit. The collection unit collects biometric information. The biometric information includes, but is not limited to, heart rate, body temperature, and activity level. For example, the collection unit measures the heart rate using a heart rate sensor. The collection unit can also measure body temperature using a thermometer. The collection unit can also measure activity level using an acceleration sensor. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor. The collection unit can also periodically measure the user's body temperature using a thermometer. The collection unit can also continuously measure the user's activity level using an acceleration sensor. The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. For example, the monitoring unit monitors the user's movements using an AI edge camera. The monitoring unit can also monitor the user's facial expressions using the AI ​​edge camera. For example, the monitoring unit monitors the user's walking movements using the AI ​​edge camera. The monitoring unit can also monitor the user's hand movements using the AI ​​edge camera. Furthermore, the monitoring unit can monitor the user's smiling or angry expressions using an AI edge camera. The recognition unit recognizes conversations using a generation AI based on abnormalities detected by the monitoring unit. The recognition unit can recognize the user's conversations using, for example, the generation AI. The recognition unit can also analyze the user's responses using the generation AI. For example, the recognition unit can analyze the user's response to the user's question, "Are you okay?" using the generation AI. The recognition unit can also analyze the user's response, "Help me!" using the generation AI. The recognition unit can also analyze the user's response, "It hurts!" using the generation AI. The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The determination unit can determine whether or not an incident has occurred based on, for example, the content of the user's response. The determination unit can also determine whether or not an incident has occurred based on additional sensor data. For example, the determination unit determines that an incident has not occurred based on the user's response, "I'm okay," to the user's question, "Are you okay?"The determination unit can also determine that an incident has occurred based on the user's response of "Help me." The determination unit can also determine that an incident has occurred based on the user's response of "It hurts." The notification unit notifies the user's family of the result of the incident determined by the determination unit. The notification unit can notify the user's family of the result of the incident using, for example, a text message. The notification unit can also notify the user's family of the result of the incident using a voice notification. For example, the notification unit can notify the user's family of the result of the incident using a text message. The notification unit can also notify the user's family of the result of the incident using a voice notification. The notification unit can also notify the user's family of the result of the incident using an email. This allows the monitoring system according to the embodiment to achieve highly accurate incident detection and notification while protecting the user's privacy.

[0068] The collection unit can collect biometric information such as heart rate, body temperature, and activity level. The collection unit, for example, measures the heart rate using a heart rate sensor. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor. The collection unit can also measure body temperature using a thermometer. For example, the collection unit can periodically measure the user's body temperature using the thermometer. Furthermore, the collection unit can also measure activity level using an acceleration sensor. For example, the collection unit can continuously measure the user's activity level using an acceleration sensor. This allows the collection unit to collect detailed biometric information of the user, enabling early detection of abnormalities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by the heart rate sensor into a generation AI to detect abnormalities in the heart rate.

[0069] The monitoring unit can monitor the user's movements or facial expressions. The monitoring unit can monitor the user's movements using, for example, an AI edge camera. For example, the monitoring unit can monitor the user's walking movements using an AI edge camera. The monitoring unit can also monitor the user's hand movements using an AI edge camera. The monitoring unit can also monitor the user's smiling or angry expressions using an AI edge camera. For example, the monitoring unit can monitor the user's smiling faces using an AI edge camera. The monitoring unit can also monitor the user's angry faces using an AI edge camera. This enables the monitoring unit to monitor the user's movements and facial expressions, thereby enabling early detection of abnormalities. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input video data acquired by the AI ​​edge camera into a generation AI to detect abnormal movements or facial expressions.

[0070] The recognition unit can recognize conversations using a generative AI. The recognition unit, for example, uses a generative AI to recognize a user's conversation. For example, the recognition unit can use a generative AI to analyze a response to a user's question, "Are you okay?". The recognition unit can also use a generative AI to analyze a user's response, "Help me!". The recognition unit can also use a generative AI to analyze a user's response, "It hurts!". In this way, the recognition unit improves the accuracy of conversation recognition by using a generative AI. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using a generative AI, or may be performed without using a generative AI. For example, the recognition unit can input user conversation data into a generative AI and analyze the content of the conversation.

[0071] The determination unit can analyze conversations or images between the user and facility employees to determine whether an accident or abuse has occurred. The determination unit, for example, analyzes conversations between the user and facility employees. For example, the determination unit analyzes the content of the conversation between the user and facility employees to determine whether an accident or abuse has occurred. The determination unit can also analyze images of the user and facility employees. For example, the determination unit analyzes image data of the user and facility employees to determine whether an accident or abuse has occurred. In this way, the determination unit can accurately determine whether an accident or abuse has occurred by analyzing the conversations or images. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input conversation data between the user and facility employees into AI to determine whether an accident or abuse has occurred.

[0072] The notification unit can notify the user's family of the determination result. The notification unit can notify the user's family of the result of the incident, for example, by using a text message. For example, the notification unit can notify the user's family of the result of the incident by using a text message. The notification unit can also notify the user's family of the result of the incident by using a voice notification. For example, the notification unit can notify the user's family of the result of the incident by using a voice notification. The notification unit can also notify the user's family of the result of the incident by email. For example, the notification unit can notify the user's family of the result of the incident by email. This allows the notification unit to quickly notify the determination result, enabling an appropriate response. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the determination result into AI and select the optimal notification method.

[0073] The recognition unit can analyze the user's response using the generation AI and confirm the authenticity of the incident. The recognition unit, for example, uses the generation AI to analyze the user's response. For example, the recognition unit can use the generation AI to analyze the user's response to the question, "Are you okay?" The recognition unit can also use the generation AI to analyze the user's response, "Help me!" The recognition unit can also use the generation AI to analyze the user's response, "It hurts!" In this way, the recognition unit can use the generation AI to confirm the authenticity of the incident with high accuracy. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the recognition unit can input the user's response data into the generative AI and confirm the authenticity of the incident.

[0074] The determination unit can interview the user and determine whether an accident or abuse has occurred. The determination unit, for example, interviews the user. For example, the determination unit may ask the user, "Have you had any troubles recently?" The determination unit may also ask the user, "How is your relationship with the facility staff?" The determination unit may also ask the user, "Has there been any change in your health recently?" In this way, the determination unit can determine with high accuracy whether an accident or abuse has occurred through interviews with the user. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's interview data into AI and determine whether an accident or abuse has occurred.

[0075] The collection unit can estimate the user's emotions and adjust the frequency of collection of biometric information based on the estimated user's emotions. The collection unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the collection unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice using voice analysis technology. The collection unit can also adjust the frequency of collection of biometric information based on the user's emotions. For example, the collection unit can increase the frequency of collection of heart rate and body temperature when the user is stressed. The collection unit can also decrease the frequency of collection of activity level when the user is relaxed. Furthermore, the collection unit can increase the frequency of collection of heart rate and decrease the frequency of collection of body temperature when the user is excited. This allows the collection unit to adjust the frequency of collection of biometric information according to the user's emotions, thereby enabling more appropriate data collection. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotional data into the AI ​​and adjust the frequency of biometric information collection.

[0076] The collection unit can analyze the user's past biometric data and select a collection method that enables early detection of abnormalities. The collection unit, for example, analyzes the user's past heart rate data. For example, the collection unit analyzes the user's past heart rate data to detect abnormal patterns early. The collection unit can also analyze the user's past body temperature data. For example, the collection unit analyzes the user's past body temperature data to detect abnormal fluctuations early. The collection unit can also analyze the user's past activity data. For example, the collection unit analyzes the user's past activity data to detect abnormal decreases early. This enables the collection unit to analyze past data and detect abnormalities early. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past biometric data into AI and select a collection method that enables early detection of abnormalities.

[0077] When collecting biometric information, the collection unit can dynamically change the type of data to be collected based on the user's current activity status. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and activity level data using a heart rate sensor and an acceleration sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is resting. For example, when the user is resting, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is sleeping. For example, when the user is sleeping, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. This allows the collection unit to change the type of data depending on the user's activity status, enabling more appropriate data collection. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user activity status data into the AI ​​and dynamically change the type of data to be collected.

[0078] The collection unit can estimate the user's emotions and determine the priority of the biometric information to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the collection unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice using voice analysis technology. The collection unit can also determine the priority of the biometric information to be collected based on the user's emotions. For example, the collection unit can prioritize collecting heart rate and body temperature data when the user is stressed. The collection unit can also prioritize collecting activity level data when the user is relaxed. The collection unit can also prioritize collecting heart rate data when the user is excited. This allows the collection unit to prioritize the biometric information according to the user's emotions, enabling more appropriate data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotional data into the AI ​​and determine the priority of the biometric information to be collected.

[0079] When collecting biometric information, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting body temperature and heart rate data. For example, when the user is at home, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. The collection unit can also prioritize collecting activity level and heart rate data when the user is out. For example, when the user is out, the collection unit prioritizes collecting activity level and heart rate data using an acceleration sensor and a heart rate sensor. The collection unit can also prioritize collecting body temperature and heart rate data when the user is in a hospital. For example, when the user is in a hospital, the collection unit prioritizes collecting body temperature and heart rate data using a thermometer and a heart rate sensor. This enables the collection unit to collect more relevant data by taking geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the AI ​​and prioritize the collection of highly relevant data.

[0080] When collecting biometric information, the collection unit can analyze the user's social media activity and collect related biometric information. For example, if the user is feeling stressed because of social media, the collection unit prioritizes collecting heart rate and body temperature data. For example, if the user is feeling stressed because of social media, the collection unit prioritizes collecting heart rate and body temperature data using a heart rate sensor and a thermometer. The collection unit can also prioritize collecting activity level data if the user is relaxing because of social media. For example, if the user is relaxing because of social media, the collection unit prioritizes collecting activity level data using an acceleration sensor. Furthermore, the collection unit can also prioritize collecting heart rate data if the user is excited because of social media. For example, if the user is excited because of social media, the collection unit prioritizes collecting heart rate data using a heart rate sensor. This enables the collection unit to collect more relevant data by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's social media activity data into AI to collect related biometric information.

[0081] The monitoring unit can estimate the user's emotions and adjust the accuracy of monitoring based on the estimated user's emotions. The monitoring unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the monitoring unit can analyze the user's smiling or angry expressions using facial expression recognition technology. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the user's voice using voice analysis technology. The monitoring unit can also adjust the accuracy of monitoring based on the user's emotions. For example, the monitoring unit can increase the accuracy of monitoring when the user is stressed. The monitoring unit can also decrease the accuracy of monitoring when the user is relaxed. The monitoring unit can also increase the accuracy of monitoring when the user is excited. This allows the monitoring unit to adjust the accuracy of monitoring according to the user's emotions, enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input user's emotional data into AI and adjust the accuracy of monitoring.

[0082] During monitoring, the monitoring unit can perform early detection of abnormalities based on the user's past motion or facial expression data. The monitoring unit, for example, refers to the user's past motion data to detect abnormal motions early. For example, the monitoring unit refers to the user's past motion data to detect abnormal motion patterns early. The monitoring unit can also refer to the user's past facial expression data to detect abnormal facial expressions early. For example, the monitoring unit refers to the user's past facial expression data to detect abnormal facial expression patterns early. Furthermore, the monitoring unit can combine the user's past motion and facial expression data to detect abnormal motions or facial expressions early. This enables the monitoring unit to detect abnormalities early by referring to past data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past motion data and facial expression data into AI to perform early detection of abnormalities.

[0083] During monitoring, the monitoring unit can dynamically change the monitoring target based on the user's current living situation. For example, when the user is exercising, the monitoring unit prioritizes monitoring of movements. For example, when the user is exercising, the monitoring unit prioritizes monitoring of movements using an AI edge camera. The monitoring unit can also prioritize monitoring of facial expressions when the user is resting. For example, when the user is resting, the monitoring unit prioritizes monitoring of facial expressions using an AI edge camera. Furthermore, when the user is sleeping, the monitoring unit can combine monitoring of movements and facial expressions. For example, when the user is sleeping, the monitoring unit combines monitoring of movements and facial expressions using an AI edge camera. This allows the monitoring unit to change the monitoring target according to the living situation, enabling more appropriate monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the user's living situation data into AI and dynamically change the monitoring target.

[0084] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user's emotions. The monitoring unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the monitoring unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the user's voice using voice analysis technology. The monitoring unit can also determine monitoring priorities based on the user's emotions. For example, the monitoring unit can prioritize monitoring of the user's movements when the user is stressed. The monitoring unit can also prioritize monitoring of the user's facial expressions when the user is relaxed. The monitoring unit can also combine monitoring of the user's movements and facial expressions when the user is excited. This enables the monitoring unit to determine monitoring priorities based on the user's emotions, thereby enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input user emotion data into AI to determine monitoring priorities.

[0085] During monitoring, the monitoring unit can prioritize monitoring highly relevant actions or facial expressions by taking into account the user's geographical location information. For example, when the user is at home, the monitoring unit prioritizes monitoring facial expressions. For example, when the user is at home, the monitoring unit prioritizes monitoring facial expressions using an AI edge camera. The monitoring unit can also prioritize monitoring actions when the user is out. For example, when the user is out, the monitoring unit prioritizes monitoring actions using an AI edge camera. Furthermore, the monitoring unit can combine monitoring of actions and facial expressions when the user is in a hospital. For example, when the user is in a hospital, the monitoring unit combines monitoring of actions and facial expressions using an AI edge camera. This enables the monitoring unit to perform more relevant monitoring by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into AI and prioritize monitoring highly relevant actions and facial expressions.

[0086] During monitoring, the monitoring unit can analyze the user's social media activity and monitor related behaviors and facial expressions. For example, if the user is feeling stressed because of social media, the monitoring unit prioritizes behavior monitoring. For example, if the user is feeling stressed because of social media, the monitoring unit prioritizes behavior monitoring using an AI edge camera. The monitoring unit can also prioritize facial expression monitoring if the user is relaxing because of social media. For example, if the user is relaxing because of social media, the monitoring unit prioritizes facial expression monitoring using an AI edge camera. Furthermore, the monitoring unit can combine monitoring of behavior and facial expression if the user is excited because of social media. For example, if the user is excited because of social media, the monitoring unit combines monitoring of behavior and facial expression using an AI edge camera. This enables the monitoring unit to analyze social media activity and perform more relevant monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into AI and monitor related behaviors and facial expressions.

[0087] The recognition unit can estimate the user's emotions and adjust the speech recognition accuracy based on the estimated user's emotions. The recognition unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the recognition unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The recognition unit can also estimate the user's emotions using voice analysis technology. For example, the recognition unit can analyze the tone and speed of the user's voice using voice analysis technology. The recognition unit can also adjust the speech recognition accuracy based on the user's emotions. For example, the recognition unit can increase the speech recognition accuracy when the user is stressed. The recognition unit can also decrease the speech recognition accuracy when the user is relaxed. The recognition unit can also increase the speech recognition accuracy when the user is excited. This allows the recognition unit to adjust the speech recognition accuracy according to the user's emotions, enabling more appropriate speech recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input user emotion data into AI and adjust the speech recognition accuracy.

[0088] During recognition, the recognition unit can improve the accuracy of recognition based on the user's past conversation data. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific phrases. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific phrases. The recognition unit can also refer to the user's past conversation data to make it easier to recognize the pronunciation of specific words. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize the pronunciation of specific words. Furthermore, the recognition unit can also refer to the user's past conversation data to make it easier to recognize specific contexts. For example, the recognition unit may refer to the user's past conversation data to make it easier to recognize specific contexts. In this way, the recognition unit improves the accuracy of recognition by referring to the past conversation data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past conversation data into AI to improve the accuracy of recognition.

[0089] The recognition unit can dynamically change the content of the conversation to be recognized based on the user's current living situation during recognition. For example, when the user is exercising, the recognition unit prioritizes recognizing conversation related to exercise. For example, when the user is exercising, the recognition unit prioritizes recognizing conversation related to exercise. Furthermore, when the user is resting, the recognition unit can also prioritize recognizing conversation related to rest. For example, when the user is resting, the recognition unit prioritizes recognizing conversation related to rest. Furthermore, when the user is sleeping, the recognition unit can also prioritize recognizing conversation related to sleep. For example, when the user is sleeping, the recognition unit prioritizes recognizing conversation related to sleep. This allows the recognition unit to change the content of the conversation depending on the living situation, enabling more appropriate conversation recognition. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input the user's living situation data into AI and dynamically change the content of the conversation to be recognized.

[0090] The recognition unit can estimate the user's emotions and determine the priority of conversations to be recognized based on the estimated user's emotions. The recognition unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the recognition unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The recognition unit can also estimate the user's emotions using voice analysis technology. For example, the recognition unit can analyze the tone and speed of the user's voice using voice analysis technology. The recognition unit can also determine the priority of conversations to be recognized based on the user's emotions. For example, if the user is feeling stressed, the recognition unit can prioritize recognizing stress-related conversations. If the user is relaxed, the recognition unit can also prioritize recognizing relaxation-related conversations. If the user is excited, the recognition unit can also prioritize recognizing excitement-related conversations. This allows the recognition unit to prioritize conversations according to the user's emotions, enabling more appropriate conversation recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input user emotional data into the AI ​​and determine the priority of conversations to be recognized.

[0091] During recognition, the recognition unit can prioritize recognizing highly relevant conversations by taking into account the user's geographical location information. For example, when the user is at home, the recognition unit prioritizes recognizing conversations related to the home. For example, when the user is at home, the recognition unit prioritizes recognizing conversations related to the home. Furthermore, when the user is out, the recognition unit can also prioritize recognizing conversations related to going out. For example, when the user is out, the recognition unit prioritizes recognizing conversations related to going out. Furthermore, when the user is in a hospital, the recognition unit can also prioritize recognizing conversations related to the hospital. For example, when the user is in a hospital, the recognition unit prioritizes recognizing conversations related to the hospital. This enables the recognition unit to recognize more relevant conversations by taking the geographical location information into account. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input the user's geographical location information to AI and prioritize recognizing highly relevant conversations.

[0092] During recognition, the recognition unit can analyze the user's social media activity and recognize relevant conversations. For example, if the user feels stressed on social media, the recognition unit prioritizes recognizing conversations related to stress. For example, if the user feels stressed on social media, the recognition unit prioritizes recognizing conversations related to stress. Furthermore, if the user feels relaxed on social media, the recognition unit can prioritize recognizing conversations related to relaxation. For example, if the user feels relaxed on social media, the recognition unit prioritizes recognizing conversations related to relaxation. Furthermore, if the user feels excited on social media, the recognition unit can prioritize recognizing conversations related to excitement. For example, if the user feels excited on social media, the recognition unit prioritizes recognizing conversations related to excitement. This enables the recognition unit to recognize more relevant conversations by analyzing social media activity. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's social media activity data into AI to recognize relevant conversations.

[0093] The determination unit can estimate the user's emotions and adjust the incident determination criteria based on the estimated user's emotions. The determination unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the determination unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The determination unit can also estimate the user's emotions using voice analysis technology. For example, the determination unit can analyze the tone and speed of the user's voice using voice analysis technology. The determination unit can also adjust the incident determination criteria based on the user's emotions. For example, the determination unit can tighten the incident determination criteria when the user is stressed. The determination unit can also loosen the incident determination criteria when the user is relaxed. The determination unit can also tighten the incident determination criteria when the user is excited. This allows the determination unit to adjust the incident determination criteria according to the user's emotions, thereby enabling more appropriate incident determination. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input user emotional data into the AI ​​and adjust the criteria for judging incidents.

[0094] When making a judgment, the judgment unit can improve the accuracy of the judgment based on the user's past incident data. The judgment unit, for example, references the user's past incident data to make it easier to recognize a specific pattern. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific pattern. The judgment unit can also reference the user's past incident data to make it easier to recognize a specific situation. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific situation. The judgment unit can also reference the user's past incident data to make it easier to recognize a specific behavior. For example, the judgment unit references the user's past incident data to make it easier to recognize a specific behavior. As a result, the judgment unit improves the accuracy of the judgment by referring to the past incident data. Some or all of the above-described processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past incident data into AI to improve the accuracy of the judgment.

[0095] The determination unit can dynamically change the content of the incident to be determined based on the user's current living situation during the determination. For example, when the user is exercising, the determination unit determines a fall or excessive exercise as an incident. For example, when the user is exercising, the determination unit determines a fall or excessive exercise as an incident. The determination unit can also determine an abnormal heart rate or body temperature fluctuation as an incident when the user is resting. For example, when the user is resting, the determination unit determines an abnormal heart rate or body temperature fluctuation as an incident. The determination unit can also determine an abnormal movement or change in breathing as an incident when the user is sleeping. For example, when the user is sleeping, the determination unit determines an abnormal movement or change in breathing as an incident. This enables the determination unit to change the content of the incident depending on the living situation, thereby enabling more appropriate incident determination. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the user's living situation data into AI and dynamically change the content of the incident to be determined.

[0096] The determination unit can estimate the user's emotions and prioritize incidents based on the estimated user's emotions. The determination unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the determination unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The determination unit can also estimate the user's emotions using voice analysis technology. For example, the determination unit can analyze the tone and speed of the user's voice using voice analysis technology. The determination unit can also prioritize incidents based on the user's emotions. For example, the determination unit can prioritize stress-related incidents when the user is stressed. The determination unit can also prioritize relaxation-related incidents when the user is relaxed. The determination unit can also prioritize excitement-related incidents when the user is excited. This allows the determination unit to prioritize incidents based on the user's emotions, enabling more appropriate incident determination. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input user emotion data into AI to determine the priority of incidents.

[0097] When making a determination, the determination unit can prioritize highly relevant incidents based on the user's geographical location information. For example, when the user is at home, the determination unit prioritizes determining incidents related to the home. For example, when the user is at home, the determination unit prioritizes determining incidents related to the home. Furthermore, when the user is out, the determination unit can also prioritize determining incidents related to going out. For example, when the user is out, the determination unit prioritizes determining incidents related to going out. Furthermore, when the user is in a hospital, the determination unit can also prioritize determining incidents related to the hospital. For example, when the user is in a hospital, the determination unit prioritizes determining incidents related to the hospital. This enables the determination unit to determine incidents with higher relevance by taking the geographical location information into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's geographical location information to AI and prioritize highly relevant incidents.

[0098] At the time of determination, the determination unit can analyze the user's social media activity and determine a related incident. For example, if the user feels stressed because of social media, the determination unit prioritizes determining a stress-related incident. For example, if the user feels stressed because of social media, the determination unit prioritizes determining a stress-related incident. Furthermore, if the user is relaxing because of social media, the determination unit can also prioritize determining an incident related to relaxation. For example, if the user is relaxing because of social media, the determination unit prioritizes determining an incident related to relaxation. Furthermore, if the user is excited because of social media, the determination unit can also prioritize determining an incident related to excitement. For example, if the user is excited because of social media, the determination unit prioritizes determining an incident related to excitement. This enables the determination unit to determine a more relevant incident by analyzing social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's social media activity data into AI to determine a related incident.

[0099] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the notification unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice using voice analysis technology. The notification unit can also adjust the content of the notification based on the user's emotions. For example, the notification unit can provide concise and reassuring notification content when the user is stressed. The notification unit can also provide detailed notification content when the user is relaxed. The notification unit can also provide quick and concise notification content when the user is excited. This allows the notification unit to adjust the content of the notification according to the user's emotions, thereby enabling more appropriate notifications. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input user emotion data into AI and adjust the content of the notification.

[0100] The notification unit can select the optimal notification method based on the user's past notification history when sending a notification. For example, the notification unit prioritizes selecting a notification method (email, SMS, etc.) that the user has previously preferred. For example, the notification unit prioritizes selecting a notification method (email, SMS, etc.) that the user has previously preferred. The notification unit can also select the optimal notification method for a specific time period from the user's past notification history. For example, the notification unit selects the optimal notification method for a specific time period from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. For example, the notification unit analyzes the user's past notification history and selects the most effective notification method. In this way, the notification unit can select the optimal notification method by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification history into AI and select the optimal notification method.

[0101] The notification unit can dynamically change the timing of notification based on the user's current living situation when notifying. For example, if the user is exercising, the notification unit can notify after the user has finished exercising. For example, if the user is exercising, the notification unit can notify after the user has finished exercising. Furthermore, if the user is resting, the notification unit can notify after the user has finished resting. For example, if the user is resting, the notification unit can notify after the user has finished resting. Furthermore, if the user is sleeping, the notification unit can notify after the user has finished sleeping. For example, if the user is sleeping, the notification unit can notify after the user has finished sleeping. This allows the notification unit to change the timing of notification according to the living situation, thereby enabling more appropriate notification. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data into AI and dynamically change the timing of notification.

[0102] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the notification unit can analyze the user's smiling or angry facial expressions using facial expression recognition technology. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice using voice analysis technology. The notification unit can also determine the priority of notifications based on the user's emotions. For example, if the user is feeling stressed, the notification unit can prioritize notifications related to stress. If the user is relaxed, the notification unit can prioritize notifications related to relaxation. If the user is excited, the notification unit can prioritize notifications related to excitement. This allows the notification unit to determine the priority of notifications according to the user's emotions, enabling more appropriate notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input user emotion data into AI to determine the priority of notifications.

[0103] When providing a notification, the notification unit can prioritize notifications that are highly relevant based on the user's geographical location information. For example, when the user is at home, the notification unit prioritizes notifications related to the home. For example, when the user is at home, the notification unit prioritizes notifications related to the home. Furthermore, when the user is out, the notification unit can also prioritize notifications related to the user's outing. For example, when the user is out, the notification unit prioritizes notifications related to the user's outing. Furthermore, when the user is in a hospital, the notification unit can also prioritize notifications related to the hospital. For example, when the user is in a hospital, the notification unit prioritizes notifications related to the hospital. This enables the notification unit to provide more relevant notifications by taking the geographical location information into consideration. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information into AI and prioritize notifications that are highly relevant.

[0104] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, if the user is feeling stressed on social media, the notification unit can prioritize notifications related to stress. For example, if the user is feeling stressed on social media, the notification unit can prioritize notifications related to stress. Furthermore, if the user is relaxing on social media, the notification unit can prioritize notifications related to relaxation. For example, if the user is relaxing on social media, the notification unit can prioritize notifications related to relaxation. Furthermore, if the user is excited on social media, the notification unit can prioritize notifications related to excitement. For example, if the user is excited on social media, the notification unit can prioritize notifications related to excitement. This enables the notification unit to analyze social media activity and provide more relevant notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's social media activity data into AI and provide relevant notifications. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, monitoring unit, recognition unit, determination unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized using a heart rate sensor, a thermometer, and an acceleration sensor of the smart device 14. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera of the smart device 14. The recognition unit recognizes conversations using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines whether or not an incident has occurred using the identification processing unit 290 of the data processing device 12. The notification unit notifies the user's family of the results of the incident using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, monitoring unit, recognition unit, determination unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized using a heart rate sensor, a thermometer, and an acceleration sensor of the smart glasses 214. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera of the smart glasses 214. The recognition unit recognizes conversations using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines whether or not an incident has occurred using the identification processing unit 290 of the data processing device 12. The notification unit notifies the user's family of the results of the incident using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, monitoring unit, recognition unit, determination unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized using a heart rate sensor, a thermometer, and an acceleration sensor of the headset-type terminal 314. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera of the headset-type terminal 314. The recognition unit recognizes conversations using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines whether or not an incident has occurred using the identification processing unit 290 of the data processing device 12. The notification unit notifies the user's family of the results of the incident using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, monitoring unit, recognition unit, determination unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized using a heart rate sensor, a thermometer, and an acceleration sensor of the robot 414. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera of the robot 414. The recognition unit recognizes conversations using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines whether or not an incident has occurred using the identification processing unit 290 of the data processing device 12. The notification unit notifies the user's family of the results of the incident using the identification processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can record the user's dietary content and analyze the nutritional balance. For example, when the user eats, the collection unit can capture the dietary content with a camera and identify nutrients using image analysis technology. The collection unit can also allow the user to input the dietary content by voice, and can analyze the voice data to evaluate the nutritional balance. Furthermore, the collection unit can accumulate the user's dietary history and compare it with past data to monitor fluctuations in nutritional balance. This allows the collection unit to provide detailed nutritional information to support the user's health management.

[0107] The monitoring unit can analyze the user's sleep patterns and evaluate the quality of their sleep. For example, the monitoring unit can monitor the user's movements and breathing patterns while they sleep using an AI edge camera to detect abnormal movements or changes in breathing. The monitoring unit can also collect fluctuations in the user's heart rate and body temperature to evaluate the depth and quality of their sleep. Furthermore, the monitoring unit can monitor the user's sleep environment (e.g., room temperature and humidity) and provide advice to provide an optimal sleeping environment. This allows the monitoring unit to provide detailed information to improve the user's sleep quality.

[0108] The recognition unit can analyze a user's hobbies and interests and provide personalized content. For example, the recognition unit can analyze the user's conversation and behavioral data to identify topics and activities in which the user is interested. The recognition unit can also analyze the user's past content viewing history to understand trends in the content the user prefers. Furthermore, the recognition unit can suggest optimal content based on the user's current mood and emotions. This allows the recognition unit to provide the user with a more personalized experience.

[0109] The determination unit can analyze the user's exercise habits and evaluate health risks. For example, the determination unit collects data on the user's activity level and detects insufficient or excessive exercise. The determination unit can also analyze fluctuations in the user's heart rate and body temperature to evaluate health risks during exercise. Furthermore, the determination unit can accumulate the user's exercise history and compare it with past data to monitor changes in exercise habits. This allows the determination unit to detect health risks in the user early and provide appropriate exercise advice.

[0110] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit can delay the notification to give the user time to relax. Also, if the user is relaxed, the notification unit can provide a notification immediately. Furthermore, if the user is excited, the notification unit can refrain from providing a notification. In this way, the notification unit can provide a notification at the optimal timing according to the user's emotions, thereby reducing the user's stress and realizing more effective communication.

[0111] The collection unit can use the user's geographical location information to assess region-specific health risks. For example, if the user is at high altitude, the collection unit can assess high altitude-specific health risks (e.g., hypoxia). If the user is in an urban area, the collection unit can also assess urban area-specific health risks (e.g., air pollution). Furthermore, if the user is traveling, the collection unit can also assess health risks at the travel destination (e.g., infectious disease outbreaks). This allows the collection unit to make more appropriate health risk assessments by taking the user's geographical location information into consideration.

[0112] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency to detect abnormalities early. The monitoring unit can also decrease the monitoring frequency if the user is relaxed. Furthermore, the monitoring unit can increase the monitoring frequency if the user is excited. In this way, the monitoring unit can adjust the monitoring frequency according to the user's emotions, enabling more appropriate monitoring.

[0113] The recognition unit can analyze the user's past conversation data and learn the user's language patterns. For example, the recognition unit can refer to the user's past conversation data to understand the frequency of use of specific words and phrases. The recognition unit can also analyze the user's past conversation data and learn how words are used in specific contexts. Furthermore, the recognition unit can learn the user's pronunciation characteristics based on the user's past conversation data. In this way, the recognition unit can improve the accuracy of conversation recognition by learning the user's language patterns.

[0114] The determination unit can estimate the user's emotion and evaluate the urgency of the incident based on the estimated user's emotion. For example, if the user is feeling stressed, the determination unit can evaluate the urgency of the incident as high. Also, if the user is relaxed, the determination unit can evaluate the urgency of the incident as low. Furthermore, if the user is excited, the determination unit can evaluate the urgency of the incident as high. This allows the determination unit to evaluate the urgency of the incident according to the user's emotion, thereby enabling a more appropriate response.

[0115] The notification unit can analyze the user's past notification history and generate optimal notification content. For example, the notification unit can refer to notification content that the user has preferred in the past and generate similar content. The notification unit can also identify notification content that was effective in a particular situation from the user's past notification history and generate a new notification based on that content. Furthermore, the notification unit can analyze the user's past notification history and generate a notification based on the notification content that received the most positive response. This allows the notification unit to utilize the past notification history to provide more effective notification content.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects biological information. The biological information includes, for example, heart rate, body temperature, and activity level. The collection unit measures heart rate using a heart rate sensor, body temperature using a thermometer, and activity level using an acceleration sensor. Step 2: The monitoring unit monitors the user's movements or facial expressions based on the biometric information collected by the collection unit. The monitoring unit monitors the user's movements and facial expressions using an AI edge camera. Step 3: The recognition unit uses the generation AI to recognize the conversation based on the anomaly detected by the monitoring unit. The recognition unit analyzes the user's conversation and responses. Step 4: The determination unit determines whether or not an incident has occurred based on the conversation recognized by the recognition unit. The determination unit determines whether or not an incident has occurred based on the content of the user's response and additional sensor data. Step 5: The notification unit notifies the user's family of the result of the incident determined by the determination unit. The notification unit notifies the family of the result of the incident using a text message, a voice notification, or an email.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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; a monitoring unit that monitors the user's movements or facial expressions based on the biometric information collected by the collecting unit; a recognition unit that detects an abnormality detected by the monitoring unit and recognizes a conversation based on the abnormality; a determination unit that determines whether or not there is an incident based on the conversation recognized by the recognition unit; a notification unit that notifies a family member of the user of the result of the incident determined by the determination unit; Equipped with A system characterized by:

2. The collecting unit Collects biometric information such as heart rate, body temperature, and activity level The system of claim 1 .

3. The monitoring unit Monitor the user's movements or facial expressions The system of claim 1 .

4. The recognition unit Recognizing speech using generative AI The system of claim 1 .

5. The determination unit Analyzes conversations or images between users and facility staff to determine whether there has been an accident or abuse The system of claim 1 .

6. The notification unit Notify the user's family of the result The system of claim 1 .

7. The recognition unit Analyze user responses using generative AI to verify the authenticity of the incident The system of claim 1 .

8. The determination unit Interview the user to determine whether there has been an accident or abuse The system of claim 1 .

Images