System

Abstract

An object of a system according to an embodiment is to ensure privacy and provide appropriate care while watching the life of an elderly person living alone.SOLUTION: A system according to an embodiment includes a video acquirer, a generation AI, a text generator, a sharer, and a warner. The video acquisition unit acquires a video of a camera or a smartphone. The generation AI analyzes the video acquired by the video acquiring unit. The text generator converts a specific action and time into text from the video analyzed by the generation AI. The sharing unit shares the information transcribed into text by the text transcribing unit with a family member or a caregiver. The warning unit detects an abnormal behavior and transmits a warning.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide appropriate care while ensuring privacy when monitoring the lives of elderly people living alone.

[0005] The system according to the embodiment aims to provide appropriate care to elderly people living alone while ensuring their privacy. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, a generation AI, a text conversion unit, a sharing unit, and a warning unit. The video acquisition unit acquires video from a camera or smartphone. The generation AI analyzes the video acquired by the video acquisition unit. The text conversion unit converts specific actions and times into text from the video analyzed by the generation AI. The sharing unit shares the information converted into text by the text conversion unit with family members and caregivers. The warning unit detects abnormal behavior and sends a warning. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate care while watching over the lives of elderly people living alone and ensuring their privacy. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​Heartful Watcher according to an embodiment of the present invention is a system that converts the behavior of elderly people into text in real time and checks their safety while protecting their privacy. This allows the AI ​​Heartful Watcher to convert the behavior of elderly people into text in real time and check their safety while protecting their privacy.

[0029] The AI ​​Heartful Watcher according to the embodiment includes a video acquisition unit, a generation AI, a text conversion unit, a sharing unit, and a warning unit. The video acquisition unit acquires video from a camera or smartphone. For example, indoor video can be acquired using a fixed camera. Additionally, a smartphone camera can be used to capture footage of the elderly person's behavior. Furthermore, a wearable camera can be used to capture video from the elderly person's perspective. The generation AI analyzes the video acquired by the video acquisition unit. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). Furthermore, the generation AI can analyze the video using a multimodal generation AI. Furthermore, the generation AI can extract and analyze particularly important parts of the video. The text conversion unit converts specific actions and times into text from the video analyzed by the generation AI. For example, the text conversion unit describes actions in detail based on the video data analyzed by the generation AI, such as "Eat breakfast at 8:30 AM." Furthermore, the text conversion unit can also describe actions in detail based on the video data analyzed by the generation AI, such as "Go for a walk at 2:15 PM." The text conversion unit can also describe actions in detail, such as "take medicine at 10 a.m.", based on the video data analyzed by the generation AI. The sharing unit shares the information converted into text by the text conversion unit with family members and caregivers. For example, the sharing unit can send the text information to family members and caregivers via email. The sharing unit can also share the text information with family members and caregivers through a dedicated application. The sharing unit can also store the text information in the cloud so that family members and caregivers can access it. The warning unit detects abnormal behavior and sends a warning. For example, the warning unit sends a warning if there is a possibility of a fall based on the video data analyzed by the generation AI. The warning unit can also send a warning if an unusual behavior pattern is detected based on the video data analyzed by the generation AI. The warning unit can also send a warning if a long period of inactivity is detected based on the video data analyzed by the generation AI.As a result, the AI ​​Heartful Watcher according to the embodiment can convert the elderly person's behavior into text in real time, enabling safety confirmation while protecting their privacy. For example, family members and caregivers can understand the elderly person's behavior through the text information and take necessary measures promptly. In addition, if the warning unit detects abnormal behavior, they can receive an immediate warning. This ensures the safety of the elderly, allowing them to continue living with peace of mind.

[0030] The video acquisition unit can automatically adjust the position and angle of the camera according to the elderly person's living environment. The video acquisition unit introduces a system that automatically adjusts the position and angle of the camera according to the elderly person's living environment, for example. For example, to monitor activities in the living room, the camera automatically moves to an optimal position and acquires video at an optimal angle. The video acquisition unit can also automatically move the camera to an optimal position and acquire video at an optimal angle to monitor activities in the bedroom. The video acquisition unit can also automatically move the camera to an optimal position and acquire video at an optimal angle to monitor activities in the kitchen. This makes it possible to acquire optimal video according to the elderly person's living environment.

[0031] The image acquisition unit can automatically track the camera according to the elderly person's movements. For example, the image acquisition unit can add a function that enables the camera to automatically track the elderly person's movements, thereby acquiring more detailed images. For example, when the elderly person walks around a room, the camera automatically tracks their movements, capturing the elderly person's figure at all times. The image acquisition unit can also automatically track the camera when the elderly person sits down in a chair, capturing the sitting action in detail. The image acquisition unit can also automatically track the camera when the elderly person stands up, capturing the standing action in detail. This makes it possible to acquire detailed images according to the elderly person's movements.

[0032] The video acquisition unit can monitor the outdoor activities of elderly people using a drone and acquire video. The video acquisition unit, for example, builds a system that monitors the outdoor activities of elderly people using a drone and acquires video. For example, the drone automatically tracks the elderly person and acquires video when the elderly person is gardening or taking a walk in the yard. The video acquisition unit can also automatically track the elderly person and acquire video when the elderly person is shopping or going out. The video acquisition unit can also automatically track the elderly person and acquire video when the elderly person is exercising or participating in recreational activities outdoors. This allows for effective monitoring of the outdoor activities of elderly people.

[0033] The image acquisition unit may be a wearable camera worn by the elderly person, allowing for image acquisition from a more personal perspective. The image acquisition unit may be a wearable camera worn by the elderly person, for example, to build a system for acquiring image from a more personal perspective. For example, a glasses-type camera or a pendant-type camera may be used. The image acquisition unit may also use a chest-worn camera or a wristwatch-type camera. The image acquisition unit may also use a hat-type camera or an earphone-type camera. This allows for detailed image acquisition from the elderly person's perspective.

[0034] Generative AI can learn the behavioral patterns of elderly people and convert predicted behavior into text in advance. For example, generative AI can add a function to learn the behavioral patterns of elderly people and convert predicted behavior into text in advance. For example, it can predict morning routines and regular activities and convert them into text in advance. Generative AI can also predict meal and walk times and convert them into text in advance. Generative AI can also predict sleep and rest times and convert them into text in advance. This makes it possible to predict elderly behavior and convert them into text in advance, making more effective monitoring possible.

[0035] Generative AI can analyze the voices of elderly people and convert what they say along with their actions into text. Generative AI can, for example, build a system that analyzes the voices of elderly people and converts what they say along with their actions into text. For example, it can analyze everyday conversations and monologues and convert them into text. Generative AI can also analyze the contents of telephone and video calls and convert them into text. Generative AI can also analyze emotional tone and important keywords and convert them into text. This allows the contents of what elderly people say to be converted into text, making it possible to provide more detailed information.

[0036] Generative AI can recreate the behavior of elderly people in 3D models and display them visually along with text. For example, generative AI can build a system that recreates the behavior of elderly people in 3D models and displays them visually along with text. For example, daily movements and activities are recreated in 3D models. Generative AI can also recreate eating and walking movements in 3D models. Generative AI can also recreate sleeping and resting movements in 3D models. This allows for more detailed information to be provided by visually displaying the behavior of elderly people.

[0037] Generative AI can animate the behavior of elderly people and provide it to their families and caregivers. For example, generative AI can build a system that animates the behavior of elderly people and provides it to their families and caregivers. For example, it can reproduce daily movements and activities as animations. Generative AI can also reproduce the movements of eating and walking as animations. Generative AI can also reproduce the movements of sleeping and resting as animations. This makes it possible to provide more detailed information by animating the behavior of elderly people.

[0038] The shared unit can be equipped with a function to read text information aloud, making it possible to accommodate visually impaired family members and caregivers. For example, the shared unit can be equipped with a function to read text information aloud, building a system that can accommodate visually impaired family members and caregivers. For example, information can be provided aloud using a smartphone or smart speaker. The shared unit can also read text information aloud using voice synthesis technology. The shared unit can also adjust the speed and volume at which the text information is read aloud. This makes it possible to provide information to visually impaired family members and caregivers.

[0039] The sharing unit can automatically compile the text information into a daily report format and periodically send it to family members or caregivers. The sharing unit, for example, builds a system that automatically compiles the text information into a daily report format and periodically sends it to family members or caregivers. For example, a daily report summarizing daily activities is sent by email. The sharing unit can also compile the text information into a daily report format and send it to family members or caregivers via a dedicated application. The sharing unit can also compile the text information into a daily report format and store it on the cloud so that family members and caregivers can access it. This allows family members and caregivers to receive information regularly.

[0040] The sharing unit can notify a smartwatch or smart speaker of the text information. The sharing unit, for example, builds a system that notifies a smartwatch or smart speaker of the text information. For example, the sharing unit displays a notification on the smartwatch and issues a voice notification on the smart speaker. The sharing unit can also notify a smartphone of the text information. The sharing unit can also notify a tablet of the text information. This allows family members or caregivers to receive the information on their smartwatch or smart speaker.

[0041] The sharing unit can link the text information with the calendar app of the family member or caregiver to help with schedule management. For example, the sharing unit can link the text information with the calendar app of the family member or caregiver to build a system that helps with schedule management. For example, important events and appointments can be automatically added to the calendar. The sharing unit can also link the text information with the calendar app to set reminders. The sharing unit can also link the text information with the calendar app to set notifications. This allows family members and caregivers to manage information in the calendar app.

[0042] The warning unit can not only detect abnormal behavior but also suggest preventive actions. For example, the warning unit can be configured to build a system that adds a function to not only detect abnormal behavior but also suggest preventive actions. For example, if there is a high risk of falling, the warning unit can suggest taking a break. The warning unit can also suggest light exercise if a long period of inactivity is detected. The warning unit can also suggest seeing a doctor if an unusual behavior pattern is detected. This makes it possible to make suggestions to prevent abnormal behavior in the elderly.

[0043] The warning unit can integrate multiple sensors (temperature, humidity, sound, etc.) to improve the accuracy of detecting abnormal behavior. For example, the warning unit builds a system that integrates multiple sensors (temperature, humidity, sound, etc.) to improve the accuracy of detecting abnormal behavior. For example, a temperature sensor and a sound sensor are combined to detect abnormal behavior. The warning unit can also detect abnormal behavior by combining a humidity sensor and a motion sensor. The warning unit can also detect abnormal behavior by combining a light sensor and a vibration sensor. This improves the accuracy of detecting abnormal behavior.

[0044] The warning unit can cooperate with medical institutions to share the abnormal behavior detection results and encourage specialized responses. The warning unit, for example, can cooperate with medical institutions to share the abnormal behavior detection results and build a system to encourage specialized responses. For example, when abnormal behavior is detected, the medical institution is automatically notified. The warning unit can also share the abnormal behavior detection results through a dedicated application of the medical institution. The warning unit can also store the abnormal behavior detection results in the medical institution's cloud system so that the medical institution can access them. This allows the abnormal behavior detection results to be shared with the medical institution and specialized responses to be made possible.

[0045] The warning unit can cooperate with a local monitoring service to report the abnormal behavior detection results, enabling a rapid response. The warning unit, for example, builds a system that cooperates with a local monitoring service to report the abnormal behavior detection results, enabling a rapid response. For example, when abnormal behavior is detected, the warning unit automatically notifies the local monitoring service. The warning unit can also share the abnormal behavior detection results through a dedicated application for the local monitoring service. The warning unit can also store the abnormal behavior detection results in the cloud system of the local monitoring service, making them accessible to the local monitoring service. This allows the abnormal behavior detection results to be shared with the local monitoring service, enabling a rapid response.

[0046] The system can be equipped with a function to analyze the behavioral history of elderly people and monitor long-term changes in their health condition. For example, a system can be constructed that adds a function to analyze the behavioral history of elderly people and monitor long-term changes in their health condition. For example, the system can analyze daily activity levels and sleep patterns to monitor changes in their health condition. The system can also analyze diet and exercise history to monitor changes in their health condition. The system can also analyze changes in weight and blood pressure to monitor changes in their health condition. This makes it possible to monitor changes in the long-term health condition of elderly people.

[0047] The system can learn the lifestyle rhythms of elderly people and automatically issue an alert when an abnormality occurs. For example, a system can be constructed that learns the lifestyle rhythms of elderly people and automatically issues an alert when an abnormality occurs. For example, an alert can be issued if an elderly person does not wake up at their usual wake-up time. The system can also issue an alert if an elderly person does not eat meals at their usual mealtimes. The system can also issue an alert if an elderly person does not go to bed at their usual bedtime. This makes it possible to quickly detect and respond to abnormalities in the elderly person's lifestyle rhythms.

[0048] The system can share information on elderly safety confirmation with the local community, and build a system for the entire community to watch over them. For example, the system can be built to share information on elderly safety confirmation with the local community, and build a system for the entire community to watch over them. For example, the system can work with local monitoring services to share safety confirmation information. The system can also work with local volunteer groups to share safety confirmation information. The system can also work with local governments to share safety confirmation information. In this way, a system can be built for the entire community to watch over the elderly.

[0049] The system can link the safety confirmation information of the elderly with a smart home system and automatically control home appliances when an abnormality occurs. For example, the system can link the safety confirmation information of the elderly with a smart home system and build a system that automatically controls home appliances when an abnormality occurs. For example, the system can turn on the lights when an abnormality is detected. The system can also operate the air conditioner when an abnormality is detected. The system can also automatically lock the doors when an abnormality is detected. In this way, home appliances can be automatically controlled when an abnormality occurs, ensuring the safety of the elderly.

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

[0051] The video acquisition unit can combine environmental sensors to acquire environmental data along with the video. For example, a temperature sensor can be used to acquire the indoor temperature and record it together with the video. A humidity sensor can also be used to acquire the indoor humidity and record it together with the video. A sound sensor can also be used to acquire the indoor sound environment and record it together with the video. This allows the video and environmental data to be combined to provide more detailed information.

[0052] The image acquisition unit can monitor the behavior of elderly people at night or in dark places using an infrared camera. For example, the behavior in the bedroom at night can be monitored using an infrared camera. The behavior in dark places can also be monitored using an infrared camera. The behavior of elderly people can also be monitored in detail in places without lighting using an infrared camera. This ensures the safety of elderly people at night or in dark places.

[0053] The video capture unit has been added with a voice recognition function, allowing it to convert what the elderly person is saying into text in real time. For example, it can recognize the voice of everyday conversations and convert them into text. It can also recognize the voice of monologue and convert it into text. It can also recognize the voice of telephone and video calls and convert them into text. This allows it to convert what the elderly person is saying into text in real time and provide detailed information.

[0054] The image acquisition unit can monitor the health condition of the elderly by combining vital sensors. For example, a heart rate sensor can be used to monitor the heart rate and issue a warning if an abnormality is detected. Alternatively, a blood pressure sensor can be used to monitor blood pressure and issue a warning if an abnormality is detected. Alternatively, an oxygen saturation sensor can be used to monitor oxygen saturation and issue a warning if an abnormality is detected. This allows for detailed monitoring of the health condition of the elderly.

[0055] The image acquisition unit can acquire location information and record the elderly person's movement route. For example, the elderly person's location information is acquired using GPS and the movement route is recorded. In addition, an indoor location information system can be used to record the movement route within an indoor space. Furthermore, a beacon can be used to record the movement route within a specific area. This allows the elderly person's movement route to be recorded in detail and necessary information to be provided.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The video acquisition unit acquires video from a camera or smartphone. For example, video of the room is acquired using a fixed camera. It is also possible to capture the behavior of the elderly person using a smartphone camera. Furthermore, it is also possible to acquire video from the elderly person's perspective using a wearable camera. Step 2: The generation AI analyzes the video acquired by the video acquisition unit. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). The generation AI can also analyze the video using a multimodal generation AI. The generation AI can also extract and analyze particularly important parts of the video. Step 3: The text conversion unit converts specific actions and times into text from the video analyzed by the generation AI. For example, based on the video data analyzed by the generation AI, the text conversion unit may describe actions in detail, such as "Eat breakfast at 8:30 AM." Based on the video data analyzed by the generation AI, the text conversion unit may also describe actions in detail, such as "Go for a walk at 2:15 PM." Based on the video data analyzed by the generation AI, the text conversion unit may also describe actions in detail, such as "Take medicine at 10 AM." Step 4: The sharing unit shares the information converted into text by the text conversion unit with family members and caregivers. For example, the sharing unit sends the converted information to family members and caregivers by email. The sharing unit can also share the converted information with family members and caregivers through a dedicated application. The sharing unit can also store the converted information on the cloud so that family members and caregivers can access it. Step 5: The warning unit detects abnormal behavior and sends a warning. For example, the warning unit sends a warning if there is a possibility of a fall based on the video data analyzed by the generation AI. The warning unit can also send a warning if an unusual behavior pattern is detected based on the video data analyzed by the generation AI. The warning unit can also send a warning if a long period of inactivity is detected based on the video data analyzed by the generation AI.

[0058] (Example 2) The AI ​​Heartful Watcher according to an embodiment of the present invention is a system that converts the behavior of elderly people into text in real time and checks their safety while protecting their privacy. This allows the AI ​​Heartful Watcher to convert the behavior of elderly people into text in real time and check their safety while protecting their privacy.

[0059] The AI ​​Heartful Watcher according to the embodiment includes a video acquisition unit, a generation AI, a text conversion unit, a sharing unit, and a warning unit. The video acquisition unit acquires video from a camera or smartphone. For example, indoor video can be acquired using a fixed camera. Additionally, a smartphone camera can be used to capture footage of the elderly person's behavior. Furthermore, a wearable camera can be used to capture video from the elderly person's perspective. The generation AI analyzes the video acquired by the video acquisition unit. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). Furthermore, the generation AI can analyze the video using a multimodal generation AI. Furthermore, the generation AI can extract and analyze particularly important parts of the video. The text conversion unit converts specific actions and times into text from the video analyzed by the generation AI. For example, the text conversion unit describes actions in detail based on the video data analyzed by the generation AI, such as "Eat breakfast at 8:30 AM." Furthermore, the text conversion unit can also describe actions in detail based on the video data analyzed by the generation AI, such as "Go for a walk at 2:15 PM." The text conversion unit can also describe actions in detail, such as "take medicine at 10 a.m.", based on the video data analyzed by the generation AI. The sharing unit shares the information converted into text by the text conversion unit with family members and caregivers. For example, the sharing unit can send the text information to family members and caregivers via email. The sharing unit can also share the text information with family members and caregivers through a dedicated application. The sharing unit can also store the text information in the cloud so that family members and caregivers can access it. The warning unit detects abnormal behavior and sends a warning. For example, the warning unit sends a warning if there is a possibility of a fall based on the video data analyzed by the generation AI. The warning unit can also send a warning if an unusual behavior pattern is detected based on the video data analyzed by the generation AI. The warning unit can also send a warning if a long period of inactivity is detected based on the video data analyzed by the generation AI.As a result, the AI ​​Heartful Watcher according to the embodiment can convert the elderly person's behavior into text in real time, enabling safety confirmation while protecting their privacy. For example, family members and caregivers can understand the elderly person's behavior through the text information and take necessary measures promptly. In addition, if the warning unit detects abnormal behavior, they can receive an immediate warning. This ensures the safety of the elderly, allowing them to continue living with peace of mind.

[0060] The video acquisition unit can automatically adjust the position and angle of the camera according to the elderly person's living environment. The video acquisition unit introduces a system that automatically adjusts the position and angle of the camera according to the elderly person's living environment, for example. For example, to monitor activities in the living room, the camera automatically moves to an optimal position and acquires video at an optimal angle. The video acquisition unit can also automatically move the camera to an optimal position and acquire video at an optimal angle to monitor activities in the bedroom. The video acquisition unit can also automatically move the camera to an optimal position and acquire video at an optimal angle to monitor activities in the kitchen. This makes it possible to acquire optimal video according to the elderly person's living environment.

[0061] The image acquisition unit can automatically track the camera according to the elderly person's movements. For example, the image acquisition unit can add a function that enables the camera to automatically track the elderly person's movements, thereby acquiring more detailed images. For example, when the elderly person walks around a room, the camera automatically tracks their movements, capturing the elderly person's figure at all times. The image acquisition unit can also automatically track the camera when the elderly person sits down in a chair, capturing the sitting action in detail. The image acquisition unit can also automatically track the camera when the elderly person stands up, capturing the standing action in detail. This makes it possible to acquire detailed images according to the elderly person's movements.

[0062] The video acquisition unit can use the emotion estimation function to estimate the emotion of the elderly person from their facial expressions and movements, and adjust the focus of the camera based on that emotion. For example, the video acquisition unit can use the emotion estimation function to estimate the emotion of the elderly person from their facial expressions and movements, and adjust the focus of the camera based on that emotion. For example, the video acquisition unit can detect a smiling or sad expression, and automatically focus the camera on that expression. The video acquisition unit can also detect an angry or surprised expression, and automatically focus the camera on that expression. The video acquisition unit can also detect hand movements or body movements, and automatically focus the camera on that movement. This makes it possible to acquire optimal video according to the elderly person's emotions.

[0063] The video acquisition unit can monitor the outdoor activities of elderly people using a drone and acquire video. The video acquisition unit, for example, builds a system that monitors the outdoor activities of elderly people using a drone and acquires video. For example, the drone automatically tracks the elderly person and acquires video when the elderly person is gardening or taking a walk in the yard. The video acquisition unit can also automatically track the elderly person and acquire video when the elderly person is shopping or going out. The video acquisition unit can also automatically track the elderly person and acquire video when the elderly person is exercising or participating in recreational activities outdoors. This allows for effective monitoring of the outdoor activities of elderly people.

[0064] The image acquisition unit may be a wearable camera worn by the elderly person, allowing for image acquisition from a more personal perspective. The image acquisition unit may be a wearable camera worn by the elderly person, for example, to build a system for acquiring image from a more personal perspective. For example, a glasses-type camera or a pendant-type camera may be used. The image acquisition unit may also use a chest-worn camera or a wristwatch-type camera. The image acquisition unit may also use a hat-type camera or an earphone-type camera. This allows for detailed image acquisition from the elderly person's perspective.

[0065] The image acquisition unit is capable of installing a smart mirror equipped with an emotion estimation function and acquiring images by analyzing the facial expressions and movements of the elderly when they look in the mirror. The image acquisition unit, for example, builds a system in which a smart mirror equipped with an emotion estimation function is installed and an image is acquired by analyzing the facial expressions and movements of the elderly when they look in the mirror. For example, the smart mirror is installed in a bathroom or bedroom. The image acquisition unit can also be installed in a living room or entrance. The image acquisition unit can also be installed in a kitchen or bathroom. This makes it possible to analyze the facial expressions and movements of the elderly in detail and acquire images.

[0066] Generative AI can learn the behavioral patterns of elderly people and convert predicted behavior into text in advance. For example, generative AI can add a function to learn the behavioral patterns of elderly people and convert predicted behavior into text in advance. For example, it can predict morning routines and regular activities and convert them into text in advance. Generative AI can also predict meal and walk times and convert them into text in advance. Generative AI can also predict sleep and rest times and convert them into text in advance. This makes it possible to predict elderly behavior and convert them into text in advance, making more effective monitoring possible.

[0067] Generative AI can analyze the voices of elderly people and convert what they say along with their actions into text. Generative AI can, for example, build a system that analyzes the voices of elderly people and converts what they say along with their actions into text. For example, it can analyze everyday conversations and monologues and convert them into text. Generative AI can also analyze the contents of telephone and video calls and convert them into text. Generative AI can also analyze emotional tone and important keywords and convert them into text. This allows the contents of what elderly people say to be converted into text, making it possible to provide more detailed information.

[0068] The generation AI can use the emotion estimation function to reflect the emotional state of the elderly in the text. For example, the generation AI can use the emotion estimation function to build a system that reflects the emotional state of the elderly in the text. For example, emotions such as joy and sadness can be reflected in the text. The generation AI can also reflect emotions such as anger and surprise in the text. The generation AI can also reflect emotions such as relief and anxiety in the text. In this way, by reflecting the emotional state of the elderly in the text, more detailed information can be provided.

[0069] Generative AI can recreate the behavior of elderly people in 3D models and display them visually along with text. For example, generative AI can build a system that recreates the behavior of elderly people in 3D models and displays them visually along with text. For example, daily movements and activities are recreated in 3D models. Generative AI can also recreate eating and walking movements in 3D models. Generative AI can also recreate sleeping and resting movements in 3D models. This allows for more detailed information to be provided by visually displaying the behavior of elderly people.

[0070] Generative AI can animate the behavior of elderly people and provide it to their families and caregivers. For example, generative AI can build a system that animates the behavior of elderly people and provides it to their families and caregivers. For example, it can reproduce daily movements and activities as animations. Generative AI can also reproduce the movements of eating and walking as animations. Generative AI can also reproduce the movements of sleeping and resting as animations. This makes it possible to provide more detailed information by animating the behavior of elderly people.

[0071] The generation AI can use the emotion estimation function to predict behavior based on the emotions of the elderly and convert it into text. For example, the generation AI can use the emotion estimation function to build a system that predicts behavior based on the emotions of the elderly and converts it into text. For example, it can predict behavior in response to changes in emotions and convert it into text. The generation AI can also predict next behavior based on changes in emotions and convert it into text. The generation AI can also predict behavior patterns based on changes in emotions and convert it into text. In this way, by predicting behavior based on the emotions of the elderly and converting it into text, more detailed information can be provided.

[0072] The shared unit can be equipped with a function to read text information aloud, making it possible to accommodate visually impaired family members and caregivers. For example, the shared unit can be equipped with a function to read text information aloud, building a system that can accommodate visually impaired family members and caregivers. For example, information can be provided aloud using a smartphone or smart speaker. The shared unit can also read text information aloud using voice synthesis technology. The shared unit can also adjust the speed and volume at which the text information is read aloud. This makes it possible to provide information to visually impaired family members and caregivers.

[0073] The sharing unit can automatically compile the text information into a daily report format and periodically send it to family members or caregivers. The sharing unit, for example, builds a system that automatically compiles the text information into a daily report format and periodically sends it to family members or caregivers. For example, a daily report summarizing daily activities is sent by email. The sharing unit can also compile the text information into a daily report format and send it to family members or caregivers via a dedicated application. The sharing unit can also compile the text information into a daily report format and store it on the cloud so that family members and caregivers can access it. This allows family members and caregivers to receive information regularly.

[0074] The sharing unit can use the emotion estimation function to assign emotion tags to the textual information and track changes in emotions. The sharing unit, for example, uses the emotion estimation function to assign emotion tags to the textual information and build a system that tracks changes in emotions. For example, the sharing unit assigns emotion tags such as joy and sadness to the text. The sharing unit can also assign emotion tags such as anger and surprise to the text. The sharing unit can also assign emotion tags such as relief and anxiety to the text. This makes it possible to track changes in the emotions of the elderly and provide detailed information.

[0075] The sharing unit can notify a smartwatch or smart speaker of the text information. The sharing unit, for example, builds a system that notifies a smartwatch or smart speaker of the text information. For example, the sharing unit displays a notification on the smartwatch and issues a voice notification on the smart speaker. The sharing unit can also notify a smartphone of the text information. The sharing unit can also notify a tablet of the text information. This allows family members or caregivers to receive the information on their smartwatch or smart speaker.

[0076] The sharing unit can link the text information with the calendar app of the family member or caregiver to help with schedule management. For example, the sharing unit can link the text information with the calendar app of the family member or caregiver to build a system that helps with schedule management. For example, important events and appointments can be automatically added to the calendar. The sharing unit can also link the text information with the calendar app to set reminders. The sharing unit can also link the text information with the calendar app to set notifications. This allows family members and caregivers to manage information in the calendar app.

[0077] The sharing unit can use the emotion estimation function to prioritize notifying family members and caregivers of information that they are most interested in. For example, the sharing unit uses the emotion estimation function to build a system that prioritizes notifying family members and caregivers of information that they are most interested in. For example, it prioritizes notifying information with a high emotion score. The sharing unit can also prioritize notifying information based on interests set by family members and caregivers. The sharing unit can also filter and notify information that they are most interested in. This allows family members and caregivers to receive the information that they are most interested in preferentially.

[0078] The warning unit can not only detect abnormal behavior but also suggest preventive actions. For example, the warning unit can be configured to build a system that adds a function to not only detect abnormal behavior but also suggest preventive actions. For example, if there is a high risk of falling, the warning unit can suggest taking a break. The warning unit can also suggest light exercise if a long period of inactivity is detected. The warning unit can also suggest seeing a doctor if an unusual behavior pattern is detected. This makes it possible to make suggestions to prevent abnormal behavior in the elderly.

[0079] The warning unit can integrate multiple sensors (temperature, humidity, sound, etc.) to improve the accuracy of detecting abnormal behavior. For example, the warning unit builds a system that integrates multiple sensors (temperature, humidity, sound, etc.) to improve the accuracy of detecting abnormal behavior. For example, a temperature sensor and a sound sensor are combined to detect abnormal behavior. The warning unit can also detect abnormal behavior by combining a humidity sensor and a motion sensor. The warning unit can also detect abnormal behavior by combining a light sensor and a vibration sensor. This improves the accuracy of detecting abnormal behavior.

[0080] The warning unit can use the emotion estimation function to detect a sudden change in emotion and issue a warning. The warning unit, for example, uses the emotion estimation function to build a system that detects a sudden change in emotion and issues a warning. For example, the warning unit issues a warning when it detects a sudden emotion of anger or sadness. The warning unit can also issue a warning when it detects a sudden emotion of anxiety or excitement. The warning unit can also issue a warning when it detects a sudden emotion of relief or joy. This makes it possible to detect a sudden change in emotion and respond quickly.

[0081] The warning unit can cooperate with medical institutions to share the abnormal behavior detection results and encourage specialized responses. The warning unit, for example, can cooperate with medical institutions to share the abnormal behavior detection results and build a system to encourage specialized responses. For example, when abnormal behavior is detected, the medical institution is automatically notified. The warning unit can also share the abnormal behavior detection results through a dedicated application of the medical institution. The warning unit can also store the abnormal behavior detection results in the medical institution's cloud system so that the medical institution can access them. This allows the abnormal behavior detection results to be shared with the medical institution and specialized responses to be made possible.

[0082] The warning unit can cooperate with a local monitoring service to report the abnormal behavior detection results, enabling a rapid response. The warning unit, for example, builds a system that cooperates with a local monitoring service to report the abnormal behavior detection results, enabling a rapid response. For example, when abnormal behavior is detected, the warning unit automatically notifies the local monitoring service. The warning unit can also share the abnormal behavior detection results through a dedicated application for the local monitoring service. The warning unit can also store the abnormal behavior detection results in the cloud system of the local monitoring service, making them accessible to the local monitoring service. This allows the abnormal behavior detection results to be shared with the local monitoring service, enabling a rapid response.

[0083] The warning unit can use the emotion estimation function to analyze the emotion behind the abnormal behavior and propose appropriate countermeasures. The warning unit, for example, uses the emotion estimation function to build a system that analyzes the emotion behind the abnormal behavior and proposes appropriate countermeasures. For example, the warning unit analyzes changes in emotion and proposes appropriate countermeasures. The warning unit can also identify the cause of stress or anxiety based on the changes in emotion and propose countermeasures. The warning unit can also identify the cause of loneliness or isolation based on the changes in emotion and propose countermeasures. In this way, the emotion behind the abnormal behavior can be analyzed and appropriate countermeasures can be proposed.

[0084] The system can be equipped with a function to analyze the behavioral history of elderly people and monitor long-term changes in their health condition. For example, a system can be constructed that adds a function to analyze the behavioral history of elderly people and monitor long-term changes in their health condition. For example, the system can analyze daily activity levels and sleep patterns to monitor changes in their health condition. The system can also analyze diet and exercise history to monitor changes in their health condition. The system can also analyze changes in weight and blood pressure to monitor changes in their health condition. This makes it possible to monitor changes in the long-term health condition of elderly people.

[0085] The system can learn the lifestyle rhythms of elderly people and automatically issue an alert when an abnormality occurs. For example, a system can be constructed that learns the lifestyle rhythms of elderly people and automatically issues an alert when an abnormality occurs. For example, an alert can be issued if an elderly person does not wake up at their usual wake-up time. The system can also issue an alert if an elderly person does not eat meals at their usual mealtimes. The system can also issue an alert if an elderly person does not go to bed at their usual bedtime. This makes it possible to quickly detect and respond to abnormalities in the elderly person's lifestyle rhythms.

[0086] The system can use the emotion estimation function to periodically report the emotional state of the elderly person to family members or caregivers. For example, the system can use the emotion estimation function to build a system that periodically reports the emotional state of the elderly person to family members or caregivers. For example, the system can send a report of the daily emotional state. The system can also notify the emotional state in real time. The system can also display changes in the emotional state in graphs or charts and report them to family members or caregivers. This allows the elderly person's emotional state to be periodically reported, allowing family members or caregivers to understand the situation.

[0087] The system can share information on elderly safety confirmation with the local community, and build a system for the entire community to watch over them. For example, the system can be built to share information on elderly safety confirmation with the local community, and build a system for the entire community to watch over them. For example, the system can work with local monitoring services to share safety confirmation information. The system can also work with local volunteer groups to share safety confirmation information. The system can also work with local governments to share safety confirmation information. In this way, a system can be built for the entire community to watch over the elderly.

[0088] The system can link the safety confirmation information of the elderly with a smart home system and automatically control home appliances when an abnormality occurs. For example, the system can link the safety confirmation information of the elderly with a smart home system and build a system that automatically controls home appliances when an abnormality occurs. For example, the system can turn on the lights when an abnormality is detected. The system can also operate the air conditioner when an abnormality is detected. The system can also automatically lock the doors when an abnormality is detected. In this way, home appliances can be automatically controlled when an abnormality occurs, ensuring the safety of the elderly.

[0089] The system can use the emotion estimation function to automatically generate and periodically send messages that give the elderly a sense of security. For example, a system can be constructed that uses the emotion estimation function to automatically generate and periodically send messages that give the elderly a sense of security. For example, messages of encouragement or words of gratitude can be sent. The system can also send reassuring information or advice. The system can also automatically generate and send appropriate messages in response to changes in emotions. In this way, psychological support can be provided by the elderly receiving messages that give them a sense of security on a regular basis.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The video acquisition unit can combine environmental sensors to acquire environmental data along with the video. For example, a temperature sensor can be used to acquire the indoor temperature and record it together with the video. A humidity sensor can also be used to acquire the indoor humidity and record it together with the video. A sound sensor can also be used to acquire the indoor sound environment and record it together with the video. This allows the video and environmental data to be combined to provide more detailed information.

[0092] The image acquisition unit can monitor the behavior of elderly people at night or in dark places using an infrared camera. For example, the behavior in the bedroom at night can be monitored using an infrared camera. The behavior in dark places can also be monitored using an infrared camera. The behavior of elderly people can also be monitored in detail in places without lighting using an infrared camera. This ensures the safety of elderly people at night or in dark places.

[0093] The video capture unit can use the emotion estimation function to adjust the camera's zoom function based on the elderly person's emotions. For example, the camera can automatically zoom in when it detects a smiling or sad expression. It can also automatically zoom out when it detects an angry or surprised expression. The camera's zoom level can also be adjusted according to changes in emotions. This makes it possible to capture optimal video according to the elderly person's emotions.

[0094] The image acquisition unit can use the emotion estimation function to adjust the color tone of the image based on the emotion of the elderly person. For example, it can detect the emotion of joy and make the color tone of the image brighter. It can also detect the emotion of sadness and make the color tone of the image calmer. It can also detect the emotion of anger and make the color tone of the image cooler. This makes it possible to adjust the color tone of the image according to the emotion of the elderly person.

[0095] The video acquisition unit can use the emotion estimation function to adjust the frame rate of the video based on the emotion of the elderly person. For example, it can detect an excited emotion and increase the frame rate. It can also detect a relaxed emotion and decrease the frame rate. It can also dynamically adjust the frame rate according to changes in emotion. This makes it possible to adjust the frame rate of the video according to the emotion of the elderly person.

[0096] The video acquisition unit can use the emotion estimation function to adjust the video resolution based on the emotion of the elderly person. For example, it can detect a tense emotion and increase the resolution. It can also detect a relaxed emotion and decrease the resolution. It can also dynamically adjust the resolution according to changes in emotion. This makes it possible to adjust the video resolution according to the emotion of the elderly person.

[0097] The image acquisition unit can use the emotion estimation function to adjust the brightness of the image based on the emotion of the elderly person. For example, it can detect the emotion of joy and increase the brightness of the image. It can also detect the emotion of sadness and decrease the brightness of the image. It can also dynamically adjust the brightness of the image according to changes in emotion. This makes it possible to adjust the brightness of the image according to the emotion of the elderly person.

[0098] The video capture unit has been added with a voice recognition function, allowing it to convert what the elderly person is saying into text in real time. For example, it can recognize the voice of everyday conversations and convert them into text. It can also recognize the voice of monologue and convert it into text. It can also recognize the voice of telephone and video calls and convert them into text. This allows it to convert what the elderly person is saying into text in real time and provide detailed information.

[0099] The image acquisition unit can monitor the health condition of the elderly by combining vital sensors. For example, a heart rate sensor can be used to monitor the heart rate and issue a warning if an abnormality is detected. Alternatively, a blood pressure sensor can be used to monitor blood pressure and issue a warning if an abnormality is detected. Alternatively, an oxygen saturation sensor can be used to monitor oxygen saturation and issue a warning if an abnormality is detected. This allows for detailed monitoring of the health condition of the elderly.

[0100] The image acquisition unit can acquire location information and record the elderly person's movement route. For example, the elderly person's location information is acquired using GPS and the movement route is recorded. In addition, an indoor location information system can be used to record the movement route within an indoor space. Furthermore, a beacon can be used to record the movement route within a specific area. This allows the elderly person's movement route to be recorded in detail and necessary information to be provided.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The video acquisition unit acquires video from a camera or smartphone. For example, video of the room is acquired using a fixed camera. It is also possible to capture the behavior of the elderly person using a smartphone camera. Furthermore, it is also possible to acquire video from the elderly person's perspective using a wearable camera. Step 2: The generation AI analyzes the video acquired by the video acquisition unit. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). The generation AI can also analyze the video using a multimodal generation AI. The generation AI can also extract and analyze particularly important parts of the video. Step 3: The text conversion unit converts specific actions and times into text from the video analyzed by the generation AI. For example, based on the video data analyzed by the generation AI, the text conversion unit may describe actions in detail, such as "Eat breakfast at 8:30 AM." Based on the video data analyzed by the generation AI, the text conversion unit may also describe actions in detail, such as "Go for a walk at 2:15 PM." Based on the video data analyzed by the generation AI, the text conversion unit may also describe actions in detail, such as "Take medicine at 10 AM." Step 4: The sharing unit shares the information converted into text by the text conversion unit with family members and caregivers. For example, the sharing unit sends the converted information to family members and caregivers by email. The sharing unit can also share the converted information with family members and caregivers through a dedicated application. The sharing unit can also store the converted information on the cloud so that family members and caregivers can access it. Step 5: The warning unit detects abnormal behavior and sends a warning. For example, the warning unit sends a warning if there is a possibility of a fall based on the video data analyzed by the generation AI. The warning unit can also send a warning if an unusual behavior pattern is detected based on the video data analyzed by the generation AI. The warning unit can also send a warning if a long period of inactivity is detected based on the video data analyzed by the generation AI.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 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. an image acquisition unit that acquires images from a camera or smartphone; a generation AI that analyzes the image acquired by the image acquisition unit; a text generation unit that converts specific actions and times from the video analyzed by the generating AI into text; a sharing unit that shares the information converted into text by the text conversion unit with family members or caregivers; a warning unit that detects abnormal behavior and sends a warning A system characterized by:

2. The image acquisition unit The position and angle of the camera are automatically adjusted according to the elderly person's living environment.

2. The system of claim 1.

3. The image acquisition unit Using drones to monitor and capture footage of elderly people's outdoor activities 2. The system of claim 1.

4. The generated AI is Learning the behavioral patterns of elderly people and converting predicted behavior into text in advance 2. The system of claim 1.

5. The common part is A function to read out the text information aloud has been added to accommodate visually impaired family members and caregivers.

2. The system of claim 1.

6. The warning unit In addition to detecting abnormal behavior, it also suggests preventative actions.

2. The system of claim 1.

7. The system comprises: Regularly reporting on the older person's emotional state to family members and caregivers 2. The system of claim 1.

8. The image acquisition unit The system estimates the elderly person's emotions from their facial expressions and movements, and adjusts the camera's focus based on those emotions.

2. The system of claim 1.

Images