System

Abstract

An object of a system according to an embodiment is to support the daily life of an elderly person and to quickly respond to an emergency.SOLUTION: A system according to an embodiment includes a pupil tracker, a generation and AI unit, an IoT sensor unit, and a notifier. The eye tracking unit tracks the line of sight of the elderly person using an eye tracking technique. The generation and AI unit provides audio guidance based on the information acquired by the pupil tracing unit. The IoT sensor unit detects an abnormality using the IoT sensor technology. The notification unit notifies a designated contact or medical institution in case of emergency.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] Conventional technologies do not adequately support the daily lives of elderly people through gaze tracking and anomaly detection, and there is room for improvement.

[0005] The system according to the embodiment aims to support the daily lives of elderly people and to respond quickly in emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a pupil tracking unit, a generation AI unit, an IoT sensor unit, and a notification unit. The pupil tracking unit tracks the gaze of an elderly person using pupil tracking technology. The generation AI unit provides audio guidance based on information acquired by the pupil tracking unit. The IoT sensor unit detects abnormalities using IoT sensor technology. The notification unit notifies designated contacts and medical institutions in the event of an emergency. [Effects of the Invention]

[0007] The system according to the embodiment supports the daily lives of elderly people and can respond quickly in emergencies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The elderly support system according to an embodiment of the present invention uses pupil tracking technology to track the gaze of the elderly, a generation AI provides voice guidance, IoT sensor technology is used to detect abnormalities, and in the event of an emergency, it notifies designated contacts and medical institutions. As a result, the elderly support system can support the daily lives of the elderly and respond quickly in the event of an emergency.

[0029] An elderly support system according to an embodiment includes a pupil tracking unit, a generation AI unit, an IoT sensor unit, and a notification unit. The pupil tracking unit tracks the gaze of an elderly person using pupil tracking technology. For example, it detects the gaze using an infrared camera and analyzes the gaze movement using an image processing algorithm. The pupil tracking unit can also set a gaze detection range and adjust the gaze movement analysis method. The generation AI unit provides voice guidance based on information acquired by the pupil tracking unit. For example, the generation AI unit generates voice guidance using a text generation AI (e.g., LLM). The generation AI unit can also provide appropriate voice guidance to the elderly person using natural language processing technology. The IoT sensor unit detects abnormalities using IoT sensor technology. For example, it monitors the elderly person's movements and detects abnormal movements using a motion sensor. The IoT sensor unit can also detect environmental changes using a temperature sensor. The notification unit notifies designated contacts and medical institutions in the event of an emergency. For example, the notification unit can send an SMS notification and make a phone call in the event of an emergency. The notification unit can also set the timing of notifications and notify at an appropriate time. As a result, the elderly support system according to the embodiment can support the daily lives of elderly people and respond quickly in emergencies. For example, the pupil tracking unit tracks the gaze of the elderly person, the generation AI unit provides voice guidance based on that information, the IoT sensor unit detects abnormalities, and the notification unit notifies designated contacts and medical institutions in the event of an emergency, allowing elderly people to continue living at home with peace of mind.

[0030] The pupil tracking unit can estimate an elderly person's interests and concerns from their gaze movements and send that information to the generation AI unit. For example, the pupil tracking unit uses pupil tracking technology to measure the amount of time an elderly person spends looking at a particular object and estimates their interests and concerns based on that data. For example, if an elderly person stares at a medicine bottle for a long time, it determines that the elderly person has a high level of interest in that medicine. The pupil tracking unit also analyzes gaze movements and the length of time the gaze remains fixed to identify which objects the elderly person is interested in. For example, it determines that an object whose gaze moves frequently is of little interest, and conversely, determines that an object whose gaze remains fixed is of high interest. The pupil tracking unit also analyzes pupil tracking data in real time, and when the elderly person's gaze is directed at a specific object, it sends information about that object to the generation AI. For example, the moment the gaze is directed at a medicine bottle, audio instructions on how to use the medicine can be provided. This allows appropriate audio guidance to be provided based on the elderly person's interests and concerns.

[0031] The pupil tracking unit simultaneously analyzes gaze movements and facial expressions, allowing the generation AI unit to make detailed situation assessments. For example, the pupil tracking unit combines pupil tracking technology with facial recognition technology to simultaneously analyze the gaze movements and facial expressions of an elderly person. For example, when the gaze is directed at a specific object, the facial expression can be used to estimate the elderly person's feelings toward that object. The pupil tracking unit also integrates gaze movement and facial expression data to allow the generation AI to assess the elderly person's situation in more detail. For example, if the gaze is directed at a medicine bottle and the facial expression shows confusion, the generation AI can provide detailed instructions on how to use the medicine. The pupil tracking unit also analyzes pupil tracking data and facial expression data in real time, and when the elderly person's gaze is directed at a specific object, it sends detailed information about that object to the generation AI. For example, the moment the gaze is directed at a medicine bottle, the generation AI can determine the elderly person's level of understanding of the medicine from their facial expression and provide the necessary information. This allows for more detailed assessments of the elderly person's situation and enables appropriate responses.

[0032] The generation AI unit can learn the elderly person's past behavioral history and provide individually customized voice guidance. For example, the generation AI unit has the generation AI learn the elderly person's past behavioral history and provide individually customized voice guidance. For example, if the elderly person has forgotten how to take their medicine in the past, detailed explanations about that medicine will be provided. The generation AI unit also analyzes the elderly person's behavioral history data and builds a system in which the generation AI provides individually customized voice guidance. For example, voice guidance is provided at the appropriate time based on past behavioral patterns. The generation AI unit also learns the elderly person's past behavioral history and provides individually customized voice guidance based on that data. For example, if the elderly person has made a mistake in how to take their medicine in the past, detailed explanations about that medicine will be provided. This makes it possible to provide individually customized voice guidance based on the elderly person's past behavioral history.

[0033] The generation AI unit can adjust the content of the voice guidance according to the elderly person's level of comprehension and provide information at an appropriate time. For example, the generation AI unit will build a system in which the generation AI analyzes the elderly person's level of comprehension in real time and adjusts the content of the voice guidance. For example, if the level of comprehension is low, a more detailed explanation will be provided. The generation AI unit will also adjust the content of the voice guidance based on the elderly person's comprehension data and provide information at an appropriate time. For example, if the level of comprehension is high, a concise explanation will be provided. The generation AI unit will also develop a system in which the generation AI analyzes the elderly person's level of comprehension and adjusts the content of the voice guidance based on that data. For example, if the level of comprehension is low, the explanation will be repeated. This makes it possible to provide appropriate voice guidance according to the elderly person's level of comprehension.

[0034] The generation AI unit can provide visual guidance in addition to audio guidance. For example, the generation AI unit will build a system in which the generation AI provides visual guidance in addition to audio guidance. For example, it will display instructions for using medicine on the screen of a smartphone or tablet. The generation AI unit will also combine audio guidance and visual guidance to provide information that is easier to understand for the elderly. For example, it will explain how to use medicine by audio while displaying the steps on the screen. The generation AI unit will also develop a system in which the generation AI provides visual guidance in addition to audio guidance. For example, it will display instructions for using medicine on the screen of a tablet and explain the steps by audio. In this way, by providing visual guidance in addition to audio guidance, it will be possible to provide information that is easier to understand for the elderly.

[0035] The IoT sensor unit can detect not only the elderly's movements but also changes in the environment and send the information to the generation AI unit. For example, using IoT sensor technology, the IoT sensor unit will build a system that monitors the elderly's movements as well as indoor temperature and humidity in real time. For example, if there is a sudden change in temperature, it will send information to the generation AI. The IoT sensor unit will also combine environmental sensors to detect changes in the elderly's living environment and send the information to the generation AI. For example, if the humidity increases, it will provide voice guidance on appropriate measures. The IoT sensor unit will also use IoT sensor technology to develop a system that simultaneously monitors the elderly's movements and changes in the environment and sends information to the generation AI if an abnormality is detected. For example, if the temperature suddenly drops, it will provide voice instructions to turn on the heating. This will enable the elderly's movements and changes in the environment to be monitored simultaneously, enabling appropriate responses.

[0036] The IoT sensor unit can integrate and analyze data from multiple sensors to improve the accuracy of anomaly detection. For example, the IoT sensor unit integrates data from multiple IoT sensors to build a system that improves the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors and environmental sensors. The IoT sensor unit also integrates data from different types of sensors in real time to improve the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors and temperature sensors. The IoT sensor unit also develops algorithms that integrate data from multiple sensors to improve the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors, temperature sensors, and humidity sensors. This improves the accuracy of anomaly detection.

[0037] The IoT sensor unit can not only detect abnormalities but also monitor daily health conditions and send information to the generation AI unit. For example, the IoT sensor unit will use IoT sensor technology to build a system that monitors the daily health conditions of elderly people. For example, it will monitor heart rate and blood pressure in real time and send information to the generation AI if an abnormality is detected. The IoT sensor unit will also combine health sensors to monitor the daily health conditions of elderly people and send information to the generation AI. For example, if the heart rate becomes abnormally high, it will provide voice guidance on appropriate measures. The IoT sensor unit will also use IoT sensor technology to develop a system that monitors the daily health conditions of elderly people and sends information to the generation AI if an abnormality is detected. For example, if blood pressure suddenly rises, it will notify a medical institution. This will enable the daily health conditions of elderly people to be monitored and appropriate action to be taken if an abnormality is detected.

[0038] The IoT sensor unit can provide not only audio but also visual alerts when an abnormality is detected. For example, the IoT sensor unit uses IoT sensor technology to build a system that provides audio and visual alerts simultaneously when an abnormality is detected. For example, if an abnormality is detected, a notification is sent to a smartphone. In addition, the IoT sensor unit provides a visual alert along with audio guidance when an abnormality is detected. For example, if an abnormality is detected, a warning message is displayed on the smartphone screen. In addition, the IoT sensor unit uses IoT sensor technology to develop a system that provides audio and visual alerts when an abnormality is detected. For example, if an abnormality is detected, a notification is sent to a smartphone and an audio warning is issued. As a result, by providing audio and visual alerts when an abnormality is detected, a faster response can be made.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The elderly support system can further include a health management unit. For example, the health management unit monitors the elderly's heart rate and blood pressure in real time using a wearable device, and sends information to the generation AI unit if an abnormality is detected. The health management unit can also periodically record health conditions and send the data to medical institutions. For example, daily heart rate and blood pressure data can be stored in the cloud so that doctors can access it. The health management unit can also provide appropriate exercise and dietary advice based on the health condition. This allows the elderly's health condition to be continuously monitored and appropriate measures to be taken.

[0041] The elderly support system can further include a reminder unit. The reminder unit can, for example, provide audio or visual notifications of when to take medicine or when to make medical appointments. The reminder unit can also manage the elderly person's schedule and help them not forget important appointments. For example, it can use a calendar function to notify them of times for regular health checks or exercise. The reminder unit can also work with family members and caregivers to share the elderly person's schedule. This helps the elderly person not forget important appointments in their daily lives.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The pupil tracking unit uses pupil tracking technology to track the elderly person's gaze. For example, it uses an infrared camera to detect the gaze and then uses an image processing algorithm to analyze the gaze movement. It is also possible to set the gaze detection range and adjust the gaze movement analysis method. Step 2: The generation AI unit provides voice guidance based on the information acquired by the pupil tracking unit. For example, the voice guidance can be generated using a text generation AI (e.g., LLM) and appropriate voice guidance for the elderly can be provided using natural language processing technology. Step 3: The IoT sensor unit uses IoT sensor technology to detect abnormalities. For example, a motion sensor can be used to monitor the elderly person's movements and detect abnormal movements. A temperature sensor can also be used to detect changes in the environment. Step 4: The notification unit notifies designated contacts and medical institutions in the event of an emergency. For example, it can send an SMS notification and make a phone call in the event of an emergency. It can also set the timing of notifications to be sent at the appropriate time.

[0044] (Example 2) The elderly support system according to an embodiment of the present invention uses pupil tracking technology to track the gaze of the elderly, a generation AI provides voice guidance, IoT sensor technology is used to detect abnormalities, and in the event of an emergency, it notifies designated contacts and medical institutions. As a result, the elderly support system can support the daily lives of the elderly and respond quickly in the event of an emergency.

[0045] An elderly support system according to an embodiment includes a pupil tracking unit, a generation AI unit, an IoT sensor unit, and a notification unit. The pupil tracking unit tracks the gaze of an elderly person using pupil tracking technology. For example, it detects the gaze using an infrared camera and analyzes the gaze movement using an image processing algorithm. The pupil tracking unit can also set a gaze detection range and adjust the gaze movement analysis method. The generation AI unit provides voice guidance based on information acquired by the pupil tracking unit. For example, the generation AI unit generates voice guidance using a text generation AI (e.g., LLM). The generation AI unit can also provide appropriate voice guidance to the elderly person using natural language processing technology. The IoT sensor unit detects abnormalities using IoT sensor technology. For example, it monitors the elderly person's movements and detects abnormal movements using a motion sensor. The IoT sensor unit can also detect environmental changes using a temperature sensor. The notification unit notifies designated contacts and medical institutions in the event of an emergency. For example, the notification unit can send an SMS notification and make a phone call in the event of an emergency. The notification unit can also set the timing of notifications and notify at an appropriate time. As a result, the elderly support system according to the embodiment can support the daily lives of elderly people and respond quickly in emergencies. For example, the pupil tracking unit tracks the gaze of the elderly person, the generation AI unit provides voice guidance based on that information, the IoT sensor unit detects abnormalities, and the notification unit notifies designated contacts and medical institutions in the event of an emergency, allowing elderly people to continue living at home with peace of mind.

[0046] The pupil tracking unit can estimate an elderly person's interests and concerns from their gaze movements and send that information to the generation AI unit. For example, the pupil tracking unit uses pupil tracking technology to measure the amount of time an elderly person spends looking at a particular object and estimates their interests and concerns based on that data. For example, if an elderly person stares at a medicine bottle for a long time, it determines that the elderly person has a high level of interest in that medicine. The pupil tracking unit also analyzes gaze movements and the length of time the gaze remains fixed to identify which objects the elderly person is interested in. For example, it determines that an object whose gaze moves frequently is of little interest, and conversely, determines that an object whose gaze remains fixed is of high interest. The pupil tracking unit also analyzes pupil tracking data in real time, and when the elderly person's gaze is directed at a specific object, it sends information about that object to the generation AI. For example, the moment the gaze is directed at a medicine bottle, audio instructions on how to use the medicine can be provided. This allows appropriate audio guidance to be provided based on the elderly person's interests and concerns.

[0047] The pupil tracking unit simultaneously analyzes gaze movements and facial expressions, allowing the generation AI unit to make detailed situation assessments. For example, the pupil tracking unit combines pupil tracking technology with facial recognition technology to simultaneously analyze the gaze movements and facial expressions of an elderly person. For example, when the gaze is directed at a specific object, the facial expression can be used to estimate the elderly person's feelings toward that object. The pupil tracking unit also integrates gaze movement and facial expression data to allow the generation AI to assess the elderly person's situation in more detail. For example, if the gaze is directed at a medicine bottle and the facial expression shows confusion, the generation AI can provide detailed instructions on how to use the medicine. The pupil tracking unit also analyzes pupil tracking data and facial expression data in real time, and when the elderly person's gaze is directed at a specific object, it sends detailed information about that object to the generation AI. For example, the moment the gaze is directed at a medicine bottle, the generation AI can determine the elderly person's level of understanding of the medicine from their facial expression and provide the necessary information. This allows for more detailed assessments of the elderly person's situation and enables appropriate responses.

[0048] The pupil tracking unit can estimate the elderly person's emotions from their gaze movements and send that information to the generation AI unit. For example, using pupil tracking technology, the pupil tracking unit analyzes the elderly person's gaze movements and pupil changes to estimate their emotions. For example, if the pupils dilate when the gaze is directed at a specific object, it determines that this indicates interest or surprise in that object. The pupil tracking unit also analyzes gaze movements and pupil responses in real time to build a system that estimates the elderly person's emotions. For example, when the gaze is directed at a medicine bottle, it infers anxiety or doubt about the medicine from changes in the pupils. Furthermore, based on the pupil tracking data, the pupil tracking unit transmits the elderly person's emotions toward that object to the generation AI when their gaze is directed at a specific object. For example, the moment the gaze is directed at a medicine bottle, it determines the elderly person's emotions toward the medicine from changes in the pupils and provides appropriate audio guidance. This allows appropriate audio guidance to be provided based on the elderly person's emotions.

[0049] The generation AI unit can learn the elderly person's past behavioral history and provide individually customized voice guidance. For example, the generation AI unit has the generation AI learn the elderly person's past behavioral history and provide individually customized voice guidance. For example, if the elderly person has forgotten how to take their medicine in the past, detailed explanations about that medicine will be provided. The generation AI unit also analyzes the elderly person's behavioral history data and builds a system in which the generation AI provides individually customized voice guidance. For example, voice guidance is provided at the appropriate time based on past behavioral patterns. The generation AI unit also learns the elderly person's past behavioral history and provides individually customized voice guidance based on that data. For example, if the elderly person has made a mistake in how to take their medicine in the past, detailed explanations about that medicine will be provided. This makes it possible to provide individually customized voice guidance based on the elderly person's past behavioral history.

[0050] The generation AI unit can adjust the content of the voice guidance according to the elderly person's level of comprehension and provide information at an appropriate time. For example, the generation AI unit will build a system in which the generation AI analyzes the elderly person's level of comprehension in real time and adjusts the content of the voice guidance. For example, if the level of comprehension is low, a more detailed explanation will be provided. The generation AI unit will also adjust the content of the voice guidance based on the elderly person's comprehension data and provide information at an appropriate time. For example, if the level of comprehension is high, a concise explanation will be provided. The generation AI unit will also develop a system in which the generation AI analyzes the elderly person's level of comprehension and adjusts the content of the voice guidance based on that data. For example, if the level of comprehension is low, the explanation will be repeated. This makes it possible to provide appropriate voice guidance according to the elderly person's level of comprehension.

[0051] The generation AI unit can estimate the emotions of the elderly and provide voice guidance according to those emotions. For example, the generation AI unit will build a system in which the generation AI analyzes the emotions of the elderly in real time and provides voice guidance according to those emotions. For example, if the elderly is feeling anxious, it will provide a reassuring explanation. The generation AI unit will also provide voice guidance according to the emotions based on the elderly's emotional data. For example, if the elderly is feeling happy, it will provide a positive message. The generation AI unit will also develop a system in which the generation AI analyzes the emotions of the elderly and provides voice guidance according to the emotions based on that data. For example, if the elderly is feeling sad, it will provide an encouraging message. This makes it possible to provide appropriate voice guidance based on the elderly's emotions.

[0052] The generation AI unit can provide visual guidance in addition to audio guidance. For example, the generation AI unit will build a system in which the generation AI provides visual guidance in addition to audio guidance. For example, it will display instructions for using medicine on the screen of a smartphone or tablet. The generation AI unit will also combine audio guidance and visual guidance to provide information that is easier to understand for the elderly. For example, it will explain how to use medicine by audio while displaying the steps on the screen. The generation AI unit will also develop a system in which the generation AI provides visual guidance in addition to audio guidance. For example, it will display instructions for using medicine on the screen of a tablet and explain the steps by audio. In this way, by providing visual guidance in addition to audio guidance, it will be possible to provide information that is easier to understand for the elderly.

[0053] The IoT sensor unit can detect not only the elderly's movements but also changes in the environment and send the information to the generation AI unit. For example, using IoT sensor technology, the IoT sensor unit will build a system that monitors the elderly's movements as well as indoor temperature and humidity in real time. For example, if there is a sudden change in temperature, it will send information to the generation AI. The IoT sensor unit will also combine environmental sensors to detect changes in the elderly's living environment and send the information to the generation AI. For example, if the humidity increases, it will provide voice guidance on appropriate measures. The IoT sensor unit will also use IoT sensor technology to develop a system that simultaneously monitors the elderly's movements and changes in the environment and sends information to the generation AI if an abnormality is detected. For example, if the temperature suddenly drops, it will provide voice instructions to turn on the heating. This will enable the elderly's movements and changes in the environment to be monitored simultaneously, enabling appropriate responses.

[0054] The IoT sensor unit can integrate and analyze data from multiple sensors to improve the accuracy of anomaly detection. For example, the IoT sensor unit integrates data from multiple IoT sensors to build a system that improves the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors and environmental sensors. The IoT sensor unit also integrates data from different types of sensors in real time to improve the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors and temperature sensors. The IoT sensor unit also develops algorithms that integrate data from multiple sensors to improve the accuracy of anomaly detection. For example, it combines and analyzes data from motion sensors, temperature sensors, and humidity sensors. This improves the accuracy of anomaly detection.

[0055] The IoT sensor unit can estimate the emotions of elderly people and detect abnormalities according to those emotions. For example, using IoT sensor technology, the IoT sensor unit will build a system that analyzes the movements and environmental changes of elderly people and estimates their emotions. For example, if there is little movement, it will estimate that they are feeling depressed. The IoT sensor unit will also use IoT sensor technology to estimate emotions based on the movements and environmental data of elderly people and detect abnormalities according to those emotions. For example, if there is little movement, it will detect an abnormality. The IoT sensor unit will also use IoT sensor technology to develop a system that estimates the emotions of elderly people and detects abnormalities according to those emotions. For example, if there is little movement, it will estimate that they are feeling depressed and detect an abnormality. This will enable more appropriate responses by detecting abnormalities based on the emotions of elderly people.

[0056] The IoT sensor unit can not only detect abnormalities but also monitor daily health conditions and send information to the generation AI unit. For example, the IoT sensor unit will use IoT sensor technology to build a system that monitors the daily health conditions of elderly people. For example, it will monitor heart rate and blood pressure in real time and send information to the generation AI if an abnormality is detected. The IoT sensor unit will also combine health sensors to monitor the daily health conditions of elderly people and send information to the generation AI. For example, if the heart rate becomes abnormally high, it will provide voice guidance on appropriate measures. The IoT sensor unit will also use IoT sensor technology to develop a system that monitors the daily health conditions of elderly people and sends information to the generation AI if an abnormality is detected. For example, if blood pressure suddenly rises, it will notify a medical institution. This will enable the daily health conditions of elderly people to be monitored and appropriate action to be taken if an abnormality is detected.

[0057] The IoT sensor unit can provide not only audio but also visual alerts when an abnormality is detected. For example, the IoT sensor unit uses IoT sensor technology to build a system that provides audio and visual alerts simultaneously when an abnormality is detected. For example, if an abnormality is detected, a notification is sent to a smartphone. In addition, the IoT sensor unit provides a visual alert along with audio guidance when an abnormality is detected. For example, if an abnormality is detected, a warning message is displayed on the smartphone screen. In addition, the IoT sensor unit uses IoT sensor technology to develop a system that provides audio and visual alerts when an abnormality is detected. For example, if an abnormality is detected, a notification is sent to a smartphone and an audio warning is issued. As a result, by providing audio and visual alerts when an abnormality is detected, a faster response can be made.

[0058] The IoT sensor unit can estimate the emotions of elderly people and detect abnormalities according to those emotions. For example, using IoT sensor technology, the IoT sensor unit will build a system that analyzes the movements and environmental changes of elderly people and estimates their emotions. For example, if there is little movement, it will estimate that they are feeling depressed. The IoT sensor unit will also use IoT sensor technology to estimate emotions based on the movements and environmental data of elderly people and detect abnormalities according to those emotions. For example, if there is little movement, it will detect an abnormality. The IoT sensor unit will also use IoT sensor technology to develop a system that estimates the emotions of elderly people and detects abnormalities according to those emotions. For example, if there is little movement, it will estimate that they are feeling depressed and detect an abnormality. This will enable more appropriate responses by detecting abnormalities based on the emotions of elderly people.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The elderly support system can further include a health management unit. For example, the health management unit monitors the elderly's heart rate and blood pressure in real time using a wearable device, and sends information to the generation AI unit if an abnormality is detected. The health management unit can also periodically record health conditions and send the data to medical institutions. For example, daily heart rate and blood pressure data can be stored in the cloud so that doctors can access it. The health management unit can also provide appropriate exercise and dietary advice based on the health condition. This allows the elderly's health condition to be continuously monitored and appropriate measures to be taken.

[0061] The elderly support system can further include a reminder unit. The reminder unit can, for example, provide audio or visual notifications of when to take medicine or when to make medical appointments. The reminder unit can also manage the elderly person's schedule and help them not forget important appointments. For example, it can use a calendar function to notify them of times for regular health checks or exercise. The reminder unit can also work with family members and caregivers to share the elderly person's schedule. This helps the elderly person not forget important appointments in their daily lives.

[0062] The elderly support system may further include a communication unit. The communication unit may, for example, use a video call function to easily contact family and friends. The communication unit may also enable elderly people to send voice messages using voice recognition technology. For example, a message may be input by voice and sent as a text message. The communication unit may also use generative AI to estimate the emotions of the elderly and support appropriate communication. This allows elderly people to maintain their connections with society without becoming isolated.

[0063] The elderly support system can further include an entertainment unit. The entertainment unit provides content such as music, movies, and reading. The entertainment unit can also suggest customized content based on the elderly's hobbies and interests. For example, it can recommend new movies based on the genre of movies previously viewed. The entertainment unit can also use generative AI to estimate the elderly's emotions and provide content according to those emotions. This allows the elderly to spend their time having fun.

[0064] The elderly support system can further include a safety management unit. The safety management unit can detect suspicious movements using, for example, door and window sensors and issue an alarm. The safety management unit can also detect emergencies such as fires and gas leaks and respond quickly. For example, it can detect abnormalities using smoke sensors and gas sensors and send information to the notification unit. The safety management unit can also use generative AI to estimate the emotions of the elderly and take appropriate action in emergencies. This allows the elderly to continue living with peace of mind.

[0065] The elderly support system can further include a rehabilitation unit. The rehabilitation unit can monitor the elderly's rehabilitation progress using, for example, a motion sensor and provide an appropriate exercise program. The rehabilitation unit can also use generative AI to estimate the elderly's emotions and provide a rehabilitation program based on those emotions. For example, if the elderly's motivation is low, it can provide an encouraging message. The rehabilitation unit can also collaborate with family members and caregivers and share rehabilitation progress with them. This allows the elderly to undergo rehabilitation effectively.

[0066] The elderly support system can further include a nutrition management unit. The nutrition management unit, for example, records the contents of meals and manages nutritional balance. The nutrition management unit can also propose appropriate meal plans based on the elderly person's health condition. For example, if a specific nutrient is lacking, it can suggest ingredients containing that nutrient. The nutrition management unit can also use generative AI to estimate the elderly person's emotions and provide a meal plan based on those emotions. This allows the elderly person to maintain a healthy diet.

[0067] The elderly support system may further include an exercise management unit. The exercise management unit may, for example, use a wearable device to monitor the amount of exercise performed by the elderly in real time and provide an appropriate exercise program. The exercise management unit may also record the elderly's exercise history and send the information to the generation AI unit. For example, the exercise management unit may suggest an appropriate exercise program based on past exercise data. The exercise management unit may also use the generation AI to estimate the elderly's emotions and provide an exercise program based on those emotions. This allows the elderly to maintain healthy exercise habits.

[0068] The elderly support system can further include a learning support unit. The learning support unit provides elderly people with new knowledge and skills, for example, through an online learning platform. The learning support unit can also propose customized learning plans based on the elderly person's interests. For example, it can recommend courses related to hobbies and interests. The learning support unit can also use generative AI to estimate the elderly person's emotions and provide learning support according to those emotions. This allows elderly people to enjoy lifelong learning.

[0069] The elderly support system can further include a travel support unit. For example, the travel support unit provides appropriate information when elderly people plan trips. The travel support unit can also propose customized travel plans based on the elderly person's health condition and interests. For example, it can recommend travel destinations and activities according to the elderly person's health condition. The travel support unit can also use generative AI to estimate the elderly person's emotions and provide travel plans according to those emotions. This allows elderly people to enjoy their trips with peace of mind.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The pupil tracking unit uses pupil tracking technology to track the elderly person's gaze. For example, it uses an infrared camera to detect the gaze and then uses an image processing algorithm to analyze the gaze movement. It is also possible to set the gaze detection range and adjust the gaze movement analysis method. Step 2: The generation AI unit provides voice guidance based on the information acquired by the pupil tracking unit. For example, the voice guidance can be generated using a text generation AI (e.g., LLM) and appropriate voice guidance for the elderly can be provided using natural language processing technology. Step 3: The IoT sensor unit uses IoT sensor technology to detect abnormalities. For example, a motion sensor can be used to monitor the elderly person's movements and detect abnormal movements. A temperature sensor can also be used to detect changes in the environment. Step 4: The notification unit notifies designated contacts and medical institutions in the event of an emergency. For example, it can send an SMS notification and make a phone call in the event of an emergency. It can also set the timing of notifications to be sent at the appropriate time.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0086] 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.

[0087] 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.

[0088] 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 AI 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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 AI 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 AI 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 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a pupil tracking unit that tracks the gaze of the elderly person using pupil tracking technology; a generation AI unit that provides voice guidance based on the information acquired by the pupil tracking unit; An IoT sensor unit that uses IoT sensor technology to detect abnormalities; A notification unit that notifies designated contact points and medical institutions in the event of an emergency. A system characterized by:

2. The pupil tracking unit The interests and concerns of the elderly are estimated from their eye movements, and that information is sent to the generation AI unit.

2. The system of claim 1.

3. The pupil tracking unit The AI ​​generator analyzes eye movements and facial expressions simultaneously, and makes detailed judgments about the situation.

2. The system of claim 1.

4. The pupil tracking unit The emotions of the elderly person are estimated from their gaze movements and this information is sent to the generation AI unit.

2. The system of claim 1.

5. The generation AI unit Learns the elderly person's past behavioral history and provides individually customized voice guidance 2. The system of claim 1.

6. The generation AI unit The content of the audio guidance is adjusted according to the elderly person's level of understanding, and information is provided at the appropriate time.

2. The system of claim 1.

7. The generation AI unit Estimating the emotions of elderly people and providing voice guidance according to those emotions 2. The system of claim 1.

8. The generation AI unit Providing visual guidance in addition to audio guidance 2. The system of claim 1.

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