System

Abstract

An object of the system according to the embodiment is to fundamentally avoid lonely death of elderly people.SOLUTION: A system according to an embodiment includes an interactive AI, a monitoring unit, and a reporting unit. The interactive AI includes an interactive AI using the generated AI and promotes communication with the elderly person. The monitoring unit monitors the daily life of the elderly person and detects an abnormality. The notification unit automatically notifies an emergency contact address when an abnormality is detected.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 technology has not done enough to fundamentally prevent elderly people from dying alone, and there is room for improvement.

[0005] The system according to the embodiment aims to fundamentally prevent elderly people from dying alone. [Means for solving the problem]

[0006] The system according to the embodiment comprises an interactive AI, a monitoring unit, and a reporting unit. The interactive AI is equipped with an interactive AI that uses a generative AI and promotes communication with the elderly. The monitoring unit monitors the elderly's daily life and detects abnormalities. The reporting unit automatically reports an abnormality to an emergency contact when it detects an abnormality. [Effects of the Invention]

[0007] The system according to the embodiment can fundamentally prevent elderly people from dying alone. [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 surveillance camera system according to an embodiment of the present invention is a system that is equipped with a conversational AI that uses a generative AI, promotes communication with the elderly, reduces feelings of loneliness, and prevents lonely deaths. As a result, the surveillance camera system can reduce feelings of loneliness and prevent lonely deaths through dialogue with the elderly.

[0029] A surveillance camera system according to an embodiment includes a conversational AI, a monitoring unit, and a reporting unit. The conversational AI promotes communication with the elderly using a generation AI. For example, the conversational AI asks the elderly, "How was your day today?" and generates a response. The conversational AI also understands what the elderly is saying and generates an appropriate response. For example, the conversational AI can ask, "What did you have for lunch?" and generate a response. The conversational AI can also analyze the elderly's emotional state and generate an appropriate response. For example, the conversational AI can offer words of encouragement if the elderly is feeling lonely. The monitoring unit monitors the elderly's daily life and detects abnormalities. For example, the monitoring unit monitors the air conditioner's startup time and the light on / off operation to detect abnormalities. The monitoring unit can also monitor the number of times the refrigerator is opened and closed to detect abnormalities. The monitoring unit can also analyze the elderly's movement patterns and detect abnormalities. For example, the monitoring unit monitors the elderly's walking speed and changes in posture to detect abnormalities. The reporting unit automatically notifies the emergency contact when it detects an abnormality. For example, the reporting unit notifies the emergency contact when an elderly person collapses. The reporting unit can also notify the emergency contact when an elderly person remains motionless for a long period of time. The reporting unit can also monitor the elderly person's health data in real time and immediately notify the emergency contact if an abnormality is detected. For example, the reporting unit notifies the emergency contact when an abnormality in heart rate or blood pressure is detected. This allows the surveillance camera system according to the embodiment to promote communication with the elderly, reduce feelings of loneliness, and prevent lonely deaths. For example, the interactive AI reduces feelings of loneliness through dialogue with the elderly. The monitoring unit monitors the daily lives of the elderly and detects abnormalities early. The reporting unit notifies the emergency contact when an abnormality is detected, enabling a prompt response.

[0030] Conversational AI can analyze an elderly person's past conversation history and generate personalized conversations based on their individual hobbies and interests. For example, conversational AI can analyze an elderly person's past conversation history and generate personalized conversations based on their hobbies and interests. For example, it can arouse interest by revisiting movies or books that have been discussed in the past. Conversational AI can also provide news and information related to specific hobbies and interests based on the elderly person's conversation history. For example, an elderly person who enjoys gardening can be provided with information on the latest gardening techniques and plants. Conversational AI can also analyze an elderly person's conversation history, remember specific events and anniversaries, and provide topics related to that day. For example, it can send congratulatory messages on birthdays and wedding anniversaries. In this way, by generating conversations based on the elderly person's hobbies and interests, it can arouse their interest and reduce feelings of loneliness.

[0031] Conversational AI can be equipped with the function of periodically recording elderly people's stories and sharing them with family members. For example, conversational AI can periodically record elderly people's stories and share them with family members. For example, once a week, it can elicit stories, convert them into text, and send them to family members. Conversational AI can also record elderly people's stories and share them with family members as audio data. For example, it can record specific episodes so that family members can listen to them at any time. Conversational AI can also record elderly people's stories and share them with family members in combination with photos and videos. For example, it can elicit stories while looking at old photos and send them to family members. In this way, sharing elderly people's stories with family members deepens bonds with them.

[0032] Conversational AI has the ability to automatically play music or radio programs that the elderly like, and can be used as part of a conversation. Conversational AI can be equipped with the ability to automatically play music that the elderly like, and used as part of a conversation. For example, playing a favorite song during a conversation to help relax. Conversational AI can also be equipped with the ability to automatically play a radio program that the elderly like, and used as part of a conversation. For example, it can provide topics about radio programs to encourage conversation. Conversational AI can also automatically play music or radio programs that the elderly like, and engage in conversations about the content. For example, it can ask about the elderly's impressions of the music or discuss the content of the radio program. In this way, playing the elderly's favorite music or radio programs enhances the relaxation effect.

[0033] The monitoring unit can analyze the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. The monitoring unit, for example, analyzes the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. For example, it monitors changes in walking speed and posture in real time. The monitoring unit also analyzes the movement patterns of the elderly and detects movements that are different from normal. For example, it detects signs of falls and abnormal movements early. The monitoring unit also analyzes the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. For example, it records changes in daily movements and issues an alert if an abnormality is found. In this way, by analyzing the movement patterns of the elderly in detail, it is possible to detect even subtle abnormalities.

[0034] The monitoring unit can monitor the elderly person's eating and fluid intake patterns and issue a warning if an abnormality is detected. The monitoring unit, for example, monitors the elderly person's eating patterns and issues a warning if an abnormality is detected. For example, it records the number of meals and the amount of meals and notifies the elderly person if an abnormality is detected. The monitoring unit also monitors the elderly person's fluid intake patterns and issues a warning if an abnormality is detected. For example, it issues a warning if the fluid intake is low. The monitoring unit also monitors the elderly person's eating and fluid intake patterns and issues a warning if an abnormality is detected. For example, it records the time and content of meals and notifies the elderly person if an abnormality is detected. In this way, the monitoring unit can monitor the elderly person's eating and fluid intake patterns and issue a warning if an abnormality is detected.

[0035] The monitoring unit may have a function to analyze the elderly person's lifestyle rhythm and provide health advice at the optimal timing. The monitoring unit, for example, analyzes the elderly person's lifestyle rhythm and provides health advice at the optimal timing. For example, it may suggest meal timings and exercise times. The monitoring unit also has a function to analyze the elderly person's lifestyle rhythm and provide health advice. For example, it may analyze sleep patterns and suggest appropriate sleep times. The monitoring unit also analyzes the elderly person's lifestyle rhythm and provide health advice at the optimal timing. For example, it may suggest the timing of regular health checks. In this way, health management is supported by analyzing the elderly person's lifestyle rhythm and providing health advice at the optimal timing.

[0036] The monitoring unit may monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, the monitoring unit may monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the room temperature is too high, the monitoring unit may suggest using an air conditioner. The monitoring unit may also monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the humidity is low, the monitoring unit may suggest using a humidifier. The monitoring unit may also monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the lighting is dim, the monitoring unit may suggest adjusting the brightness. In this way, the elderly person's living environment is monitored and advice for maintaining a comfortable environment is provided, thereby improving their quality of life.

[0037] The reporting unit can monitor the elderly person's health data (heart rate, blood pressure, etc.) in real time and immediately report any abnormalities. The reporting unit, for example, monitors the elderly person's heart rate in real time and immediately reports any abnormalities. For example, it issues a warning if the heart rate rises suddenly. The reporting unit also monitors the elderly person's blood pressure in real time and immediately reports any abnormalities. For example, it issues a warning if the blood pressure is abnormally high. The reporting unit also monitors the elderly person's health data in real time and immediately reports any abnormalities. For example, it notifies emergency contacts if it detects an abnormality in the heart rate or blood pressure. This makes it possible to monitor the elderly person's health data in real time and immediately report any abnormalities, enabling a rapid response.

[0038] The reporting unit can track the location information of the elderly person and report when an abnormal movement pattern is detected. The reporting unit, for example, tracks the location information of the elderly person in real time and reports when an abnormal movement pattern is detected. For example, it issues a warning if the elderly person stays in a place they normally do not go to for a long time. The reporting unit also analyzes the movement pattern of the elderly person and reports when abnormal movement is detected. For example, it issues a warning if the elderly person is out at night. The reporting unit also tracks the location information of the elderly person and reports when an abnormal movement pattern is detected. For example, it issues a warning if the elderly person is moving off their normal route. In this way, by tracking the location information of the elderly person and reporting when an abnormal movement pattern is detected, a rapid response is possible.

[0039] The reporting unit can be equipped with a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. The reporting unit, for example, has a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. For example, the contact information of nearby residents is registered in an emergency contact list. The reporting unit also has a function to send notifications to nearby residents in the event of an emergency, encouraging a quick response. For example, it sends notifications to nearby residents by SMS or email. The reporting unit also has a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. For example, it sends notifications to nearby residents via an app. This enables a quick response by sending notifications to nearby residents in the event of an emergency.

[0040] The reporting unit can automatically generate a detailed report including the elderly person's past health data and lifestyle patterns and transmit it to a medical institution when an emergency call is made. For example, the reporting unit automatically generates a detailed report including the elderly person's past health data and lifestyle patterns and transmits it to a medical institution when an emergency call is made. For example, a report including heart rate and blood pressure history is generated. The reporting unit also analyzes the elderly person's lifestyle patterns when an emergency call is made, and transmits a detailed report to a medical institution when an abnormality occurs. For example, a report including abnormal movement patterns and dietary records is generated. The reporting unit also automatically generates a detailed report including the elderly person's past health data and lifestyle patterns and transmits it to a medical institution when an emergency call is made. For example, a report including changes in emotional state and abnormality detection history is generated. As a result, by transmitting a detailed report to a medical institution when an emergency call is made, prompt and appropriate medical treatment can be provided.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The surveillance camera system can also be equipped with a health checkup unit that periodically checks the health of the elderly. For example, the health checkup unit may periodically collect the elderly's health data using a blood pressure monitor or thermometer and notify the elderly if any abnormalities are found. The health checkup unit may also periodically collect blood samples using a blood test kit and send the analysis results to family members or medical institutions. Furthermore, the health checkup unit may provide individual health advice based on the results of the regular health checks. This allows for continuous monitoring of the elderly's health and early detection of abnormalities.

[0043] The monitoring camera system can also be equipped with a reminder unit that analyzes the elderly person's daily rhythm and provides reminders at optimal times. For example, the reminder unit can notify them when it's time to take their medicine. The reminder unit can also notify them when it's time to do regular exercise or stretching. The reminder unit can also notify them of important appointments and events to help them remember. This can support the elderly person's daily rhythm and promote health management.

[0044] The surveillance camera system can further include a content provider that provides content based on the elderly person's hobbies and interests. For example, the content provider can recommend movies and TV shows that the elderly person likes. The content provider can also introduce online courses and events related to the elderly person's hobbies. Furthermore, the content provider can provide news and articles that interest the elderly person, creating opportunities for conversation. This can pique the elderly person's interest and reduce their sense of loneliness.

[0045] The surveillance camera system can further include an environment monitoring unit that monitors the elderly's living environment and provides advice on maintaining a comfortable environment. For example, the environment monitoring unit can monitor room temperature and humidity and suggest appropriate use of an air conditioner or humidifier. The environment monitoring unit can also monitor lighting brightness and suggest appropriate brightness. Furthermore, the environment monitoring unit can monitor noise levels and provide advice on maintaining a quiet environment. This can help maintain a comfortable living environment for the elderly and improve their quality of life.

[0046] The surveillance camera system can further include a meal monitoring unit that monitors the elderly person's eating and water intake patterns and issues a warning if any abnormalities are detected. For example, the meal monitoring unit can record the number of meals and the amount of food eaten and notify the elderly person if any abnormalities are detected. The meal monitoring unit can also monitor the amount of water intake and issue a warning if any abnormalities are detected. Furthermore, the meal monitoring unit can record the time and content of meals and notify the elderly person if any abnormalities are detected. This makes it possible to monitor the elderly person's eating and water intake patterns and support their health management.

[0047] The surveillance camera system can further include a location tracking unit that tracks the location information of the elderly person and issues a warning if an abnormal movement pattern is detected. For example, the location tracking unit tracks the location information of the elderly person in real time and issues a warning if the elderly person stays in a place they normally do not go to for a long time. The location tracking unit can also analyze the movement pattern of the elderly person and issue a warning if the elderly person is out at night. Furthermore, the location tracking unit can track the location information of the elderly person and issue a warning if the elderly person is moving away from their normal route. This allows for a quick response by tracking the location information of the elderly person and reporting if an abnormal movement pattern is detected.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: Conversational AI uses generative AI to promote communication with the elderly. For example, conversational AI can ask the elderly, "How was your day today?" and generate a response. Conversational AI can also understand what the elderly is saying and generate an appropriate response. For example, it can ask, "What did you have for lunch?" and generate a response. Furthermore, conversational AI can analyze the elderly's emotional state and generate an appropriate response. For example, if the elderly person is feeling lonely, it can offer words of encouragement. Step 2: The monitoring unit monitors the elderly person's daily life and detects any abnormalities. For example, it monitors the air conditioner startup time and the on / off operation of lights to detect any abnormalities. It can also monitor the number of times the refrigerator is opened and closed to detect any abnormalities. It can also analyze the elderly person's movement patterns to detect any abnormalities. For example, it can monitor changes in walking speed and posture to detect any abnormalities. Step 3: The reporting unit automatically notifies emergency contacts when it detects an abnormality. For example, if an elderly person collapses or remains motionless for a long period of time, it will notify emergency contacts. It can also notify emergency contacts when it detects abnormalities in heart rate or blood pressure.

[0050] (Example 2) The surveillance camera system according to an embodiment of the present invention is a system that is equipped with a conversational AI that uses a generative AI, promotes communication with the elderly, reduces feelings of loneliness, and prevents lonely deaths. As a result, the surveillance camera system can reduce feelings of loneliness and prevent lonely deaths through dialogue with the elderly.

[0051] A surveillance camera system according to an embodiment includes a conversational AI, a monitoring unit, and a reporting unit. The conversational AI promotes communication with the elderly using a generation AI. For example, the conversational AI asks the elderly, "How was your day today?" and generates a response. The conversational AI also understands what the elderly is saying and generates an appropriate response. For example, the conversational AI can ask, "What did you have for lunch?" and generate a response. The conversational AI can also analyze the elderly's emotional state and generate an appropriate response. For example, the conversational AI can offer words of encouragement if the elderly is feeling lonely. The monitoring unit monitors the elderly's daily life and detects abnormalities. For example, the monitoring unit monitors the air conditioner's startup time and the light on / off operation to detect abnormalities. The monitoring unit can also monitor the number of times the refrigerator is opened and closed to detect abnormalities. The monitoring unit can also analyze the elderly's movement patterns and detect abnormalities. For example, the monitoring unit monitors the elderly's walking speed and changes in posture to detect abnormalities. The reporting unit automatically notifies the emergency contact when it detects an abnormality. For example, the reporting unit notifies the emergency contact when an elderly person collapses. The reporting unit can also notify the emergency contact when an elderly person remains motionless for a long period of time. The reporting unit can also monitor the elderly person's health data in real time and immediately notify the emergency contact if an abnormality is detected. For example, the reporting unit notifies the emergency contact when an abnormality in heart rate or blood pressure is detected. This allows the surveillance camera system according to the embodiment to promote communication with the elderly, reduce feelings of loneliness, and prevent lonely deaths. For example, the interactive AI reduces feelings of loneliness through dialogue with the elderly. The monitoring unit monitors the daily lives of the elderly and detects abnormalities early. The reporting unit notifies the emergency contact when an abnormality is detected, enabling a prompt response.

[0052] Conversational AI can analyze an elderly person's past conversation history and generate personalized conversations based on their individual hobbies and interests. For example, conversational AI can analyze an elderly person's past conversation history and generate personalized conversations based on their hobbies and interests. For example, it can arouse interest by revisiting movies or books that have been discussed in the past. Conversational AI can also provide news and information related to specific hobbies and interests based on the elderly person's conversation history. For example, an elderly person who enjoys gardening can be provided with information on the latest gardening techniques and plants. Conversational AI can also analyze an elderly person's conversation history, remember specific events and anniversaries, and provide topics related to that day. For example, it can send congratulatory messages on birthdays and wedding anniversaries. In this way, by generating conversations based on the elderly person's hobbies and interests, it can arouse their interest and reduce feelings of loneliness.

[0053] Conversational AI can analyze changes in an elderly person's tone of voice and speaking style to infer their emotional state and generate appropriate responses. For example, conversational AI can analyze changes in an elderly person's tone of voice and speaking style to infer their emotional state. For example, if their voice sounds low, it can offer words of encouragement. Conversational AI can also analyze the speed and rhythm of an elderly person's speech to infer their emotional state. For example, if their speech slows down, it can ask questions to show concern for their health. Conversational AI can also analyze changes in an elderly person's tone of voice and speaking style in real time to generate responses that correspond to their emotional state. For example, if they are excited, it can offer calming topics. In this way, by generating responses that correspond to the elderly person's emotional state, it can reduce their sense of loneliness.

[0054] Conversational AI can use its emotion estimation function to generate dialogue content that corresponds to the emotional state of the elderly, thereby reducing their sense of loneliness. For example, conversational AI uses its emotion estimation function to generate dialogue content that corresponds to the emotional state of the elderly. For example, if the elderly person is feeling lonely, it can provide pleasant topics to talk about. Conversational AI can also analyze the elderly person's emotional state in real time and generate dialogue content to reduce their sense of loneliness. For example, it can elicit stories about past memories. Conversational AI can also use its emotion estimation function to generate dialogue content that corresponds to the elderly person's emotional state, thereby reducing their sense of loneliness. For example, it can send words of gratitude or messages of encouragement. In this way, dialogue content that corresponds to the elderly person's emotional state can be generated, thereby reducing their sense of loneliness.

[0055] Conversational AI can be equipped with the function of periodically recording elderly people's stories and sharing them with family members. For example, conversational AI can periodically record elderly people's stories and share them with family members. For example, once a week, it can elicit stories, convert them into text, and send them to family members. Conversational AI can also record elderly people's stories and share them with family members as audio data. For example, it can record specific episodes so that family members can listen to them at any time. Conversational AI can also record elderly people's stories and share them with family members in combination with photos and videos. For example, it can elicit stories while looking at old photos and send them to family members. In this way, sharing elderly people's stories with family members deepens bonds with them.

[0056] Conversational AI has the ability to automatically play music or radio programs that the elderly like, and can be used as part of a conversation. Conversational AI can be equipped with the ability to automatically play music that the elderly like, and used as part of a conversation. For example, playing a favorite song during a conversation to help relax. Conversational AI can also be equipped with the ability to automatically play a radio program that the elderly like, and used as part of a conversation. For example, it can provide topics about radio programs to encourage conversation. Conversational AI can also automatically play music or radio programs that the elderly like, and engage in conversations about the content. For example, it can ask about the elderly's impressions of the music or discuss the content of the radio program. In this way, playing the elderly's favorite music or radio programs enhances the relaxation effect.

[0057] Conversational AI can use its emotion estimation function to automatically play videos or music with a relaxing effect when an elderly person shows a specific emotion. For example, using its emotion estimation function, conversational AI can automatically play music with a relaxing effect when an elderly person feels stressed. For example, it can play classical music or nature sounds. Conversational AI can also automatically play videos with a relaxing effect when an elderly person feels anxious. For example, it can display videos of landscapes or animals. Conversational AI can also use its emotion estimation function to automatically play videos or music with a relaxing effect when an elderly person shows a specific emotion. For example, it can play meditation music or relaxation videos. In this way, by playing videos or music with a relaxing effect when an elderly person shows a specific emotion, stress can be reduced.

[0058] The monitoring unit can analyze the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. The monitoring unit, for example, analyzes the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. For example, it monitors changes in walking speed and posture in real time. The monitoring unit also analyzes the movement patterns of the elderly and detects movements that are different from normal. For example, it detects signs of falls and abnormal movements early. The monitoring unit also analyzes the movement patterns of the elderly in detail, making it possible to detect even subtle abnormalities. For example, it records changes in daily movements and issues an alert if an abnormality is found. In this way, by analyzing the movement patterns of the elderly in detail, it is possible to detect even subtle abnormalities.

[0059] The monitoring unit can monitor the elderly person's eating and fluid intake patterns and issue a warning if an abnormality is detected. The monitoring unit, for example, monitors the elderly person's eating patterns and issues a warning if an abnormality is detected. For example, it records the number of meals and the amount of meals and notifies the elderly person if an abnormality is detected. The monitoring unit also monitors the elderly person's fluid intake patterns and issues a warning if an abnormality is detected. For example, it issues a warning if the fluid intake is low. The monitoring unit also monitors the elderly person's eating and fluid intake patterns and issues a warning if an abnormality is detected. For example, it records the time and content of meals and notifies the elderly person if an abnormality is detected. In this way, the monitoring unit can monitor the elderly person's eating and fluid intake patterns and issue a warning if an abnormality is detected.

[0060] The monitoring unit can use the emotion estimation function to issue a warning when the emotional state of the elderly person is abnormal. The monitoring unit, for example, uses the emotion estimation function to issue a warning when the emotional state of the elderly person is abnormal. For example, it issues a warning when negative emotions continue for a long period of time. The monitoring unit also monitors the emotional state of the elderly person in real time and issues a warning if an abnormality is detected. For example, it notifies the elderly person when there is a sudden change in emotion. The monitoring unit also uses the emotion estimation function to issue a warning when the emotional state of the elderly person is abnormal. For example, it issues a warning when the emotion score falls below a certain threshold. In this way, by issuing a warning when the emotional state of the elderly person is abnormal, early action can be taken.

[0061] The monitoring unit may have a function to analyze the elderly person's lifestyle rhythm and provide health advice at the optimal timing. The monitoring unit, for example, analyzes the elderly person's lifestyle rhythm and provides health advice at the optimal timing. For example, it may suggest meal timings and exercise times. The monitoring unit also has a function to analyze the elderly person's lifestyle rhythm and provide health advice. For example, it may analyze sleep patterns and suggest appropriate sleep times. The monitoring unit also analyzes the elderly person's lifestyle rhythm and provide health advice at the optimal timing. For example, it may suggest the timing of regular health checks. In this way, health management is supported by analyzing the elderly person's lifestyle rhythm and providing health advice at the optimal timing.

[0062] The monitoring unit may monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, the monitoring unit may monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the room temperature is too high, the monitoring unit may suggest using an air conditioner. The monitoring unit may also monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the humidity is low, the monitoring unit may suggest using a humidifier. The monitoring unit may also monitor the elderly person's living environment and provide advice for maintaining a comfortable environment. For example, if the lighting is dim, the monitoring unit may suggest adjusting the brightness. In this way, the elderly person's living environment is monitored and advice for maintaining a comfortable environment is provided, thereby improving their quality of life.

[0063] The monitoring unit can use the emotion estimation function to suggest relaxation methods when the elderly person is feeling stressed. The monitoring unit, for example, uses the emotion estimation function to suggest relaxation methods when the elderly person is feeling stressed. For example, it may teach deep breathing or meditation techniques. The monitoring unit may also suggest music or videos that have a relaxing effect when the elderly person is feeling stressed. For example, it may play relaxation music or videos of natural scenery. The monitoring unit may also use the emotion estimation function to suggest relaxation methods when the elderly person is feeling stressed. For example, it may teach light exercise or stretching techniques. In this way, by suggesting relaxation methods when the elderly person is feeling stressed, stress is reduced.

[0064] The reporting unit can monitor the elderly person's health data (heart rate, blood pressure, etc.) in real time and immediately report any abnormalities. The reporting unit, for example, monitors the elderly person's heart rate in real time and immediately reports any abnormalities. For example, it issues a warning if the heart rate rises suddenly. The reporting unit also monitors the elderly person's blood pressure in real time and immediately reports any abnormalities. For example, it issues a warning if the blood pressure is abnormally high. The reporting unit also monitors the elderly person's health data in real time and immediately reports any abnormalities. For example, it notifies emergency contacts if it detects an abnormality in the heart rate or blood pressure. This makes it possible to monitor the elderly person's health data in real time and immediately report any abnormalities, enabling a rapid response.

[0065] The reporting unit can track the location information of the elderly person and report when an abnormal movement pattern is detected. The reporting unit, for example, tracks the location information of the elderly person in real time and reports when an abnormal movement pattern is detected. For example, it issues a warning if the elderly person stays in a place they normally do not go to for a long time. The reporting unit also analyzes the movement pattern of the elderly person and reports when abnormal movement is detected. For example, it issues a warning if the elderly person is out at night. The reporting unit also tracks the location information of the elderly person and reports when an abnormal movement pattern is detected. For example, it issues a warning if the elderly person is moving off their normal route. In this way, by tracking the location information of the elderly person and reporting when an abnormal movement pattern is detected, a rapid response is possible.

[0066] The reporting unit can use the emotion estimation function to automatically report when the elderly person feels strong anxiety or fear. The reporting unit, for example, uses the emotion estimation function to automatically report when the elderly person feels strong anxiety. For example, if the emotion score exceeds a certain threshold, it notifies an emergency contact. The reporting unit also uses the emotion estimation function to automatically report when the elderly person feels fear. For example, it issues a warning when a sudden change in emotion is detected. The reporting unit also uses the emotion estimation function to automatically report when the elderly person feels strong anxiety or fear. For example, it notifies an emergency contact when the emotion score is high. This allows for automatic reporting even when the elderly person feels strong anxiety or fear, enabling a rapid response.

[0067] The reporting unit can be equipped with a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. The reporting unit, for example, has a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. For example, the contact information of nearby residents is registered in an emergency contact list. The reporting unit also has a function to send notifications to nearby residents in the event of an emergency, encouraging a quick response. For example, it sends notifications to nearby residents by SMS or email. The reporting unit also has a function to send notifications to nearby residents in addition to the elderly person's family and caregivers in the event of an emergency. For example, it sends notifications to nearby residents via an app. This enables a quick response by sending notifications to nearby residents in the event of an emergency.

[0068] The reporting unit can automatically generate a detailed report including the elderly person's past health data and lifestyle patterns and transmit it to a medical institution when an emergency call is made. For example, the reporting unit automatically generates a detailed report including the elderly person's past health data and lifestyle patterns and transmits it to a medical institution when an emergency call is made. For example, a report including heart rate and blood pressure history is generated. The reporting unit also analyzes the elderly person's lifestyle patterns when an emergency call is made, and transmits a detailed report to a medical institution when an abnormality occurs. For example, a report including abnormal movement patterns and dietary records is generated. The reporting unit also automatically generates a detailed report including the elderly person's past health data and lifestyle patterns and transmits it to a medical institution when an emergency call is made. For example, a report including changes in emotional state and abnormality detection history is generated. As a result, by transmitting a detailed report to a medical institution when an emergency call is made, prompt and appropriate medical treatment can be provided.

[0069] The reporting unit can use the emotion estimation function to generate a dialogue that calms the elderly so that they do not panic in an emergency. The reporting unit, for example, uses the emotion estimation function to generate a dialogue that calms the elderly so that they do not panic in an emergency. For example, it sends a message urging the elderly to remain calm. The reporting unit also uses the emotion estimation function to generate a dialogue that calms the elderly so that they do not panic in an emergency. For example, it sends a message urging the elderly to take a deep breath. The reporting unit also uses the emotion estimation function to generate a dialogue that calms the elderly so that they do not panic in an emergency. For example, it speaks words that give a sense of security. In this way, a dialogue that calms the elderly so that they do not panic in an emergency can be generated, enabling them to respond calmly.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The surveillance camera system can also be equipped with a health checkup unit that periodically checks the health of the elderly. For example, the health checkup unit may periodically collect the elderly's health data using a blood pressure monitor or thermometer and notify the elderly if any abnormalities are found. The health checkup unit may also periodically collect blood samples using a blood test kit and send the analysis results to family members or medical institutions. Furthermore, the health checkup unit may provide individual health advice based on the results of the regular health checks. This allows for continuous monitoring of the elderly's health and early detection of abnormalities.

[0072] The monitoring camera system can also be equipped with a reminder unit that analyzes the elderly person's daily rhythm and provides reminders at optimal times. For example, the reminder unit can notify them when it's time to take their medicine. The reminder unit can also notify them when it's time to do regular exercise or stretching. The reminder unit can also notify them of important appointments and events to help them remember. This can support the elderly person's daily rhythm and promote health management.

[0073] The surveillance camera system can further include a content provider that provides content based on the elderly person's hobbies and interests. For example, the content provider can recommend movies and TV shows that the elderly person likes. The content provider can also introduce online courses and events related to the elderly person's hobbies. Furthermore, the content provider can provide news and articles that interest the elderly person, creating opportunities for conversation. This can pique the elderly person's interest and reduce their sense of loneliness.

[0074] The monitoring camera system can further include a relaxation suggestion unit that estimates the emotional state of the elderly person and suggests appropriate relaxation methods based on the estimated emotions. For example, if the elderly person is feeling stressed, the relaxation suggestion unit can suggest deep breathing or meditation. If the elderly person is feeling anxious, the relaxation suggestion unit can also play relaxation music or videos of natural scenery. Furthermore, the relaxation suggestion unit can also suggest light exercise or stretching methods to help the elderly person relax. In this way, relaxation methods can be suggested according to the elderly person's emotional state, thereby reducing stress.

[0075] The surveillance camera system can further include a dialogue generation unit that estimates the emotional state of the elderly person and generates appropriate dialogue content based on the estimated emotion. For example, if the elderly person feels lonely, the dialogue generation unit can provide a pleasant topic. If the elderly person feels anxious, the dialogue generation unit can also speak words that give a sense of security. If the elderly person is excited, the dialogue generation unit can also provide a calming topic. In this way, dialogue content according to the elderly person's emotional state can be generated, reducing the elderly person's sense of loneliness.

[0076] The surveillance camera system can further include an environment monitoring unit that monitors the elderly's living environment and provides advice on maintaining a comfortable environment. For example, the environment monitoring unit can monitor room temperature and humidity and suggest appropriate use of an air conditioner or humidifier. The environment monitoring unit can also monitor lighting brightness and suggest appropriate brightness. Furthermore, the environment monitoring unit can monitor noise levels and provide advice on maintaining a quiet environment. This can help maintain a comfortable living environment for the elderly and improve their quality of life.

[0077] The monitoring camera system can further include an exercise suggestion unit that estimates the emotional state of the elderly person and suggests appropriate exercises based on the estimated emotions. For example, if the elderly person is feeling stressed, the exercise suggestion unit can suggest yoga or stretching, which have a relaxing effect. If the elderly person is feeling low in energy, the exercise suggestion unit can also suggest light walking or gymnastics. Furthermore, if the elderly person is excited, the exercise suggestion unit can suggest deep breathing or meditation to calm them down. In this way, exercises can be suggested according to the elderly person's emotional state, and health can be supported.

[0078] The surveillance camera system can further include a meal monitoring unit that monitors the elderly person's eating and water intake patterns and issues a warning if any abnormalities are detected. For example, the meal monitoring unit can record the number of meals and the amount of food eaten and notify the elderly person if any abnormalities are detected. The meal monitoring unit can also monitor the amount of water intake and issue a warning if any abnormalities are detected. Furthermore, the meal monitoring unit can record the time and content of meals and notify the elderly person if any abnormalities are detected. This makes it possible to monitor the elderly person's eating and water intake patterns and support their health management.

[0079] The monitoring camera system can further include an activity suggestion unit that estimates the emotional state of the elderly person and suggests appropriate activities based on the estimated emotion. For example, if the elderly person feels lonely, the activity suggestion unit can suggest a video call with friends or family. If the elderly person feels bored, the activity suggestion unit can also suggest a new hobby or activity. Furthermore, if the elderly person feels stressed, the activity suggestion unit can also suggest an activity that has a relaxing effect. In this way, activities can be suggested according to the elderly person's emotional state, improving their quality of life.

[0080] The surveillance camera system can further include a location tracking unit that tracks the location information of the elderly person and issues a warning if an abnormal movement pattern is detected. For example, the location tracking unit tracks the location information of the elderly person in real time and issues a warning if the elderly person stays in a place they normally do not go to for a long time. The location tracking unit can also analyze the movement pattern of the elderly person and issue a warning if the elderly person is out at night. Furthermore, the location tracking unit can track the location information of the elderly person and issue a warning if the elderly person is moving away from their normal route. This allows for a quick response by tracking the location information of the elderly person and reporting if an abnormal movement pattern is detected.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: Conversational AI uses generative AI to promote communication with the elderly. For example, conversational AI can ask the elderly, "How was your day today?" and generate a response. Conversational AI can also understand what the elderly is saying and generate an appropriate response. For example, it can ask, "What did you have for lunch?" and generate a response. Furthermore, conversational AI can analyze the elderly's emotional state and generate an appropriate response. For example, if the elderly person is feeling lonely, it can offer words of encouragement. Step 2: The monitoring unit monitors the elderly person's daily life and detects any abnormalities. For example, it monitors the air conditioner startup time and the on / off operation of lights to detect any abnormalities. It can also monitor the number of times the refrigerator is opened and closed to detect any abnormalities. It can also analyze the elderly person's movement patterns to detect any abnormalities. For example, it can monitor changes in walking speed and posture to detect any abnormalities. Step 3: The reporting unit automatically notifies emergency contacts when it detects an abnormality. For example, if an elderly person collapses or remains motionless for a long period of time, it will notify emergency contacts. It can also notify emergency contacts when it detects abnormalities in heart rate or blood pressure.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. It is equipped with a conversational AI that uses generative AI to promote communication with the elderly, A monitoring unit that monitors the daily life of the elderly and detects abnormalities; A reporting unit that automatically reports to an emergency contact when an abnormality is detected. A system characterized by:

2. The conversational AI is Analyzing the elderly person's past conversation history and generating personalized conversations based on their individual hobbies and interests 2. The system of claim 1.

3. The conversational AI is Analyzing changes in the elderly person's tone of voice and speaking style, inferring their emotional state and generating appropriate responses.

2. The system of claim 1.

4. The conversational AI is Generate dialogue content according to the emotional state of the elderly person to reduce their sense of loneliness 2. The system of claim 1.

5. The conversational AI is It has a function to periodically record the elderly person's memories and share them with their family.

2. The system of claim 1.

6. The conversational AI is The device will have a function to automatically play music or radio programs that the elderly person likes, and will be used as part of the conversation.

2. The system of claim 1.

7. The conversational AI is When the elderly person shows a specific emotion, relaxing videos or music are automatically played.

2. The system of claim 1.

8. The monitoring unit By analyzing the elderly person's movement patterns in detail, it becomes possible to detect even the most minute abnormalities.

2. The system of claim 1.

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