System

Abstract

An object of a system according to an embodiment is to support health management and communication of an elderly person living alone and provide information necessary for a family.SOLUTION: A system according to an embodiment includes a speaking unit, a recording unit, an analysis unit, a notification unit, a reminder unit, and a camera interlocking unit. The speaking unit speaks in the voice of a child or grandchild. The recording unit records the content of the conversation performed by the speaking unit. The analysis unit analyzes the conversation content recorded by the recording unit and detects a sign of dementia or depression. The notifier notifies the family member of the sign of dementia or depression detected by the analyzer. The reminder unit prevents forgetting to take the medicine. The camera interlocking part detects the fall of the aged person by interlocking with the camera, and communicates with the family and the hospital.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively support the health management and communication of elderly people living alone, or to provide necessary information to family members who live far away.

[0005] The system according to the embodiment aims to support health management and communication for elderly people living alone and to provide necessary information to their families. [Means for solving the problem]

[0006] The system according to the embodiment comprises a speaking unit, a recording unit, an analysis unit, a notification unit, a reminder unit, and a camera linkage unit. The speaking unit speaks in the voice of a child or grandchild. The recording unit records the content of the conversation carried out by the speaking unit. The analysis unit analyzes the content of the conversation recorded by the recording unit and detects signs of dementia or depression. The notification unit notifies family members of signs of dementia or depression detected by the analysis unit. The reminder unit prevents forgetting to take medication. The camera linkage unit works in conjunction with the camera to detect when an elderly person falls and contacts the family and the hospital. [Effects of the Invention]

[0007] The system according to the embodiment can support health management and communication for elderly people living alone and provide necessary information to their families. [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 health management system according to an embodiment of the present invention uses generative AI to support health management and communication for the elderly and provide necessary information to their families. As a result, the health management system supports health management and communication for the elderly and provides necessary information to their families, ensuring the peace of mind and safety of both parties.

[0029] A health management system according to an embodiment includes a conversation unit, a recording unit, an analysis unit, a notification unit, a reminder unit, and a camera linkage unit. The conversation unit speaks to an elderly person using the voice of a child or grandchild. For example, the generation AI speaks to the elderly person using the voice of a child or grandchild, asking, "Grandma, how was your day?" The generation AI can also speak to the elderly person using the voice of a child or grandchild, asking, "Grandma, how are you?" The generation AI can also speak to the elderly person using the voice of a child or grandchild, asking, "Grandma, how are you doing?" The recording unit records the conversation content conducted by the conversation unit. For example, the generation AI can save the conversation content as text data. The generation AI can also save the conversation content as audio data. The generation AI can also save the conversation content as video data. The analysis unit analyzes the conversation content recorded by the recording unit to detect signs of dementia or depression. For example, the generation AI can analyze the conversation content using natural language processing technology to detect specific keywords and phrases. The generation AI can also analyze the conversation content using machine learning algorithms to detect behavioral patterns. The generation AI can also analyze the conversation content using emotion analysis technology to detect changes in emotions. The notification unit notifies family members of signs of dementia or depression detected by the analysis unit. For example, the generation AI can notify family members by email. The generation AI can also send app notifications to family members. The generation AI can also notify family members by SMS. The reminder unit provides reminders to prevent forgetting to take medication. For example, the generation AI can notify, "Grandma, it's almost time to take your medicine." The generation AI can also notify, "Grandma, don't forget to take your medicine." The camera linkage unit works with the camera to detect falls by the elderly person and contact the family and hospital. For example, the generation AI can analyze camera footage and send an alert to the family if a fall is detected. The generation AI can also analyze camera footage and send an alert to the hospital if a fall is detected. The generation AI can also analyze camera footage and call an ambulance if a fall is detected.As a result, the health management system according to the embodiment can support the health management and communication of the elderly and provide necessary information to their families, thereby ensuring the peace of mind and safety of both parties.

[0030] The conversation unit can refer to the conversation history and provide topics based on the elderly person's individual interests and hobbies. For example, the generation AI analyzes past conversation history to identify topics that interest the elderly person. For example, if the elderly person has previously expressed an interest in growing flowers, the generation AI can ask, "How are you taking care of your flowers lately?" The generation AI can also provide the latest information related to the elderly person's hobbies and interests based on past conversation history. For example, if the elderly person is interested in cooking, it can say, "I found a new recipe." The generation AI can also refer to past conversation history and remember when the elderly person talked about specific events or happenings. For example, it can ask, "How was your grandchild's birthday party last week?" This allows for more intimate communication by providing topics based on the elderly person's interests and hobbies.

[0031] The speaking part can achieve more realistic communication by using not only voice but also avatars that include facial expressions and gestures. For example, the speaking part uses an avatar in which the generation AI adds facial expressions and gestures when speaking to an elderly person. For example, the avatar may say with a smile, "Grandma, how are you?" The generation AI can also use the avatar to speak to the elderly person while making gestures such as waving. For example, it can say, "Grandma, did you go out anywhere today?" while waving. The generation AI can also use the avatar to express emotions to the elderly. For example, the avatar can say with a surprised expression, "Grandma, I see you've started a new hobby!" In this way, using avatars that include facial expressions and gestures makes it possible to achieve more realistic communication.

[0032] The interlocutor can simultaneously display family photos and video messages, strengthening the visual connection. For example, when the generation AI speaks, it will speak while displaying family photos on the screen. For example, it could say, "Grandma, this photo is from your grandchild's sports day." The generation AI can also speak while playing video messages from family members. For example, it can say, "Grandma, I have a message for you from your grandchild," and play the video message. The generation AI can also speak while displaying a family photo album in slideshow format. For example, it can say, "Grandma, this photo is from our family trip last year." This strengthens the visual connection by displaying family photos and video messages.

[0033] The conversation unit can provide local news and event information to promote social connections. For example, when the generation AI speaks, the conversation unit can provide local news. For example, it can say, "Grandma, there's going to be a fireworks display at a nearby park." Also, when the generation AI speaks, it can provide local event information. For example, it can say, "Grandma, there's going to be a local bazaar this weekend." Also, when the generation AI speaks, it can introduce local community activities. For example, it can say, "Grandma, there's going to be a reading group at a nearby library." This allows for the provision of local news and event information to promote social connections.

[0034] The recording unit can analyze the content of the conversation, extract important information about the elderly person's health condition and lifestyle habits, and provide it to family members as a report. For example, the generation AI in the recording unit analyzes the content of the conversation and extracts information about the elderly person's health condition. For example, it can send a report to the family saying, "Grandma said she hasn't had much of an appetite lately." The generation AI can also analyze the content of the conversation and extract information about the elderly person's lifestyle habits. For example, it can send a report to the family saying, "Grandma said she's been staying up late lately." The generation AI can also analyze the content of the conversation and periodically send reports to the family summarizing important information about the elderly person's health condition and lifestyle habits. For example, it can notify the family saying, "I'm sending you a report about Grandma's recent health condition and lifestyle habits." By providing important information about the elderly person's health condition and lifestyle habits to the family, the family can take appropriate action.

[0035] The recording unit can analyze the conversation content and automatically generate dietary and exercise advice to suggest to the elderly. For example, the generation AI in the recording unit can analyze the conversation content and automatically generate dietary advice for the elderly. For example, it could suggest, "Grandma, it seems you haven't been eating many vegetables lately. Try to eat more." The generation AI can also analyze the conversation content and automatically generate exercise advice for the elderly. For example, it could suggest, "Grandma, it would be good for you to take a short walk every day." The generation AI can also analyze the conversation content and periodically provide the elderly with a report summarizing dietary and exercise advice. For example, it could suggest, "For your health, we recommend the following diet and exercise." This makes health management easier by automatically generating dietary and exercise advice and suggesting it to the elderly.

[0036] The recording unit can analyze the conversation content and suggest events and activities based on the elderly person's hobbies and interests. For example, the generation AI in the recording unit analyzes the conversation content and suggests events based on the elderly person's hobbies. For example, it could suggest, "Grandma, there's a handicraft class being held nearby." The generation AI can also analyze the conversation content and suggest activities based on the elderly person's interests. For example, it could suggest, "Grandma, you said you were interested in gardening recently. There's a gardening event going on at a nearby park." The generation AI can also analyze the conversation content and periodically provide the elderly with a list of events and activities based on their hobbies and interests. For example, it could suggest, "I'll send you a list of events and activities that suit your hobbies." This can improve the quality of life of the elderly by suggesting events and activities based on their hobbies and interests.

[0037] The recording unit can analyze the content of the conversation and introduce local support groups and community activities. For example, the generation AI in the recording unit can analyze the content of the conversation and introduce local support groups to the elderly. For example, it can introduce, "Grandma, there's a support group for seniors nearby." The generation AI can also analyze the content of the conversation and introduce local community activities to the elderly. For example, it can introduce, "Grandma, there's an event this weekend at the nearby community center." The generation AI can also analyze the content of the conversation and periodically provide the elderly with a list of local support groups and community activities. For example, it can introduce, "Grandma, we'll send you a list of support groups and community activities near you." This can strengthen the social connections of the elderly by introducing them to local support groups and community activities.

[0038] The analysis unit can analyze the content of conversations and develop algorithms for early detection of signs of cognitive decline and depression. For example, the analysis unit develops an algorithm in which the generation AI analyzes the content of conversations and detects patterns that indicate cognitive decline. For example, an alert can be sent if the frequency of repeating the same story increases. The generation AI can also analyze the content of conversations and develop algorithms that detect patterns that indicate signs of depression. For example, an alert can be sent if there is an increase in negative topics. The generation AI can also analyze the content of conversations and develop algorithms that combine multiple indicators to early detect signs of cognitive decline and depression. For example, it can comprehensively evaluate memory decline and mood fluctuations. This allows the development of algorithms that early detect signs of cognitive decline and depression, enabling early and appropriate response.

[0039] The analysis unit can analyze the content of the conversation, identify risk factors for dementia and depression, and suggest preventive measures. For example, the generation AI analyzes the content of the conversation and identifies risk factors for dementia. For example, it can detect memory decline and inattention and suggest preventive measures. The generation AI can also analyze the content of the conversation and identify risk factors for depression. For example, it can detect increased negative emotions and social isolation and suggest preventive measures. The generation AI can also analyze the content of the conversation and comprehensively evaluate risk factors for dementia and depression and suggest preventive measures. For example, it can recommend regular exercise and participation in social activities. In this way, by identifying risk factors for dementia and depression and suggesting preventive measures, it becomes possible to prevent the diseases.

[0040] The analysis unit can analyze the content of the conversation and, if there is a high risk of dementia or depression, send a notification urging the family to consult a specialist. For example, the generation AI analyzes the content of the conversation and, if there is a high risk of dementia, sends a notification to the family urging them to consult a specialist. For example, the generation AI may notify the family, "There is abnormality in Grandma's cognitive function. Please consult a specialist." The generation AI can also analyze the content of the conversation and, if there is a high risk of depression, send a notification to the family urging them to consult a specialist. For example, the generation AI can notify the family, "There is abnormality in Grandma's emotional state. Please consult a specialist." The generation AI can also analyze the content of the conversation and, if there is a high risk of dementia or depression, periodically send a notification to the family urging them to consult a specialist. For example, the generation AI can notify the family, "We recommend that you consult a specialist about Grandma's health condition." This allows for early and appropriate medical treatment by sending a notification urging them to consult a specialist if there is a high risk of dementia or depression.

[0041] The analysis unit can analyze the conversation content and suggest brain training and relaxation exercises that are effective in preventing dementia and depression. For example, the analysis unit uses a generation AI to analyze the conversation content and suggest brain training exercises that are effective in preventing dementia. For example, it could suggest, "Grandma, it's a good idea to solve crossword puzzles every day." The generation AI can also analyze the conversation content and suggest relaxation exercises that are effective in preventing depression. For example, it could suggest, "Grandma, try doing relaxation exercises such as deep breathing every day." The generation AI can also analyze the conversation content and periodically provide the elderly with a list of brain training and relaxation exercises that are effective in preventing dementia and depression. For example, it could suggest, "I recommend the following exercises for Grandma's health." This makes it possible to prevent diseases by suggesting brain training and relaxation exercises that are effective in preventing dementia and depression.

[0042] The reminder unit manages medication history to prevent forgetting to take medication, and can strengthen reminders if a dose is forgotten. For example, the generation AI manages medication history and strengthens reminders if a dose is forgotten. For example, it notifies the user, "Grandma, it's time to take your medicine." The generation AI also analyzes medication history and increases the frequency of reminders if there are frequent forgettings. For example, it can periodically notify the user, "Grandma, please don't forget to take your medicine." The generation AI also manages medication history and sends notifications to family members if a dose is forgotten. For example, it can notify family members, "Grandma forgot to take her medicine." This strengthens reminders to prevent forgetting to take medication, improving the reliability of medication administration.

[0043] The reminder section can analyze the nutritional balance of meals and suggest healthy meal menus. For example, the generation AI can analyze the nutritional balance of meals and suggest healthy meal menus to the elderly. For example, it could suggest, "Grandma, it would be good for you to eat more vegetables today." The generation AI can also analyze the nutritional balance of meals and suggest meal menus to make up for nutritional deficiencies. For example, it could suggest, "Grandma, it would be good for you to eat more protein today." The generation AI can also analyze the nutritional balance of meals and periodically provide the elderly with a list of healthy meal menus. For example, it could suggest, "For your health, we recommend the following meal menu." This makes it easier for the elderly to manage their health by analyzing the nutritional balance of meals and suggesting healthy meal menus.

[0044] The reminder section can analyze drug side effects and interactions and issue a warning if there is a risk. For example, the generative AI can analyze drug side effects and issue a warning to the elderly if there is a risk. For example, it can warn, "Grandma, this drug may have side effects. Be careful." The generative AI can also analyze drug interactions and issue a warning to the elderly if there is a risk. For example, it can warn, "Grandma, it is dangerous to take this drug with other drugs. Be careful." The generative AI can also analyze drug side effects and interactions and issue a warning to family members if there is a risk. For example, it can notify family members, "There is a risk of side effects from the drug Grandma is taking." This allows safe medication to be taken by analyzing drug side effects and interactions and issuing a warning if there is a risk.

[0045] The reminder section can automatically generate a shopping list and order necessary items in conjunction with online shopping. For example, the generation AI in the reminder section analyzes the lifestyle habits of an elderly person, lists the necessary items, and automatically generates a shopping list. For example, it might suggest, "Grandma, you need milk and bread this week." The generation AI can also link the automatically generated shopping list with online shopping to automatically order the necessary items. For example, it can notify the user, "Grandma, you have ordered the necessary items online." The generation AI can also automatically generate a shopping list and share it with family members. For example, it can notify the user, "Grandma has sent a list of necessary items to the family." This automatically generating a shopping list and linking it with online shopping to order the necessary items improves the convenience of daily life.

[0046] The camera linkage unit can analyze camera footage, detect movement patterns that are a sign of a fall, and send preventative alerts. In the camera linkage unit, for example, the generation AI analyzes camera footage and detects movement patterns that are a sign of a fall. For example, an alert is sent if an elderly person makes a movement that could cause them to lose their balance. The generation AI can also analyze camera footage and detect movement patterns that pose a high risk of falling. For example, an alert can be sent if an elderly person suddenly stands up. The generation AI can also analyze camera footage, detect movement patterns that are a sign of a fall, and send preventative alerts to family members. For example, it can notify family members that "Grandma is making a movement that could cause her to fall." In this way, by detecting movement patterns that are a sign of a fall and sending preventative alerts, it is possible to prevent falls from occurring.

[0047] After the camera linkage unit detects a fall, the generating AI can automatically execute emergency response procedures and provide the necessary support. For example, the camera linkage unit automatically executes emergency response procedures after the generating AI detects a fall. For example, it can contact the family and the hospital immediately after detecting a fall. Also, after the generating AI detects a fall, it can automatically execute emergency response procedures and provide the necessary support. For example, it can call an ambulance immediately after detecting a fall. Also, after the generating AI detects a fall, it can automatically execute emergency response procedures and report the detailed situation to the family. For example, it can notify the family, "Grandma has fallen. Please check the current situation." This enables a rapid response by automatically executing emergency response procedures and providing the necessary support after detecting a fall.

[0048] The camera linkage unit can analyze camera footage, learn movement patterns in daily life, and send an alert if abnormal movement is detected. In the camera linkage unit, for example, the generation AI analyzes camera footage and learns movement patterns in daily life. For example, an alert is sent if an elderly person moves differently from usual. The generation AI can also analyze camera footage and send an alert if abnormal movement is detected. For example, it can notify if an elderly person walks in an unusual way. The generation AI can also analyze camera footage, learn movement patterns in daily life, and send an alert to family members if abnormal movement is detected. For example, it can notify, "Grandma has moved differently than usual." In this way, by learning movement patterns in daily life and sending an alert if abnormal movement is detected, abnormal situations can be detected early.

[0049] The camera linking unit can link the fall detection system with other home security systems to provide comprehensive safety measures. In the camera linking unit, for example, the generation AI links the fall detection system with other home security systems to provide comprehensive safety measures. For example, it activates the door lock system upon detecting a fall. The generation AI also links the fall detection system with other home security systems to automatically turn on lights in an emergency. For example, it can turn on the lights in a room upon detecting a fall. The generation AI also links the fall detection system with other home security systems to provide comprehensive safety measures. For example, it can send security camera footage to family members upon detecting a fall. In this way, by linking the fall detection system with other home security systems, comprehensive safety measures are possible.

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

[0051] The health management system can further include a voice recognition unit. The voice recognition unit can analyze the elderly person's voice and evaluate the clarity and speed of speech. For example, if the elderly person's speech becomes unclear, a notification can be sent to family members. Also, if the speaking rate slows, an alert can be sent as a sign of cognitive decline. Furthermore, the voice recognition unit can analyze the content of speech and detect specific keywords and phrases, allowing for early detection of changes in health status. This makes it possible to monitor health status through changes in speech using the voice recognition unit.

[0052] The health management system may further include an exercise monitoring unit. The exercise monitoring unit can record the amount of daily exercise an elderly person performs and detect lack of exercise. For example, it can use a pedometer to record the number of steps taken each day and send a reminder if the target number of steps has not been reached. The exercise monitoring unit can also suggest a specific exercise program and monitor the progress of the program. For example, it can suggest daily stretching or light exercises and record the progress of the program. Furthermore, the exercise monitoring unit can evaluate the effectiveness of the exercise and report any improvements in health to family members. In this way, the exercise monitoring unit can support daily exercise habits and help maintain and improve health.

[0053] The health management system can further include a dietary management unit. The dietary management unit can record the elderly person's dietary content and evaluate nutritional balance. For example, it can take photos of meals and calculate nutrient intake using image analysis technology. The dietary management unit can also suggest meal menus to improve nutritional imbalance if the nutritional balance is unbalanced. For example, if the elderly person is not consuming enough vegetables, it can suggest, "Eat more vegetables today." Furthermore, the dietary management unit can report the dietary content to family members and share the progress of dietary improvements. In this way, the dietary management unit can support a nutritionally balanced diet and maintain and improve health.

[0054] The health management system can further include a sleep monitoring unit. The sleep monitoring unit can record the sleep patterns of the elderly and evaluate their sleep quality. For example, it can use a sensor to detect movements during sleep and analyze the depth of sleep and the frequency of interruptions. The sleep monitoring unit can also provide advice for improving sleep quality if the quality of sleep is declining. For example, it can suggest, "Grandma, listen to some relaxing music before bed." Furthermore, the sleep monitoring unit can report sleep data to family members and share the progress of sleep improvement. In this way, the sleep monitoring unit can support quality sleep and maintain and improve health.

[0055] The health management system may further include a social participation promotion unit. The social participation promotion unit may provide opportunities for elderly people to participate in local events and community activities. For example, it may collect information about local events and suggest, "Grandma, there's a handicraft class nearby." The social participation promotion unit may also keep a record of events and activities participated in and report them to family members. For example, it may notify family members that "Grandma participated in a handicraft class." Furthermore, the social participation promotion unit may record impressions of events and activities participated in and provide feedback to increase motivation to participate next time. In this way, the social participation promotion unit may be used to strengthen the social connections of elderly people and support their psychological health.

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

[0057] Step 1: The conversational part speaks to the elderly in the voice of a child or grandchild. For example, the AI ​​generator might say, "Grandma, how was your day?", "Grandma, how are you?", or "Grandma, how are you doing these days?" Step 2: The recording unit records the conversation conducted by the speaking unit. For example, the generation AI saves the conversation content as text data, audio data, and video data. Step 3: The analysis unit analyzes the conversation recorded by the recording unit to detect signs of dementia or depression. For example, the generative AI uses natural language processing technology to detect specific keywords and phrases, machine learning algorithms to detect behavioral patterns, and emotion analysis technology to detect changes in emotions. Step 4: The notification unit notifies the family of any signs of dementia or depression detected by the analysis unit. For example, the generation AI notifies the family via email, app notification, or SMS. Step 5: The reminder section provides reminders to prevent forgetting to take medicine. For example, the generative AI may notify the user, "Grandma, it's time to take your medicine," "Grandma, don't forget to take your medicine," or "Grandma, don't forget to take your medicine." Step 6: The camera linkage unit works with the camera to detect when an elderly person falls and notify the family and hospital. For example, the generation AI analyzes the camera footage, and if it detects a fall, it sends an alert to the family and hospital, and calls an ambulance if necessary.

[0058] (Example 2) The health management system according to an embodiment of the present invention uses generative AI to support health management and communication for the elderly and provide necessary information to their families. As a result, the health management system supports health management and communication for the elderly and provides necessary information to their families, ensuring the peace of mind and safety of both parties.

[0059] A health management system according to an embodiment includes a conversation unit, a recording unit, an analysis unit, a notification unit, a reminder unit, and a camera linkage unit. The conversation unit speaks to an elderly person using the voice of a child or grandchild. For example, the generation AI speaks to the elderly person using the voice of a child or grandchild, asking, "Grandma, how was your day?" The generation AI can also speak to the elderly person using the voice of a child or grandchild, asking, "Grandma, how are you?" The generation AI can also speak to the elderly person using the voice of a child or grandchild, asking, "Grandma, how are you doing?" The recording unit records the conversation content conducted by the conversation unit. For example, the generation AI can save the conversation content as text data. The generation AI can also save the conversation content as audio data. The generation AI can also save the conversation content as video data. The analysis unit analyzes the conversation content recorded by the recording unit to detect signs of dementia or depression. For example, the generation AI can analyze the conversation content using natural language processing technology to detect specific keywords and phrases. The generation AI can also analyze the conversation content using machine learning algorithms to detect behavioral patterns. The generation AI can also analyze the conversation content using emotion analysis technology to detect changes in emotions. The notification unit notifies family members of signs of dementia or depression detected by the analysis unit. For example, the generation AI can notify family members by email. The generation AI can also send app notifications to family members. The generation AI can also notify family members by SMS. The reminder unit provides reminders to prevent forgetting to take medication. For example, the generation AI can notify, "Grandma, it's almost time to take your medicine." The generation AI can also notify, "Grandma, don't forget to take your medicine." The camera linkage unit works with the camera to detect falls by the elderly person and contact the family and hospital. For example, the generation AI can analyze camera footage and send an alert to the family if a fall is detected. The generation AI can also analyze camera footage and send an alert to the hospital if a fall is detected. The generation AI can also analyze camera footage and call an ambulance if a fall is detected.As a result, the health management system according to the embodiment can support the health management and communication of the elderly and provide necessary information to their families, thereby ensuring the peace of mind and safety of both parties.

[0060] The conversation unit can refer to the conversation history and provide topics based on the elderly person's individual interests and hobbies. For example, the generation AI analyzes past conversation history to identify topics that interest the elderly person. For example, if the elderly person has previously expressed an interest in growing flowers, the generation AI can ask, "How are you taking care of your flowers lately?" The generation AI can also provide the latest information related to the elderly person's hobbies and interests based on past conversation history. For example, if the elderly person is interested in cooking, it can say, "I found a new recipe." The generation AI can also refer to past conversation history and remember when the elderly person talked about specific events or happenings. For example, it can ask, "How was your grandchild's birthday party last week?" This allows for more intimate communication by providing topics based on the elderly person's interests and hobbies.

[0061] The speaking part can achieve more realistic communication by using not only voice but also avatars that include facial expressions and gestures. For example, the speaking part uses an avatar in which the generation AI adds facial expressions and gestures when speaking to an elderly person. For example, the avatar may say with a smile, "Grandma, how are you?" The generation AI can also use the avatar to speak to the elderly person while making gestures such as waving. For example, it can say, "Grandma, did you go out anywhere today?" while waving. The generation AI can also use the avatar to express emotions to the elderly. For example, the avatar can say with a surprised expression, "Grandma, I see you've started a new hobby!" In this way, using avatars that include facial expressions and gestures makes it possible to achieve more realistic communication.

[0062] The conversation unit uses the emotion estimation function to analyze the elderly person's emotional state in real time and can offer words of encouragement or comfort at the appropriate time. For example, the generation AI uses the emotion estimation function to analyze the elderly person's tone of voice and facial expressions to grasp their emotional state in real time. For example, if the elderly person sounds sad, the generation AI can offer words of encouragement such as, "Grandma, are you okay?" The generation AI also uses the emotion estimation function to select appropriate words according to the elderly person's emotional state. For example, if the elderly person appears tired, the generation AI can offer words of comfort such as, "Grandma, you should get some rest." The generation AI also uses the emotion estimation function to analyze the elderly person's emotional state and provide topics to elicit positive emotions. For example, if the elderly person is feeling down, the generation AI can ask, "Grandma, what have you enjoyed recently?" This makes it possible to provide psychological support by offering appropriate words according to the elderly person's emotional state.

[0063] The interlocutor can simultaneously display family photos and video messages, strengthening the visual connection. For example, when the generation AI speaks, it will speak while displaying family photos on the screen. For example, it could say, "Grandma, this photo is from your grandchild's sports day." The generation AI can also speak while playing video messages from family members. For example, it can say, "Grandma, I have a message for you from your grandchild," and play the video message. The generation AI can also speak while displaying a family photo album in slideshow format. For example, it can say, "Grandma, this photo is from our family trip last year." This strengthens the visual connection by displaying family photos and video messages.

[0064] The conversation unit can provide local news and event information to promote social connections. For example, when the generation AI speaks, the conversation unit can provide local news. For example, it can say, "Grandma, there's going to be a fireworks display at a nearby park." Also, when the generation AI speaks, it can provide local event information. For example, it can say, "Grandma, there's going to be a local bazaar this weekend." Also, when the generation AI speaks, it can introduce local community activities. For example, it can say, "Grandma, there's going to be a reading group at a nearby library." This allows for the provision of local news and event information to promote social connections.

[0065] The conversation unit can use the emotion estimation function to automatically play music or relaxation content that corresponds to the elderly person's emotional state. For example, the conversation unit uses the emotion estimation function to analyze the elderly person's emotional state and automatically play relaxing music. For example, if the elderly person is feeling stressed, the conversation unit can say, "Grandma, I'll play some relaxing music," and play music. The conversation unit can also use the emotion estimation function to provide relaxation content that corresponds to the elderly person's emotional state. For example, if the elderly person is feeling anxious, the conversation unit can say, "Grandma, let's watch a relaxation video," and play a video. The conversation unit can also use the emotion estimation function to grasp the elderly person's emotional state in real time and select appropriate relaxation content. For example, if the elderly person is tired, the conversation unit can say, "Grandma, let's watch a relaxing yoga video," and play a video. This allows for psychological support by automatically playing music or relaxation content that corresponds to the elderly person's emotional state.

[0066] The recording unit can analyze the content of the conversation, extract important information about the elderly person's health condition and lifestyle habits, and provide it to family members as a report. For example, the generation AI in the recording unit analyzes the content of the conversation and extracts information about the elderly person's health condition. For example, it can send a report to the family saying, "Grandma said she hasn't had much of an appetite lately." The generation AI can also analyze the content of the conversation and extract information about the elderly person's lifestyle habits. For example, it can send a report to the family saying, "Grandma said she's been staying up late lately." The generation AI can also analyze the content of the conversation and periodically send reports to the family summarizing important information about the elderly person's health condition and lifestyle habits. For example, it can notify the family saying, "I'm sending you a report about Grandma's recent health condition and lifestyle habits." By providing important information about the elderly person's health condition and lifestyle habits to the family, the family can take appropriate action.

[0067] The recording unit can analyze the conversation content and automatically generate dietary and exercise advice to suggest to the elderly. For example, the generation AI in the recording unit can analyze the conversation content and automatically generate dietary advice for the elderly. For example, it could suggest, "Grandma, it seems you haven't been eating many vegetables lately. Try to eat more." The generation AI can also analyze the conversation content and automatically generate exercise advice for the elderly. For example, it could suggest, "Grandma, it would be good for you to take a short walk every day." The generation AI can also analyze the conversation content and periodically provide the elderly with a report summarizing dietary and exercise advice. For example, it could suggest, "For your health, we recommend the following diet and exercise." This makes health management easier by automatically generating dietary and exercise advice and suggesting it to the elderly.

[0068] The recording unit can use the emotion estimation function to track changes in emotions during conversations and notify family members of signs of stress or anxiety. For example, the generation AI in the recording unit uses the emotion estimation function to track changes in the elderly person's emotions during conversations in real time. For example, if the elderly person is feeling stressed, the generation AI can notify the family member, "Grandma seems to be feeling stressed lately." The generation AI can also use the emotion estimation function to detect signs of anxiety during conversations. For example, if the elderly person is feeling anxious, the generation AI can notify the family member, "Grandma seems to be feeling anxious lately." The generation AI can also use the emotion estimation function to track changes in the elderly person's emotions during conversations and periodically send reports summarizing signs of stress and anxiety to the family member. For example, the generation AI can notify the family member, "I'm sending you a report on Grandma's recent emotional state." This allows the generation AI to track changes in emotions during conversations and notify family members of signs of stress or anxiety, enabling early and appropriate response.

[0069] The recording unit can analyze the conversation content and suggest events and activities based on the elderly person's hobbies and interests. For example, the generation AI in the recording unit analyzes the conversation content and suggests events based on the elderly person's hobbies. For example, it could suggest, "Grandma, there's a handicraft class being held nearby." The generation AI can also analyze the conversation content and suggest activities based on the elderly person's interests. For example, it could suggest, "Grandma, you said you were interested in gardening recently. There's a gardening event going on at a nearby park." The generation AI can also analyze the conversation content and periodically provide the elderly with a list of events and activities based on their hobbies and interests. For example, it could suggest, "I'll send you a list of events and activities that suit your hobbies." This can improve the quality of life of the elderly by suggesting events and activities based on their hobbies and interests.

[0070] The recording unit can analyze the content of the conversation and introduce local support groups and community activities. For example, the generation AI in the recording unit can analyze the content of the conversation and introduce local support groups to the elderly. For example, it can introduce, "Grandma, there's a support group for seniors nearby." The generation AI can also analyze the content of the conversation and introduce local community activities to the elderly. For example, it can introduce, "Grandma, there's an event this weekend at the nearby community center." The generation AI can also analyze the content of the conversation and periodically provide the elderly with a list of local support groups and community activities. For example, it can introduce, "Grandma, we'll send you a list of support groups and community activities near you." This can strengthen the social connections of the elderly by introducing them to local support groups and community activities.

[0071] The recording unit can use the emotion estimation function to automatically suggest topics that will elicit positive emotions during a conversation. For example, the generation AI in the recording unit can use the emotion estimation function to analyze the emotional state of an elderly person during a conversation and suggest topics that will elicit positive emotions. For example, it can provide topics that will make the elderly person smile. The generation AI can also use the emotion estimation function to automatically select positive topics that correspond to the elderly person's emotional state during a conversation. For example, it can also provide topics that the elderly person is interested in. The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state during a conversation in real time and provide topics that will elicit positive emotions. For example, it can talk about the elderly person's favorite music or movies. In this way, the psychological health of the elderly can be supported by automatically suggesting topics that will elicit positive emotions.

[0072] The analysis unit can analyze the content of conversations and develop algorithms for early detection of signs of cognitive decline and depression. For example, the analysis unit develops an algorithm in which the generation AI analyzes the content of conversations and detects patterns that indicate cognitive decline. For example, an alert can be sent if the frequency of repeating the same story increases. The generation AI can also analyze the content of conversations and develop algorithms that detect patterns that indicate signs of depression. For example, an alert can be sent if there is an increase in negative topics. The generation AI can also analyze the content of conversations and develop algorithms that combine multiple indicators to early detect signs of cognitive decline and depression. For example, it can comprehensively evaluate memory decline and mood fluctuations. This allows the development of algorithms that early detect signs of cognitive decline and depression, enabling early and appropriate response.

[0073] The analysis unit can analyze the content of the conversation, identify risk factors for dementia and depression, and suggest preventive measures. For example, the generation AI analyzes the content of the conversation and identifies risk factors for dementia. For example, it can detect memory decline and inattention and suggest preventive measures. The generation AI can also analyze the content of the conversation and identify risk factors for depression. For example, it can detect increased negative emotions and social isolation and suggest preventive measures. The generation AI can also analyze the content of the conversation and comprehensively evaluate risk factors for dementia and depression and suggest preventive measures. For example, it can recommend regular exercise and participation in social activities. In this way, by identifying risk factors for dementia and depression and suggesting preventive measures, it becomes possible to prevent the diseases.

[0074] The analysis unit can use the emotion estimation function to analyze patterns of emotional fluctuations and send an alert if abnormal fluctuations are detected. In the analysis unit, for example, the generation AI uses the emotion estimation function to analyze patterns of emotional fluctuations of an elderly person in real time. For example, an alert is sent if a sudden change in emotion is detected. The generation AI can also use the emotion estimation function to track patterns of emotional fluctuations over a long period of time and send an alert if abnormal fluctuations are detected. For example, a notification can be sent if emotional stability is lost. The generation AI can also use the emotion estimation function to analyze patterns of emotional fluctuations and send an alert to family members or medical institutions if abnormal fluctuations are detected. For example, a notification can be sent if signs of depression are observed. In this way, by analyzing patterns of emotional fluctuations and sending an alert if abnormal fluctuations are detected, early and appropriate action can be taken.

[0075] The analysis unit can analyze the content of the conversation and, if there is a high risk of dementia or depression, send a notification urging the family to consult a specialist. For example, the generation AI analyzes the content of the conversation and, if there is a high risk of dementia, sends a notification to the family urging them to consult a specialist. For example, the generation AI may notify the family, "There is abnormality in Grandma's cognitive function. Please consult a specialist." The generation AI can also analyze the content of the conversation and, if there is a high risk of depression, send a notification to the family urging them to consult a specialist. For example, the generation AI can notify the family, "There is abnormality in Grandma's emotional state. Please consult a specialist." The generation AI can also analyze the content of the conversation and, if there is a high risk of dementia or depression, periodically send a notification to the family urging them to consult a specialist. For example, the generation AI can notify the family, "We recommend that you consult a specialist about Grandma's health condition." This allows for early and appropriate medical treatment by sending a notification urging them to consult a specialist if there is a high risk of dementia or depression.

[0076] The analysis unit can analyze the conversation content and suggest brain training and relaxation exercises that are effective in preventing dementia and depression. For example, the analysis unit uses a generation AI to analyze the conversation content and suggest brain training exercises that are effective in preventing dementia. For example, it could suggest, "Grandma, it's a good idea to solve crossword puzzles every day." The generation AI can also analyze the conversation content and suggest relaxation exercises that are effective in preventing depression. For example, it could suggest, "Grandma, try doing relaxation exercises such as deep breathing every day." The generation AI can also analyze the conversation content and periodically provide the elderly with a list of brain training and relaxation exercises that are effective in preventing dementia and depression. For example, it could suggest, "I recommend the following exercises for Grandma's health." This makes it possible to prevent diseases by suggesting brain training and relaxation exercises that are effective in preventing dementia and depression.

[0077] The analysis unit can use the emotion estimation function to provide mental health support according to emotional fluctuations. For example, the generation AI in the analysis unit uses the emotion estimation function to provide mental health support according to the emotional fluctuations of the elderly person. For example, if the elderly person is feeling stressed, the generation AI can say, "Grandma, I'll play some relaxing music," and play music. The generation AI can also use the emotion estimation function to suggest counseling according to the elderly person's emotional fluctuations. For example, if the elderly person is feeling anxious, the generation AI can suggest, "Grandma, why don't you talk to a counselor?" The generation AI can also use the emotion estimation function to periodically provide a list of mental health support according to the elderly person's emotional fluctuations. For example, the generation AI can suggest, "I'll send you a list of mental health support according to Grandma's emotional state." This allows the psychological health of the elderly to be supported by providing mental health support according to emotional fluctuations.

[0078] The reminder unit manages medication history to prevent forgetting to take medication, and can strengthen reminders if a dose is forgotten. For example, the generation AI manages medication history and strengthens reminders if a dose is forgotten. For example, it notifies the user, "Grandma, it's time to take your medicine." The generation AI also analyzes medication history and increases the frequency of reminders if there are frequent forgettings. For example, it can periodically notify the user, "Grandma, please don't forget to take your medicine." The generation AI also manages medication history and sends notifications to family members if a dose is forgotten. For example, it can notify family members, "Grandma forgot to take her medicine." This strengthens reminders to prevent forgetting to take medication, improving the reliability of medication administration.

[0079] The reminder section can analyze the nutritional balance of meals and suggest healthy meal menus. For example, the generation AI can analyze the nutritional balance of meals and suggest healthy meal menus to the elderly. For example, it could suggest, "Grandma, it would be good for you to eat more vegetables today." The generation AI can also analyze the nutritional balance of meals and suggest meal menus to make up for nutritional deficiencies. For example, it could suggest, "Grandma, it would be good for you to eat more protein today." The generation AI can also analyze the nutritional balance of meals and periodically provide the elderly with a list of healthy meal menus. For example, it could suggest, "For your health, we recommend the following meal menu." This makes it easier for the elderly to manage their health by analyzing the nutritional balance of meals and suggesting healthy meal menus.

[0080] The reminder unit can use the emotion estimation function to provide dietary and exercise advice based on the elderly person's emotional state. For example, the generation AI can use the emotion estimation function to analyze the elderly person's emotional state and provide dietary advice based on that emotional state. For example, if the elderly person is feeling stressed, the generation AI can suggest, "Grandma, eat a relaxing meal." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state and provide exercise advice based on that emotional state. For example, if the elderly person is tired, the generation AI can suggest, "Grandma, it would be good for you to do some light stretching." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state in real time and provide dietary and exercise advice based on that emotional state. For example, if the elderly person is feeling anxious, the generation AI can suggest, "Grandma, do some relaxing yoga." This makes health management for the elderly more effective by providing dietary and exercise advice based on their emotional state.

[0081] The reminder section can analyze drug side effects and interactions and issue a warning if there is a risk. For example, the generative AI can analyze drug side effects and issue a warning to the elderly if there is a risk. For example, it can warn, "Grandma, this drug may have side effects. Be careful." The generative AI can also analyze drug interactions and issue a warning to the elderly if there is a risk. For example, it can warn, "Grandma, it is dangerous to take this drug with other drugs. Be careful." The generative AI can also analyze drug side effects and interactions and issue a warning to family members if there is a risk. For example, it can notify family members, "There is a risk of side effects from the drug Grandma is taking." This allows safe medication to be taken by analyzing drug side effects and interactions and issuing a warning if there is a risk.

[0082] The reminder section can automatically generate a shopping list and order necessary items in conjunction with online shopping. For example, the generation AI in the reminder section analyzes the lifestyle habits of an elderly person, lists the necessary items, and automatically generates a shopping list. For example, it might suggest, "Grandma, you need milk and bread this week." The generation AI can also link the automatically generated shopping list with online shopping to automatically order the necessary items. For example, it can notify the user, "Grandma, you have ordered the necessary items online." The generation AI can also automatically generate a shopping list and share it with family members. For example, it can notify the user, "Grandma has sent a list of necessary items to the family." This automatically generating a shopping list and linking it with online shopping to order the necessary items improves the convenience of daily life.

[0083] The reminder unit can use the emotion estimation function to suggest relaxation and stress relief activities based on the elderly person's emotional state. For example, the generation AI in the reminder unit uses the emotion estimation function to analyze the elderly person's emotional state and suggest relaxation activities. For example, if the elderly person is feeling stressed, the generation AI can suggest, "Grandma, let's listen to some relaxing music." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state and suggest stress relief activities. For example, if the elderly person is feeling anxious, the generation AI can suggest, "Grandma, it would be good for you to go for a walk." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state in real time and suggest relaxation and stress relief activities based on their emotional state. For example, if the elderly person is tired, the generation AI can suggest, "Grandma, let's do some yoga." This supports the psychological health of the elderly by suggesting relaxation and stress relief activities based on their emotional state.

[0084] The camera linkage unit can analyze camera footage, detect movement patterns that are a sign of a fall, and send preventative alerts. In the camera linkage unit, for example, the generation AI analyzes camera footage and detects movement patterns that are a sign of a fall. For example, an alert is sent if an elderly person makes a movement that could cause them to lose their balance. The generation AI can also analyze camera footage and detect movement patterns that pose a high risk of falling. For example, an alert can be sent if an elderly person suddenly stands up. The generation AI can also analyze camera footage, detect movement patterns that are a sign of a fall, and send preventative alerts to family members. For example, it can notify family members that "Grandma is making a movement that could cause her to fall." In this way, by detecting movement patterns that are a sign of a fall and sending preventative alerts, it is possible to prevent falls from occurring.

[0085] After the camera linkage unit detects a fall, the generating AI can automatically execute emergency response procedures and provide the necessary support. For example, the camera linkage unit automatically executes emergency response procedures after the generating AI detects a fall. For example, it can contact the family and the hospital immediately after detecting a fall. Also, after the generating AI detects a fall, it can automatically execute emergency response procedures and provide the necessary support. For example, it can call an ambulance immediately after detecting a fall. Also, after the generating AI detects a fall, it can automatically execute emergency response procedures and report the detailed situation to the family. For example, it can notify the family, "Grandma has fallen. Please check the current situation." This enables a rapid response by automatically executing emergency response procedures and providing the necessary support after detecting a fall.

[0086] The camera-linked unit can use the emotion estimation function to provide psychological support and a sense of security after a fall. For example, the generation AI in the camera-linked unit uses the emotion estimation function to analyze the emotional state of an elderly person after a fall and provide psychological support. For example, if an elderly person is feeling anxious, the generation AI can reassure them by saying, "Grandma, are you okay? Help will be here soon." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state after a fall in real time and provide appropriate support. For example, if an elderly person is panicking, the generation AI can instruct them to "Grandma, take a deep breath and stay calm." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state after a fall and provide a message to reassure them. For example, the generation AI can say, "Grandma, don't worry, your family will be here soon." This provides psychological support and reassurance after a fall, thereby reducing the elderly person's anxiety.

[0087] The camera linkage unit can analyze camera footage, learn movement patterns in daily life, and send an alert if abnormal movement is detected. In the camera linkage unit, for example, the generation AI analyzes camera footage and learns movement patterns in daily life. For example, an alert is sent if an elderly person moves differently from usual. The generation AI can also analyze camera footage and send an alert if abnormal movement is detected. For example, it can notify if an elderly person walks in an unusual way. The generation AI can also analyze camera footage, learn movement patterns in daily life, and send an alert to family members if abnormal movement is detected. For example, it can notify, "Grandma has moved differently than usual." In this way, by learning movement patterns in daily life and sending an alert if abnormal movement is detected, abnormal situations can be detected early.

[0088] The camera linking unit can link the fall detection system with other home security systems to provide comprehensive safety measures. In the camera linking unit, for example, the generation AI links the fall detection system with other home security systems to provide comprehensive safety measures. For example, it activates the door lock system upon detecting a fall. The generation AI also links the fall detection system with other home security systems to automatically turn on lights in an emergency. For example, it can turn on the lights in a room upon detecting a fall. The generation AI also links the fall detection system with other home security systems to provide comprehensive safety measures. For example, it can send security camera footage to family members upon detecting a fall. In this way, by linking the fall detection system with other home security systems, comprehensive safety measures are possible.

[0089] The camera linkage unit can use the emotion estimation function to provide relaxation content to reduce stress and anxiety after a fall. For example, the generation AI in the camera linkage unit uses the emotion estimation function to analyze the emotional state of an elderly person after a fall and provide relaxation content. For example, if the elderly person is feeling stressed, the generation AI can suggest, "Grandma, let's listen to some relaxing music." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state after a fall in real time and provide appropriate relaxation content. For example, if the elderly person is feeling anxious, the generation AI can suggest, "Grandma, let's watch a relaxation video." The generation AI can also use the emotion estimation function to analyze the elderly person's emotional state after a fall and provide relaxation content to reduce stress and anxiety. For example, the generation AI can suggest, "Grandma, let's watch a relaxing yoga video." This allows the generation AI to support the elderly person's psychological health by providing relaxation content to reduce stress and anxiety after a fall.

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

[0091] The health management system can further include a voice recognition unit. The voice recognition unit can analyze the elderly person's voice and evaluate the clarity and speed of speech. For example, if the elderly person's speech becomes unclear, a notification can be sent to family members. Also, if the speaking rate slows, an alert can be sent as a sign of cognitive decline. Furthermore, the voice recognition unit can analyze the content of speech and detect specific keywords and phrases, allowing for early detection of changes in health status. This makes it possible to monitor health status through changes in speech using the voice recognition unit.

[0092] The health management system may further include an exercise monitoring unit. The exercise monitoring unit can record the amount of daily exercise an elderly person performs and detect lack of exercise. For example, it can use a pedometer to record the number of steps taken each day and send a reminder if the target number of steps has not been reached. The exercise monitoring unit can also suggest a specific exercise program and monitor the progress of the program. For example, it can suggest daily stretching or light exercises and record the progress of the program. Furthermore, the exercise monitoring unit can evaluate the effectiveness of the exercise and report any improvements in health to family members. In this way, the exercise monitoring unit can support daily exercise habits and help maintain and improve health.

[0093] The health management system can further include a dietary management unit. The dietary management unit can record the elderly person's dietary content and evaluate nutritional balance. For example, it can take photos of meals and calculate nutrient intake using image analysis technology. The dietary management unit can also suggest meal menus to improve nutritional imbalance if the nutritional balance is unbalanced. For example, if the elderly person is not consuming enough vegetables, it can suggest, "Eat more vegetables today." Furthermore, the dietary management unit can report the dietary content to family members and share the progress of dietary improvements. In this way, the dietary management unit can support a nutritionally balanced diet and maintain and improve health.

[0094] The health management system can further include a sleep monitoring unit. The sleep monitoring unit can record the sleep patterns of the elderly and evaluate their sleep quality. For example, it can use a sensor to detect movements during sleep and analyze the depth of sleep and the frequency of interruptions. The sleep monitoring unit can also provide advice for improving sleep quality if the quality of sleep is declining. For example, it can suggest, "Grandma, listen to some relaxing music before bed." Furthermore, the sleep monitoring unit can report sleep data to family members and share the progress of sleep improvement. In this way, the sleep monitoring unit can support quality sleep and maintain and improve health.

[0095] The health management system may further include a social participation promotion unit. The social participation promotion unit may provide opportunities for elderly people to participate in local events and community activities. For example, it may collect information about local events and suggest, "Grandma, there's a handicraft class nearby." The social participation promotion unit may also keep a record of events and activities participated in and report them to family members. For example, it may notify family members that "Grandma participated in a handicraft class." Furthermore, the social participation promotion unit may record impressions of events and activities participated in and provide feedback to increase motivation to participate next time. In this way, the social participation promotion unit may be used to strengthen the social connections of elderly people and support their psychological health.

[0096] The health management system can further use the emotion estimation function to provide relaxation content according to the elderly person's emotional state. For example, if an elderly person is feeling stressed, the system can suggest, "Grandma, let's listen to some relaxing music." The emotion estimation function can also be used to provide relaxation exercises according to the elderly person's emotional state. For example, if an elderly person is feeling anxious, the system can suggest, "Grandma, let's do some deep breathing relaxation exercises." The emotion estimation function can also be used to analyze the elderly person's emotional state in real time and provide appropriate relaxation content. For example, if an elderly person is tired, the system can suggest, "Grandma, let's watch a relaxing yoga video." In this way, the emotion estimation function can provide relaxation content according to the elderly person's emotional state and support their psychological health.

[0097] The health management system can further use the emotion estimation function to provide mental health support tailored to the elderly person's emotional state. For example, if an elderly person is feeling stressed, the system can say, "Grandma, I'll play some relaxing music," and play music accordingly. The emotion estimation function can also be used to suggest counseling tailored to the elderly person's emotional state. For example, if an elderly person is feeling anxious, the system can suggest, "Grandma, why don't you talk to a counselor?" The emotion estimation function can also be used to analyze the elderly person's emotional state in real time and provide appropriate mental health support. For example, if an elderly person is feeling depressed, the system can say, "Grandma, try to remember something fun you've had recently." This makes it possible to provide mental health support tailored to the elderly person's emotional state and support their psychological health.

[0098] The health management system can also use the emotion estimation function to suggest exercise programs tailored to the elderly person's emotional state. For example, if an elderly person is feeling stressed, the system can suggest, "Grandma, let's do some relaxing yoga." The emotion estimation function can also be used to suggest walking programs tailored to the elderly person's emotional state. For example, if an elderly person is tired, the system can suggest, "Grandma, it would be good for you to take a light walk." The emotion estimation function can also be used to analyze the elderly person's emotional state in real time and provide an appropriate exercise program. For example, if an elderly person is feeling anxious, the system can suggest, "Grandma, let's do some relaxing stretches." This makes it possible to provide exercise programs tailored to the elderly person's emotional state, thereby helping to maintain and improve their health.

[0099] The health management system can also use the emotion estimation function to suggest a meal menu based on the emotional state of the elderly person. For example, if the elderly person is feeling stressed, the system can suggest, "Grandma, let's eat a relaxing meal." The emotion estimation function can also be used to suggest a nutritionally balanced meal menu based on the elderly person's emotional state. For example, if the elderly person is tired, the system can suggest, "Grandma, let's eat more protein to replenish our energy." The emotion estimation function can also be used to analyze the elderly person's emotional state in real time and suggest an appropriate meal menu. For example, if the elderly person is feeling anxious, the system can suggest, "Grandma, let's drink some relaxing herbal tea." This makes it possible to suggest a meal menu based on the elderly person's emotional state, thereby maintaining and improving their health.

[0100] The health management system can also use emotion estimation to suggest hobbies and activities based on the elderly person's emotional state. For example, if an elderly person is feeling stressed, the system can suggest, "Grandma, let's start doing some relaxing arts and crafts." The emotion estimation function can also be used to suggest social activities based on the elderly person's emotional state. For example, if an elderly person is feeling lonely, the system can suggest, "Grandma, why not join a local support group." Furthermore, the emotion estimation function can analyze the elderly person's emotional state in real time and suggest appropriate hobbies and activities. For example, if an elderly person is feeling depressed, the system can suggest, "Grandma, let's try remembering something fun you've done recently." This makes it possible to use the emotion estimation function to suggest hobbies and activities based on the elderly person's emotional state and support their psychological health.

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

[0102] Step 1: The conversational part speaks to the elderly in the voice of a child or grandchild. For example, the AI ​​generator might say, "Grandma, how was your day?", "Grandma, how are you?", or "Grandma, how are you doing these days?" Step 2: The recording unit records the conversation conducted by the speaking unit. For example, the generation AI saves the conversation content as text data, audio data, and video data. Step 3: The analysis unit analyzes the conversation recorded by the recording unit to detect signs of dementia or depression. For example, the generative AI uses natural language processing technology to detect specific keywords and phrases, machine learning algorithms to detect behavioral patterns, and emotion analysis technology to detect changes in emotions. Step 4: The notification unit notifies the family of any signs of dementia or depression detected by the analysis unit. For example, the generation AI notifies the family via email, app notification, or SMS. Step 5: The reminder section provides reminders to prevent forgetting to take medicine. For example, the generative AI may notify the user, "Grandma, it's time to take your medicine," "Grandma, don't forget to take your medicine," or "Grandma, don't forget to take your medicine." Step 6: The camera linkage unit works with the camera to detect when an elderly person falls and notify the family and hospital. For example, the generation AI analyzes the camera footage, and if it detects a fall, it sends an alert to the family and hospital, and calls an ambulance if necessary.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the 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.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0147] 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. This system uses generative AI to support health management and communication for the elderly and provides necessary information to their families. A talking part that speaks in the voices of children and grandchildren, a recording unit that records the content of the conversation carried out by the speaking unit; an analysis unit that analyzes the conversation content recorded by the recording unit and detects signs of dementia or depression; a notification unit that notifies a family member of signs of dementia or depression detected by the analysis unit; A reminder section to prevent forgetting to take medicine, It is equipped with a camera linkage unit that works in conjunction with the camera to detect falls of elderly people and notify their families and hospitals. A system characterized by:

2. The speaking unit is Refer to the conversation history and provide topics based on the elderly person's individual interests and hobbies 2. The system of claim 1.

3. The speaking unit is Realize more realistic communication by using avatars that include not only voice but also facial expressions and gestures.

2. The system of claim 1.

4. The speaking unit is Analyzing the emotional state of the elderly person in real time and providing the encouraging or comforting words at the appropriate time 2. The system of claim 1.

5. The speaking unit is Simultaneously display family photos and video messages to strengthen the visual connection 2. The system of claim 1.

6. The speaking unit is Providing local news and event information and promoting social connections 2. The system of claim 1.

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