System

Abstract

To provide a system according to the embodiment which relieves loneliness of the elderly and effectively monitors health conditions.SOLUTION: A system according to the embodiment includes a communication part, a training part, and a monitoring part. The communication part communicates with the elderly. The training part sets a quiz or question on the basis of information obtained by the communication part. The monitoring part monitors health conditions of the elderly on the basis of the information obtained by the communication part.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 not been able to adequately address the loneliness and social isolation felt by the elderly and effectively monitor their health status.

[0005] The system according to the embodiment aims to alleviate the sense of loneliness felt by the elderly and to effectively monitor their health conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication unit, a training unit, and a monitoring unit. The communication unit communicates with the elderly. The training unit asks quizzes or questions based on information obtained by the communication unit. The monitoring unit monitors the health condition of the elderly based on the information obtained by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate the sense of loneliness of the elderly and effectively monitor their health condition. [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 pet robot service according to an embodiment of the present invention is a system designed to eliminate feelings of loneliness and social isolation among the elderly. This system allows elderly people to enjoy reminiscing and chatting by communicating with them. Using a generation AI, the system can provide appropriate responses to the elderly's reminiscences and chats, allowing the conversation to continue. This allows the elderly to spend their time enjoying themselves without feeling lonely. The pet robot also periodically presents quizzes and questions to train the elderly for dementia. For example, simple calculation problems or memory quizzes can be presented, and by having the elderly answer these questions, brain activity can be enhanced. Furthermore, the pet robot monitors the elderly's health and stress levels. For example, if an elderly person complains of poor health, the generation AI can analyze the information and contact or inquire with a center as necessary. This allows for comprehensive support by providing information on health management and prevention for the elderly. For example, the pet robot can measure the elderly's body temperature and heart rate and notify the center if any abnormalities are detected. The pet robot can also learn the elderly's preferences and past conversation history to provide more personalized communication. This allows the elderly to spend their time having fun without feeling lonely or socially isolated. Additionally, dementia training and health management can improve the quality of life for the elderly. The Pet Robo Service can thus reduce the elderly's feelings of loneliness and social isolation and support health management.

[0029] A pet robot service according to an embodiment includes a communication unit, a training unit, and a monitoring unit. The communication unit communicates with the elderly. Communication with the elderly can be achieved, for example, in person, by telephone, or by video call, but is not limited to these examples. The communication unit uses a generation AI to listen to the elderly reminisce or chat. The generation AI provides appropriate responses to the elderly's stories using, for example, deep learning or natural language processing technology. For example, when the elderly talks about old memories, the generation AI can provide appropriate responses to the story and continue the conversation. The training unit presents quizzes and questions based on the information obtained by the communication unit. Examples of quizzes and questions include, but are not limited to, simple calculation problems and memory quizzes. The training unit uses the generation AI to train the elderly for dementia. For example, the generation AI presents simple calculation problems such as "What is 5 + 3?" to the elderly, and brain activation can be achieved by the elderly answering the questions. The monitoring unit monitors the elderly's health status based on the information obtained by the communication unit. Health conditions include, but are not limited to, body temperature, heart rate, and blood pressure. The monitoring unit uses the generation AI to analyze the elderly person's health condition and contact or inquire with the center as necessary. For example, if the elderly person complains of feeling unwell, the generation AI analyzes the information and notifies the center if there is an abnormality. As a result, the pet robot service according to the embodiment can reduce the elderly person's sense of loneliness and social isolation and support their health management.

[0030] The communication unit communicates with the elderly. Communication with the elderly can be achieved, for example, in person, by telephone, or by video call, but is not limited to these examples. The communication unit uses a generation AI to listen to the elderly reminisce or chat. The generation AI uses deep learning and natural language processing technology to provide appropriate responses to the elderly's speech. Specifically, the generation AI converts the elderly's speech into text using speech recognition technology and analyzes the text to generate appropriate responses. For example, if the elderly says, "I once went on a family trip," the generation AI can respond with, "That's wonderful. Where did you go?" Furthermore, the generation AI can analyze the elderly's emotions and respond accordingly. For example, if the elderly speaks in a sad voice, the generation AI can respond empathetically with, "Is there something bothering you?" This allows the communication unit to deepen dialogue with the elderly and reduce their sense of loneliness. The generation AI can also record the content of the elderly's speech and use it for the next conversation. For example, the system can remember what was discussed in the previous conversation and continue the conversation in the next conversation by asking, "Regarding the trip you talked about last time, what happened afterwards?" This allows the elderly person to feel that they are being listened to properly, and to communicate with greater confidence.

[0031] The training unit presents quizzes and questions based on the information obtained by the communication unit. Examples of quizzes and questions include, but are not limited to, simple calculation problems and memory tests. The training unit uses a generative AI to provide dementia training for elderly people. Specifically, the generative AI evaluates the elderly's cognitive function and provides an appropriate training program based on the results. For example, the generative AI may present simple calculation questions such as "What is 5 + 3?" to the elderly, and by having the elderly answer these questions, brain activation can be achieved. The generative AI can also record the elderly's answers and monitor changes in cognitive function by analyzing the accuracy rate and response time. Furthermore, the generative AI can present quizzes and questions tailored to the elderly's interests. For example, if an elderly person is interested in history, it can present a quiz related to history, allowing cognitive function training to be enjoyed. This allows the training unit to not only maintain and improve the elderly's cognitive function, but also to provide training while having fun. The generative AI can also record the elderly's training results and periodically create evaluation reports. This allows the elderly, their families, and medical professionals to check the effectiveness of the training and adjust the training program as needed.

[0032] The monitoring unit monitors the elderly person's health status based on the information obtained by the communication unit. Health status information includes, but is not limited to, body temperature, heart rate, and blood pressure. The monitoring unit uses the generation AI to analyze the elderly person's health status and contact or inquire with the center as necessary. Specifically, the generation AI analyzes the elderly person's speech content, tone of voice, facial expressions, etc. to detect changes in their health status. For example, if an elderly person says, "I haven't had much of an appetite lately," the generation AI analyzes that information and notifies the center if the loss of appetite persists. The generation AI also monitors the elderly person's vital signs in real time and can immediately issue an alert if an abnormality is detected. For example, if the heart rate suddenly increases or blood pressure becomes abnormally high, the generation AI notifies the center and prompts a prompt response. Furthermore, the generation AI can monitor the elderly person's health status over the long term and analyze trends. This allows for early detection of changes in health status and the implementation of preventive measures. For example, if the body temperature continues to rise, it is recommended that the elderly person consult a medical institution early as it could be a sign of an infectious disease. This allows the monitoring department to continuously monitor the health status of the elderly and support prompt and appropriate responses. The generation AI also safely manages the elderly's health data and takes measures to protect their privacy, allowing the elderly to use the service with peace of mind.

[0033] The communication unit includes a learning unit that learns the elderly person's preferences and past conversation history. The learning unit uses a generation AI to learn the elderly person's preferences and past conversation history. The generation AI analyzes the elderly person's preferences and past conversation history using, for example, deep learning or natural language processing technology. For example, the generation AI can learn the elderly person's preferences based on what the elderly person has said in the past. This allows the learning unit to learn the elderly person's preferences and past conversation history and provide more personalized communication. For example, the generation AI can provide related topics based on the elderly person's past hobbies and interests. The generation AI can also optimize the way the conversation progresses based on the elderly person's past conversation history. For example, the generation AI can conduct the conversation in a way that avoids topics that the elderly person has avoided in the past. This allows the learning unit to learn the elderly person's preferences and past conversation history and provide more appropriate communication.

[0034] The sensor unit can measure body temperature and heart rate. The sensor unit measures body temperature and heart rate using, for example, a temperature sensor and a heart rate sensor. The temperature sensor can measure body temperature, for example, in the ear, mouth, or armpit. The heart rate sensor can measure, for example, the number of heartbeats per minute or the resting heart rate. This allows the sensor unit to measure body temperature and heart rate and more accurately monitor the health condition of the elderly. For example, the sensor unit can periodically measure the body temperature of the elderly and notify the center if there is an abnormality. The sensor unit can also monitor the elderly's heart rate and issue an alert if there is an abnormality. This allows the sensor unit to grasp the elderly's health condition in detail and take appropriate measures.

[0035] The communication unit can enjoy listening to the elderly reminisce and chat. The communication unit uses the generation AI to enjoy listening to the elderly reminisce and chat. The generation AI, for example, uses deep learning and natural language processing technology to provide appropriate responses to what the elderly is saying. For example, when an elderly person talks about old memories, the generation AI can provide an appropriate response to that story and continue the conversation. This allows the communication unit to enjoy listening to the elderly reminisce and chat. For example, when an elderly person talks about past events or travel memories, the generation AI can provide an appropriate response to that story and continue the conversation. The generation AI can also provide an appropriate response when the elderly person talks about everyday events or hobbies and continue the conversation. This allows the communication unit to reduce the elderly's sense of loneliness and provide them with a fun time.

[0036] The training unit can pose simple calculation problems and quizzes to test memory. The training unit uses the generation AI to pose simple calculation problems and quizzes to test memory. The generation AI uses, for example, deep learning and natural language processing technology to pose appropriate quizzes to elderly people. For example, the generation AI can pose simple calculation problems such as "What is 5 + 3?" to elderly people, and by having the elderly answer the questions, brain activation can be promoted. The generation AI can also pose quizzes to test memory, such as "Please remember this word," to elderly people, and by having the elderly answer the questions, memory training can be promoted. In this way, the training unit can train elderly people for dementia. For example, the generation AI can pose calculation problems such as addition, subtraction, and multiplication to elderly people, and by having the elderly answer the questions, brain activation can be promoted. In addition, the generation AI can pose quizzes such as word memory and number memory to elderly people, and by having the elderly answer the questions, memory training can be promoted. In this way, the training unit can train elderly people for dementia and brain activation.

[0037] The monitoring unit analyzes the elderly person's poor health and can contact or inquire with the center as necessary. The monitoring unit uses the generation AI to analyze the elderly person's poor health. The generation AI analyzes the elderly person's poor health using, for example, deep learning or natural language processing technology. For example, if an elderly person complains of feeling unwell, the generation AI can analyze the information and notify the center if there is an abnormality. This allows the monitoring unit to analyze the elderly person's poor health and contact or inquire with the center as necessary. For example, if an elderly person complains of symptoms such as fever, headache, or fatigue, the generation AI can analyze the information and notify the center if there is an abnormality. The generation AI can also analyze the elderly person's poor health and contact or inquire with a medical center or nursing care center as necessary. This allows the monitoring unit to quickly grasp the elderly person's poor health and take appropriate action.

[0038] The communication unit can analyze the elderly person's past conversation history and select the optimal way to proceed with the conversation. The communication unit uses the generation AI to analyze the elderly person's past conversation history. The generation AI analyzes the elderly person's past conversation history using, for example, deep learning or natural language processing technology. For example, the generation AI can optimize the elderly person's conversation progression method based on what the elderly person has said in the past. This allows the communication unit to analyze the elderly person's past conversation history and select the optimal way to proceed with the conversation. For example, the generation AI can suggest new related topics based on topics the elderly person has talked about in the past. The generation AI can also provide detailed information related to topics that the elderly person has shown interest in in the past. Furthermore, the generation AI can proceed with the conversation in a way that avoids topics that the elderly person has avoided in the past. This allows the communication unit to analyze the elderly person's past conversation history and provide a smoother conversation progression.

[0039] The communication unit can filter topics based on the elderly person's current interests and concerns during a conversation. The communication unit uses a generation AI to analyze the elderly person's current interests and concerns. The generation AI can analyze the elderly person's current interests and concerns using, for example, deep learning or natural language processing technology. For example, the generation AI can select topics related to the elderly person's current hobbies and activities. This allows the communication unit to filter topics based on the elderly person's current interests and concerns during a conversation. For example, the generation AI can select topics related to the elderly person's current hobbies. The generation AI can also select topics related to the content of movies or television programs the elderly person has recently watched. Furthermore, the generation AI can select topics related to events the elderly person recently attended. This allows the communication unit to select topics based on the elderly person's current interests and concerns and provide more interesting conversations.

[0040] The communication unit can prioritize relevant topics during conversations by taking into account the elderly person's geographic location information. The communication unit uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, the generation AI can select topics related to news and events in the area where the elderly person lives. This allows the communication unit to prioritize relevant topics during conversations by taking into account the elderly person's geographic location information. For example, the generation AI can select topics related to news and events in the area where the elderly person lives. The generation AI can also select topics related to places the elderly person frequently visits. Furthermore, the generation AI can select topics related to places where the elderly person has lived in the past. This allows the communication unit to provide relevant topics by taking into account the elderly person's geographic location information, thereby providing a more relevant conversation.

[0041] The communication unit can analyze the social media activity of the elderly person and select relevant topics during conversations. The communication unit uses a generation AI to analyze the social media activity of the elderly person. The generation AI analyzes the social media activity of the elderly person using, for example, deep learning or natural language processing technology. For example, the generation AI can select related topics for articles shared by the elderly person on social media or posts on which the elderly person has commented. This allows the communication unit to analyze the social media activity of the elderly person and select relevant topics during conversations. For example, the generation AI can select topics related to articles shared by the elderly person on social media. The generation AI can also select topics related to posts on which the elderly person has commented. Furthermore, the generation AI can select topics related to accounts followed by the elderly person on social media. This allows the communication unit to analyze the social media activity of the elderly person and provide more relevant topics.

[0042] When creating a quiz, the training department can analyze the elderly person's past answer history and select the most appropriate questions. The training department uses the generation AI to analyze the elderly person's past answer history. The generation AI analyzes the elderly person's past answer history, for example, using deep learning or natural language processing technology. For example, the generation AI can select new questions related to the trends in questions that the elderly person has answered correctly in the past. This allows the training department to analyze the elderly person's past answer history and select the most appropriate questions when creating a quiz. For example, the generation AI can select new questions related to the trends in questions that the elderly person has answered correctly in the past. The generation AI can also select review questions based on the trends in questions that the elderly person has answered incorrectly in the past. Furthermore, the generation AI can select questions related to topics that the elderly person has shown interest in in the past. This allows the training department to analyze the elderly person's past answer history and provide more effective training.

[0043] When generating quizzes, the training department can filter the types of questions based on the elderly person's current cognitive ability. The training department uses the generation AI to analyze the elderly person's current cognitive ability. The generation AI analyzes the elderly person's current cognitive ability using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's cognitive ability based on their cognitive test results or medical diagnosis. This allows the training department to filter the types of questions based on the elderly person's current cognitive ability when generating quizzes. For example, the generation AI can generate complex questions if the elderly person has high cognitive ability. The generation AI can also generate easy questions if the elderly person has low cognitive ability. Furthermore, the generation AI can select questions of an appropriate level of difficulty depending on the elderly person's cognitive ability. This allows the training department to select questions based on the elderly person's current cognitive ability and provide more appropriate training.

[0044] When generating quizzes, the training department can prioritize relevant questions by taking into account the elderly person's geographic location information. The training department uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, the generation AI can select questions related to the history or geography of the area where the elderly person lives. This allows the training department to prioritize relevant questions by taking into account the elderly person's geographic location information when generating quizzes. For example, the generation AI can select questions related to the history or geography of the area where the elderly person lives. The generation AI can also select questions related to places the elderly person frequently visits. Furthermore, the generation AI can select questions related to places the elderly person has lived in the past. This allows the training department to provide relevant questions by taking into account the elderly person's geographic location information, thereby providing more effective training.

[0045] The training department can analyze the social media activity of the elderly and provide relevant questions when creating quizzes. The training department uses a generation AI to analyze the social media activity of the elderly. The generation AI analyzes the social media activity of the elderly using, for example, deep learning or natural language processing technology. For example, the generation AI can select questions related to articles shared by the elderly on social media or posts on which the elderly have commented. This allows the training department to analyze the social media activity of the elderly and provide relevant questions when creating quizzes. For example, the generation AI can provide questions related to articles shared by the elderly on social media. The generation AI can also provide questions related to posts on which the elderly have commented on social media. Furthermore, the generation AI can provide questions related to accounts the elderly follow on social media. This allows the training department to analyze the social media activity of the elderly and provide more relevant questions.

[0046] The monitoring unit can analyze the elderly person's past health data and select the optimal monitoring method when monitoring their health status. The monitoring unit uses the generation AI to analyze the elderly person's past health data. The generation AI analyzes the elderly person's past health data using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's health status based on their body temperature records, heart rate records, etc. This allows the monitoring unit to analyze the elderly person's past health data and select the optimal monitoring method when monitoring their health status. For example, the generation AI can select an appropriate monitoring method based on the elderly person's past health data. The generation AI can also predict specific health risks from the elderly person's past health data and adjust the monitoring method. Furthermore, the generation AI can analyze the elderly person's past health data and propose an optimal monitoring schedule. This allows the monitoring unit to analyze the elderly person's past health data and provide more effective monitoring.

[0047] The monitoring unit can customize the monitoring method based on the elderly person's current living situation when monitoring their health condition. The monitoring unit uses the generation AI to analyze the elderly person's current living situation. The generation AI analyzes the elderly person's current living situation using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's living situation based on their daily activity level, dietary habits, etc. This allows the monitoring unit to customize the monitoring method based on the elderly person's current living situation when monitoring their health condition. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. Also, if the elderly person is out, the generation AI can suggest a monitoring method while they are away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a monitoring method while they are traveling. This allows the monitoring unit to customize the monitoring method based on the elderly person's current living situation and provide more appropriate health management.

[0048] The monitoring unit can select the optimal monitoring method by taking into account the geographical location information of the elderly person when monitoring their health condition. The monitoring unit uses a generation AI to analyze the geographical location information of the elderly person. The generation AI analyzes the geographical location information of the elderly person, for example, using deep learning or natural language processing technology. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. This allows the monitoring unit to select the optimal monitoring method by taking into account the geographical location information of the elderly person when monitoring their health condition. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. Furthermore, if the elderly person is out, the generation AI can suggest a monitoring method while away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a monitoring method at their travel destination. This allows the monitoring unit to select the optimal monitoring method by taking into account the geographical location information of the elderly person and provide more appropriate health management.

[0049] The monitoring unit can analyze the social media activity of the elderly person and suggest monitoring measures when monitoring their health condition. The monitoring unit uses a generation AI to analyze the social media activity of the elderly person. The generation AI analyzes the social media activity of the elderly person using, for example, deep learning or natural language processing technology. For example, if the elderly person posts about health on social media, the generation AI can suggest monitoring methods based on the content of the posts. This allows the monitoring unit to analyze the social media activity of the elderly person and suggest monitoring measures when monitoring their health condition. For example, if the elderly person posts about health on social media, the generation AI can suggest monitoring methods based on the content of the posts. Furthermore, if the elderly person posts about stress on social media, the generation AI can suggest stress monitoring. Furthermore, if the elderly person posts about exercise on social media, the generation AI can suggest exercise monitoring. This allows the monitoring unit to analyze the social media activity of the elderly person and provide more appropriate monitoring measures.

[0050] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit analyzes past learning data using a generation AI. The generation AI analyzes past learning data using, for example, deep learning or natural language processing technology. For example, the generation AI can select new learning data based on what the elderly person has learned in the past. This allows the learning unit to optimize the learning algorithm by referring to past learning data during learning. For example, the generation AI can select new learning data based on what the elderly person has learned in the past. The generation AI can also analyze the elderly person's past learning history and suggest an optimal learning method. Furthermore, the generation AI can adjust the learning algorithm based on the elderly person's past learning data. This allows the learning unit to provide more effective learning by referring to past learning data.

[0051] During learning, the learning unit can weight the learning data based on the time when the conversation history was submitted. The learning unit uses the generation AI to analyze the time when the conversation history was submitted. The generation AI analyzes the time when the conversation history was submitted, for example, using deep learning or natural language processing technology. For example, the generation AI can emphasize learning data related to content recently spoken by the elderly person based on that content. This allows the learning unit to weight the learning data based on the time when the conversation history was submitted during learning. For example, the generation AI can emphasize learning data related to content recently spoken by the elderly person based on that content. The generation AI can also emphasize learning data related to content previously spoken by the elderly person based on that content. Furthermore, the generation AI can analyze the elderly person's conversation history and optimally weight the learning data. This allows the learning unit to weight the learning data based on the time when the conversation history was submitted, thereby providing more effective learning.

[0052] The sensor unit can select the optimal measurement method by referring to the elderly person's past health data when performing a measurement using the sensor. The sensor unit uses a generation AI to analyze the elderly person's past health data. The generation AI analyzes the elderly person's past health data using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's health condition based on their body temperature records, heart rate records, etc. This allows the sensor unit to select the optimal measurement method by referring to the elderly person's past health data when performing a measurement using the sensor. For example, the generation AI can select an appropriate measurement method based on the elderly person's past health data. The generation AI can also predict specific health risks from the elderly person's past health data and adjust the measurement method. Furthermore, the generation AI can analyze the elderly person's past health data and propose an optimal measurement schedule. This allows the sensor unit to refer to the elderly person's past health data and provide a more appropriate measurement method.

[0053] The sensor unit can select the optimal measurement method by taking into account the elderly person's geographic location information when taking measurements using the sensor. The sensor unit uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, if the elderly person is at home, the generation AI can suggest a measurement method for the elderly person at home. This allows the sensor unit to select the optimal measurement method by taking into account the elderly person's geographic location information when taking measurements using the sensor. For example, if the elderly person is at home, the generation AI can suggest a measurement method for the elderly person at home. Furthermore, if the elderly person is out, the generation AI can suggest a measurement method for when the elderly person is away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a measurement method for when the elderly person is traveling. This allows the sensor unit to select the optimal measurement method by taking into account the elderly person's geographic location information and provide more appropriate data acquisition.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The PetRobo service can also be equipped with a reminder unit. The reminder unit manages the elderly person's schedule and medication times, and notifies them at the appropriate time. For example, if an elderly person needs to take their medicine at a specific time every day, the reminder unit can notify them by voice or on-screen display when it is time. The reminder unit can also manage the elderly person's schedule and send reminders before important appointments or events. Furthermore, the reminder unit can also work with the elderly person's family or caregivers to share necessary information. This allows the elderly person to live their daily lives with peace of mind, without having to forget to manage their schedule or take their medicine.

[0056] The pet robot service may further include a feedback unit. The feedback unit collects reactions and feedback from the elderly and uses the collected feedback to improve the service. For example, the feedback unit may collect and analyze what the elderly think about conversations and training with the pet robot. The feedback unit may also evaluate whether the elderly are satisfied with the service provided and adjust the content of the service as necessary. Furthermore, the feedback unit may collect opinions and requests from the elderly's family members and caregivers and reflect them in improving the service. This allows the pet robot service to provide a service that is more satisfying to the elderly.

[0057] The Petrobo service can further include an exercise unit. The exercise unit provides exercise programs aimed at maintaining the physical strength and promoting health of the elderly. For example, the exercise unit can provide guidance on stretching and light exercises that the elderly can easily do at home. The exercise unit can also suggest appropriate exercise programs based on the elderly's physical strength and health condition. Furthermore, the exercise unit can record the elderly's exercise history and monitor their progress. This allows the elderly to continue exercising without straining themselves and maintain their health.

[0058] The pet robot service may further include an education department, which provides educational content for seniors to learn new knowledge and skills. For example, the education department may provide online courses and video lessons in areas that interest seniors. The education department may also suggest appropriate learning materials and study plans based on the topics that seniors want to learn. Furthermore, the education department may monitor the seniors' learning progress and provide support as needed. This allows seniors to acquire new knowledge and skills and experience a sense of personal growth.

[0059] The PetRobo service can also be equipped with a nutrition management unit, which manages the elderly's meals and nutritional balance to support a healthy diet. For example, the nutrition management unit can propose an appropriate meal plan based on the elderly's health condition and food preferences. The nutrition management unit can also provide guidance on how to select ingredients and how to cook them so that the elderly can consume the necessary nutrients. Furthermore, the nutrition management unit can record the elderly's dietary history and monitor their nutritional balance. This allows the elderly to maintain a healthy diet and maintain a balanced nutritional intake.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The communication department communicates with the elderly person. For example, face-to-face meetings, telephone calls, video calls, and other methods can be used. The generation AI is used to listen to the elderly person's reminiscences and chat. The generation AI uses deep learning and natural language processing technology to provide appropriate responses to what the elderly person is saying. Step 2: The training department presents quizzes and questions based on the information obtained by the communication department. These quizzes and questions include simple calculation problems and quizzes to test memory. Generative AI is used to train elderly people against dementia. For example, simple calculation problems such as "What is 5 + 3?" are presented, and the elderly's answers are used to stimulate their brains. Step 3: The monitoring unit monitors the elderly person's health condition based on the information obtained by the communication unit. Health conditions include body temperature, heart rate, blood pressure, etc. The generation AI is used to analyze the elderly person's health condition and contact or inquire with the center as necessary. For example, if an elderly person complains of feeling unwell, the generation AI analyzes the information and notifies the center if there is an abnormality.

[0062] (Example 2) The pet robot service according to an embodiment of the present invention is a system designed to eliminate feelings of loneliness and social isolation among the elderly. This system allows elderly people to enjoy reminiscing and chatting by communicating with them. Using a generation AI, the system can provide appropriate responses to the elderly's reminiscences and chats, allowing the conversation to continue. This allows the elderly to spend their time enjoying themselves without feeling lonely. The pet robot also periodically presents quizzes and questions to train the elderly for dementia. For example, simple calculation problems or memory quizzes can be presented, and by having the elderly answer these questions, brain activity can be enhanced. Furthermore, the pet robot monitors the elderly's health and stress levels. For example, if an elderly person complains of poor health, the generation AI can analyze the information and contact or inquire with a center as necessary. This allows for comprehensive support by providing information on health management and prevention for the elderly. For example, the pet robot can measure the elderly's body temperature and heart rate and notify the center if any abnormalities are detected. The pet robot can also learn the elderly's preferences and past conversation history to provide more personalized communication. This allows the elderly to spend their time having fun without feeling lonely or socially isolated. Additionally, dementia training and health management can improve the quality of life for the elderly. The Pet Robo Service can thus reduce the elderly's feelings of loneliness and social isolation and support health management.

[0063] A pet robot service according to an embodiment includes a communication unit, a training unit, and a monitoring unit. The communication unit communicates with the elderly. Communication with the elderly can be achieved, for example, in person, by telephone, or by video call, but is not limited to these examples. The communication unit uses a generation AI to listen to the elderly reminisce or chat. The generation AI provides appropriate responses to the elderly's stories using, for example, deep learning or natural language processing technology. For example, when the elderly talks about old memories, the generation AI can provide appropriate responses to the story and continue the conversation. The training unit presents quizzes and questions based on the information obtained by the communication unit. Examples of quizzes and questions include, but are not limited to, simple calculation problems and memory quizzes. The training unit uses the generation AI to train the elderly for dementia. For example, the generation AI presents simple calculation problems such as "What is 5 + 3?" to the elderly, and brain activation can be achieved by the elderly answering the questions. The monitoring unit monitors the elderly's health status based on the information obtained by the communication unit. Health conditions include, but are not limited to, body temperature, heart rate, and blood pressure. The monitoring unit uses the generation AI to analyze the elderly person's health condition and contact or inquire with the center as necessary. For example, if the elderly person complains of feeling unwell, the generation AI analyzes the information and notifies the center if there is an abnormality. As a result, the pet robot service according to the embodiment can reduce the elderly person's sense of loneliness and social isolation and support their health management.

[0064] The communication unit communicates with the elderly. Communication with the elderly can be achieved, for example, in person, by telephone, or by video call, but is not limited to these examples. The communication unit uses a generation AI to listen to the elderly reminisce or chat. The generation AI uses deep learning and natural language processing technology to provide appropriate responses to the elderly's speech. Specifically, the generation AI converts the elderly's speech into text using speech recognition technology and analyzes the text to generate appropriate responses. For example, if the elderly says, "I once went on a family trip," the generation AI can respond with, "That's wonderful. Where did you go?" Furthermore, the generation AI can analyze the elderly's emotions and respond accordingly. For example, if the elderly speaks in a sad voice, the generation AI can respond empathetically with, "Is there something bothering you?" This allows the communication unit to deepen dialogue with the elderly and reduce their sense of loneliness. The generation AI can also record the content of the elderly's speech and use it for the next conversation. For example, the system can remember what was discussed in the previous conversation and continue the conversation in the next conversation by asking, "Regarding the trip you talked about last time, what happened afterwards?" This allows the elderly person to feel that they are being listened to properly, and to communicate with greater confidence.

[0065] The training unit presents quizzes and questions based on the information obtained by the communication unit. Examples of quizzes and questions include, but are not limited to, simple calculation problems and memory tests. The training unit uses a generative AI to provide dementia training for elderly people. Specifically, the generative AI evaluates the elderly's cognitive function and provides an appropriate training program based on the results. For example, the generative AI may present simple calculation questions such as "What is 5 + 3?" to the elderly, and by having the elderly answer these questions, brain activation can be achieved. The generative AI can also record the elderly's answers and monitor changes in cognitive function by analyzing the accuracy rate and response time. Furthermore, the generative AI can present quizzes and questions tailored to the elderly's interests. For example, if an elderly person is interested in history, it can present a quiz related to history, allowing cognitive function training to be enjoyed. This allows the training unit to not only maintain and improve the elderly's cognitive function, but also to provide training while having fun. The generative AI can also record the elderly's training results and periodically create evaluation reports. This allows the elderly, their families, and medical professionals to check the effectiveness of the training and adjust the training program as needed.

[0066] The monitoring unit monitors the elderly person's health status based on the information obtained by the communication unit. Health status information includes, but is not limited to, body temperature, heart rate, and blood pressure. The monitoring unit uses the generation AI to analyze the elderly person's health status and contact or inquire with the center as necessary. Specifically, the generation AI analyzes the elderly person's speech content, tone of voice, facial expressions, etc. to detect changes in their health status. For example, if an elderly person says, "I haven't had much of an appetite lately," the generation AI analyzes that information and notifies the center if the loss of appetite persists. The generation AI also monitors the elderly person's vital signs in real time and can immediately issue an alert if an abnormality is detected. For example, if the heart rate suddenly increases or blood pressure becomes abnormally high, the generation AI notifies the center and prompts a prompt response. Furthermore, the generation AI can monitor the elderly person's health status over the long term and analyze trends. This allows for early detection of changes in health status and the implementation of preventive measures. For example, if the body temperature continues to rise, it is recommended that the elderly person consult a medical institution early as it could be a sign of an infectious disease. This allows the monitoring department to continuously monitor the health status of the elderly and support prompt and appropriate responses. The generation AI also safely manages the elderly's health data and takes measures to protect their privacy, allowing the elderly to use the service with peace of mind.

[0067] The communication unit includes a learning unit that learns the elderly person's preferences and past conversation history. The learning unit uses a generation AI to learn the elderly person's preferences and past conversation history. The generation AI analyzes the elderly person's preferences and past conversation history using, for example, deep learning or natural language processing technology. For example, the generation AI can learn the elderly person's preferences based on what the elderly person has said in the past. This allows the learning unit to learn the elderly person's preferences and past conversation history and provide more personalized communication. For example, the generation AI can provide related topics based on the elderly person's past hobbies and interests. The generation AI can also optimize the way the conversation progresses based on the elderly person's past conversation history. For example, the generation AI can conduct the conversation in a way that avoids topics that the elderly person has avoided in the past. This allows the learning unit to learn the elderly person's preferences and past conversation history and provide more appropriate communication.

[0068] The sensor unit can measure body temperature and heart rate. The sensor unit measures body temperature and heart rate using, for example, a temperature sensor and a heart rate sensor. The temperature sensor can measure body temperature, for example, in the ear, mouth, or armpit. The heart rate sensor can measure, for example, the number of heartbeats per minute or the resting heart rate. This allows the sensor unit to measure body temperature and heart rate and more accurately monitor the health condition of the elderly. For example, the sensor unit can periodically measure the body temperature of the elderly and notify the center if there is an abnormality. The sensor unit can also monitor the elderly's heart rate and issue an alert if there is an abnormality. This allows the sensor unit to grasp the elderly's health condition in detail and take appropriate measures.

[0069] The communication unit can enjoy listening to the elderly reminisce and chat. The communication unit uses the generation AI to enjoy listening to the elderly reminisce and chat. The generation AI, for example, uses deep learning and natural language processing technology to provide appropriate responses to what the elderly is saying. For example, when an elderly person talks about old memories, the generation AI can provide an appropriate response to that story and continue the conversation. This allows the communication unit to enjoy listening to the elderly reminisce and chat. For example, when an elderly person talks about past events or travel memories, the generation AI can provide an appropriate response to that story and continue the conversation. The generation AI can also provide an appropriate response when the elderly person talks about everyday events or hobbies and continue the conversation. This allows the communication unit to reduce the elderly's sense of loneliness and provide them with a fun time.

[0070] The training unit can pose simple calculation problems and quizzes to test memory. The training unit uses the generation AI to pose simple calculation problems and quizzes to test memory. The generation AI uses, for example, deep learning and natural language processing technology to pose appropriate quizzes to elderly people. For example, the generation AI can pose simple calculation problems such as "What is 5 + 3?" to elderly people, and by having the elderly answer the questions, brain activation can be promoted. The generation AI can also pose quizzes to test memory, such as "Please remember this word," to elderly people, and by having the elderly answer the questions, memory training can be promoted. In this way, the training unit can train elderly people for dementia. For example, the generation AI can pose calculation problems such as addition, subtraction, and multiplication to elderly people, and by having the elderly answer the questions, brain activation can be promoted. In addition, the generation AI can pose quizzes such as word memory and number memory to elderly people, and by having the elderly answer the questions, memory training can be promoted. In this way, the training unit can train elderly people for dementia and brain activation.

[0071] The monitoring unit analyzes the elderly person's poor health and can contact or inquire with the center as necessary. The monitoring unit uses the generation AI to analyze the elderly person's poor health. The generation AI analyzes the elderly person's poor health using, for example, deep learning or natural language processing technology. For example, if an elderly person complains of feeling unwell, the generation AI can analyze the information and notify the center if there is an abnormality. This allows the monitoring unit to analyze the elderly person's poor health and contact or inquire with the center as necessary. For example, if an elderly person complains of symptoms such as fever, headache, or fatigue, the generation AI can analyze the information and notify the center if there is an abnormality. The generation AI can also analyze the elderly person's poor health and contact or inquire with a medical center or nursing care center as necessary. This allows the monitoring unit to quickly grasp the elderly person's poor health and take appropriate action.

[0072] The communication unit can estimate the elderly person's emotions and select a conversation topic based on the estimated elderly person's emotions. The communication unit uses a generation AI to estimate the elderly person's emotions. The generation AI can estimate the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the communication unit to estimate the elderly person's emotions and select a conversation topic based on the estimated elderly person's emotions. For example, if the elderly person is sad, the generation AI can select a story about happy memories from the past as the topic. Also, if the elderly person is excited, the generation AI can select interesting news or topics as the topic. Furthermore, if the elderly person is relaxed, the generation AI can select topics about relaxing hobbies and activities. This allows the communication unit to select a conversation topic based on the elderly person's emotions and provide more appropriate conversation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0073] The communication unit can analyze the elderly person's past conversation history and select the optimal way to proceed with the conversation. The communication unit uses the generation AI to analyze the elderly person's past conversation history. The generation AI analyzes the elderly person's past conversation history using, for example, deep learning or natural language processing technology. For example, the generation AI can optimize the elderly person's conversation progression method based on what the elderly person has said in the past. This allows the communication unit to analyze the elderly person's past conversation history and select the optimal way to proceed with the conversation. For example, the generation AI can suggest new related topics based on topics the elderly person has talked about in the past. The generation AI can also provide detailed information related to topics that the elderly person has shown interest in in the past. Furthermore, the generation AI can proceed with the conversation in a way that avoids topics that the elderly person has avoided in the past. This allows the communication unit to analyze the elderly person's past conversation history and provide a smoother conversation progression.

[0074] The communication unit can filter topics based on the elderly person's current interests and concerns during a conversation. The communication unit uses a generation AI to analyze the elderly person's current interests and concerns. The generation AI can analyze the elderly person's current interests and concerns using, for example, deep learning or natural language processing technology. For example, the generation AI can select topics related to the elderly person's current hobbies and activities. This allows the communication unit to filter topics based on the elderly person's current interests and concerns during a conversation. For example, the generation AI can select topics related to the elderly person's current hobbies. The generation AI can also select topics related to the content of movies or television programs the elderly person has recently watched. Furthermore, the generation AI can select topics related to events the elderly person recently attended. This allows the communication unit to select topics based on the elderly person's current interests and concerns and provide more interesting conversations.

[0075] The communication unit can estimate the elderly person's emotions and adjust the tempo of the conversation based on the estimated emotions. The communication unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the communication unit to estimate the elderly person's emotions and adjust the tempo of the conversation based on the estimated emotions. For example, the generation AI can conduct the conversation at a slower tempo if the elderly person is tired. Furthermore, the generation AI can conduct the conversation at a faster tempo if the elderly person is excited. Furthermore, the generation AI can conduct the conversation at a more appropriate tempo if the elderly person is relaxed. This allows the communication unit to adjust the tempo of the conversation based on the elderly person's emotions and provide a more appropriate conversation progression. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0076] The communication unit can prioritize relevant topics during conversations by taking into account the elderly person's geographic location information. The communication unit uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, the generation AI can select topics related to news and events in the area where the elderly person lives. This allows the communication unit to prioritize relevant topics during conversations by taking into account the elderly person's geographic location information. For example, the generation AI can select topics related to news and events in the area where the elderly person lives. The generation AI can also select topics related to places the elderly person frequently visits. Furthermore, the generation AI can select topics related to places where the elderly person has lived in the past. This allows the communication unit to provide relevant topics by taking into account the elderly person's geographic location information, thereby providing a more relevant conversation.

[0077] The communication unit can analyze the social media activity of the elderly person and select relevant topics during conversations. The communication unit uses a generation AI to analyze the social media activity of the elderly person. The generation AI analyzes the social media activity of the elderly person using, for example, deep learning or natural language processing technology. For example, the generation AI can select related topics for articles shared by the elderly person on social media or posts on which the elderly person has commented. This allows the communication unit to analyze the social media activity of the elderly person and select relevant topics during conversations. For example, the generation AI can select topics related to articles shared by the elderly person on social media. The generation AI can also select topics related to posts on which the elderly person has commented. Furthermore, the generation AI can select topics related to accounts followed by the elderly person on social media. This allows the communication unit to analyze the social media activity of the elderly person and provide more relevant topics.

[0078] The training unit can estimate the elderly person's emotions and adjust the difficulty of the quiz based on the estimated elderly person's emotions. The training unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the training unit to estimate the elderly person's emotions and adjust the difficulty of the quiz based on the estimated elderly person's emotions. For example, the generation AI can present a somewhat difficult quiz if the elderly person is relaxed. The generation AI can also present an easy quiz if the elderly person is tired. Furthermore, the generation AI can present a medium-difficulty quiz if the elderly person is excited. This allows the training unit to adjust the difficulty of the quiz based on the elderly person's emotions and provide more appropriate training. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] When creating a quiz, the training department can analyze the elderly person's past answer history and select the most appropriate questions. The training department uses the generation AI to analyze the elderly person's past answer history. The generation AI analyzes the elderly person's past answer history, for example, using deep learning or natural language processing technology. For example, the generation AI can select new questions related to the trends in questions that the elderly person has answered correctly in the past. This allows the training department to analyze the elderly person's past answer history and select the most appropriate questions when creating a quiz. For example, the generation AI can select new questions related to the trends in questions that the elderly person has answered correctly in the past. The generation AI can also select review questions based on the trends in questions that the elderly person has answered incorrectly in the past. Furthermore, the generation AI can select questions related to topics that the elderly person has shown interest in in the past. This allows the training department to analyze the elderly person's past answer history and provide more effective training.

[0080] When generating quizzes, the training department can filter the types of questions based on the elderly person's current cognitive ability. The training department uses the generation AI to analyze the elderly person's current cognitive ability. The generation AI analyzes the elderly person's current cognitive ability using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's cognitive ability based on their cognitive test results or medical diagnosis. This allows the training department to filter the types of questions based on the elderly person's current cognitive ability when generating quizzes. For example, the generation AI can generate complex questions if the elderly person has high cognitive ability. The generation AI can also generate easy questions if the elderly person has low cognitive ability. Furthermore, the generation AI can select questions of an appropriate level of difficulty depending on the elderly person's cognitive ability. This allows the training department to select questions based on the elderly person's current cognitive ability and provide more appropriate training.

[0081] The training unit can estimate the elderly person's emotions and adjust the frequency of quizzes based on the estimated emotions. The training unit uses a generation AI to estimate the elderly person's emotions. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the training unit to estimate the elderly person's emotions and adjust the frequency of quizzes based on the estimated emotions. For example, the generation AI can frequently present quizzes to the elderly person when they are relaxed. Furthermore, the generation AI can reduce the frequency of quizzes to the elderly person when they are tired. Furthermore, the generation AI can present quizzes at an appropriate frequency when the elderly person is excited. This allows the training unit to adjust the frequency of quizzes based on the elderly person's emotions and provide more appropriate training. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0082] When generating quizzes, the training department can prioritize relevant questions by taking into account the elderly person's geographic location information. The training department uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, the generation AI can select questions related to the history or geography of the area where the elderly person lives. This allows the training department to prioritize relevant questions by taking into account the elderly person's geographic location information when generating quizzes. For example, the generation AI can select questions related to the history or geography of the area where the elderly person lives. The generation AI can also select questions related to places the elderly person frequently visits. Furthermore, the generation AI can select questions related to places the elderly person has lived in the past. This allows the training department to provide relevant questions by taking into account the elderly person's geographic location information, thereby providing more effective training.

[0083] The training department can analyze the social media activity of the elderly and provide relevant questions when creating quizzes. The training department uses a generation AI to analyze the social media activity of the elderly. The generation AI analyzes the social media activity of the elderly using, for example, deep learning or natural language processing technology. For example, the generation AI can select questions related to articles shared by the elderly on social media or posts on which the elderly have commented. This allows the training department to analyze the social media activity of the elderly and provide relevant questions when creating quizzes. For example, the generation AI can provide questions related to articles shared by the elderly on social media. The generation AI can also provide questions related to posts on which the elderly have commented on social media. Furthermore, the generation AI can provide questions related to accounts the elderly follow on social media. This allows the training department to analyze the social media activity of the elderly and provide more relevant questions.

[0084] The monitoring unit can estimate the elderly person's emotions and adjust the health status monitoring method based on the estimated elderly person's emotions. The monitoring unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. As a result, the monitoring unit can estimate the elderly person's emotions and adjust the health status monitoring method based on the estimated elderly person's emotions. For example, if the generation AI is stressed, it can monitor the elderly in a relaxing environment. If the elderly person is relaxed, it can perform a detailed health check. Furthermore, if the elderly person is excited, it can perform a simple health check. As a result, the monitoring unit can adjust the monitoring method based on the elderly person's emotions and provide more appropriate health management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The monitoring unit can analyze the elderly person's past health data and select the optimal monitoring method when monitoring their health status. The monitoring unit uses the generation AI to analyze the elderly person's past health data. The generation AI analyzes the elderly person's past health data using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's health status based on their body temperature records, heart rate records, etc. This allows the monitoring unit to analyze the elderly person's past health data and select the optimal monitoring method when monitoring their health status. For example, the generation AI can select an appropriate monitoring method based on the elderly person's past health data. The generation AI can also predict specific health risks from the elderly person's past health data and adjust the monitoring method. Furthermore, the generation AI can analyze the elderly person's past health data and propose an optimal monitoring schedule. This allows the monitoring unit to analyze the elderly person's past health data and provide more effective monitoring.

[0086] The monitoring unit can customize the monitoring method based on the elderly person's current living situation when monitoring their health condition. The monitoring unit uses the generation AI to analyze the elderly person's current living situation. The generation AI analyzes the elderly person's current living situation using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's living situation based on their daily activity level, dietary habits, etc. This allows the monitoring unit to customize the monitoring method based on the elderly person's current living situation when monitoring their health condition. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. Also, if the elderly person is out, the generation AI can suggest a monitoring method while they are away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a monitoring method while they are traveling. This allows the monitoring unit to customize the monitoring method based on the elderly person's current living situation and provide more appropriate health management.

[0087] The monitoring unit can estimate the elderly person's emotions and determine monitoring priorities based on the estimated emotions. The monitoring unit uses a generation AI to estimate the elderly person's emotions. The generation AI can estimate the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the monitoring unit to estimate the elderly person's emotions and determine monitoring priorities based on the estimated emotions. For example, if the elderly person is feeling stressed, the generation AI can prioritize stress-related monitoring. Also, if the elderly person is relaxed, the generation AI can prioritize regular health checks. Furthermore, if the elderly person is excited, the generation AI can prioritize heart rate and blood pressure monitoring. This allows the monitoring unit to determine monitoring priorities based on the elderly person's emotions and provide more effective health management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0088] The monitoring unit can select the optimal monitoring method by taking into account the geographical location information of the elderly person when monitoring their health condition. The monitoring unit uses a generation AI to analyze the geographical location information of the elderly person. The generation AI analyzes the geographical location information of the elderly person, for example, using deep learning or natural language processing technology. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. This allows the monitoring unit to select the optimal monitoring method by taking into account the geographical location information of the elderly person when monitoring their health condition. For example, if the elderly person is at home, the generation AI can suggest a monitoring method at home. Furthermore, if the elderly person is out, the generation AI can suggest a monitoring method while away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a monitoring method at their travel destination. This allows the monitoring unit to select the optimal monitoring method by taking into account the geographical location information of the elderly person and provide more appropriate health management.

[0089] The monitoring unit can analyze the social media activity of the elderly person and suggest monitoring measures when monitoring their health condition. The monitoring unit uses a generation AI to analyze the social media activity of the elderly person. The generation AI analyzes the social media activity of the elderly person using, for example, deep learning or natural language processing technology. For example, if the elderly person posts about health on social media, the generation AI can suggest monitoring methods based on the content of the posts. This allows the monitoring unit to analyze the social media activity of the elderly person and suggest monitoring measures when monitoring their health condition. For example, if the elderly person posts about health on social media, the generation AI can suggest monitoring methods based on the content of the posts. Furthermore, if the elderly person posts about stress on social media, the generation AI can suggest stress monitoring. Furthermore, if the elderly person posts about exercise on social media, the generation AI can suggest exercise monitoring. This allows the monitoring unit to analyze the social media activity of the elderly person and provide more appropriate monitoring measures.

[0090] The learning unit can estimate the emotions of the elderly and select training data based on the estimated emotions of the elderly. The learning unit uses a generation AI to estimate the emotions of the elderly. The generation AI estimates the emotions of the elderly using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the facial expressions, voice, text, etc. of the elderly to estimate the emotions of the elderly. This allows the learning unit to estimate the emotions of the elderly and select training data based on the estimated emotions of the elderly. For example, if the elderly is relaxed, the generation AI selects training data with relaxing content. Also, if the elderly is excited, the generation AI can select training data with interesting content. Furthermore, if the elderly is tired, the generation AI can select training data with simple and easy-to-understand content. This allows the learning unit to select training data based on the emotions of the elderly and provide more effective learning. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit analyzes past learning data using a generation AI. The generation AI analyzes past learning data using, for example, deep learning or natural language processing technology. For example, the generation AI can select new learning data based on what the elderly person has learned in the past. This allows the learning unit to optimize the learning algorithm by referring to past learning data during learning. For example, the generation AI can select new learning data based on what the elderly person has learned in the past. The generation AI can also analyze the elderly person's past learning history and suggest an optimal learning method. Furthermore, the generation AI can adjust the learning algorithm based on the elderly person's past learning data. This allows the learning unit to provide more effective learning by referring to past learning data.

[0092] The learning unit can estimate the elderly person's emotions and adjust the frequency of learning based on the estimated elderly person's emotions. The learning unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the learning unit to estimate the elderly person's emotions and adjust the frequency of learning based on the estimated elderly person's emotions. For example, the generation AI can suggest frequent learning sessions if the elderly person is relaxed. Furthermore, the generation AI can reduce the frequency of learning sessions if the elderly person is tired. Furthermore, the generation AI can suggest learning sessions at an appropriate frequency if the elderly person is excited. This allows the learning unit to adjust the frequency of learning based on the elderly person's emotions and provide more effective learning. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] During learning, the learning unit can weight the learning data based on the time when the conversation history was submitted. The learning unit uses the generation AI to analyze the time when the conversation history was submitted. The generation AI analyzes the time when the conversation history was submitted, for example, using deep learning or natural language processing technology. For example, the generation AI can emphasize learning data related to content recently spoken by the elderly person based on that content. This allows the learning unit to weight the learning data based on the time when the conversation history was submitted during learning. For example, the generation AI can emphasize learning data related to content recently spoken by the elderly person based on that content. The generation AI can also emphasize learning data related to content previously spoken by the elderly person based on that content. Furthermore, the generation AI can analyze the elderly person's conversation history and optimally weight the learning data. This allows the learning unit to weight the learning data based on the time when the conversation history was submitted, thereby providing more effective learning.

[0094] The sensor unit can estimate the elderly person's emotions and adjust the sensor sensitivity based on the estimated elderly person's emotions. The sensor unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the sensor unit to estimate the elderly person's emotions and adjust the sensor sensitivity based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, the generation AI can increase the sensor sensitivity to obtain detailed data. Also, if the elderly person is excited, the generation AI can decrease the sensor sensitivity to obtain simplified data. Furthermore, if the elderly person is tired, the generation AI can adjust the sensor sensitivity appropriately to obtain data. This allows the sensor unit to adjust the sensor sensitivity based on the elderly person's emotions and provide more appropriate data acquisition. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0095] The sensor unit can select the optimal measurement method by referring to the elderly person's past health data when performing a measurement using the sensor. The sensor unit uses a generation AI to analyze the elderly person's past health data. The generation AI analyzes the elderly person's past health data using, for example, deep learning or natural language processing technology. For example, the generation AI can evaluate the elderly person's health condition based on their body temperature records, heart rate records, etc. This allows the sensor unit to select the optimal measurement method by referring to the elderly person's past health data when performing a measurement using the sensor. For example, the generation AI can select an appropriate measurement method based on the elderly person's past health data. The generation AI can also predict specific health risks from the elderly person's past health data and adjust the measurement method. Furthermore, the generation AI can analyze the elderly person's past health data and propose an optimal measurement schedule. This allows the sensor unit to refer to the elderly person's past health data and provide a more appropriate measurement method.

[0096] The sensor unit can estimate the elderly person's emotions and adjust the sensor measurement frequency based on the estimated elderly person's emotions. The sensor unit estimates the elderly person's emotions using a generation AI. The generation AI estimates the elderly person's emotions using, for example, deep learning or natural language processing technology. For example, the generation AI can analyze the elderly person's facial expressions, voice, text, etc. to estimate the elderly person's emotions. This allows the sensor unit to estimate the elderly person's emotions and adjust the sensor measurement frequency based on the estimated elderly person's emotions. For example, the generation AI can increase the sensor measurement frequency when the elderly person is relaxed. Also, the generation AI can decrease the sensor measurement frequency when the elderly person is tired. Furthermore, the generation AI can appropriately adjust the sensor measurement frequency when the elderly person is excited. This allows the sensor unit to adjust the sensor measurement frequency based on the elderly person's emotions and provide more appropriate data acquisition. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The sensor unit can select the optimal measurement method by taking into account the elderly person's geographic location information when taking measurements using the sensor. The sensor unit uses a generation AI to analyze the elderly person's geographic location information. The generation AI analyzes the elderly person's geographic location information using, for example, deep learning or natural language processing technology. For example, if the elderly person is at home, the generation AI can suggest a measurement method for the elderly person at home. This allows the sensor unit to select the optimal measurement method by taking into account the elderly person's geographic location information when taking measurements using the sensor. For example, if the elderly person is at home, the generation AI can suggest a measurement method for the elderly person at home. Furthermore, if the elderly person is out, the generation AI can suggest a measurement method for when the elderly person is away from home. Furthermore, if the elderly person is traveling, the generation AI can suggest a measurement method for when the elderly person is traveling. This allows the sensor unit to select the optimal measurement method by taking into account the elderly person's geographic location information and provide more appropriate data acquisition.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The PetRobo service can also be equipped with a reminder unit. The reminder unit manages the elderly person's schedule and medication times, and notifies them at the appropriate time. For example, if an elderly person needs to take their medicine at a specific time every day, the reminder unit can notify them by voice or on-screen display when it is time. The reminder unit can also manage the elderly person's schedule and send reminders before important appointments or events. Furthermore, the reminder unit can also work with the elderly person's family or caregivers to share necessary information. This allows the elderly person to live their daily lives with peace of mind, without having to forget to manage their schedule or take their medicine.

[0100] The pet robot service may further include an entertainment unit. The entertainment unit provides entertainment content such as music, movies, and games based on the elderly person's hobbies and interests. For example, the entertainment unit may play the elderly person's favorite music or show old movies. The entertainment unit may also provide simple games that the elderly person can enjoy, allowing them to train their brains while having fun. Furthermore, the entertainment unit may select appropriate entertainment content based on the elderly person's emotions and mood. This allows the elderly person to spend their time having fun and reduce their sense of loneliness.

[0101] The pet robot service may further include a feedback unit. The feedback unit collects reactions and feedback from the elderly and uses the collected feedback to improve the service. For example, the feedback unit may collect and analyze what the elderly think about conversations and training with the pet robot. The feedback unit may also evaluate whether the elderly are satisfied with the service provided and adjust the content of the service as necessary. Furthermore, the feedback unit may collect opinions and requests from the elderly's family members and caregivers and reflect them in improving the service. This allows the pet robot service to provide a service that is more satisfying to the elderly.

[0102] The Pet Robo Service can further include a relaxation unit. The relaxation unit provides functions aimed at helping the elderly relax and reduce stress. For example, the relaxation unit can play music or natural sounds that help the elderly relax. The relaxation unit can also provide guidance on breathing techniques and simple stretches that will help the elderly relax. Furthermore, the relaxation unit can monitor the emotions and stress levels of the elderly and suggest relaxation techniques as needed. This allows the elderly to live their daily lives in a relaxed state and reduce stress.

[0103] The pet robot service may further include a social networking unit. The social networking unit provides a function for seniors to interact online with other seniors, family, and friends. For example, the social networking unit may allow seniors to interact with other seniors through video calls or chats. The social networking unit may also allow seniors to join groups with common hobbies or interests to exchange information and interact. Furthermore, the social networking unit may suggest appropriate contacts or groups depending on the seniors' emotions and moods. This allows seniors to have social connections and reduce feelings of loneliness.

[0104] The Petrobo service can further include an exercise unit. The exercise unit provides exercise programs aimed at maintaining the physical strength and promoting health of the elderly. For example, the exercise unit can provide guidance on stretching and light exercises that the elderly can easily do at home. The exercise unit can also suggest appropriate exercise programs based on the elderly's physical strength and health condition. Furthermore, the exercise unit can record the elderly's exercise history and monitor their progress. This allows the elderly to continue exercising without straining themselves and maintain their health.

[0105] The pet robot service may further include an education department, which provides educational content for seniors to learn new knowledge and skills. For example, the education department may provide online courses and video lessons in areas that interest seniors. The education department may also suggest appropriate learning materials and study plans based on the topics that seniors want to learn. Furthermore, the education department may monitor the seniors' learning progress and provide support as needed. This allows seniors to acquire new knowledge and skills and experience a sense of personal growth.

[0106] The robotic pet service may further include an art therapy section. The art therapy section provides a function for seniors to relax and express their emotions through creative activities. For example, the art therapy section may provide materials and instruction for seniors to enjoy drawing or handicrafts. The art therapy section may also provide an online gallery to display artwork created by seniors, which can be shared with other seniors and family members. Furthermore, the art therapy section may suggest appropriate creative activities according to the seniors' emotions and moods. This allows seniors to relax and express their emotions through creative activities.

[0107] The petrobo service may further include a trip planning unit. The trip planning unit provides support for elderly people to plan and enjoy their trips. For example, the trip planning unit may suggest appropriate travel destinations and activities based on the elderly person's interests and physical strength. The trip planning unit may also support travel schedules and accommodation arrangements, and provide information to help elderly people enjoy their trips with peace of mind. Furthermore, the trip planning unit may suggest relaxing travel destinations and activities based on the elderly person's emotions and mood. This allows elderly people to enjoy their trips with peace of mind and refresh themselves through new experiences.

[0108] The PetRobo service can also be equipped with a nutrition management unit, which manages the elderly's meals and nutritional balance to support a healthy diet. For example, the nutrition management unit can propose an appropriate meal plan based on the elderly's health condition and food preferences. The nutrition management unit can also provide guidance on how to select ingredients and how to cook them so that the elderly can consume the necessary nutrients. Furthermore, the nutrition management unit can record the elderly's dietary history and monitor their nutritional balance. This allows the elderly to maintain a healthy diet and maintain a balanced nutritional intake.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The communication department communicates with the elderly person. For example, face-to-face meetings, telephone calls, video calls, and other methods can be used. The generation AI is used to listen to the elderly person's reminiscences and chat. The generation AI uses deep learning and natural language processing technology to provide appropriate responses to what the elderly person is saying. Step 2: The training department presents quizzes and questions based on the information obtained by the communication department. These quizzes and questions include simple calculation problems and quizzes to test memory. Generative AI is used to train elderly people against dementia. For example, simple calculation problems such as "What is 5 + 3?" are presented, and the elderly's answers are used to stimulate their brains. Step 3: The monitoring unit monitors the elderly person's health condition based on the information obtained by the communication unit. Health conditions include body temperature, heart rate, blood pressure, etc. The generation AI is used to analyze the elderly person's health condition and contact or inquire with the center as necessary. For example, if an elderly person complains of feeling unwell, the generation AI analyzes the information and notifies the center if there is an abnormality.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] 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.

[0114] Each of the multiple elements including the communication unit, training unit, and monitoring unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the smart device 14 and engages in dialogue with the elderly. The training unit is realized by the specific processing unit 290 of the data processing device 12 and presents quizzes and questions. The monitoring unit monitors the health condition of the elderly using, for example, the camera 42 or sensors of the smart device 14, and the monitoring result is analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] Each of the multiple elements including the communication unit, training unit, and monitoring unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the smart glasses 214 and engages in dialogue with the elderly. The training unit is realized by the specific processing unit 290 of the data processing device 12 and provides quizzes and questions. The monitoring unit monitors the elderly's health status using, for example, the camera 42 or a sensor of the smart glasses 214, and the monitoring unit analyzes the health status by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] Each of the multiple elements including the communication unit, training unit, and monitoring unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the headset type terminal 314 and engages in dialogue with the elderly. The training unit is realized by the specific processing unit 290 of the data processing device 12 and presents quizzes and questions. The monitoring unit monitors the health condition of the elderly using, for example, the camera 42 or sensors of the headset type terminal 314, and the monitoring result is analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] Each of the multiple elements including the communication unit, training unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the communication unit is realized by the control unit 46A of the robot 414 and engages in dialogue with the elderly. The training unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and presents quizzes and questions. The monitoring unit monitors the health condition of the elderly using, for example, the camera 42 or sensors of the robot 414, and the monitoring result is analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) The communication department, which communicates with the elderly, a training unit that asks quizzes or questions based on the information obtained by the communication unit; a monitoring unit that monitors the health condition of the elderly person based on the information obtained by the communication unit. A system characterized by: (Appendix 2) The generative AI has a learning component that learns the preferences and past conversation history of the elderly. 2. The system of claim 1. (Appendix 3) Equipped with a sensor unit that measures body temperature and heart rate using sensors 2. The system of claim 1. (Appendix 4) The communication unit Enjoying elderly people's reminiscences or casual conversation 2. The system of claim 1. (Appendix 5) The training section Ask simple math problems or quizzes to test your memory 2. The system of claim 1. (Appendix 6) The monitoring unit Analyze the health problems of elderly people and contact or refer them to the center as necessary. 2. The system of claim 1. (Appendix 7) The communication unit Estimating the emotions of elderly people and selecting conversation topics based on the estimated emotions of elderly people 2. The system of claim 1. (Appendix 8) The communication unit Analyzing the elderly person's past conversation history and selecting the optimal way to proceed with the conversation 2. The system of claim 1. (Appendix 9) The communication unit During a conversation, filter topics based on the senior's current interests or concerns 2. The system of claim 1. (Appendix 10) The communication unit Estimate the emotions of the elderly and adjust the tempo of the conversation based on the estimated emotions of the elderly. 2. The system of claim 1. (Appendix 11) The communication unit During conversations, the system prioritizes relevant topics based on the elderly person's geographic location. 2. The system of claim 1. (Appendix 12) The communication unit Analyzing the elderly person's social media activity and selecting relevant topics during conversations 2. The system of claim 1. (Appendix 13) The training section Estimate the emotions of the elderly and adjust the difficulty of the quiz based on the estimated emotions of the elderly. 2. The system of claim 1. (Appendix 14) The training section When creating a quiz, the system analyzes the elderly person's past answer history to select the most appropriate questions. 2. The system of claim 1. (Appendix 15) The training section When quizzing, filter question types based on the senior's current cognitive ability. 2. The system of claim 1. (Appendix 16) The training section Estimate the emotions of the elderly and adjust the frequency of quizzes based on the estimated emotions of the elderly. 2. The system of claim 1. (Appendix 17) The training section When presenting quizzes, questions that are relevant to the elderly person are prioritized based on their geographic location information. 2. The system of claim 1. (Appendix 18) The training section When preparing quizzes, the social media activity of seniors is analyzed and relevant questions are presented. 2. The system of claim 1. (Appendix 19) The monitoring unit Estimating the emotions of elderly people and adjusting health monitoring methods based on the estimated emotions of elderly people 2. The system of claim 1. (Appendix 20) The monitoring unit When monitoring health status, analyze the elderly person's past health data to select the most appropriate monitoring method. 2. The system of claim 1. (Appendix 21) The monitoring unit When monitoring health status, customize monitoring measures based on the elderly person's current living situation. 2. The system of claim 1. (Appendix 22) The monitoring unit Estimate the emotions of the elderly and determine monitoring priorities based on the estimated emotions of the elderly. 2. The system of claim 1. (Appendix 23) The monitoring unit When monitoring health status, select the most appropriate monitoring method based on the elderly person's geographic location information. 2. The system of claim 1. (Appendix 24) The monitoring unit Analyzing elderly people's social media activity and proposing monitoring methods when monitoring their health status 2. The system of claim 1. (Appendix 25) The learning unit Estimate the emotions of the elderly and select training data based on the estimated emotions of the elderly. 3. The system of claim 2. (Appendix 26) The learning unit During learning, the learning algorithm is optimized by referencing past learning data. 3. The system of claim 2. (Appendix 27) The learning unit Estimate the emotions of the elderly and adjust the frequency of learning based on the estimated emotions of the elderly. 3. The system of claim 2. (Appendix 28) The learning unit During training, the training data is weighted based on when the conversation history was submitted. 3. The system of claim 2. (Appendix 29) The sensor unit Estimate the emotion of the elderly person and adjust the sensitivity of the sensor based on the estimated emotion of the elderly person. 4. The system of claim 3. (Appendix 30) The sensor unit When measuring with sensors, the optimal measurement method is selected by referring to the elderly person's past health data. 4. The system of claim 3. (Appendix 31) The sensor unit The emotion of the elderly person is estimated, and the frequency of measurement by the sensor is adjusted based on the estimated emotion of the elderly person. 4. The system of claim 3. (Appendix 32) The sensor unit When measuring with sensors, the optimal measurement method is selected taking into account the elderly person's geographical location information. 4. The system of claim 3. [Explanation of symbols]

[0183] 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. The Communication Department, which communicates with the elderly, a training unit that asks quizzes or questions based on the information obtained by the communication unit; A monitoring unit that monitors the health condition of the elderly person based on the information obtained by the communication unit. A system characterized by:

2. The generative AI will have a learning component that will learn about the preferences and past conversation history of the elderly.

2. The system of claim 1 .

3. Equipped with a sensor unit that uses sensors to measure body temperature and heart rate 2. The system of claim 1 .

4. The communication unit includes: Enjoying elderly people's reminiscences or casual conversation 2. The system of claim 1 .

5. The training department includes: Ask simple math problems or quizzes to test your memory 2. The system of claim 1 .

6. The monitoring unit is Analyze the health problems of elderly people and contact or refer them to the center as necessary.

2. The system of claim 1 .

7. The communication unit includes: Estimating the emotions of elderly people and selecting conversation topics based on the estimated emotions of elderly people 2. The system of claim 1 .

8. The communication unit includes: Analyzing the elderly person's past conversation history and selecting the optimal way to proceed with the conversation 2. The system of claim 1 .

9. The communication unit includes: During conversations, filter topics based on the senior’s current interests or concerns 2. The system of claim 1 .

10. The communication unit includes: Estimate the emotions of the elderly and adjust the tempo of the conversation based on the estimated emotions of the elderly 2. The system of claim 1 .

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