System

Abstract

An object of the system according to the embodiment is for people living away from elderly parents to grasp the health condition of the parents and eliminate anxiety.SOLUTION: A system according to an embodiment includes a AI avatar, a generation AI, a server, and a service user. The AI avatar starts a conversation at a fixed time every day. The generation AI summarizes the content of the conversation between the AI avatar and the parent and stores it in the server. The service user confirms the summarized conversation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for people who lived far away from their elderly parents to understand their parents' health status, leaving them with limited means to alleviate their concerns.

[0005] The system according to the embodiment aims to enable people who live far away from their elderly parents to understand their parents' health conditions and alleviate their concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI avatar, a generation AI, a server, and a service user. The AI ​​avatar starts a conversation at a fixed time every day. The generation AI summarizes the conversation between the AI ​​avatar and the parent and saves it on the server. The service user can view the summarized conversation. [Effects of the Invention]

[0007] The system according to the embodiment allows people who live far away from their elderly parents to understand their parents' health conditions and alleviate their concerns. [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) A monitoring system according to an embodiment of the present invention allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety. In this system, an AI avatar initiates a conversation at a fixed time each day, and the generating AI summarizes and saves the conversation on a server, allowing service users to view the summarized conversation. This allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety.

[0029] A monitoring system according to an embodiment includes an AI avatar, a generation AI, a server, and a service user. The AI ​​avatar initiates a conversation at a fixed time every day. For example, the AI ​​avatar asks questions such as, "Good morning. How was your day today?" and "How are you feeling lately?" The generation AI summarizes the conversation between the AI ​​avatar and the parent. For example, if the parent says, "I had a slight headache today," the generation AI summarizes the conversation as, "The parent had a headache." The generation AI can also analyze the conversation in real time and extract important information. The server stores the conversation summarized by the generation AI. For example, the server stores the summarized conversation in a database for later access. The service user can view the summarized conversation. For example, the service user can access the server via a smartphone or computer to keep track of their parent's health and daily events. This allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety.

[0030] The AI ​​avatar can learn a parent's past conversation history and ask personalized questions based on their health status or hobbies. For example, the AI ​​avatar can analyze a parent's past conversation history and ask questions based on the parent's previous conversations and interests. For example, if a parent previously said, "I like gardening," the AI ​​avatar could ask, "How's your gardening going lately?" The AI ​​avatar can also learn past data about the parent's health status and ask questions related to specific health issues. For example, if a parent previously said, "My knee hurts," the AI ​​avatar could ask, "How's your knee pain?" The AI ​​avatar can also provide topics that the parent will enjoy based on the parent's hobbies and interests. For example, if a parent says, "I like cooking," the AI ​​avatar could ask, "What dish have you cooked recently?" This allows the AI ​​avatar to ask questions based on the parent's individual health status and hobbies, thereby attracting the parent's interest and enriching the conversation.

[0031] When summarizing conversation content, the generation AI can automatically highlight and visually emphasize important keywords related to the parent's health condition. For example, the generation AI analyzes the conversation content and automatically highlights important keywords related to the parent's health condition. For example, it highlights keywords such as "headache" and "can't sleep." It also visually emphasizes important information related to the parent's health condition in the summarized conversation content. For example, if the parent says, "I haven't had much of an appetite lately," the phrase "I have no appetite" is displayed in bold or colored. The generation AI also analyzes the conversation content and automatically extracts important keywords related to the parent's health condition and reflects them in the summary. For example, it includes keywords such as "high blood pressure." This visually emphasizes important information related to the parent's health condition, allowing service users to quickly grasp important information.

[0032] When summarizing the content of the conversation, the generation AI can automatically generate graphs that can track changes in the parent's health condition over time. For example, the generation AI analyzes the summarized content of the conversation and automatically generates graphs that can track changes in the parent's health condition over time. For example, it plots changes in the parent's physical condition by date. It also generates graphs that visually display changes in the parent's health condition based on the summarized content of the conversation. For example, it displays changes in the parent's sleep state and appetite in a graph. The generation AI also analyzes the content of the conversation, extracts data to track changes in the parent's health condition over time, and automatically generates graphs. For example, it displays changes in the parent's blood pressure and weight in a graph. This allows the service user to visually track changes in the parent's health condition and more accurately understand it.

[0033] When summarizing conversation content, the generation AI can evaluate it against expert opinions about the parent's health condition. For example, the generation AI analyzes the summarized conversation content and evaluates it against expert opinions about the parent's health condition. For example, it evaluates the parent's health condition based on a doctor's opinion. In addition, a function can be added to evaluate the summarized conversation content against expert opinions to more accurately understand the parent's health condition. For example, the expert's advice can be reflected in the summary. The generation AI can also analyze the summarized conversation content and build a system that evaluates it based on expert opinions about the parent's health condition. For example, it can evaluate the parent's health condition based on expert opinions and reflect this in the summary. This allows for a more accurate evaluation of the parent's health condition by comparing it with expert opinions.

[0034] When summarizing the conversation content, the generative AI can integrate and store it with other data related to the parent's health condition. For example, the generative AI can integrate the summarized conversation content with the parent's medical records to obtain a comprehensive understanding of the parent's health condition. For example, the generative AI can store the parent's medical records and the conversation content in a single database. The generative AI can also integrate the summarized conversation content with the parent's fitness data to obtain a more accurate understanding of the parent's health condition. For example, the generative AI can integrate and store the parent's exercise data and the conversation content. The generative AI can also integrate the summarized conversation content with other data related to the parent's health condition to build a system for comprehensive health management. For example, the generative AI can integrate and store the parent's medical records and fitness data. This makes it possible to provide comprehensive health management by integrating it with other data related to the parent's health condition.

[0035] The generation AI can enhance search functionality by tagging summarized conversation content to make it easier for users to search. For example, the generation AI can automatically tag summarized conversation content based on the parent's health condition or topics. For example, tags such as "health," "diet," and "exercise" are added. The generation AI can also tag summarized conversation content to make it easier for users to search for specific keywords. For example, searching for "headache" will display conversations in which the parent talked about headaches. The generation AI can also tag summarized conversation content based on the parent's emotional state to enhance search functionality. For example, emotion tags such as "happy," "sad," and "anxious" are added. This makes it easier for users to search, allowing them to quickly obtain the information they need.

[0036] The generation AI can convert summarized conversation content into infographics that are easy for users to understand visually. For example, the generation AI can analyze the summarized conversation content and automatically generate infographics that visually display a parent's health condition and emotional state. For example, it can display changes in a parent's physical condition using graphs and charts. The generation AI can also convert the summarized conversation content into infographics that allow users to understand a parent's health condition at a glance. For example, it can show changes in a parent's sleep state and appetite using icons and colors. The generation AI can also build a system that analyzes the summarized conversation content and generates infographics that visually display a parent's health condition and emotional state. For example, it can display changes in a parent's emotions over time. This makes it easier for users to understand the information by providing it in a format that is visually easy to understand.

[0037] The generation AI can incorporate speech synthesis technology so that the user can check the summarized conversation content by voice. For example, the generation AI can convert the summarized conversation content into audio using speech synthesis technology, allowing the user to check it by voice. For example, the summary content can be played back through a smartphone or speaker. The generation AI can also convert the summarized conversation content into audio using speech synthesis technology, allowing the user to check it while driving or doing housework. For example, the summary content can be played back through speakers or earphones in the car. The generation AI can also convert the summarized conversation content into audio using speech synthesis technology, allowing the user to obtain information even in situations where they cannot check it visually. For example, the summary content can be provided by voice to the visually impaired. This allows the user to check the information by voice, allowing them to obtain information even in situations where they cannot check it visually.

[0038] The generative AI can add a function that allows users to share summarized conversation content with other family members or medical professionals. For example, the generative AI analyzes the summarized conversation content and adds a function that allows users to share it with other family members or medical professionals. For example, the summary content can be sent by email or message. A platform for sharing summarized conversation content can also be built, allowing users to easily share information with other family members or medical professionals. For example, the summary content can be shared through a dedicated app or website. The generative AI can also analyze the summarized conversation content and automatically extract the information necessary for users to share it with other family members or medical professionals. For example, important information about a parent's health condition can be highlighted and shared. This allows users to share information with other family members and medical professionals, thereby more effectively managing their parent's health.

[0039] When analyzing conversation content, the generative AI can automatically detect patterns related to a parent's health condition and detect abnormalities early. For example, the generative AI analyzes conversation content and automatically detects patterns related to a parent's health condition. For example, if a parent says, "I haven't been sleeping well lately," it detects a pattern of sleep deprivation. It also analyzes conversation content and builds a system that detects abnormalities related to a parent's health condition early. For example, if a parent says, "I haven't had much of an appetite lately," it detects a pattern of loss of appetite. It also analyzes conversation content and automatically detects patterns related to a parent's health condition and detects abnormalities early. For example, if a parent says, "I've been having chest pains lately," it detects the possibility of a heart problem. This makes it possible to automatically detect patterns related to a parent's health condition and detect abnormalities early, enabling prompt response.

[0040] The generative AI can evaluate the health monitoring results against the parent's medical records and notify medical professionals. For example, the generative AI may evaluate the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's blood pressure is high, it may notify a doctor. A system may also be built that evaluates the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's blood sugar level is high, it may notify a doctor. The generative AI may also evaluate the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's heart rate is abnormally high, it may notify a doctor. This allows the generative AI to evaluate the parent's health monitoring results against the medical records and notify medical professionals, enabling appropriate medical treatment.

[0041] The generating AI can integrate and analyze health monitoring results with parent's lifestyle data. For example, the generating AI integrates health monitoring results with parent's lifestyle data and performs a comprehensive analysis. For example, it integrates and analyzes parent's dietary data and health status. It also builds a system that integrates health monitoring results with parent's exercise data and performs a comprehensive analysis. For example, it integrates and analyzes parent's exercise volume and health status. The generating AI also integrates health monitoring results with parent's lifestyle data and performs a comprehensive analysis. For example, it integrates and analyzes parent's sleep data and health status. This makes it possible to integrate and analyze parent's health monitoring results with lifestyle data, enabling comprehensive health management.

[0042] The generative AI can evaluate health monitoring results based on a predictive model of the parent's health condition and predict future risks. For example, the generative AI analyzes health monitoring results and predicts future risks based on a predictive model of the parent's health condition. For example, it predicts future heart disease risk based on the parent's blood pressure data. It also builds a system that evaluates health monitoring results based on a predictive model of the parent's health condition and predicts future risks. For example, it predicts future diabetes risk based on the parent's blood sugar level data. The generative AI also analyzes health monitoring results and predicts future risks based on a predictive model of the parent's health condition. For example, it predicts future obesity risk based on the parent's weight data. This enables preventive health management by evaluating the parent's health monitoring results based on a predictive model and predicting future risks.

[0043] When detecting an emergency from the content of a conversation, the generation AI can assess the level of urgency by referring to the parent's past health history. For example, the generation AI analyzes the content of the conversation and assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I have chest pain," the level of urgency is assessed based on their past history of heart disease. In addition, a system can be built that, when detecting an emergency from the content of a conversation, assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I have a headache," the level of urgency is assessed based on their past history of migraines. In addition, the generation AI analyzes the content of the conversation and assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I'm having trouble breathing," the level of urgency is assessed based on their past history of respiratory disease. This allows for a more appropriate response by assessing the level of urgency by referring to the parent's past health history.

[0044] When an emergency is detected, the generation AI can automatically obtain the parent's current location information and notify emergency contacts. For example, when an emergency is detected from the content of a conversation, the generation AI automatically obtains the parent's current location information and notifies emergency contacts. For example, if a parent says, "I fell and can't move," the current location information is obtained and notified. We will also build a system that automatically obtains the parent's current location information and notifies emergency contacts when an emergency is detected. For example, if a parent says, "I have chest pain," the current location information is obtained and notified. When an emergency is detected from the content of a conversation, the generation AI automatically obtains the parent's current location information and notifies emergency contacts. For example, if a parent says, "I'm having trouble breathing," the current location information is obtained and notified. This allows for a rapid response by automatically obtaining the parent's current location information and notifying emergency contacts.

[0045] When the generating AI detects an emergency, it can also send an emergency notification to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I fell and can't move," it will notify friends and family living in the neighborhood. A system can also be built to send emergency notifications to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I have chest pain," it will notify friends and family living in the neighborhood. When the generating AI detects an emergency, it can also send emergency notifications to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I'm having trouble breathing," it will notify friends and family living in the neighborhood. This allows for a quick response by sending emergency notifications to friends and family living in the parent's neighborhood, enabling them to respond quickly.

[0046] When the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, when the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I have chest pain," it will notify the medical institution. We will also build a system that automatically sends an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I'm having trouble breathing," it will notify the medical institution. When the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I fell and can't move," it will notify the nursing facility. This allows emergency notifications to be sent to the parent's medical institution or nursing facility, enabling specialized treatment to be provided quickly.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The monitoring system can also collect data on parents' lifestyle habits and use it to monitor their health. For example, it can record the parents' diet and exercise habits to track changes in their health. It can also analyze the parents' sleep patterns and evaluate their sleep quality. It can also provide advice on maintaining health based on the parents' lifestyle habits. This allows for a comprehensive understanding of parents' lifestyle habits and more comprehensive health management.

[0049] The monitoring system can provide entertainment content based on the parent's hobbies and interests. For example, it can recommend music and movies that the parent likes. It can also provide news and articles related to the parent's interests. It can also introduce online communities and events that the parent can participate in. This can bring enjoyment to the parent's life and support their mental health.

[0050] The monitoring system can have the ability to share data about a parent's health with other family members or medical professionals. For example, reports about the parent's health can be sent by email periodically. Data about the parent's health can also be shared on an online platform where family members and medical professionals can access it. Furthermore, a function can be added to notify important information about the parent's health in real time. This allows parents to share their health status with family members and medical professionals, enabling more effective health management.

[0051] The monitoring system can analyze data on a parent's health condition and predict future health risks. For example, it can predict future risks of heart disease and diabetes based on a parent's blood pressure and blood sugar data. It can also assess future health risks based on a parent's lifestyle data. It can also provide preventative health management advice based on predicted health risks. This allows parents to predict future health risks and take early action.

[0052] The monitoring system can be equipped with a function to analyze data on the parent's health condition and detect abnormalities early. For example, it can monitor the parent's blood pressure and temperature data in real time and issue an alert if an abnormality is detected. It can also analyze the parent's lifestyle data and detect abnormal patterns. Furthermore, it can add a function to notify family members or medical professionals if an abnormality is detected. This allows abnormalities in the parent's health condition to be detected early and responded to promptly.

[0053] The monitoring system can be equipped with a function to analyze data on a parent's health condition and track changes in that condition over time. For example, the system can plot a parent's blood pressure and temperature data by date to visually display changes in their health condition. It can also analyze a parent's lifestyle data over time to track changes in their health condition. It can also display changes in their health condition in graphs and charts so that family members and medical professionals can easily understand them. This allows parents to track changes in their health condition over time and enable comprehensive health management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The AI ​​avatar will start a conversation at a set time each day, asking questions such as, "Good morning. How was your day today?" or "How are you feeling these days?" Step 2: The generative AI summarizes the conversation between the AI ​​avatar and the parent. For example, if the parent says, "I had a slight headache today," the generative AI summarizes it as, "The parent had a headache." The generative AI can also analyze the conversation in real time and extract important information. Step 3: The server stores the conversation summarized by the generation AI, for example, by storing the summarized conversation in a database for later access. Step 4: The service user can review the summarized conversations, for example by accessing the server via a smartphone or computer, to keep track of their parent's health status and daily events.

[0056] (Example 2) A monitoring system according to an embodiment of the present invention allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety. In this system, an AI avatar initiates a conversation at a fixed time each day, and the generating AI summarizes and saves the conversation on a server, allowing service users to view the summarized conversation. This allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety.

[0057] A monitoring system according to an embodiment includes an AI avatar, a generation AI, a server, and a service user. The AI ​​avatar initiates a conversation at a fixed time every day. For example, the AI ​​avatar asks questions such as, "Good morning. How was your day today?" and "How are you feeling lately?" The generation AI summarizes the conversation between the AI ​​avatar and the parent. For example, if the parent says, "I had a slight headache today," the generation AI summarizes the conversation as, "The parent had a headache." The generation AI can also analyze the conversation in real time and extract important information. The server stores the conversation summarized by the generation AI. For example, the server stores the summarized conversation in a database for later access. The service user can view the summarized conversation. For example, the service user can access the server via a smartphone or computer to keep track of their parent's health and daily events. This allows people who live far from their elderly parents to keep track of their parents' health and alleviate their anxiety.

[0058] The AI ​​avatar can learn a parent's past conversation history and ask personalized questions based on their health status or hobbies. For example, the AI ​​avatar can analyze a parent's past conversation history and ask questions based on the parent's previous conversations and interests. For example, if a parent previously said, "I like gardening," the AI ​​avatar could ask, "How's your gardening going lately?" The AI ​​avatar can also learn past data about the parent's health status and ask questions related to specific health issues. For example, if a parent previously said, "My knee hurts," the AI ​​avatar could ask, "How's your knee pain?" The AI ​​avatar can also provide topics that the parent will enjoy based on the parent's hobbies and interests. For example, if a parent says, "I like cooking," the AI ​​avatar could ask, "What dish have you cooked recently?" This allows the AI ​​avatar to ask questions based on the parent's individual health status and hobbies, thereby attracting the parent's interest and enriching the conversation.

[0059] The AI ​​avatar can detect changes in a parent's tone of voice and speaking style, infer emotional changes, and adjust the content of the conversation accordingly. For example, the AI ​​avatar can analyze a parent's tone of voice to determine whether they are tired. For example, if a parent's voice is low and listless, the AI ​​avatar might ask, "You seem a little tired today. Is there anything you're worried about?" The AI ​​avatar can also analyze the speed and rhythm of a parent's speech to infer their emotional state. For example, if a parent speaks quickly, the AI ​​avatar might ask, "Did anything exciting happen today?" The AI ​​avatar can also detect changes in a parent's voice to determine whether they are stressed. For example, if a parent's voice is trembling, the AI ​​avatar might ask, "Have you been feeling stressed recently?" This allows the AI ​​avatar to detect changes in a parent's emotions and provide appropriate conversation content to provide psychological support to the parent.

[0060] When summarizing conversation content, the generation AI can automatically highlight and visually emphasize important keywords related to the parent's health condition. For example, the generation AI analyzes the conversation content and automatically highlights important keywords related to the parent's health condition. For example, it highlights keywords such as "headache" and "can't sleep." It also visually emphasizes important information related to the parent's health condition in the summarized conversation content. For example, if the parent says, "I haven't had much of an appetite lately," the phrase "I have no appetite" is displayed in bold or colored. The generation AI also analyzes the conversation content and automatically extracts important keywords related to the parent's health condition and reflects them in the summary. For example, it includes keywords such as "high blood pressure." This visually emphasizes important information related to the parent's health condition, allowing service users to quickly grasp important information.

[0061] When summarizing the content of the conversation, the generation AI can automatically generate graphs that can track changes in the parent's health condition over time. For example, the generation AI analyzes the summarized content of the conversation and automatically generates graphs that can track changes in the parent's health condition over time. For example, it plots changes in the parent's physical condition by date. It also generates graphs that visually display changes in the parent's health condition based on the summarized content of the conversation. For example, it displays changes in the parent's sleep state and appetite in a graph. The generation AI also analyzes the content of the conversation, extracts data to track changes in the parent's health condition over time, and automatically generates graphs. For example, it displays changes in the parent's blood pressure and weight in a graph. This allows the service user to visually track changes in the parent's health condition and more accurately understand it.

[0062] When summarizing the content of a conversation, the generation AI can add the parent's emotional state and record emotional changes. For example, the generation AI analyzes the content of the conversation, estimates the parent's emotional state, and adds it to the summary. For example, if the parent says, "Today was fun," the emotion "fun" is included in the summary. The generation AI also adds the parent's emotional state to the summarized content of the conversation and records emotional changes. For example, if the parent says, "Today was a little sad," the emotion "sad" is included in the summary. The emotion estimation function also adds the parent's emotional state to the summarized content of the conversation and records emotional changes chronologically. For example, the parent's emotional changes can be recorded by date and displayed visually. In this way, by recording the parent's emotional state, the parent's mental health can also be understood.

[0063] When summarizing conversation content, the generation AI can evaluate it against expert opinions about the parent's health condition. For example, the generation AI analyzes the summarized conversation content and evaluates it against expert opinions about the parent's health condition. For example, it evaluates the parent's health condition based on a doctor's opinion. In addition, a function can be added to evaluate the summarized conversation content against expert opinions to more accurately understand the parent's health condition. For example, the expert's advice can be reflected in the summary. The generation AI can also analyze the summarized conversation content and build a system that evaluates it based on expert opinions about the parent's health condition. For example, it can evaluate the parent's health condition based on expert opinions and reflect this in the summary. This allows for a more accurate evaluation of the parent's health condition by comparing it with expert opinions.

[0064] When summarizing the conversation content, the generative AI can integrate and store it with other data related to the parent's health condition. For example, the generative AI can integrate the summarized conversation content with the parent's medical records to obtain a comprehensive understanding of the parent's health condition. For example, the generative AI can store the parent's medical records and the conversation content in a single database. The generative AI can also integrate the summarized conversation content with the parent's fitness data to obtain a more accurate understanding of the parent's health condition. For example, the generative AI can integrate and store the parent's exercise data and the conversation content. The generative AI can also integrate the summarized conversation content with other data related to the parent's health condition to build a system for comprehensive health management. For example, the generative AI can integrate and store the parent's medical records and fitness data. This makes it possible to provide comprehensive health management by integrating it with other data related to the parent's health condition.

[0065] When summarizing the conversation, the generative AI can analyze the parent's emotional response and provide feedback to elicit positive emotions. For example, if a parent says, "I had fun today," the generative AI can emphasize the emotion of "fun." It also uses emotion estimation to analyze the parent's emotional response to the summarized conversation and provide advice to elicit positive emotions. For example, if a parent says, "I'm feeling a little down today," the generative AI can encourage them by saying, "Tomorrow will definitely be a good day." The generative AI can also analyze the parent's emotional response to the summarized conversation and provide specific suggestions to elicit positive emotions. For example, if a parent says, "I'm feeling down lately because I haven't been getting enough exercise," the generative AI can suggest, "Why don't you go for a short walk?" This analysis of the parent's emotional response and eliciting positive emotions can support the parent's mental health.

[0066] The generation AI can enhance search functionality by tagging summarized conversation content to make it easier for users to search. For example, the generation AI can automatically tag summarized conversation content based on the parent's health condition or topics. For example, tags such as "health," "diet," and "exercise" are added. The generation AI can also tag summarized conversation content to make it easier for users to search for specific keywords. For example, searching for "headache" will display conversations in which the parent talked about headaches. The generation AI can also tag summarized conversation content based on the parent's emotional state to enhance search functionality. For example, emotion tags such as "happy," "sad," and "anxious" are added. This makes it easier for users to search, allowing them to quickly obtain the information they need.

[0067] The generation AI can convert summarized conversation content into infographics that are easy for users to understand visually. For example, the generation AI can analyze the summarized conversation content and automatically generate infographics that visually display a parent's health condition and emotional state. For example, it can display changes in a parent's physical condition using graphs and charts. The generation AI can also convert the summarized conversation content into infographics that allow users to understand a parent's health condition at a glance. For example, it can show changes in a parent's sleep state and appetite using icons and colors. The generation AI can also build a system that analyzes the summarized conversation content and generates infographics that visually display a parent's health condition and emotional state. For example, it can display changes in a parent's emotions over time. This makes it easier for users to understand the information by providing it in a format that is visually easy to understand.

[0068] The generation AI can collect users' emotional reactions to the summarized conversation content and improve the accuracy of the summary based on the feedback. For example, the generation AI collects users' emotional reactions to the summarized conversation content and improves the accuracy of the summary based on that data. For example, if a user evaluates the summary as "easy to understand," the AI ​​reflects that feedback. The generation AI also uses an emotion estimation function to analyze the user's emotional reactions to the summarized conversation content and improve the accuracy of the summary. For example, if a user evaluates the summary as "insufficient," the AI ​​regenerates the summary based on that feedback. The generation AI also collects users' emotional reactions to the summarized conversation content and builds a system that continuously improves the accuracy of the summary based on that data. For example, the AI ​​reflects user feedback in real time. This allows the system to collect users' emotional reactions and improve the accuracy of the summary, making it possible to provide more accurate information.

[0069] The generation AI can incorporate speech synthesis technology so that the user can check the summarized conversation content by voice. For example, the generation AI can convert the summarized conversation content into audio using speech synthesis technology, allowing the user to check it by voice. For example, the summary content can be played back through a smartphone or speaker. The generation AI can also convert the summarized conversation content into audio using speech synthesis technology, allowing the user to check it while driving or doing housework. For example, the summary content can be played back through speakers or earphones in the car. The generation AI can also convert the summarized conversation content into audio using speech synthesis technology, allowing the user to obtain information even in situations where they cannot check it visually. For example, the summary content can be provided by voice to the visually impaired. This allows the user to check the information by voice, allowing them to obtain information even in situations where they cannot check it visually.

[0070] The generative AI can add a function that allows users to share summarized conversation content with other family members or medical professionals. For example, the generative AI analyzes the summarized conversation content and adds a function that allows users to share it with other family members or medical professionals. For example, the summary content can be sent by email or message. A platform for sharing summarized conversation content can also be built, allowing users to easily share information with other family members or medical professionals. For example, the summary content can be shared through a dedicated app or website. The generative AI can also analyze the summarized conversation content and automatically extract the information necessary for users to share it with other family members or medical professionals. For example, important information about a parent's health condition can be highlighted and shared. This allows users to share information with other family members and medical professionals, thereby more effectively managing their parent's health.

[0071] The generation AI can analyze the user's emotional response to the summarized conversation content and make suggestions to elicit positive emotions. For example, if a user evaluates a summary as "helpful," the generation AI can recommend a summary format. The generation AI can also use emotion estimation to analyze the user's emotional response to the summarized conversation content and provide advice to elicit positive emotions. For example, if a user evaluates a summary as "difficult to understand," the generation AI can suggest areas for improvement. The generation AI can also analyze the user's emotional response to the summarized conversation content and make specific suggestions to elicit positive emotions. For example, if a user evaluates a summary as "reassuring," the generation AI can apply that summary format to other summaries. This analysis of the user's emotional response and suggestions to elicit positive emotions improves user satisfaction.

[0072] When analyzing conversation content, the generative AI can automatically detect patterns related to a parent's health condition and detect abnormalities early. For example, the generative AI analyzes conversation content and automatically detects patterns related to a parent's health condition. For example, if a parent says, "I haven't been sleeping well lately," it detects a pattern of sleep deprivation. It also analyzes conversation content and builds a system that detects abnormalities related to a parent's health condition early. For example, if a parent says, "I haven't had much of an appetite lately," it detects a pattern of loss of appetite. It also analyzes conversation content and automatically detects patterns related to a parent's health condition and detects abnormalities early. For example, if a parent says, "I've been having chest pains lately," it detects the possibility of a heart problem. This makes it possible to automatically detect patterns related to a parent's health condition and detect abnormalities early, enabling prompt response.

[0073] The generative AI can evaluate the health monitoring results against the parent's medical records and notify medical professionals. For example, the generative AI may evaluate the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's blood pressure is high, it may notify a doctor. A system may also be built that evaluates the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's blood sugar level is high, it may notify a doctor. The generative AI may also evaluate the health monitoring results against the parent's medical records and notify medical professionals if an abnormality is detected. For example, if the parent's heart rate is abnormally high, it may notify a doctor. This allows the generative AI to evaluate the parent's health monitoring results against the medical records and notify medical professionals, enabling appropriate medical treatment.

[0074] The generative AI can use its emotion estimation function to analyze the relationship between a parent's emotional state and their health condition, and evaluate the impact of emotional changes on their health. For example, the generative AI can analyze the content of a conversation and analyze the relationship between a parent's emotional state and their health condition. For example, if a parent says, "I've been feeling a lot of stress lately," the system can evaluate the impact of stress on their health. The generative AI can also use its emotion estimation function to analyze the relationship between a parent's emotional state and their health condition, and build a system to evaluate the impact of emotional changes on their health. For example, if a parent says, "I've been feeling depressed lately," the system can evaluate the impact of mood changes on their health. The generative AI can also analyze the content of a conversation, analyze the relationship between a parent's emotional state and their health condition, and evaluate the impact of emotional changes on their health. For example, if a parent says, "I haven't had any fun lately," the system can evaluate the impact of emotional changes on their health. This enables comprehensive health management by analyzing the relationship between a parent's emotional state and their health condition and evaluating the impact of emotional changes on their health.

[0075] The generating AI can integrate and analyze health monitoring results with parent's lifestyle data. For example, the generating AI integrates health monitoring results with parent's lifestyle data and performs a comprehensive analysis. For example, it integrates and analyzes parent's dietary data and health status. It also builds a system that integrates health monitoring results with parent's exercise data and performs a comprehensive analysis. For example, it integrates and analyzes parent's exercise volume and health status. The generating AI also integrates health monitoring results with parent's lifestyle data and performs a comprehensive analysis. For example, it integrates and analyzes parent's sleep data and health status. This makes it possible to integrate and analyze parent's health monitoring results with lifestyle data, enabling comprehensive health management.

[0076] The generative AI can evaluate health monitoring results based on a predictive model of the parent's health condition and predict future risks. For example, the generative AI analyzes health monitoring results and predicts future risks based on a predictive model of the parent's health condition. For example, it predicts future heart disease risk based on the parent's blood pressure data. It also builds a system that evaluates health monitoring results based on a predictive model of the parent's health condition and predicts future risks. For example, it predicts future diabetes risk based on the parent's blood sugar level data. The generative AI also analyzes health monitoring results and predicts future risks based on a predictive model of the parent's health condition. For example, it predicts future obesity risk based on the parent's weight data. This enables preventive health management by evaluating the parent's health monitoring results based on a predictive model and predicting future risks.

[0077] The generation AI can use its emotion estimation function to automatically generate health advice based on the parent's emotional state and provide it to the parent. For example, the generation AI analyzes the parent's emotional state and automatically generates health advice based on the results. For example, if a parent says, "I've been feeling stressed lately," the system could advise, "Try taking deep breaths to relax." We also use the emotion estimation function to build a system that automatically generates health advice based on the parent's emotional state and provides it to the parent. For example, if a parent says, "I've been feeling depressed lately," the system could advise, "Try taking a walk to change your mood." The generation AI can also analyze the parent's emotional state and automatically generate health advice based on the results and provide it to the parent. For example, if a parent says, "I haven't had any fun lately," the system could advise, "Why don't you take up a new hobby?" This allows for more effective health advice based on the parent's emotional state, enabling more effective health management for parents.

[0078] When detecting an emergency from the content of a conversation, the generation AI can assess the level of urgency by referring to the parent's past health history. For example, the generation AI analyzes the content of the conversation and assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I have chest pain," the level of urgency is assessed based on their past history of heart disease. In addition, a system can be built that, when detecting an emergency from the content of a conversation, assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I have a headache," the level of urgency is assessed based on their past history of migraines. In addition, the generation AI analyzes the content of the conversation and assesses the level of urgency by referring to the parent's past health history. For example, if a parent says, "I'm having trouble breathing," the level of urgency is assessed based on their past history of respiratory disease. This allows for a more appropriate response by assessing the level of urgency by referring to the parent's past health history.

[0079] When an emergency is detected, the generation AI can automatically obtain the parent's current location information and notify emergency contacts. For example, when an emergency is detected from the content of a conversation, the generation AI automatically obtains the parent's current location information and notifies emergency contacts. For example, if a parent says, "I fell and can't move," the current location information is obtained and notified. We will also build a system that automatically obtains the parent's current location information and notifies emergency contacts when an emergency is detected. For example, if a parent says, "I have chest pain," the current location information is obtained and notified. When an emergency is detected from the content of a conversation, the generation AI automatically obtains the parent's current location information and notifies emergency contacts. For example, if a parent says, "I'm having trouble breathing," the current location information is obtained and notified. This allows for a rapid response by automatically obtaining the parent's current location information and notifying emergency contacts.

[0080] Using the emotion estimation function, the generation AI can also issue an emergency notification if a parent's emotional state changes suddenly. For example, the generation AI analyzes the parent's emotional state and issues an emergency notification if a sudden change is detected. For example, if a parent says, "I suddenly felt a pain in my chest," the system will detect this sudden change in emotion and notify the parent. We will also build a system using the emotion estimation function to issue an emergency notification if a parent's emotional state changes suddenly. For example, if a parent says, "I suddenly felt sick," the system will detect this sudden change in emotion and notify the parent. The generation AI also analyzes the parent's emotional state and issues an emergency notification if a sudden change is detected. For example, if a parent says, "I suddenly felt short of breath," the system will detect this sudden change in emotion and notify the parent. This allows for a rapid response by issuing an emergency notification even if a parent's emotional state changes suddenly.

[0081] When the generating AI detects an emergency, it can also send an emergency notification to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I fell and can't move," it will notify friends and family living in the neighborhood. A system can also be built to send emergency notifications to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I have chest pain," it will notify friends and family living in the neighborhood. When the generating AI detects an emergency, it can also send emergency notifications to friends and family living in the parent's neighborhood, encouraging them to respond quickly. For example, if a parent says, "I'm having trouble breathing," it will notify friends and family living in the neighborhood. This allows for a quick response by sending emergency notifications to friends and family living in the parent's neighborhood, enabling them to respond quickly.

[0082] When the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, when the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I have chest pain," it will notify the medical institution. We will also build a system that automatically sends an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I'm having trouble breathing," it will notify the medical institution. When the generating AI detects an emergency, it can automatically send an emergency notification to the parent's medical institution or nursing facility, requesting specialized treatment. For example, if a parent says, "I fell and can't move," it will notify the nursing facility. This allows emergency notifications to be sent to the parent's medical institution or nursing facility, enabling specialized treatment to be provided quickly.

[0083] The generation AI can use its emotion estimation function to automatically generate and provide advice to parents to stabilize their emotions in the event of an emergency. For example, the generation AI analyzes a parent's emotional state and automatically generates advice to stabilize their emotions in the event of an emergency. For example, if a parent says, "I have chest pain," the system could advise them to "take a deep breath and relax." Furthermore, using the emotion estimation function, a system can be built that automatically generates and provides advice to parents to stabilize their emotions in the event of an emergency. For example, if a parent says, "I'm having trouble breathing," the system could advise them to "take slow, steady breaths." The generation AI can also analyze a parent's emotional state and automatically generate and provide advice to stabilize their emotions in the event of an emergency. For example, if a parent says, "I fell and can't move," the system could advise them to "calm down and call for help." This allows parents to maintain their mental stability by providing advice to stabilize their emotions in the event of an emergency.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The monitoring system can also collect data on parents' lifestyle habits and use it to monitor their health. For example, it can record the parents' diet and exercise habits to track changes in their health. It can also analyze the parents' sleep patterns and evaluate their sleep quality. It can also provide advice on maintaining health based on the parents' lifestyle habits. This allows for a comprehensive understanding of parents' lifestyle habits and more comprehensive health management.

[0086] The monitoring system can provide entertainment content based on the parent's hobbies and interests. For example, it can recommend music and movies that the parent likes. It can also provide news and articles related to the parent's interests. It can also introduce online communities and events that the parent can participate in. This can bring enjoyment to the parent's life and support their mental health.

[0087] The monitoring system can estimate the parent's emotional state and suggest relaxation methods according to the emotion. For example, if the parent is feeling stressed, it can suggest deep breathing or meditation. If the parent is feeling anxious, it can also provide relaxing music or natural sounds. It can also provide advice on creating an environment where the parent can relax. This allows the system to provide relaxation methods according to the parent's emotional state and support their mental health.

[0088] The monitoring system can have the ability to share data about a parent's health with other family members or medical professionals. For example, reports about the parent's health can be sent by email periodically. Data about the parent's health can also be shared on an online platform where family members and medical professionals can access it. Furthermore, a function can be added to notify important information about the parent's health in real time. This allows parents to share their health status with family members and medical professionals, enabling more effective health management.

[0089] The monitoring system can analyze data on a parent's health condition and predict future health risks. For example, it can predict future risks of heart disease and diabetes based on a parent's blood pressure and blood sugar data. It can also assess future health risks based on a parent's lifestyle data. It can also provide preventative health management advice based on predicted health risks. This allows parents to predict future health risks and take early action.

[0090] The monitoring system can estimate the parent's emotional state and provide entertainment content according to that emotion. For example, if the parent is feeling sad, it can recommend movies or music that will brighten their mood. If the parent is feeling stressed, it can provide relaxing music or guided meditation. It can also introduce online games and puzzles that the parent can enjoy. In this way, entertainment content according to the parent's emotional state can be provided to support their mental health.

[0091] The monitoring system can be equipped with a function to analyze data on the parent's health condition and detect abnormalities early. For example, it can monitor the parent's blood pressure and temperature data in real time and issue an alert if an abnormality is detected. It can also analyze the parent's lifestyle data and detect abnormal patterns. Furthermore, it can add a function to notify family members or medical professionals if an abnormality is detected. This allows abnormalities in the parent's health condition to be detected early and responded to promptly.

[0092] The monitoring system can estimate the parent's emotional state and provide health advice according to the emotion. For example, if the parent is feeling stressed, it can provide advice on exercise and diet to reduce stress. If the parent is feeling anxious, it can provide relaxation techniques and information on support groups. Furthermore, if the parent is feeling depressed, it can suggest activities or hobbies to lift their spirits. In this way, it is possible to provide health advice according to the parent's emotional state and support comprehensive health management.

[0093] The monitoring system can be equipped with a function to analyze data on a parent's health condition and track changes in that condition over time. For example, the system can plot a parent's blood pressure and temperature data by date to visually display changes in their health condition. It can also analyze a parent's lifestyle data over time to track changes in their health condition. It can also display changes in their health condition in graphs and charts so that family members and medical professionals can easily understand them. This allows parents to track changes in their health condition over time and enable comprehensive health management.

[0094] The monitoring system can estimate a parent's emotional state and suggest communication methods according to their emotions. For example, if a parent feels lonely, it can suggest a video call with family or friends. If a parent feels stressed, it can provide relaxing conversation topics. It can also introduce online communities and events that parents can enjoy. In this way, it can suggest communication methods according to a parent's emotional state and support their mental health.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The AI ​​avatar will start a conversation at a set time each day, asking questions such as, "Good morning. How was your day today?" or "How are you feeling these days?" Step 2: The generative AI summarizes the conversation between the AI ​​avatar and the parent. For example, if the parent says, "I had a slight headache today," the generative AI summarizes it as, "The parent had a headache." The generative AI can also analyze the conversation in real time and extract important information. Step 3: The server stores the conversation summarized by the generation AI, for example, by storing the summarized conversation in a database for later access. Step 4: The service user can review the summarized conversations, for example by accessing the server via a smartphone or computer, to keep track of their parent's health status and daily events.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[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 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.

[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. 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.

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

[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 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.

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

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

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

[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 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.

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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. AI avatars and Generative AI and The server and a service user; The AI ​​avatar is Start the conversation at a set time each day The generated AI is Summarize the conversation between the AI ​​avatar and the parent, Store it on the server, The service user: Check out the summary of the conversation A system characterized by:

2. The AI ​​avatar is learning the parent's past conversation history; Ask personalized questions based on your health and interests 2. The system of claim 1.

3. The AI ​​avatar is detecting a change in the parent's tone of voice or speaking style; Predicting changes in emotions and adjusting conversation content 2. The system of claim 1.

4. The generated AI is In summarizing the conversation, automatically highlighting important keywords related to the parent's health condition; Visual emphasis 2. The system of claim 1.

5. The generated AI is When summarizing the conversation, Automatically generate a graph that tracks changes in the parent's health over time 2. The system of claim 1.

Images