System

Abstract

An object of the system according to the embodiment is to efficiently perform health management and daily life support of elderly people.SOLUTION: A system according to an embodiment includes a response unit, a monitoring unit, a providing unit, and a support unit. The response unit analyzes the speech content of the elderly person and makes a response. The monitoring unit monitors the health data based on the information obtained by the response unit. The providing unit provides a health management advice based on the data collected by the monitoring unit. The support unit supports daily life based on the advice provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately support the health management and daily life of the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide health management and daily life support for elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a response unit, a monitoring unit, a providing unit, and a support unit. The response unit analyzes the content of statements made by the elderly person and provides a response. The monitoring unit monitors health data based on information obtained by the response unit. The providing unit provides health management advice based on the data collected by the monitoring unit. The support unit supports daily life based on the advice provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide health management and daily life support for elderly people. [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 system according to an embodiment of the present invention utilizes generative AI to address health issues and loneliness in the elderly. In this system, the generative AI functions as a conversation partner, preventing dementia through dialogue with the elderly. The generative AI then monitors the elderly's health status and provides appropriate health management. Furthermore, the generative AI supports daily life, enabling the elderly to live their lives with minimal smartphone use. For example, the generative AI can stimulate the elderly's brain by asking questions or listening to their stories about past events. The generative AI also analyzes the elderly's speech and provides appropriate responses, enabling natural conversation. The generative AI then collects and analyzes data such as the elderly's body temperature, blood pressure, and heart rate to understand their health status. This allows for early intervention if an abnormality is detected. The generative AI can also provide appropriate health management advice to the elderly. For example, it can support health maintenance by providing advice on diet and exercise. Furthermore, the generative AI manages the elderly's schedule and sets reminders to ensure important appointments are not forgotten. The generative AI can also monitor the elderly's living environment and provide necessary support. For example, the system can detect areas where there is a high risk of falling and warn people to help them live safely. This helps to resolve health issues and loneliness among the elderly, allowing them to live with peace of mind. For example, by utilizing generative AI, elderly people can receive support without using smartphone functions much, improving usability.

[0029] A health management system according to an embodiment includes a response unit, a monitoring unit, a providing unit, and a support unit. The response unit analyzes the content of a speech made by an elderly person and provides an appropriate response. For example, the response unit uses a generation AI to analyze the content of a speech made by an elderly person and realize natural conversation. The response unit can also ask questions to the elderly person to stimulate their brain. The monitoring unit monitors health data based on the information obtained by the response unit. For example, the monitoring unit uses a generation AI to collect and analyze data such as the elderly person's body temperature, blood pressure, and heart rate. If an abnormality is detected, the monitoring unit can take early action. The providing unit provides health management advice based on the data collected by the monitoring unit. For example, the providing unit uses a generation AI to provide advice on diet and exercise to the elderly person. The providing unit can also provide specific advice to support health maintenance. The support unit supports daily life based on the advice provided by the providing unit. For example, the support unit uses a generation AI to manage the elderly person's schedule and set reminders. The support unit can also monitor the elderly person's living environment and provide necessary support. As a result, the health management system according to the embodiment can solve the health problems and loneliness of the elderly, allowing them to live with peace of mind. For example, by utilizing generative AI, elderly people can receive support without using smartphone functions much, improving usability.

[0030] The response unit can pose questions to the elderly to activate their brains. The response unit poses questions to the elderly using, for example, a generation AI. For example, the response unit can ask the elderly questions about past events. The response unit can also ask the elderly questions about topics of interest. The response unit can also ask the elderly questions about their daily lives. This can promote brain activation in the elderly. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the content of the elderly's utterances into the generation AI, which can then generate appropriate questions.

[0031] The monitoring unit can collect and analyze data on body temperature, blood pressure, and heart rate. The monitoring unit, for example, collects the body temperature of the elderly person using a body temperature sensor. For example, the monitoring unit attaches the body temperature sensor to the elderly person's body and measures their body temperature in real time. The monitoring unit can also collect the blood pressure of the elderly person using a blood pressure monitor. For example, the monitoring unit attaches the blood pressure monitor to the elderly person's arm and measures their blood pressure periodically. The monitoring unit can also collect the heart rate of the elderly person using a heart rate sensor. For example, the monitoring unit attaches the heart rate sensor to the elderly person's chest and measures their heart rate in real time. This makes it possible to accurately grasp the elderly person's health condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the collected body temperature, blood pressure, and heart rate data into the generation AI, which can analyze the data.

[0032] The providing unit can provide dietary and exercise advice. The providing unit, for example, uses a generating AI to provide dietary advice to the elderly. For example, the providing unit suggests a balanced meal menu based on the elderly's health condition. The providing unit can also use the generating AI to provide exercise advice to the elderly. For example, the providing unit suggests an appropriate exercise program based on the elderly's physical strength and health condition. The providing unit can also use the generating AI to provide advice on improving lifestyle habits to the elderly. For example, the providing unit analyzes the elderly's lifestyle habits and suggests areas for improvement. This can support the elderly in maintaining their health. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generating AI. For example, the providing unit can input the elderly's health data into the generating AI, which can then generate appropriate advice.

[0033] The support unit can manage the elderly person's schedule and set reminders. The support unit can manage the elderly person's schedule using, for example, a generation AI. For example, the support unit can register the elderly person's schedule in a calendar and set reminders. The support unit can also notify the elderly person of reminders using the generation AI. For example, the support unit can display reminders on the elderly person's smartphone or tablet. The support unit can also monitor the elderly person's living environment using the generation AI and provide necessary support. For example, the support unit can detect places with a high risk of falling and warn the elderly person. This can prevent the elderly person from forgetting important appointments. Some or all of the above-mentioned processing in the support unit can be performed using, or without, the generation AI. For example, the support unit can input the elderly person's schedule data into the generation AI, which can then generate reminders.

[0034] The support unit can detect places where there is a risk of falling and provide warnings. The support unit, for example, uses a generating AI to monitor the elderly person's living environment. For example, the support unit can monitor the elderly person's living environment using cameras and sensors and detect places where there is a high risk of falling. The support unit can also use a generating AI to provide warnings to the elderly person. For example, the support unit can display warning messages on the elderly person's smartphone or tablet. The support unit can also use a generating AI to provide advice on improving the elderly person's living environment. For example, the support unit can suggest furniture arrangements and lighting improvements to reduce the risk of falling. This can support the elderly person's safe living. Some or all of the above-mentioned processing in the support unit may be performed using or without a generating AI. For example, the support unit can input the elderly person's living environment data into a generating AI, which can detect the risk of falling and provide warnings.

[0035] The response unit can analyze the elderly person's past conversation history and select optimal questions and topics. The response unit can analyze the elderly person's past conversation history using, for example, a generation AI. For example, the response unit can ask the elderly person again about hobbies that they have talked about in the past. The response unit can also revisit topics that the elderly person has shown interest in in the past. The response unit can also avoid topics that the elderly person has avoided in the past. This makes it possible to provide a conversation based on the elderly person's interests and concerns. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's past conversation history data into the generation AI, which can then select optimal questions and topics.

[0036] The response unit can select an appropriate topic based on the elderly person's current health condition. The response unit can, for example, use a generation AI to evaluate the elderly person's current health condition. For example, the response unit can analyze the elderly person's latest health data and select an appropriate topic. The response unit can also use a generation AI to adjust the content of the response based on the elderly person's health condition. For example, if the elderly person is in poor health, the response unit can select a topic including health advice. If the elderly person is in good health, the response unit can select an active topic. If the elderly person is tired, the response unit can select a relaxing topic. This makes it possible to provide an appropriate conversation based on the elderly person's health condition. Some or all of the above-mentioned processing in the response unit can be performed using or without a generation AI. For example, the response unit can input the elderly person's health data into the generation AI, which can then select an appropriate topic.

[0037] The response unit can customize the content of the conversation based on the elderly person's hobbies and interests. The response unit, for example, uses a generation AI to identify the elderly person's hobbies and interests. For example, the response unit analyzes data related to the hobbies and interests that the elderly person has previously discussed. The response unit can also customize the content of the conversation based on the elderly person's hobbies and interests using the generation AI. For example, if the elderly person is interested in gardening, the response unit can talk about how to grow plants. If the elderly person is interested in music, the response unit can talk about their favorite songs. If the elderly person is interested in traveling, the response unit can talk about places they would like to visit. This makes it possible to provide a conversation tailored to the elderly person's hobbies and interests. Some or all of the above-described processing in the response unit may be performed using or without the generation AI. For example, the response unit can input data related to the elderly person's hobbies and interests into the generation AI, which can then customize the content of the conversation.

[0038] The response unit can provide topics related to the area by taking into account the geographical location information of the elderly person. The response unit, for example, uses a generation AI to obtain the geographical location information of the elderly person. For example, the response unit collects GPS data of the elderly person and determines their current location information. The response unit can also use the generation AI to provide topics related to the area based on the geographical location information of the elderly person. For example, the response unit can provide event information in the area where the elderly person lives. The response unit can also provide weather information in the area where the elderly person lives. The response unit can also provide news in the area where the elderly person lives. This makes it possible to provide information related to the area where the elderly person lives. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the geographical location information of the elderly person to the generation AI, which can then provide topics related to the area.

[0039] The response unit can analyze the social media activity of the elderly person and provide related topics. The response unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the response unit can talk about articles the elderly person shared on social media. The response unit can also talk about comments the elderly person made on social media. The response unit can also provide topics related to accounts the elderly person follows on social media. This makes it possible to provide conversations based on the elderly person's social media activity. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's social media activity data into the generation AI, which can then provide related topics.

[0040] The response unit can customize the response method by reflecting the elderly person's past feedback. The response unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the response unit can reuse a response method that the elderly person preferred in the past. The response unit can also avoid a response method that the elderly person avoided in the past. The response unit can also improve the response method based on the elderly person's past feedback. This makes it possible to provide a response based on the elderly person's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's past feedback data into the generation AI, which can then customize the response method.

[0041] The monitoring unit can perform early detection of abnormalities by referring to past health data. The monitoring unit, for example, uses a generation AI to refer to the elderly person's past health data. For example, the monitoring unit can refer to the elderly person's past blood pressure data and detect abnormal fluctuations. The monitoring unit can also refer to the elderly person's past heart rate data and detect abnormal patterns. The monitoring unit can also refer to the elderly person's past body temperature data and detect abnormal increases or decreases. This allows for early detection of abnormalities in the elderly person's health condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit inputs the elderly person's past health data into the generation AI, which can then detect abnormalities early.

[0042] The monitoring unit can adjust the timing of data collection based on the elderly person's lifestyle rhythm. The monitoring unit, for example, uses a generation AI to evaluate the elderly person's lifestyle rhythm. For example, the monitoring unit analyzes the elderly person's daily activity patterns and sleep cycles. The monitoring unit can also adjust the timing of data collection based on the elderly person's lifestyle rhythm using a generation AI. For example, the monitoring unit can measure the elderly person's body temperature immediately after waking up in the morning. The monitoring unit can also measure the elderly person's blood pressure after they finish eating. The monitoring unit can also measure the elderly person's heart rate before they go to bed. This allows appropriate data collection according to the elderly person's lifestyle rhythm. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's lifestyle rhythm data into the generation AI, which can then adjust the timing of data collection.

[0043] The monitoring unit can evaluate the health condition of the elderly person by taking into account the environmental information of the elderly person. The monitoring unit, for example, uses a generating AI to collect environmental information of the elderly person. For example, the monitoring unit can collect environmental information of the elderly person's room using a temperature sensor and a humidity sensor. The monitoring unit can also evaluate the health condition of the elderly person based on the environmental information of the elderly person using the generating AI. For example, the monitoring unit can evaluate the risk of heatstroke if the temperature in the elderly person's room is high. The monitoring unit can also evaluate the health risk due to dryness if the humidity in the elderly person's room is low. The monitoring unit can also evaluate the risk to the respiratory system if the air quality in the elderly person's room is poor. This makes it possible to perform a health evaluation based on the environmental information of the elderly person. Some or all of the above-mentioned processing in the monitoring unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the monitoring unit can input environmental information data of the elderly person into the generating AI, which can then evaluate the health condition.

[0044] The monitoring unit can evaluate region-specific health risks by taking into account the elderly person's geographic location information. The monitoring unit, for example, uses a generating AI to acquire the elderly person's geographic location information. For example, the monitoring unit collects the elderly person's GPS data and determines their current location information. The monitoring unit can also evaluate region-specific health risks based on the elderly person's geographic location information using the generating AI. For example, the monitoring unit can evaluate allergy risk based on pollen information in the area where the elderly person lives. The monitoring unit can also evaluate heatstroke risk based on temperature information in the area where the elderly person lives. The monitoring unit can also evaluate infection risk based on infectious disease information in the area where the elderly person lives. This makes it possible to evaluate health risks associated with the area where the elderly person lives. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generating AI. For example, the monitoring unit can input the elderly person's geographic location information into the generating AI, which can then evaluate region-specific health risks.

[0045] The monitoring unit can analyze the social media activity of the elderly person and collect information related to their health condition. The monitoring unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the monitoring unit can collect health information shared by the elderly person on social media. The monitoring unit can also analyze health-related posts that the elderly person comments on on social media. The monitoring unit can also collect information on health-related accounts that the elderly person follows on social media. This makes it possible to provide health information based on the elderly person's social media activity. Some or all of the above-mentioned processing in the monitoring unit can be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's social media activity data into the generation AI, which can then collect information related to their health condition.

[0046] The monitoring unit can customize the monitoring method by reflecting the elderly person's past feedback. The monitoring unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the monitoring unit can reuse a monitoring method that the elderly person previously preferred. The monitoring unit can also avoid a monitoring method that the elderly person previously avoided. The monitoring unit can also improve the monitoring method based on the elderly person's past feedback. This makes it possible to provide monitoring based on the elderly person's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's past feedback data into the generation AI, which can then customize the monitoring method.

[0047] The providing unit can adjust the level of detail of the advice based on the importance of the health data. The providing unit, for example, uses a generation AI to evaluate the health data of the elderly person. For example, the providing unit analyzes the urgency and impact of the health data and evaluates the importance. The providing unit can also adjust the level of detail of the advice based on the importance of the health data using the generation AI. For example, the providing unit provides detailed advice based on important health data. The providing unit can also provide concise advice based on general health data. The providing unit can also provide quick advice based on health data with high urgency. This makes it possible to provide appropriate advice based on important health data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, and the generation AI can adjust the level of detail of the advice.

[0048] The providing unit can apply different advice algorithms depending on the category of health data. For example, the providing unit uses a generation AI to classify the health data of the elderly person by category. For example, the providing unit classifies data related to diet, exercise, sleep, etc. The providing unit can also apply different advice algorithms depending on the category of health data using the generation AI. For example, the providing unit can provide nutrition advice based on diet data. The providing unit can also provide exercise advice based on exercise data. The providing unit can also provide sleep advice based on sleep data. This makes it possible to provide appropriate advice depending on the category of health data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the health data of the elderly person into the generation AI, which then classifies the data by category and applies an appropriate advice algorithm.

[0049] The providing unit can improve the accuracy of advice by referring to the elderly person's past advice results. The providing unit, for example, uses a generation AI to analyze the elderly person's past advice results. For example, the providing unit analyzes the results of advice the elderly person received in the past and improves the accuracy. The providing unit can also evaluate the effectiveness of advice the elderly person received in the past and improve the accuracy. The providing unit can also improve the accuracy based on feedback of advice the elderly person received in the past. This makes it possible to provide highly accurate advice based on the elderly person's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0050] The providing unit can determine the priority of advice based on the collection time of the health data. For example, the providing unit can use a generation AI to evaluate the collection time of the elderly person's health data. For example, the providing unit can analyze the freshness and collection frequency of the health data. The providing unit can also determine the priority of advice based on the collection time of the health data using the generation AI. For example, the providing unit can prioritize advice based on recently collected health data. The providing unit can also adjust the priority of advice based on health data collected in the past. The providing unit can also quickly provide advice based on health data with high urgency. This makes it possible to provide appropriate advice priorities based on the collection time. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, which can then determine the priority of advice.

[0051] The providing unit can adjust the order of advice based on the relevance of the health data. The providing unit, for example, uses a generation AI to evaluate the relevance of the elderly person's health data. For example, the providing unit analyzes the correlations and influences between the health data. The providing unit can also adjust the order of advice based on the relevance of the health data using the generation AI. For example, the providing unit provides advice based on data most relevant to the elderly person's health condition. The providing unit can also adjust the order of advice based on data related to the elderly person's lifestyle habits. The providing unit can also determine the order of advice based on the elderly person's past health data. This makes it possible to provide an appropriate order of advice based on the relevance. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, which can then adjust the order of advice.

[0052] The providing unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. The providing unit, for example, uses a generation AI to evaluate the elderly person's level of expertise. For example, the providing unit analyzes the elderly person's questionnaire survey and past learning history. The providing unit can also adjust the use of technical terms in the advice according to the elderly person's level of expertise using the generation AI. For example, if the elderly person has technical expertise, the providing unit can provide detailed advice using technical terms. If the elderly person does not have technical expertise, the providing unit can provide advice in simple language. The providing unit can also adjust the use of technical terms based on the elderly person's past feedback. This makes it possible to provide appropriate advice according to the elderly person's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's level of expertise data into the generation AI, which can then adjust the use of technical terms in the advice.

[0053] The support unit can analyze the elderly person's past lifestyle history and select the optimal support method. The support unit can analyze the elderly person's past lifestyle history using, for example, a generation AI. For example, the support unit can analyze the elderly person's diary and activity records. The support unit can also use the generation AI to select the optimal support method based on the elderly person's past lifestyle history. For example, the support unit can reuse support methods that the elderly person previously preferred. The support unit can also avoid support methods that the elderly person previously avoided. The support unit can also select the optimal support method based on the elderly person's past lifestyle history. This makes it possible to provide appropriate support based on the elderly person's past lifestyle history. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input the elderly person's past lifestyle history data into the generation AI, which can then select the optimal support method.

[0054] The support unit can customize support measures based on the elderly person's current living situation. The support unit, for example, uses a generation AI to evaluate the elderly person's current living situation. For example, the support unit analyzes the elderly person's daily activity patterns and health status. The support unit can also customize support measures based on the elderly person's current living situation using a generation AI. For example, if the elderly person lives alone, the support unit can enhance the reminder function. If the elderly person lives with their family, the support unit can also strengthen collaboration with the family. If the elderly person is in a nursing home, the support unit can also collaborate with facility staff to provide support. This makes it possible to provide appropriate support according to the elderly person's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's current living situation data into the generation AI, which can then customize the support measures.

[0055] The support unit can improve the support method by reflecting the elderly person's feedback. The support unit can analyze the elderly person's feedback using, for example, a generation AI. For example, the support unit can reuse a support method that the elderly person previously preferred. The support unit can also avoid a support method that the elderly person previously avoided. The support unit can also improve the support method based on the elderly person's past feedback. This makes it possible to provide appropriate support based on the elderly person's feedback. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's feedback data into the generation AI, which can then improve the support method.

[0056] The support unit can select the optimal support method taking into account the geographical location information of the elderly person. The support unit, for example, uses a generation AI to acquire the geographical location information of the elderly person. For example, the support unit collects GPS data of the elderly person and determines their current location information. The support unit can also use a generation AI to select the optimal support method based on the geographical location information of the elderly person. For example, the support unit can provide information on medical institutions in the area where the elderly person lives. The support unit can also provide traffic information in the area where the elderly person lives. The support unit can also provide event information in the area where the elderly person lives. This makes it possible to provide appropriate support based on the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the geographical location information of the elderly person into the generation AI, which can then select the optimal support method.

[0057] The support unit can analyze the social media activity of the elderly person and suggest means of support. The support unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the support unit can provide support based on information shared by the elderly person on social media. The support unit can also provide support based on comments made by the elderly person on social media. The support unit can also provide support related to accounts the elderly person follows on social media. This makes it possible to provide appropriate support based on social media activity. Some or all of the above-mentioned processing in the support unit can be performed using or without the generation AI. For example, the support unit can input the social media activity data of the elderly person into the generation AI, which can then suggest means of support.

[0058] The support unit can customize the support method by reflecting the elderly person's past feedback. The support unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the support unit can reuse a support method that the elderly person preferred in the past. The support unit can also avoid a support method that the elderly person avoided in the past. The support unit can also improve the support method based on the elderly person's past feedback. This makes it possible to provide appropriate support based on past feedback. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's past feedback data into the generation AI, which can then customize the support method.

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

[0060] The monitoring unit can detect abnormalities early by referring to past health data. For example, the monitoring unit can refer to the elderly person's past blood pressure data to detect abnormal fluctuations. The monitoring unit can also refer to the elderly person's past heart rate data to detect abnormal patterns. Furthermore, the monitoring unit can refer to the elderly person's past body temperature data to detect abnormal increases or decreases. This allows for early detection of abnormalities in the elderly person's health condition.

[0061] The providing unit can adjust the level of detail of the advice based on the importance of the health data. For example, the providing unit can analyze the urgency and impact of the health data and evaluate the importance. The providing unit can also adjust the level of detail of the advice based on the importance of the health data. For example, detailed advice can be provided based on important health data. Brief advice can also be provided based on general health data. Furthermore, quick advice can be provided based on health data with high urgency. This makes it possible to provide appropriate advice based on important health data.

[0062] The support unit can select the optimal support method by analyzing the elderly person's past lifestyle history. For example, the support unit can analyze the elderly person's diary or activity records. The support unit can also select the optimal support method based on the elderly person's past lifestyle history. For example, the support unit can reuse a support method that the elderly person preferred in the past. The support unit can also avoid a support method that the elderly person avoided in the past. Furthermore, the optimal support method can be selected based on the elderly person's past lifestyle history. This makes it possible to provide appropriate support based on the elderly person's past lifestyle history.

[0063] The monitoring unit can adjust the timing of data collection based on the elderly person's lifestyle rhythm. For example, the monitoring unit can analyze the elderly person's daily activity patterns and sleep cycles. The monitoring unit can also adjust the timing of data collection based on the elderly person's lifestyle rhythm. For example, the monitoring unit can measure the elderly person's body temperature immediately after they wake up in the morning. The monitoring unit can also measure the elderly person's blood pressure after they finish eating. Furthermore, the monitoring unit can measure the elderly person's heart rate before they go to bed. This allows appropriate data collection to be performed according to the elderly person's lifestyle rhythm.

[0064] The providing unit can apply different advice algorithms depending on the category of health data. For example, the providing unit can classify the health data into data related to diet, data related to exercise, data related to sleep, etc. The providing unit can also apply different advice algorithms depending on the category of health data. For example, the providing unit can provide nutrition advice based on data related to diet. The providing unit can also provide exercise advice based on data related to exercise. The providing unit can also provide sleep advice based on data related to sleep. This makes it possible to provide appropriate advice according to the category of health data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The response unit analyzes what the elderly person says and provides an appropriate response. For example, the response unit can use generative AI to analyze what the elderly person says and achieve a natural conversation. The response unit can also pose questions to the elderly person to stimulate their brain. Step 2: The monitoring unit monitors health data based on the information obtained by the response unit. For example, the monitoring unit uses generative AI to collect and analyze data such as the elderly person's body temperature, blood pressure, and heart rate. If an abnormality is detected, the monitoring unit can take early action. Step 3: The provision unit provides health management advice based on the data collected by the monitoring unit. For example, the provision unit uses the generative AI to give dietary and exercise advice to the elderly. The provision unit can also provide specific advice to support health maintenance. Step 4: The support department provides support for daily life based on the advice provided by the provision department. For example, the support department uses the generation AI to manage the elderly person's schedule and set reminders. The support department can also monitor the elderly person's living environment and provide necessary support.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to address health issues and loneliness in the elderly. In this system, the generative AI functions as a conversation partner, preventing dementia through dialogue with the elderly. The generative AI then monitors the elderly's health status and provides appropriate health management. Furthermore, the generative AI supports daily life, enabling the elderly to live their lives with minimal smartphone use. For example, the generative AI can stimulate the elderly's brain by asking questions or listening to their stories about past events. The generative AI also analyzes the elderly's speech and provides appropriate responses, enabling natural conversation. The generative AI then collects and analyzes data such as the elderly's body temperature, blood pressure, and heart rate to understand their health status. This allows for early intervention if an abnormality is detected. The generative AI can also provide appropriate health management advice to the elderly. For example, it can support health maintenance by providing advice on diet and exercise. Furthermore, the generative AI manages the elderly's schedule and sets reminders to ensure important appointments are not forgotten. The generative AI can also monitor the elderly's living environment and provide necessary support. For example, the system can detect areas where there is a high risk of falling and warn people to help them live safely. This helps to resolve health issues and loneliness among the elderly, allowing them to live with peace of mind. For example, by utilizing generative AI, elderly people can receive support without using smartphone functions much, improving usability.

[0068] A health management system according to an embodiment includes a response unit, a monitoring unit, a providing unit, and a support unit. The response unit analyzes the content of a speech made by an elderly person and provides an appropriate response. For example, the response unit uses a generation AI to analyze the content of a speech made by an elderly person and realize natural conversation. The response unit can also ask questions to the elderly person to stimulate their brain. The monitoring unit monitors health data based on the information obtained by the response unit. For example, the monitoring unit uses a generation AI to collect and analyze data such as the elderly person's body temperature, blood pressure, and heart rate. If an abnormality is detected, the monitoring unit can take early action. The providing unit provides health management advice based on the data collected by the monitoring unit. For example, the providing unit uses a generation AI to provide advice on diet and exercise to the elderly person. The providing unit can also provide specific advice to support health maintenance. The support unit supports daily life based on the advice provided by the providing unit. For example, the support unit uses a generation AI to manage the elderly person's schedule and set reminders. The support unit can also monitor the elderly person's living environment and provide necessary support. As a result, the health management system according to the embodiment can solve the health problems and loneliness of the elderly, allowing them to live with peace of mind. For example, by utilizing generative AI, elderly people can receive support without using smartphone functions much, improving usability.

[0069] The response unit can pose questions to the elderly to activate their brains. The response unit poses questions to the elderly using, for example, a generation AI. For example, the response unit can ask the elderly questions about past events. The response unit can also ask the elderly questions about topics of interest. The response unit can also ask the elderly questions about their daily lives. This can promote brain activation in the elderly. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the content of the elderly's utterances into the generation AI, which can then generate appropriate questions.

[0070] The monitoring unit can collect and analyze data on body temperature, blood pressure, and heart rate. The monitoring unit, for example, collects the body temperature of the elderly person using a body temperature sensor. For example, the monitoring unit attaches the body temperature sensor to the elderly person's body and measures their body temperature in real time. The monitoring unit can also collect the blood pressure of the elderly person using a blood pressure monitor. For example, the monitoring unit attaches the blood pressure monitor to the elderly person's arm and measures their blood pressure periodically. The monitoring unit can also collect the heart rate of the elderly person using a heart rate sensor. For example, the monitoring unit attaches the heart rate sensor to the elderly person's chest and measures their heart rate in real time. This makes it possible to accurately grasp the elderly person's health condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the collected body temperature, blood pressure, and heart rate data into the generation AI, which can analyze the data.

[0071] The providing unit can provide dietary and exercise advice. The providing unit, for example, uses a generating AI to provide dietary advice to the elderly. For example, the providing unit suggests a balanced meal menu based on the elderly's health condition. The providing unit can also use the generating AI to provide exercise advice to the elderly. For example, the providing unit suggests an appropriate exercise program based on the elderly's physical strength and health condition. The providing unit can also use the generating AI to provide advice on improving lifestyle habits to the elderly. For example, the providing unit analyzes the elderly's lifestyle habits and suggests areas for improvement. This can support the elderly in maintaining their health. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generating AI. For example, the providing unit can input the elderly's health data into the generating AI, which can then generate appropriate advice.

[0072] The support unit can manage the elderly person's schedule and set reminders. The support unit can manage the elderly person's schedule using, for example, a generation AI. For example, the support unit can register the elderly person's schedule in a calendar and set reminders. The support unit can also notify the elderly person of reminders using the generation AI. For example, the support unit can display reminders on the elderly person's smartphone or tablet. The support unit can also monitor the elderly person's living environment using the generation AI and provide necessary support. For example, the support unit can detect places with a high risk of falling and warn the elderly person. This can prevent the elderly person from forgetting important appointments. Some or all of the above-mentioned processing in the support unit can be performed using, or without, the generation AI. For example, the support unit can input the elderly person's schedule data into the generation AI, which can then generate reminders.

[0073] The support unit can detect places where there is a risk of falling and provide warnings. The support unit, for example, uses a generating AI to monitor the elderly person's living environment. For example, the support unit can monitor the elderly person's living environment using cameras and sensors and detect places where there is a high risk of falling. The support unit can also use a generating AI to provide warnings to the elderly person. For example, the support unit can display warning messages on the elderly person's smartphone or tablet. The support unit can also use a generating AI to provide advice on improving the elderly person's living environment. For example, the support unit can suggest furniture arrangements and lighting improvements to reduce the risk of falling. This can support the elderly person's safe living. Some or all of the above-mentioned processing in the support unit may be performed using or without a generating AI. For example, the support unit can input the elderly person's living environment data into a generating AI, which can detect the risk of falling and provide warnings.

[0074] The response unit can estimate the elderly person's emotions and adjust the tone and content of the response based on the estimated elderly person's emotions. The response unit estimates the elderly person's emotions using, for example, a generation AI. For example, the response unit analyzes the elderly person's facial expressions and voice to estimate emotions. The response unit can also adjust the tone and content of the response based on the elderly person's emotions estimated using the generation AI. For example, if the elderly person is sad, the response unit can offer encouraging words in a gentle tone. If the elderly person is excited, the response unit can continue the conversation in a calm tone. If the elderly person is tired, the response unit can provide a short and concise response. This allows for an appropriate response based on the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input facial expression data of an elderly person into the generation AI, which can then infer their emotions and adjust the tone and content of the response.

[0075] The response unit can analyze the elderly person's past conversation history and select optimal questions and topics. The response unit can analyze the elderly person's past conversation history using, for example, a generation AI. For example, the response unit can ask the elderly person again about hobbies that they have talked about in the past. The response unit can also revisit topics that the elderly person has shown interest in in the past. The response unit can also avoid topics that the elderly person has avoided in the past. This makes it possible to provide a conversation based on the elderly person's interests and concerns. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's past conversation history data into the generation AI, which can then select optimal questions and topics.

[0076] The response unit can select an appropriate topic based on the elderly person's current health condition. The response unit can, for example, use a generation AI to evaluate the elderly person's current health condition. For example, the response unit can analyze the elderly person's latest health data and select an appropriate topic. The response unit can also use a generation AI to adjust the content of the response based on the elderly person's health condition. For example, if the elderly person is in poor health, the response unit can select a topic including health advice. If the elderly person is in good health, the response unit can select an active topic. If the elderly person is tired, the response unit can select a relaxing topic. This makes it possible to provide an appropriate conversation based on the elderly person's health condition. Some or all of the above-mentioned processing in the response unit can be performed using or without a generation AI. For example, the response unit can input the elderly person's health data into the generation AI, which can then select an appropriate topic.

[0077] The response unit can customize the content of the conversation based on the elderly person's hobbies and interests. The response unit, for example, uses a generation AI to identify the elderly person's hobbies and interests. For example, the response unit analyzes data related to the hobbies and interests that the elderly person has previously discussed. The response unit can also customize the content of the conversation based on the elderly person's hobbies and interests using the generation AI. For example, if the elderly person is interested in gardening, the response unit can talk about how to grow plants. If the elderly person is interested in music, the response unit can talk about their favorite songs. If the elderly person is interested in traveling, the response unit can talk about places they would like to visit. This makes it possible to provide a conversation tailored to the elderly person's hobbies and interests. Some or all of the above-described processing in the response unit may be performed using or without the generation AI. For example, the response unit can input data related to the elderly person's hobbies and interests into the generation AI, which can then customize the content of the conversation.

[0078] The response unit can estimate the elderly person's emotions and adjust the frequency of responses based on the estimated elderly person's emotions. The response unit estimates the elderly person's emotions using, for example, a generation AI. For example, the response unit analyzes the elderly person's facial expressions and voice to estimate emotions. The response unit can also adjust the frequency of responses based on the elderly person's emotions estimated using the generation AI. For example, the response unit can respond more frequently if the elderly person feels lonely. The response unit can also reduce the frequency of responses if the elderly person is busy. The response unit can also respond at an appropriate frequency if the elderly person is relaxed. This makes it possible to provide an appropriate response frequency according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the response unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the response unit can input facial expression data of an elderly person into the generation AI, which can then estimate the emotion and adjust the frequency of the response.

[0079] The response unit can provide topics related to the area by taking into account the geographical location information of the elderly person. The response unit, for example, uses a generation AI to obtain the geographical location information of the elderly person. For example, the response unit collects GPS data of the elderly person and determines their current location information. The response unit can also use the generation AI to provide topics related to the area based on the geographical location information of the elderly person. For example, the response unit can provide event information in the area where the elderly person lives. The response unit can also provide weather information in the area where the elderly person lives. The response unit can also provide news in the area where the elderly person lives. This makes it possible to provide information related to the area where the elderly person lives. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the geographical location information of the elderly person to the generation AI, which can then provide topics related to the area.

[0080] The response unit can analyze the social media activity of the elderly person and provide related topics. The response unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the response unit can talk about articles the elderly person shared on social media. The response unit can also talk about comments the elderly person made on social media. The response unit can also provide topics related to accounts the elderly person follows on social media. This makes it possible to provide conversations based on the elderly person's social media activity. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's social media activity data into the generation AI, which can then provide related topics.

[0081] The response unit can customize the response method by reflecting the elderly person's past feedback. The response unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the response unit can reuse a response method that the elderly person preferred in the past. The response unit can also avoid a response method that the elderly person avoided in the past. The response unit can also improve the response method based on the elderly person's past feedback. This makes it possible to provide a response based on the elderly person's past feedback. Some or all of the above-mentioned processing in the response unit may be performed using or without the generation AI. For example, the response unit can input the elderly person's past feedback data into the generation AI, which can then customize the response method.

[0082] The monitoring unit can estimate the elderly person's emotions and adjust the monitoring frequency based on the estimated elderly person's emotions. The monitoring unit estimates the elderly person's emotions using, for example, a generation AI. For example, the monitoring unit analyzes the elderly person's facial expressions and voice to estimate emotions. The monitoring unit can also adjust the monitoring frequency based on the elderly person's emotions estimated using the generation AI. For example, the monitoring unit monitors the elderly person more frequently when the elderly person is feeling anxious. The monitoring unit can also reduce the monitoring frequency when the elderly person is relaxed. The monitoring unit can also appropriately adjust the monitoring frequency when the elderly person is busy. This makes it possible to provide an appropriate monitoring frequency according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input facial expression data of an elderly person into the generation AI, which can then estimate the emotion and adjust the frequency of monitoring.

[0083] The monitoring unit can perform early detection of abnormalities by referring to past health data. The monitoring unit, for example, uses a generation AI to refer to the elderly person's past health data. For example, the monitoring unit can refer to the elderly person's past blood pressure data and detect abnormal fluctuations. The monitoring unit can also refer to the elderly person's past heart rate data and detect abnormal patterns. The monitoring unit can also refer to the elderly person's past body temperature data and detect abnormal increases or decreases. This allows for early detection of abnormalities in the elderly person's health condition. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit inputs the elderly person's past health data into the generation AI, which can then detect abnormalities early.

[0084] The monitoring unit can adjust the timing of data collection based on the elderly person's lifestyle rhythm. The monitoring unit, for example, uses a generation AI to evaluate the elderly person's lifestyle rhythm. For example, the monitoring unit analyzes the elderly person's daily activity patterns and sleep cycles. The monitoring unit can also adjust the timing of data collection based on the elderly person's lifestyle rhythm using a generation AI. For example, the monitoring unit can measure the elderly person's body temperature immediately after waking up in the morning. The monitoring unit can also measure the elderly person's blood pressure after they finish eating. The monitoring unit can also measure the elderly person's heart rate before they go to bed. This allows appropriate data collection according to the elderly person's lifestyle rhythm. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's lifestyle rhythm data into the generation AI, which can then adjust the timing of data collection.

[0085] The monitoring unit can evaluate the health condition of the elderly person by taking into account the environmental information of the elderly person. The monitoring unit, for example, uses a generating AI to collect environmental information of the elderly person. For example, the monitoring unit can collect environmental information of the elderly person's room using a temperature sensor and a humidity sensor. The monitoring unit can also evaluate the health condition of the elderly person based on the environmental information of the elderly person using the generating AI. For example, the monitoring unit can evaluate the risk of heatstroke if the temperature in the elderly person's room is high. The monitoring unit can also evaluate the health risk due to dryness if the humidity in the elderly person's room is low. The monitoring unit can also evaluate the risk to the respiratory system if the air quality in the elderly person's room is poor. This makes it possible to perform a health evaluation based on the environmental information of the elderly person. Some or all of the above-mentioned processing in the monitoring unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the monitoring unit can input environmental information data of the elderly person into the generating AI, which can then evaluate the health condition.

[0086] The monitoring unit can estimate the elderly person's emotions and prioritize the monitoring data based on the estimated emotions. The monitoring unit estimates the elderly person's emotions using, for example, a generation AI. For example, the monitoring unit analyzes the elderly person's facial expressions and voice to estimate emotions. The monitoring unit can also prioritize the monitoring data based on the elderly person's emotions estimated using the generation AI. For example, if the elderly person is feeling anxious, the monitoring unit can prioritize monitoring heart rate data. If the elderly person is relaxed, the monitoring unit can prioritize monitoring body temperature data. If the elderly person is tired, the monitoring unit can prioritize blood pressure data. This makes it possible to provide appropriate prioritization of monitoring data according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the monitoring unit may be performed using the generation AI or without the generation AI. For example, the monitoring unit can input facial expression data of an elderly person into the generation AI, which can then estimate the emotion and determine the priority of the monitoring data.

[0087] The monitoring unit can evaluate region-specific health risks by taking into account the elderly person's geographic location information. The monitoring unit, for example, uses a generating AI to acquire the elderly person's geographic location information. For example, the monitoring unit collects the elderly person's GPS data and determines their current location information. The monitoring unit can also evaluate region-specific health risks based on the elderly person's geographic location information using the generating AI. For example, the monitoring unit can evaluate allergy risk based on pollen information in the area where the elderly person lives. The monitoring unit can also evaluate heatstroke risk based on temperature information in the area where the elderly person lives. The monitoring unit can also evaluate infection risk based on infectious disease information in the area where the elderly person lives. This makes it possible to evaluate health risks associated with the area where the elderly person lives. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generating AI. For example, the monitoring unit can input the elderly person's geographic location information into the generating AI, which can then evaluate region-specific health risks.

[0088] The monitoring unit can analyze the social media activity of the elderly person and collect information related to their health condition. The monitoring unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the monitoring unit can collect health information shared by the elderly person on social media. The monitoring unit can also analyze health-related posts that the elderly person comments on on social media. The monitoring unit can also collect information on health-related accounts that the elderly person follows on social media. This makes it possible to provide health information based on the elderly person's social media activity. Some or all of the above-mentioned processing in the monitoring unit can be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's social media activity data into the generation AI, which can then collect information related to their health condition.

[0089] The monitoring unit can customize the monitoring method by reflecting the elderly person's past feedback. The monitoring unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the monitoring unit can reuse a monitoring method that the elderly person previously preferred. The monitoring unit can also avoid a monitoring method that the elderly person previously avoided. The monitoring unit can also improve the monitoring method based on the elderly person's past feedback. This makes it possible to provide monitoring based on the elderly person's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using or without the generation AI. For example, the monitoring unit can input the elderly person's past feedback data into the generation AI, which can then customize the monitoring method.

[0090] The providing unit can estimate the elderly person's emotions and adjust the way in which advice is presented based on the estimated elderly person's emotions. The providing unit estimates the elderly person's emotions using, for example, a generation AI. For example, the providing unit analyzes the elderly person's facial expressions and voice to estimate emotions. The providing unit can also adjust the way in which advice is presented based on the elderly person's emotions estimated using the generation AI. For example, if the elderly person is feeling anxious, the providing unit can provide advice in gentle words. If the elderly person is relaxed, the providing unit can also provide detailed advice. If the elderly person is in a hurry, the providing unit can also provide concise advice. This makes it possible to provide appropriate advice according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input facial expression data of an elderly person into the generating AI, which can then estimate the emotion and adjust the way the advice is expressed.

[0091] The providing unit can adjust the level of detail of the advice based on the importance of the health data. The providing unit, for example, uses a generation AI to evaluate the health data of the elderly person. For example, the providing unit analyzes the urgency and impact of the health data and evaluates the importance. The providing unit can also adjust the level of detail of the advice based on the importance of the health data using the generation AI. For example, the providing unit provides detailed advice based on important health data. The providing unit can also provide concise advice based on general health data. The providing unit can also provide quick advice based on health data with high urgency. This makes it possible to provide appropriate advice based on important health data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, and the generation AI can adjust the level of detail of the advice.

[0092] The providing unit can apply different advice algorithms depending on the category of health data. For example, the providing unit uses a generation AI to classify the health data of the elderly person by category. For example, the providing unit classifies data related to diet, exercise, sleep, etc. The providing unit can also apply different advice algorithms depending on the category of health data using the generation AI. For example, the providing unit can provide nutrition advice based on diet data. The providing unit can also provide exercise advice based on exercise data. The providing unit can also provide sleep advice based on sleep data. This makes it possible to provide appropriate advice depending on the category of health data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the health data of the elderly person into the generation AI, which then classifies the data by category and applies an appropriate advice algorithm.

[0093] The providing unit can improve the accuracy of advice by referring to the elderly person's past advice results. The providing unit, for example, uses a generation AI to analyze the elderly person's past advice results. For example, the providing unit analyzes the results of advice the elderly person received in the past and improves the accuracy. The providing unit can also evaluate the effectiveness of advice the elderly person received in the past and improve the accuracy. The providing unit can also improve the accuracy based on feedback of advice the elderly person received in the past. This makes it possible to provide highly accurate advice based on the elderly person's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0094] The providing unit can estimate the elderly person's emotions and adjust the length of advice based on the estimated elderly person's emotions. The providing unit estimates the elderly person's emotions using, for example, a generation AI. For example, the providing unit analyzes the elderly person's facial expressions and voice to estimate emotions. The providing unit can also adjust the length of advice based on the elderly person's emotions estimated using the generation AI. For example, the providing unit can provide short and concise advice when the elderly person is feeling anxious. The providing unit can also provide detailed advice when the elderly person is relaxed. The providing unit can also provide quick and concise advice when the elderly person is in a hurry. This makes it possible to provide an appropriate length of advice according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input facial expression data of an elderly person into the generating AI, which can then estimate the emotion and adjust the length of the advice.

[0095] The providing unit can determine the priority of advice based on the collection time of the health data. For example, the providing unit can use a generation AI to evaluate the collection time of the elderly person's health data. For example, the providing unit can analyze the freshness and collection frequency of the health data. The providing unit can also determine the priority of advice based on the collection time of the health data using the generation AI. For example, the providing unit can prioritize advice based on recently collected health data. The providing unit can also adjust the priority of advice based on health data collected in the past. The providing unit can also quickly provide advice based on health data with high urgency. This makes it possible to provide appropriate advice priorities based on the collection time. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, which can then determine the priority of advice.

[0096] The providing unit can adjust the order of advice based on the relevance of the health data. The providing unit, for example, uses a generation AI to evaluate the relevance of the elderly person's health data. For example, the providing unit analyzes the correlations and influences between the health data. The providing unit can also adjust the order of advice based on the relevance of the health data using the generation AI. For example, the providing unit provides advice based on data most relevant to the elderly person's health condition. The providing unit can also adjust the order of advice based on data related to the elderly person's lifestyle habits. The providing unit can also determine the order of advice based on the elderly person's past health data. This makes it possible to provide an appropriate order of advice based on the relevance. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the elderly person's health data into the generation AI, which can then adjust the order of advice.

[0097] The providing unit can adjust the use of technical terms in the advice according to the elderly person's level of expertise. The providing unit, for example, uses a generation AI to evaluate the elderly person's level of expertise. For example, the providing unit analyzes the elderly person's questionnaire survey and past learning history. The providing unit can also adjust the use of technical terms in the advice according to the elderly person's level of expertise using the generation AI. For example, if the elderly person has technical expertise, the providing unit can provide detailed advice using technical terms. If the elderly person does not have technical expertise, the providing unit can provide advice in simple language. The providing unit can also adjust the use of technical terms based on the elderly person's past feedback. This makes it possible to provide appropriate advice according to the elderly person's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the elderly person's level of expertise data into the generation AI, which can then adjust the use of technical terms in the advice.

[0098] The support unit can estimate the elderly person's emotions and adjust the support method based on the estimated elderly person's emotions. The support unit estimates the elderly person's emotions using, for example, a generation AI. For example, the support unit analyzes the elderly person's facial expressions and voice to estimate emotions. The support unit can also adjust the support method based on the elderly person's emotions estimated using the generation AI. For example, if the elderly person feels anxious, the support unit can provide support using gentle words. If the elderly person is relaxed, the support unit can provide detailed support. If the elderly person is in a hurry, the support unit can provide quick and concise support. This makes it possible to provide appropriate support according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the support department can input facial expression data of the elderly person into the generation AI, which can then infer their emotions and adjust the method of support.

[0099] The support unit can analyze the elderly person's past lifestyle history and select the optimal support method. The support unit can analyze the elderly person's past lifestyle history using, for example, a generation AI. For example, the support unit can analyze the elderly person's diary and activity records. The support unit can also use the generation AI to select the optimal support method based on the elderly person's past lifestyle history. For example, the support unit can reuse support methods that the elderly person previously preferred. The support unit can also avoid support methods that the elderly person previously avoided. The support unit can also select the optimal support method based on the elderly person's past lifestyle history. This makes it possible to provide appropriate support based on the elderly person's past lifestyle history. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input the elderly person's past lifestyle history data into the generation AI, which can then select the optimal support method.

[0100] The support unit can customize support measures based on the elderly person's current living situation. The support unit, for example, uses a generation AI to evaluate the elderly person's current living situation. For example, the support unit analyzes the elderly person's daily activity patterns and health status. The support unit can also customize support measures based on the elderly person's current living situation using a generation AI. For example, if the elderly person lives alone, the support unit can enhance the reminder function. If the elderly person lives with their family, the support unit can also strengthen collaboration with the family. If the elderly person is in a nursing home, the support unit can also collaborate with facility staff to provide support. This makes it possible to provide appropriate support according to the elderly person's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's current living situation data into the generation AI, which can then customize the support measures.

[0101] The support unit can improve the support method by reflecting the elderly person's feedback. The support unit can analyze the elderly person's feedback using, for example, a generation AI. For example, the support unit can reuse a support method that the elderly person previously preferred. The support unit can also avoid a support method that the elderly person previously avoided. The support unit can also improve the support method based on the elderly person's past feedback. This makes it possible to provide appropriate support based on the elderly person's feedback. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's feedback data into the generation AI, which can then improve the support method.

[0102] The support unit can estimate the elderly person's emotions and determine support priorities based on the estimated elderly person's emotions. The support unit estimates the elderly person's emotions using, for example, a generation AI. For example, the support unit analyzes the elderly person's facial expressions and voice to estimate emotions. The support unit can also determine support priorities based on the elderly person's emotions estimated using the generation AI. For example, the support unit can provide support preferentially when the elderly person is feeling anxious. The support unit can also provide normal support when the elderly person is relaxed. The support unit can also provide quick support when the elderly person is in a hurry. This makes it possible to provide appropriate support priorities according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the support department can input facial expression data of an elderly person into the generation AI, which can then infer their emotions and determine support priorities.

[0103] The support unit can select the optimal support method taking into account the geographical location information of the elderly person. The support unit, for example, uses a generation AI to acquire the geographical location information of the elderly person. For example, the support unit collects GPS data of the elderly person and determines their current location information. The support unit can also use a generation AI to select the optimal support method based on the geographical location information of the elderly person. For example, the support unit can provide information on medical institutions in the area where the elderly person lives. The support unit can also provide traffic information in the area where the elderly person lives. The support unit can also provide event information in the area where the elderly person lives. This makes it possible to provide appropriate support based on the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the geographical location information of the elderly person into the generation AI, which can then select the optimal support method.

[0104] The support unit can analyze the social media activity of the elderly person and suggest means of support. The support unit can analyze the social media activity of the elderly person using, for example, a generation AI. For example, the support unit can provide support based on information shared by the elderly person on social media. The support unit can also provide support based on comments made by the elderly person on social media. The support unit can also provide support related to accounts the elderly person follows on social media. This makes it possible to provide appropriate support based on social media activity. Some or all of the above-mentioned processing in the support unit can be performed using or without the generation AI. For example, the support unit can input the social media activity data of the elderly person into the generation AI, which can then suggest means of support.

[0105] The support unit can customize the support method by reflecting the elderly person's past feedback. The support unit can, for example, use a generation AI to analyze the elderly person's past feedback. For example, the support unit can reuse a support method that the elderly person preferred in the past. The support unit can also avoid a support method that the elderly person avoided in the past. The support unit can also improve the support method based on the elderly person's past feedback. This makes it possible to provide appropriate support based on past feedback. Some or all of the above-mentioned processing in the support unit may be performed using or without the generation AI. For example, the support unit can input the elderly person's past feedback data into the generation AI, which can then customize the support method. === Hard Collateral 1-1 === Each of the multiple elements, including the response unit, monitoring unit, providing unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart device 14 and analyzes the content of the elderly person's statements and provides an appropriate response. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as the elderly person's body temperature, blood pressure, and heart rate. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health management advice. The support unit is realized, for example, by the control unit 46A of the smart device 14 and manages the elderly person's schedule and sets reminders. === Hard Collateral 1-2 === Each of the multiple elements, including the response unit, monitoring unit, providing unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart glasses 214 and analyzes the content of the elderly person's statements and provides an appropriate response. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as the elderly person's body temperature, blood pressure, and heart rate. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health management advice. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and manages the elderly person's schedule and sets reminders. === Hard Collateral 1-3 === Each of the multiple elements including the response unit, monitoring unit, providing unit, and support unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the headset-type terminal 314 and analyzes the content of the elderly person's statements and provides an appropriate response. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as the elderly person's body temperature, blood pressure, and heart rate. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health management advice. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and manages the elderly person's schedule and sets reminders. === Hard Collateral 1-4 === Each of the multiple elements including the response unit, monitoring unit, providing unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the response unit is realized by the control unit 46A of the robot 414 and analyzes the content of the elderly person's statements and provides an appropriate response. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as the elderly person's body temperature, blood pressure, and heart rate. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides health management advice. The support unit is realized, for example, by the control unit 46A of the robot 414 and manages the elderly person's schedule and sets reminders.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The response unit can estimate the elderly person's emotions and adjust the tone and content of the response based on the estimated elderly person's emotions. For example, the response unit analyzes the elderly person's facial expressions and voice to estimate emotions. The response unit can also adjust the tone and content of the response based on the estimated elderly person's emotions. For example, if the elderly person is sad, the response unit can offer encouraging words in a gentle tone. If the elderly person is excited, the response unit can continue the conversation in a calm tone. Furthermore, if the elderly person is tired, the response unit can provide a short and concise response. This makes it possible to provide an appropriate response according to the elderly person's emotions.

[0108] The monitoring unit can estimate the elderly person's emotions and adjust the monitoring frequency based on the estimated elderly person's emotions. For example, the monitoring unit analyzes the elderly person's facial expressions and voice to estimate emotions. The monitoring unit can also adjust the monitoring frequency based on the estimated elderly person's emotions. For example, if the elderly person feels anxious, the monitoring unit can perform monitoring more frequently. If the elderly person feels relaxed, the monitoring frequency can be reduced. Furthermore, if the elderly person is busy, the monitoring frequency can be adjusted appropriately. This makes it possible to provide an appropriate monitoring frequency according to the elderly person's emotions.

[0109] The providing unit can estimate the elderly person's emotions and adjust the way in which advice is expressed based on the estimated elderly person's emotions. For example, the providing unit analyzes the elderly person's facial expressions and voice to estimate emotions. The providing unit can also adjust the way in which advice is expressed based on the estimated elderly person's emotions. For example, if the elderly person is feeling anxious, the providing unit can provide advice in gentle words. If the elderly person is relaxed, the providing unit can provide detailed advice. Furthermore, if the elderly person is in a hurry, the providing unit can provide concise advice. In this way, appropriate advice can be provided according to the elderly person's emotions.

[0110] The support unit can estimate the elderly person's emotions and adjust the support method based on the estimated elderly person's emotions. For example, the support unit analyzes the elderly person's facial expressions and voice to estimate emotions. The support unit can also adjust the support method based on the estimated elderly person's emotions. For example, if the elderly person feels anxious, the support unit can provide support using gentle words. If the elderly person feels relaxed, the support unit can provide detailed support. Furthermore, if the elderly person is in a hurry, the support unit can provide quick and concise support. In this way, appropriate support can be provided according to the elderly person's emotions.

[0111] The response unit can estimate the elderly person's emotions and adjust the frequency of responses based on the estimated elderly person's emotions. For example, the response unit analyzes the elderly person's facial expressions and voice to estimate emotions. The response unit can also adjust the frequency of responses based on the estimated elderly person's emotions. For example, if the elderly person feels lonely, the response unit can respond more frequently. If the elderly person is busy, the response unit can respond less frequently. Furthermore, if the elderly person is relaxed, the response unit can respond at an appropriate frequency. This makes it possible to provide an appropriate response frequency according to the elderly person's emotions.

[0112] The monitoring unit can detect abnormalities early by referring to past health data. For example, the monitoring unit can refer to the elderly person's past blood pressure data to detect abnormal fluctuations. The monitoring unit can also refer to the elderly person's past heart rate data to detect abnormal patterns. Furthermore, the monitoring unit can refer to the elderly person's past body temperature data to detect abnormal increases or decreases. This allows for early detection of abnormalities in the elderly person's health condition.

[0113] The providing unit can adjust the level of detail of the advice based on the importance of the health data. For example, the providing unit can analyze the urgency and impact of the health data and evaluate the importance. The providing unit can also adjust the level of detail of the advice based on the importance of the health data. For example, detailed advice can be provided based on important health data. Brief advice can also be provided based on general health data. Furthermore, quick advice can be provided based on health data with high urgency. This makes it possible to provide appropriate advice based on important health data.

[0114] The support unit can select the optimal support method by analyzing the elderly person's past lifestyle history. For example, the support unit can analyze the elderly person's diary or activity records. The support unit can also select the optimal support method based on the elderly person's past lifestyle history. For example, the support unit can reuse a support method that the elderly person preferred in the past. The support unit can also avoid a support method that the elderly person avoided in the past. Furthermore, the optimal support method can be selected based on the elderly person's past lifestyle history. This makes it possible to provide appropriate support based on the elderly person's past lifestyle history.

[0115] The monitoring unit can adjust the timing of data collection based on the elderly person's lifestyle rhythm. For example, the monitoring unit can analyze the elderly person's daily activity patterns and sleep cycles. The monitoring unit can also adjust the timing of data collection based on the elderly person's lifestyle rhythm. For example, the monitoring unit can measure the elderly person's body temperature immediately after they wake up in the morning. The monitoring unit can also measure the elderly person's blood pressure after they finish eating. Furthermore, the monitoring unit can measure the elderly person's heart rate before they go to bed. This allows appropriate data collection to be performed according to the elderly person's lifestyle rhythm.

[0116] The providing unit can apply different advice algorithms depending on the category of health data. For example, the providing unit can classify the health data into data related to diet, data related to exercise, data related to sleep, etc. The providing unit can also apply different advice algorithms depending on the category of health data. For example, the providing unit can provide nutrition advice based on data related to diet. The providing unit can also provide exercise advice based on data related to exercise. The providing unit can also provide sleep advice based on data related to sleep. This makes it possible to provide appropriate advice according to the category of health data.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The response unit analyzes what the elderly person says and provides an appropriate response. For example, the response unit can use generative AI to analyze what the elderly person says and achieve a natural conversation. The response unit can also pose questions to the elderly person to stimulate their brain. Step 2: The monitoring unit monitors health data based on the information obtained by the response unit. For example, the monitoring unit uses generative AI to collect and analyze data such as the elderly person's body temperature, blood pressure, and heart rate. If an abnormality is detected, the monitoring unit can take early action. Step 3: The provision unit provides health management advice based on the data collected by the monitoring unit. For example, the provision unit uses the generative AI to give dietary and exercise advice to the elderly. The provision unit can also provide specific advice to support health maintenance. Step 4: The support department provides support for daily life based on the advice provided by the provision department. For example, the support department uses the generation AI to manage the elderly person's schedule and set reminders. The support department can also monitor the elderly person's living environment and provide necessary support.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A response unit that analyzes the content of the elderly person's remarks and responds; a monitoring unit that monitors health data based on the information obtained by the response unit; a providing unit that provides health management advice based on the data collected by the monitoring unit; a support unit that provides support for daily life based on the advice provided by the providing unit. A system characterized by:

2. The response unit Asking questions to elderly people to stimulate their brains 2. The system of claim 1.

3. The monitoring unit Collect and analyze data on body temperature, blood pressure, and heart rate 2. The system of claim 1.

4. The providing unit Providing dietary and exercise advice 2. The system of claim 1.

5. The support portion is Manage seniors' schedules and set reminders 2. The system of claim 1.

6. The support portion is Detects areas where there is a risk of falling and warns 2. The system of claim 1.

7. The response unit Estimate the senior's emotions and adjust the tone and content of responses based on the estimated emotions of the senior 2. The system of claim 1.

8. The response unit Analyzing the elderly person's past conversation history and selecting the most appropriate questions and topics 2. The system of claim 1.

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