System

Abstract

An object of a system according to an embodiment is to monitor a living situation of an elderly person, grasp a problem associated with the progress of aging, and provide appropriate advice.SOLUTION: A system according to an embodiment includes a living situation monitoring unit, a problem grasping unit, an advice providing unit, and a consultation proposing unit. The living situation monitoring unit monitors a living situation of the elderly person. The problem point grasping unit analyzes the data acquired by the living situation monitoring unit to grasp a problem point associated with the progress of aging. The advice providing unit provides appropriate advice on the basis of the problem grasped by the problem grasping unit. The consultation proposing unit proposes a consultation with the helper or the household services based on the advice provided by the advice 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 monitor the living conditions of the elderly, identify problems associated with the aging process, and provide appropriate advice, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the living conditions of elderly people, identify problems that arise with the aging process, and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a living situation monitoring unit, a problem identification unit, an advice providing unit, and a consultation suggestion unit. The living situation monitoring unit monitors the living situation of the elderly person. The problem identification unit analyzes data acquired by the living situation monitoring unit to identify problems associated with the progression of aging. The advice providing unit provides appropriate advice based on the problems identified by the problem identification unit. The consultation suggestion unit suggests consultation with a helper or housekeeping service based on the advice provided by the advice providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the living conditions of elderly people, identify problems that arise with the progression of aging, and provide appropriate advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The lifestyle monitoring support system according to an embodiment of the present invention monitors the living conditions of elderly people, and the generation AI identifies problems associated with the aging process, provides appropriate advice, and suggests consultations with helpers or housekeeping services. As a result, the lifestyle monitoring support system can ensure the safety and security of elderly people and reduce the burden on their lives.

[0029] A life monitoring and support system according to an embodiment includes a life situation monitoring unit, a problem identification unit, an advice providing unit, and a consultation suggestion unit. The life situation monitoring unit monitors the life situation of an elderly person. For example, it records the elderly person's movements and behaviors using sensors and cameras and analyzes the data. The life situation monitoring unit also monitors the elderly person's daily movement patterns, meal frequency, sleep quality, and the like, and issues an alert if an abnormality is detected. The problem identification unit analyzes the data acquired by the life situation monitoring unit to identify problems associated with the progression of aging. For example, it detects changes such as unsteady walking or a decrease in food intake. The problem identification unit also identifies problems based on daily movement data and health data. The advice providing unit provides appropriate advice based on the problems identified by the problem identification unit. For example, if the elderly person's walking is unsteady, it suggests installing handrails or using walking aids. The advice providing unit also generates advice based on data related to the problems. The consultation suggestion unit suggests consulting a helper or a housekeeping service based on the advice provided by the advice providing unit. For example, if daily housework becomes difficult, it suggests using a housekeeping service. Furthermore, the consultation suggestion unit generates consultation suggestions based on data on living conditions and problems. As a result, the life monitoring support system according to the embodiment can ensure the safety and security of elderly people and reduce their burden on daily life. For example, by receiving appropriate support services, elderly people can continue to live independently. Furthermore, by monitoring living conditions and identifying problems early, appropriate measures can be taken.

[0030] The living condition monitoring unit can monitor the elderly person's living environment and automatically adjust the temperature, humidity, and lighting. The living condition monitoring unit, for example, is equipped with a temperature sensor and a humidity sensor to monitor the elderly person's living environment. For example, the unit constantly monitors the indoor temperature and humidity and automatically adjusts the air conditioner and humidifier to maintain a comfortable environment. The living condition monitoring unit also is equipped with a lighting sensor to monitor the elderly person's living environment. For example, the unit constantly monitors the indoor lighting brightness and automatically adjusts the lighting brightness to maintain a comfortable environment. The living condition monitoring unit also uses an algorithm that analyzes environmental data to monitor the elderly person's living environment. For example, the unit learns past environmental data and automatically makes optimal adjustments to maintain a comfortable environment. This improves the quality of life by maintaining a comfortable living environment for the elderly person.

[0031] The living situation monitoring unit monitors the behavior of an elderly person's pet and analyzes the pet's health condition and behavioral patterns to benefit the elderly person's life. For example, the living situation monitoring unit attaches a wearable device to the pet to monitor the behavior of the elderly person's pet. For example, it constantly monitors the pet's movements and analyzes the pet's health condition and behavioral patterns. The living situation monitoring unit also installs a pet camera to monitor the behavior of the elderly person's pet. For example, it records the pet's behavior and analyzes its behavioral patterns. The living situation monitoring unit also uses an algorithm to analyze the pet's health data to monitor the behavior of the elderly person's pet. For example, it analyzes the pet's diet and exercise data to understand its health condition. In this way, understanding the health condition and behavioral patterns of the elderly person's pet improves the quality of life of the elderly person.

[0032] The living situation monitoring unit monitors the usage of home appliances used by the elderly and can issue an alert if an abnormality is detected. The living situation monitoring unit, for example, uses a smart plug to monitor the usage of home appliances used by the elderly. For example, it constantly monitors the power consumption of the home appliance and issues an alert if an abnormality is detected. The living situation monitoring unit also uses IoT technology to monitor the usage of home appliances used by the elderly. For example, it attaches sensors to the home appliances to constantly monitor their usage and issues an alert if an abnormality is detected. The living situation monitoring unit also uses a data analysis algorithm to monitor the usage of home appliances used by the elderly. For example, it learns past usage data, detects abnormal usage patterns, and issues an alert. This allows for early detection of abnormalities in home appliances used by the elderly and ensures their safety.

[0033] The problem identification unit can analyze the dietary content of the elderly person, detect any imbalance in nutritional balance, and make suggestions for improvement. For example, to analyze the dietary content of the elderly person, the problem identification unit takes photos of the meals and analyzes the nutritional components using image recognition technology. For example, calories and nutrients are automatically calculated from the photos of the meals. The problem identification unit also uses a meal record app to analyze the dietary content of the elderly person. For example, the meal content is input and the nutritional balance is analyzed. The problem identification unit also uses an algorithm to analyze dietary data to analyze the dietary content of the elderly person. For example, past dietary data is learned and any imbalance in nutritional balance is detected. In this way, the dietary content of the elderly person can be analyzed, any imbalance in nutritional balance can be detected, and suggestions for improvement can be made to maintain health.

[0034] The problem identification unit can analyze the sleep patterns of the elderly person in detail and provide advice to improve sleep quality. The problem identification unit, for example, uses a wearable device to analyze the sleep patterns of the elderly person. For example, the heart rate and movements during sleep are constantly monitored to analyze the quality of sleep. The problem identification unit also uses a smart mattress to analyze the sleep patterns of the elderly person. For example, a sensor built into the mattress monitors movements during sleep to analyze the quality of sleep. The problem identification unit also uses an algorithm to analyze sleep data to analyze the sleep patterns of the elderly person. For example, past sleep data is learned and advice to improve sleep quality is provided. In this way, the sleep patterns of the elderly person are analyzed and advice to improve sleep quality is provided, thereby maintaining health.

[0035] The problem identification unit can monitor the exercise habits of the elderly and suggest an appropriate exercise program. The problem identification unit, for example, uses a wearable device to monitor the exercise habits of the elderly. For example, it constantly monitors the number of steps and exercise time and suggests an exercise program. The problem identification unit also uses an exercise recording app to monitor the exercise habits of the elderly. For example, it inputs the exercise details and suggests an appropriate exercise program. The problem identification unit also uses an algorithm to analyze exercise data to monitor the exercise habits of the elderly. For example, it learns past exercise data and suggests an appropriate exercise program. In this way, the exercise habits of the elderly can be monitored and an appropriate exercise program suggested, thereby maintaining health.

[0036] The problem identification unit can monitor the elderly person's medication status and provide reminders to prevent them from forgetting to take their medicine. The problem identification unit, for example, uses a smart pillbox to monitor the elderly person's medication status. For example, it monitors the removal of medicine and provides reminders to prevent them from forgetting to take their medicine. The problem identification unit also uses a medication record app to monitor the elderly person's medication status. For example, it inputs the details of medication and provides reminders to prevent them from forgetting to take their medicine. The problem identification unit also uses an algorithm that analyzes medication data to monitor the elderly person's medication status. For example, it learns past medication data and provides reminders to prevent them from forgetting to take their medicine. In this way, the elderly person's medication status can be monitored and reminders to prevent them from forgetting to take their medicine can be provided, thereby maintaining their health.

[0037] The advice providing unit can provide personalized advice tailored to the elderly person's lifestyle rhythm. The advice providing unit, for example, uses a wearable device to analyze the elderly person's lifestyle rhythm. For example, it constantly monitors daily activity data and provides advice tailored to the lifestyle rhythm. The advice providing unit also uses a lifestyle record app to analyze the elderly person's lifestyle rhythm. For example, it inputs details of daily activities and provides advice tailored to the lifestyle rhythm. The advice providing unit also uses an algorithm that analyzes lifestyle data to analyze the elderly person's lifestyle rhythm. For example, it learns past lifestyle data and provides advice tailored to the lifestyle rhythm. In this way, personalized advice tailored to the elderly person's lifestyle rhythm is provided, improving their quality of life.

[0038] The advice providing unit can predict future problems based on the elderly person's past behavioral data and propose countermeasures in advance. The advice providing unit, for example, uses a behavior recording app to analyze the elderly person's past behavioral data. For example, the content of past behavior is input and future problems are predicted. The advice providing unit also uses a data analysis algorithm to analyze the elderly person's past behavioral data. For example, the past behavioral data is trained to predict future problems. The advice providing unit also uses a machine learning algorithm to analyze the elderly person's past behavioral data. For example, future problems are predicted based on the past behavioral data and countermeasures are proposed in advance. In this way, future problems can be predicted based on the elderly person's past behavioral data and countermeasures are proposed in advance, thereby preventing problems from occurring.

[0039] The advice providing unit can provide advice to promote communication between the elderly person and family and friends. For example, the advice providing unit uses a video call app to promote communication between the elderly person and family and friends. For example, it suggests scheduling regular video calls. The advice providing unit also uses a messaging app to promote communication between the elderly person and family and friends. For example, it suggests regular message exchanges. The advice providing unit also uses social media to promote communication between the elderly person and family and friends. For example, it suggests posts to promote interaction with family and friends. In this way, social connections are strengthened by providing advice to promote communication between the elderly person and family and friends.

[0040] The advice providing unit can suggest local events and activities in which the elderly can participate, thereby strengthening social ties. For example, the advice providing unit refers to a local event calendar to suggest local events and activities in which the elderly can participate. For example, it collects local event information and suggests it to the elderly. The advice providing unit also refers to information on local community centers to suggest local events and activities in which the elderly can participate. For example, it collects activity information at community centers and suggests it to the elderly. The advice providing unit also refers to local news sites to suggest local events and activities in which the elderly can participate. For example, it collects event information from local news sites and suggests it to the elderly. In this way, social ties are strengthened by suggesting local events and activities in which the elderly can participate.

[0041] The consultation suggestion unit can match the most suitable helper or housekeeping service according to the elderly person's living situation. The consultation suggestion unit, for example, uses a life record app to analyze the elderly person's living situation. For example, details of daily activities are input and the most suitable helper or housekeeping service is matched. The consultation suggestion unit also uses a data analysis algorithm to analyze the elderly person's living situation. For example, past life data is learned and the most suitable helper or housekeeping service is matched. The consultation suggestion unit also uses a machine learning algorithm to analyze the elderly person's living situation. For example, the most suitable helper or housekeeping service is matched based on past life data. In this way, the burden on the elderly person's life is reduced by matching the most suitable helper or housekeeping service according to their living situation.

[0042] The consultation suggestion unit can make customized service suggestions based on the needs of the elderly person. The consultation suggestion unit, for example, uses a lifestyle record app to analyze the needs of the elderly person. For example, the content of daily activities is input and a customized service suggestion is made. The consultation suggestion unit also uses a data analysis algorithm to analyze the needs of the elderly person. For example, past lifestyle data is learned and a customized service suggestion is made. The consultation suggestion unit also uses a machine learning algorithm to analyze the needs of the elderly person. For example, a customized service suggestion is made based on the past lifestyle data. As a result, a customized service suggestion is made based on the needs of the elderly person, thereby providing support that meets individual needs.

[0043] The consultation suggestion unit can propose a flexible service schedule that matches the elderly person's lifestyle rhythm. The consultation suggestion unit, for example, uses a wearable device to analyze the elderly person's lifestyle rhythm. For example, it constantly monitors daily activity data and proposes a service schedule that matches the lifestyle rhythm. The consultation suggestion unit also uses a lifestyle record app to analyze the elderly person's lifestyle rhythm. For example, it inputs details of daily activities and proposes a service schedule that matches the lifestyle rhythm. The consultation suggestion unit also uses an algorithm that analyzes lifestyle data to analyze the elderly person's lifestyle rhythm. For example, it learns past lifestyle data and proposes a service schedule that matches the lifestyle rhythm. In this way, by proposing a flexible service schedule that matches the elderly person's lifestyle rhythm, the burden on their daily life is reduced.

[0044] The consultation suggestion unit can also share the service usage status with the elderly person's family, thereby increasing the sense of security for the family. For example, the consultation suggestion unit uses a dedicated app to share the service usage status with the elderly person's family. For example, it provides an app that allows the service usage status to be checked in real time. The consultation suggestion unit also provides regular reports to share the service usage status with the elderly person's family. For example, it periodically sends reports summarizing the service usage status. The consultation suggestion unit also provides an online portal to share the service usage status with the elderly person's family. For example, it provides an online portal that allows the service usage status to be checked. In this way, the service usage status can be shared with the elderly person's family, thereby increasing the sense of security for the family.

[0045] The system periodically evaluates the elderly person's living environment and makes suggestions for improvements to ensure safety. The system, for example, uses environmental sensors to periodically evaluate the elderly person's living environment. For example, it constantly monitors indoor temperature, humidity, and lighting brightness and makes suggestions for improvements to ensure safety. The system also uses a lifestyle record app to periodically evaluate the elderly person's living environment. For example, it inputs details of daily activities and makes suggestions for improvements to ensure safety. The system also uses a data analysis algorithm to periodically evaluate the elderly person's living environment. For example, it learns from past lifestyle data and makes suggestions for improvements to ensure safety. In this way, the system periodically evaluates the elderly person's living environment and makes suggestions for improvements to ensure safety, thereby improving the safety of their lives.

[0046] The system monitors the health status of the elderly and issues an alert for rapid response if an abnormality occurs. For example, the system uses a wearable device to monitor the health status of the elderly. For example, it constantly monitors heart rate and blood pressure and issues an alert if an abnormality occurs. The system also uses an algorithm to analyze vital data to monitor the health status of the elderly. For example, it learns past health data and issues an alert if an abnormality occurs. The system also uses a health record app to monitor the health status of the elderly. For example, it inputs daily health data and issues an alert if an abnormality occurs. In this way, the system monitors the health status of the elderly and issues an alert for rapid response if an abnormality occurs, thereby maintaining their health.

[0047] The system suggests a relaxation program to improve the quality of life of the elderly. For example, the system uses music therapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests music that has a relaxing effect. The system also uses aromatherapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests aromas that have a relaxing effect. The system also uses massage therapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests a massage that has a relaxing effect. In this way, by suggesting a relaxation program to improve the quality of life of the elderly, the quality of life is improved.

[0048] The system provides training to improve daily living skills so that the elderly can live independently. For example, the system uses online courses to provide training to improve daily living skills so that the elderly can live independently. For example, courses to improve cooking and cleaning skills are provided. The system also uses on-site training to provide training to improve daily living skills so that the elderly can live independently. For example, a specialist visits the elderly to provide direct instruction. The system also uses a training app to provide training to improve daily living skills so that the elderly can live independently. For example, an app for improving daily living skills is provided. Thereby, the system improves the quality of life by providing training to improve daily living skills so that the elderly can live independently.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The living situation monitoring unit can monitor the elderly person's living environment and automatically adjust the temperature, humidity, and lighting. For example, by installing a temperature sensor and a humidity sensor, the temperature and humidity in the room can be constantly monitored, and the air conditioner and humidifier can be automatically adjusted to maintain a comfortable environment. In addition, by installing a lighting sensor, the brightness of the indoor lighting can be constantly monitored and the brightness of the lighting can be automatically adjusted to maintain a comfortable environment. Furthermore, the unit can learn from past environmental data and automatically make optimal adjustments to maintain a comfortable environment. This can improve the quality of life by maintaining a comfortable living environment for the elderly person.

[0051] The living situation monitoring unit can monitor the behavior of an elderly person's pet and analyze the pet's health condition and behavioral patterns to benefit the elderly person's life. For example, a wearable device for the pet can be attached to constantly monitor the pet's movements and analyze its health condition and behavioral patterns. A pet camera can also be installed to record the pet's behavior and analyze its behavioral patterns. Furthermore, an algorithm for analyzing pet health data can be used to analyze the pet's diet and exercise data to understand its health condition. This can improve the quality of life of the elderly by understanding the health condition and behavioral patterns of the elderly person's pet.

[0052] The lifestyle monitoring unit can monitor the usage of home appliances used by the elderly and issue alerts if any abnormalities are detected. For example, a smart plug can be used to constantly monitor the power consumption of home appliances and issue alerts if any abnormalities are detected. IoT technology can also be used to attach sensors to home appliances to constantly monitor their usage and issue alerts if any abnormalities are detected. Furthermore, data analysis algorithms can be used to learn from past usage data, detect abnormal usage patterns, and issue alerts. This makes it possible to detect abnormalities in home appliances used by the elderly early on and ensure their safety.

[0053] The problem identification unit can analyze the dietary content of elderly people, detect nutritional imbalances, and make suggestions for improvement. For example, it can take photos of meals and analyze the nutritional components using image recognition technology. For example, it can automatically calculate calories and nutrients from photos of meals. It can also input meal contents using a food record app and analyze nutritional balance. Furthermore, it can use an algorithm that analyzes dietary data to learn from past dietary data and detect nutritional imbalances. This allows the dietary content of elderly people to be analyzed, detect nutritional imbalances, and make suggestions for improvement, thereby helping to maintain their health.

[0054] The problem identification unit can analyze the elderly person's sleep patterns in detail and provide advice to improve sleep quality. For example, a wearable device can be used to constantly monitor heart rate and movement during sleep to analyze sleep quality. A smart mattress can also be used to analyze sleep quality by monitoring movement during sleep with sensors built into the mattress. Furthermore, an algorithm for analyzing sleep data can be used to learn past sleep data and provide advice to improve sleep quality. By analyzing the elderly person's sleep patterns and providing advice to improve sleep quality, health can be maintained.

[0055] The advice providing unit can provide personalized advice tailored to the elderly person's lifestyle rhythm. For example, a wearable device can be used to constantly monitor daily activity data and provide advice tailored to the elderly person's lifestyle rhythm. It is also possible to input daily activity details using a lifestyle record app and provide advice tailored to the elderly person's lifestyle rhythm. Furthermore, it is possible to use an algorithm that analyzes lifestyle data to learn from past lifestyle data and provide advice tailored to the elderly person's lifestyle rhythm. This makes it possible to provide personalized advice tailored to the elderly person's lifestyle rhythm, thereby improving their quality of life.

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

[0057] Step 1: The living situation monitoring unit monitors the elderly person's living situation. For example, it uses sensors and cameras to record the elderly person's movements and behaviors and analyzes the data. It also monitors daily movement patterns, meal frequency, sleep quality, etc., and issues an alert if any abnormalities are detected. Step 2: The problem identification unit analyzes the data acquired by the lifestyle monitoring unit to identify problems associated with the aging process. For example, it detects changes such as unsteady walking or a decrease in food intake. It also identifies problems based on daily movement data and health data. Step 3: The advice provider provides appropriate advice based on the problems identified by the problem identification unit. For example, if the user's walking is unstable, the advice provider may suggest installing handrails or using walking aids. The advice provider also generates advice based on data related to the problems. Step 4: The consultation suggestion unit makes a consultation suggestion to a helper or a housekeeping service based on the advice provided by the advice providing unit. For example, if daily housework becomes difficult, it suggests using a housekeeping service. In addition, the consultation suggestion is generated based on data on the living situation and problems.

[0058] (Example 2) The lifestyle monitoring support system according to an embodiment of the present invention monitors the living conditions of elderly people, and the generation AI identifies problems associated with the aging process, provides appropriate advice, and suggests consultations with helpers or housekeeping services. As a result, the lifestyle monitoring support system can ensure the safety and security of elderly people and reduce the burden on their lives.

[0059] A life monitoring and support system according to an embodiment includes a life situation monitoring unit, a problem identification unit, an advice providing unit, and a consultation suggestion unit. The life situation monitoring unit monitors the life situation of an elderly person. For example, it records the elderly person's movements and behaviors using sensors and cameras and analyzes the data. The life situation monitoring unit also monitors the elderly person's daily movement patterns, meal frequency, sleep quality, and the like, and issues an alert if an abnormality is detected. The problem identification unit analyzes the data acquired by the life situation monitoring unit to identify problems associated with the progression of aging. For example, it detects changes such as unsteady walking or a decrease in food intake. The problem identification unit also identifies problems based on daily movement data and health data. The advice providing unit provides appropriate advice based on the problems identified by the problem identification unit. For example, if the elderly person's walking is unsteady, it suggests installing handrails or using walking aids. The advice providing unit also generates advice based on data related to the problems. The consultation suggestion unit suggests consulting a helper or a housekeeping service based on the advice provided by the advice providing unit. For example, if daily housework becomes difficult, it suggests using a housekeeping service. Furthermore, the consultation suggestion unit generates consultation suggestions based on data on living conditions and problems. As a result, the life monitoring support system according to the embodiment can ensure the safety and security of elderly people and reduce their burden on daily life. For example, by receiving appropriate support services, elderly people can continue to live independently. Furthermore, by monitoring living conditions and identifying problems early, appropriate measures can be taken.

[0060] The living situation monitoring unit can analyze changes in the elderly person's tone of voice and speaking style to detect changes in their emotions and health condition. The living situation monitoring unit, for example, uses voice recognition technology to analyze changes in the elderly person's tone of voice and speaking style. For example, it analyzes recorded data of everyday conversations to detect changes in their tone of voice and speaking style. The living situation monitoring unit also uses a machine learning algorithm to analyze changes in the elderly person's tone of voice and speaking style. For example, it learns past voice data and detects abnormal changes. The living situation monitoring unit also uses emotion analysis technology to analyze changes in the elderly person's tone of voice and speaking style. For example, it detects changes in emotions from changes in tone of voice and speaking style and infers changes in their health condition. By detecting changes in the elderly person's emotions and health condition in this way, problems can be discovered early and appropriate measures can be taken.

[0061] The living condition monitoring unit can monitor the elderly person's living environment and automatically adjust the temperature, humidity, and lighting. The living condition monitoring unit, for example, is equipped with a temperature sensor and a humidity sensor to monitor the elderly person's living environment. For example, the unit constantly monitors the indoor temperature and humidity and automatically adjusts the air conditioner and humidifier to maintain a comfortable environment. The living condition monitoring unit also is equipped with a lighting sensor to monitor the elderly person's living environment. For example, the unit constantly monitors the indoor lighting brightness and automatically adjusts the lighting brightness to maintain a comfortable environment. The living condition monitoring unit also uses an algorithm that analyzes environmental data to monitor the elderly person's living environment. For example, the unit learns past environmental data and automatically makes optimal adjustments to maintain a comfortable environment. This improves the quality of life by maintaining a comfortable living environment for the elderly person.

[0062] The living situation monitoring unit can use an emotion estimation function to monitor the emotional state of the elderly person in real time and provide music or video to reduce stress and anxiety. The living situation monitoring unit, for example, uses facial expression recognition technology to monitor the emotional state of the elderly person in real time using the emotion estimation function. For example, the emotional state is estimated by analyzing the facial expression of the elderly person using a camera. The living situation monitoring unit also uses voice recognition technology to monitor the emotional state of the elderly person in real time using the emotion estimation function. For example, the tone of voice and speaking style are analyzed to estimate the emotional state. The living situation monitoring unit also analyzes vital sign data to monitor the emotional state of the elderly person in real time using the emotion estimation function. For example, the heart rate and blood pressure are constantly monitored to estimate the emotional state. This makes it possible to monitor the emotional state of the elderly person in real time and take appropriate measures to reduce stress and anxiety.

[0063] The living situation monitoring unit monitors the behavior of an elderly person's pet and analyzes the pet's health condition and behavioral patterns to benefit the elderly person's life. For example, the living situation monitoring unit attaches a wearable device to the pet to monitor the behavior of the elderly person's pet. For example, it constantly monitors the pet's movements and analyzes the pet's health condition and behavioral patterns. The living situation monitoring unit also installs a pet camera to monitor the behavior of the elderly person's pet. For example, it records the pet's behavior and analyzes its behavioral patterns. The living situation monitoring unit also uses an algorithm to analyze the pet's health data to monitor the behavior of the elderly person's pet. For example, it analyzes the pet's diet and exercise data to understand its health condition. In this way, understanding the health condition and behavioral patterns of the elderly person's pet improves the quality of life of the elderly person.

[0064] The living situation monitoring unit monitors the usage of home appliances used by the elderly and can issue an alert if an abnormality is detected. The living situation monitoring unit, for example, uses a smart plug to monitor the usage of home appliances used by the elderly. For example, it constantly monitors the power consumption of the home appliance and issues an alert if an abnormality is detected. The living situation monitoring unit also uses IoT technology to monitor the usage of home appliances used by the elderly. For example, it attaches sensors to the home appliances to constantly monitor their usage and issues an alert if an abnormality is detected. The living situation monitoring unit also uses a data analysis algorithm to monitor the usage of home appliances used by the elderly. For example, it learns past usage data, detects abnormal usage patterns, and issues an alert. This allows for early detection of abnormalities in home appliances used by the elderly and ensures their safety.

[0065] The living situation monitoring unit can use the emotion estimation function to suggest hobbies and activities that the elderly person likes, thereby improving their quality of life. The living situation monitoring unit, for example, analyzes past emotion data to suggest hobbies and activities that the elderly person likes using the emotion estimation function. For example, it suggests activities that the elderly person has previously expressed positive emotions about. The living situation monitoring unit also analyzes real-time emotion data to suggest hobbies and activities that the elderly person likes using the emotion estimation function. For example, it suggests hobbies and activities that correspond to the elderly person's current emotional state. The living situation monitoring unit also refers to data on other elderly people to suggest hobbies and activities that the elderly person likes using the emotion estimation function. For example, it suggests hobbies and activities that elderly people of the same age group and gender like. In this way, the quality of life is improved by suggesting hobbies and activities that the elderly person likes.

[0066] The problem identification unit can analyze the dietary content of the elderly person, detect any imbalance in nutritional balance, and make suggestions for improvement. For example, to analyze the dietary content of the elderly person, the problem identification unit takes photos of the meals and analyzes the nutritional components using image recognition technology. For example, calories and nutrients are automatically calculated from the photos of the meals. The problem identification unit also uses a meal record app to analyze the dietary content of the elderly person. For example, the meal content is input and the nutritional balance is analyzed. The problem identification unit also uses an algorithm to analyze dietary data to analyze the dietary content of the elderly person. For example, past dietary data is learned and any imbalance in nutritional balance is detected. In this way, the dietary content of the elderly person can be analyzed, any imbalance in nutritional balance can be detected, and suggestions for improvement can be made to maintain health.

[0067] The problem identification unit can analyze the sleep patterns of the elderly person in detail and provide advice to improve sleep quality. The problem identification unit, for example, uses a wearable device to analyze the sleep patterns of the elderly person. For example, the heart rate and movements during sleep are constantly monitored to analyze the quality of sleep. The problem identification unit also uses a smart mattress to analyze the sleep patterns of the elderly person. For example, a sensor built into the mattress monitors movements during sleep to analyze the quality of sleep. The problem identification unit also uses an algorithm to analyze sleep data to analyze the sleep patterns of the elderly person. For example, past sleep data is learned and advice to improve sleep quality is provided. In this way, the sleep patterns of the elderly person are analyzed and advice to improve sleep quality is provided, thereby maintaining health.

[0068] The problem identification unit can use the emotion estimation function to detect loneliness and social isolation in the elderly and make suggestions to increase opportunities for communication. The problem identification unit, for example, uses facial expression recognition technology to detect loneliness and social isolation in the elderly using the emotion estimation function. For example, it analyzes the facial expressions of the elderly using a camera to estimate loneliness. The problem identification unit also uses voice recognition technology to detect loneliness and social isolation in the elderly using the emotion estimation function. For example, it analyzes the tone of voice and speaking style to estimate loneliness. The problem identification unit also analyzes vital sign data to detect loneliness and social isolation in the elderly using the emotion estimation function. For example, it constantly monitors heart rate and blood pressure to estimate loneliness. In this way, it is possible to detect loneliness and social isolation in the elderly and provide psychological support by making suggestions to increase opportunities for communication.

[0069] The problem identification unit can monitor the exercise habits of the elderly and suggest an appropriate exercise program. The problem identification unit, for example, uses a wearable device to monitor the exercise habits of the elderly. For example, it constantly monitors the number of steps and exercise time and suggests an exercise program. The problem identification unit also uses an exercise recording app to monitor the exercise habits of the elderly. For example, it inputs the exercise details and suggests an appropriate exercise program. The problem identification unit also uses an algorithm to analyze exercise data to monitor the exercise habits of the elderly. For example, it learns past exercise data and suggests an appropriate exercise program. In this way, the exercise habits of the elderly can be monitored and an appropriate exercise program suggested, thereby maintaining health.

[0070] The problem identification unit can monitor the elderly person's medication status and provide reminders to prevent them from forgetting to take their medicine. The problem identification unit, for example, uses a smart pillbox to monitor the elderly person's medication status. For example, it monitors the removal of medicine and provides reminders to prevent them from forgetting to take their medicine. The problem identification unit also uses a medication record app to monitor the elderly person's medication status. For example, it inputs the details of medication and provides reminders to prevent them from forgetting to take their medicine. The problem identification unit also uses an algorithm that analyzes medication data to monitor the elderly person's medication status. For example, it learns past medication data and provides reminders to prevent them from forgetting to take their medicine. In this way, the elderly person's medication status can be monitored and reminders to prevent them from forgetting to take their medicine can be provided, thereby maintaining their health.

[0071] The problem identification unit can use the emotion estimation function to suggest new hobbies and activities that will interest the elderly, thereby enriching their lives. The problem identification unit, for example, analyzes past emotion data to suggest new hobbies and activities that will interest the elderly using the emotion estimation function. For example, it suggests activities that have shown positive emotions in the past. The problem identification unit also analyzes real-time emotion data to suggest new hobbies and activities that will interest the elderly using the emotion estimation function. For example, it suggests hobbies and activities that correspond to the elderly's current emotional state. The problem identification unit also refers to data on other elderly people to suggest new hobbies and activities that will interest the elderly using the emotion estimation function. For example, it suggests hobbies and activities that are popular among elderly people of the same age group and gender. In this way, new hobbies and activities that will interest the elderly can be suggested, thereby enriching their lives.

[0072] The advice providing unit can provide personalized advice tailored to the elderly person's lifestyle rhythm. The advice providing unit, for example, uses a wearable device to analyze the elderly person's lifestyle rhythm. For example, it constantly monitors daily activity data and provides advice tailored to the lifestyle rhythm. The advice providing unit also uses a lifestyle record app to analyze the elderly person's lifestyle rhythm. For example, it inputs details of daily activities and provides advice tailored to the lifestyle rhythm. The advice providing unit also uses an algorithm that analyzes lifestyle data to analyze the elderly person's lifestyle rhythm. For example, it learns past lifestyle data and provides advice tailored to the lifestyle rhythm. In this way, personalized advice tailored to the elderly person's lifestyle rhythm is provided, improving their quality of life.

[0073] The advice providing unit can predict future problems based on the elderly person's past behavioral data and propose countermeasures in advance. The advice providing unit, for example, uses a behavior recording app to analyze the elderly person's past behavioral data. For example, the content of past behavior is input and future problems are predicted. The advice providing unit also uses a data analysis algorithm to analyze the elderly person's past behavioral data. For example, the past behavioral data is trained to predict future problems. The advice providing unit also uses a machine learning algorithm to analyze the elderly person's past behavioral data. For example, future problems are predicted based on the past behavioral data and countermeasures are proposed in advance. In this way, future problems can be predicted based on the elderly person's past behavioral data and countermeasures are proposed in advance, thereby preventing problems from occurring.

[0074] The advice providing unit can provide advice according to the emotional state of the elderly person using the emotion estimation function, thereby providing psychological support. The advice providing unit uses, for example, facial expression recognition technology to provide advice according to the emotional state of the elderly person using the emotion estimation function. For example, the advice providing unit analyzes the facial expression of the elderly person using a camera and provides advice according to the emotional state. The advice providing unit also uses voice recognition technology to provide advice according to the emotional state of the elderly person using the emotion estimation function. For example, the advice providing unit analyzes the tone of voice and speaking style and provides advice according to the emotional state. The advice providing unit also analyzes vital sign data to provide advice according to the emotional state of the elderly person using the emotion estimation function. For example, the advice providing unit constantly monitors the heart rate and blood pressure and provides advice according to the emotional state. In this way, advice according to the emotional state of the elderly person is provided and psychological support is provided, thereby maintaining mental health.

[0075] The advice providing unit can provide advice to promote communication between the elderly person and family and friends. For example, the advice providing unit uses a video call app to promote communication between the elderly person and family and friends. For example, it suggests scheduling regular video calls. The advice providing unit also uses a messaging app to promote communication between the elderly person and family and friends. For example, it suggests regular message exchanges. The advice providing unit also uses social media to promote communication between the elderly person and family and friends. For example, it suggests posts to promote interaction with family and friends. In this way, social connections are strengthened by providing advice to promote communication between the elderly person and family and friends.

[0076] The advice providing unit can suggest local events and activities in which the elderly can participate, thereby strengthening social ties. For example, the advice providing unit refers to a local event calendar to suggest local events and activities in which the elderly can participate. For example, it collects local event information and suggests it to the elderly. The advice providing unit also refers to information on local community centers to suggest local events and activities in which the elderly can participate. For example, it collects activity information at community centers and suggests it to the elderly. The advice providing unit also refers to local news sites to suggest local events and activities in which the elderly can participate. For example, it collects event information from local news sites and suggests it to the elderly. In this way, social ties are strengthened by suggesting local events and activities in which the elderly can participate.

[0077] The advice providing unit can use the emotion estimation function to provide advice on creating a relaxing environment for the elderly. The advice providing unit uses, for example, facial expression recognition technology to provide advice on creating a relaxing environment for the elderly using the emotion estimation function. For example, the advice providing unit analyzes the facial expression of the elderly using a camera and provides advice on creating a relaxing environment. The advice providing unit also uses voice recognition technology to provide advice on creating a relaxing environment for the elderly using the emotion estimation function. For example, the advice providing unit analyzes the tone of voice and speaking style and provides advice on creating a relaxing environment. The advice providing unit also analyzes vital sign data to provide advice on creating a relaxing environment for the elderly using the emotion estimation function. For example, the advice providing unit constantly monitors the heart rate and blood pressure and provides advice on creating a relaxing environment. In this way, providing advice on creating a relaxing environment for the elderly maintains their mental health.

[0078] The consultation suggestion unit can match the most suitable helper or housekeeping service according to the elderly person's living situation. The consultation suggestion unit, for example, uses a life record app to analyze the elderly person's living situation. For example, details of daily activities are input and the most suitable helper or housekeeping service is matched. The consultation suggestion unit also uses a data analysis algorithm to analyze the elderly person's living situation. For example, past life data is learned and the most suitable helper or housekeeping service is matched. The consultation suggestion unit also uses a machine learning algorithm to analyze the elderly person's living situation. For example, the most suitable helper or housekeeping service is matched based on past life data. In this way, the burden on the elderly person's life is reduced by matching the most suitable helper or housekeeping service according to their living situation.

[0079] The consultation suggestion unit can make customized service suggestions based on the needs of the elderly person. The consultation suggestion unit, for example, uses a lifestyle record app to analyze the needs of the elderly person. For example, the content of daily activities is input and a customized service suggestion is made. The consultation suggestion unit also uses a data analysis algorithm to analyze the needs of the elderly person. For example, past lifestyle data is learned and a customized service suggestion is made. The consultation suggestion unit also uses a machine learning algorithm to analyze the needs of the elderly person. For example, a customized service suggestion is made based on the past lifestyle data. As a result, a customized service suggestion is made based on the needs of the elderly person, thereby providing support that meets individual needs.

[0080] The consultation suggestion unit can use the emotion estimation function to suggest services that the elderly can use with confidence. The consultation suggestion unit uses, for example, facial expression recognition technology to suggest services that the elderly can use with confidence using the emotion estimation function. For example, it analyzes the facial expressions of the elderly using a camera and suggests services that the elderly can use with confidence. The consultation suggestion unit also uses voice recognition technology to suggest services that the elderly can use with confidence using the emotion estimation function. For example, it analyzes the tone of voice and speaking style and suggests services that the elderly can use with confidence. The consultation suggestion unit also analyzes vital sign data to suggest services that the elderly can use with confidence using the emotion estimation function. For example, it constantly monitors heart rate and blood pressure and suggests services that the elderly can use with confidence. In this way, by suggesting services that the elderly can use with confidence, a sense of psychological security is provided.

[0081] The consultation suggestion unit can propose a flexible service schedule that matches the elderly person's lifestyle rhythm. The consultation suggestion unit, for example, uses a wearable device to analyze the elderly person's lifestyle rhythm. For example, it constantly monitors daily activity data and proposes a service schedule that matches the lifestyle rhythm. The consultation suggestion unit also uses a lifestyle record app to analyze the elderly person's lifestyle rhythm. For example, it inputs details of daily activities and proposes a service schedule that matches the lifestyle rhythm. The consultation suggestion unit also uses an algorithm that analyzes lifestyle data to analyze the elderly person's lifestyle rhythm. For example, it learns past lifestyle data and proposes a service schedule that matches the lifestyle rhythm. In this way, by proposing a flexible service schedule that matches the elderly person's lifestyle rhythm, the burden on their daily life is reduced.

[0082] The consultation suggestion unit can also share the service usage status with the elderly person's family, thereby increasing the sense of security for the family. For example, the consultation suggestion unit uses a dedicated app to share the service usage status with the elderly person's family. For example, it provides an app that allows the service usage status to be checked in real time. The consultation suggestion unit also provides regular reports to share the service usage status with the elderly person's family. For example, it periodically sends reports summarizing the service usage status. The consultation suggestion unit also provides an online portal to share the service usage status with the elderly person's family. For example, it provides an online portal that allows the service usage status to be checked. In this way, the service usage status can be shared with the elderly person's family, thereby increasing the sense of security for the family.

[0083] The consultation suggestion unit can use the emotion estimation function to make suggestions to reduce stress felt by the elderly when using a service. The consultation suggestion unit uses, for example, facial expression recognition technology to make suggestions to reduce stress felt by the elderly when using a service using the emotion estimation function. For example, the consultation suggestion unit analyzes the facial expression of the elderly using a camera and makes suggestions to reduce stress. The consultation suggestion unit also uses voice recognition technology to make suggestions to reduce stress felt by the elderly when using a service using the emotion estimation function. For example, the consultation suggestion unit analyzes tone of voice and speaking style and makes suggestions to reduce stress. The consultation suggestion unit also analyzes vital sign data to make suggestions to reduce stress felt by the elderly when using a service using the emotion estimation function. For example, the consultation suggestion unit constantly monitors heart rate and blood pressure and makes suggestions to reduce stress. In this way, suggestions to reduce stress felt by the elderly when using a service are made, thereby reducing psychological burden.

[0084] The system periodically evaluates the elderly person's living environment and makes suggestions for improvements to ensure safety. The system, for example, uses environmental sensors to periodically evaluate the elderly person's living environment. For example, it constantly monitors indoor temperature, humidity, and lighting brightness and makes suggestions for improvements to ensure safety. The system also uses a lifestyle record app to periodically evaluate the elderly person's living environment. For example, it inputs details of daily activities and makes suggestions for improvements to ensure safety. The system also uses a data analysis algorithm to periodically evaluate the elderly person's living environment. For example, it learns from past lifestyle data and makes suggestions for improvements to ensure safety. In this way, the system periodically evaluates the elderly person's living environment and makes suggestions for improvements to ensure safety, thereby improving the safety of their lives.

[0085] The system monitors the health status of the elderly and issues an alert for rapid response if an abnormality occurs. For example, the system uses a wearable device to monitor the health status of the elderly. For example, it constantly monitors heart rate and blood pressure and issues an alert if an abnormality occurs. The system also uses an algorithm to analyze vital data to monitor the health status of the elderly. For example, it learns past health data and issues an alert if an abnormality occurs. The system also uses a health record app to monitor the health status of the elderly. For example, it inputs daily health data and issues an alert if an abnormality occurs. In this way, the system monitors the health status of the elderly and issues an alert for rapid response if an abnormality occurs, thereby maintaining their health.

[0086] The system provides support for enhancing the elderly's sense of psychological security using an emotion estimation function. The system uses, for example, facial expression recognition technology to provide support for enhancing the elderly's sense of psychological security using the emotion estimation function. For example, the system analyzes the elderly's facial expressions using a camera to provide support for enhancing the psychological security. The system also uses voice recognition technology to provide support for enhancing the elderly's sense of psychological security using the emotion estimation function. For example, the system analyzes tone of voice and speaking style to provide support for enhancing the psychological security. The system also analyzes vital sign data to provide support for enhancing the elderly's sense of psychological security using the emotion estimation function. For example, the system constantly monitors heart rate and blood pressure to provide support for enhancing the psychological security. In this way, support for enhancing the elderly's sense of psychological security is provided, thereby maintaining mental health.

[0087] The system suggests a relaxation program to improve the quality of life of the elderly. For example, the system uses music therapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests music that has a relaxing effect. The system also uses aromatherapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests aromas that have a relaxing effect. The system also uses massage therapy to suggest a relaxation program to improve the quality of life of the elderly. For example, the system suggests a massage that has a relaxing effect. In this way, by suggesting a relaxation program to improve the quality of life of the elderly, the quality of life is improved.

[0088] The system provides training to improve daily living skills so that the elderly can live independently. For example, the system uses online courses to provide training to improve daily living skills so that the elderly can live independently. For example, courses to improve cooking and cleaning skills are provided. The system also uses on-site training to provide training to improve daily living skills so that the elderly can live independently. For example, a specialist visits the elderly to provide direct instruction. The system also uses a training app to provide training to improve daily living skills so that the elderly can live independently. For example, an app for improving daily living skills is provided. Thereby, the system improves the quality of life by providing training to improve daily living skills so that the elderly can live independently.

[0089] The system uses an emotion estimation function to make suggestions for creating an environment where the elderly can live safely. The system uses, for example, facial expression recognition technology to make suggestions for creating an environment where the elderly can live safely using the emotion estimation function. For example, the system analyzes the facial expressions of the elderly using a camera and makes suggestions for creating an environment where the elderly can live safely. The system also uses voice recognition technology to make suggestions for creating an environment where the elderly can live safely using the emotion estimation function. For example, the system analyzes tone of voice and speaking style and makes suggestions for creating an environment where the elderly can live safely using the emotion estimation function. The system also analyzes vital sign data to make suggestions for creating an environment where the elderly can live safely using the emotion estimation function. For example, the system constantly monitors heart rate and blood pressure and makes suggestions for creating an environment where the elderly can live safely. In this way, the system improves the quality of life by making suggestions for creating an environment where the elderly can live safely.

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

[0091] The living situation monitoring unit can monitor the elderly person's living environment and automatically adjust the temperature, humidity, and lighting. For example, by installing a temperature sensor and a humidity sensor, the temperature and humidity in the room can be constantly monitored, and the air conditioner and humidifier can be automatically adjusted to maintain a comfortable environment. In addition, by installing a lighting sensor, the brightness of the indoor lighting can be constantly monitored and the brightness of the lighting can be automatically adjusted to maintain a comfortable environment. Furthermore, the unit can learn from past environmental data and automatically make optimal adjustments to maintain a comfortable environment. This can improve the quality of life by maintaining a comfortable living environment for the elderly person.

[0092] The living situation monitoring unit can analyze changes in the elderly person's tone of voice and speaking style to detect changes in their emotions and health condition. For example, it can use voice recognition technology to analyze recorded data of everyday conversations and detect changes in tone of voice and speaking style. It can also use machine learning algorithms to learn from past voice data and detect abnormal changes. Furthermore, it can use emotion analysis technology to detect changes in emotions from changes in tone of voice and speaking style and infer changes in health condition. By detecting changes in the elderly person's emotions and health condition, it is possible to discover problems early and take appropriate measures.

[0093] The living situation monitoring unit can monitor the behavior of an elderly person's pet and analyze the pet's health condition and behavioral patterns to benefit the elderly person's life. For example, a wearable device for the pet can be attached to constantly monitor the pet's movements and analyze its health condition and behavioral patterns. A pet camera can also be installed to record the pet's behavior and analyze its behavioral patterns. Furthermore, an algorithm for analyzing pet health data can be used to analyze the pet's diet and exercise data to understand its health condition. This can improve the quality of life of the elderly by understanding the health condition and behavioral patterns of the elderly person's pet.

[0094] The living situation monitoring unit can use an emotion estimation function to monitor the emotional state of the elderly person in real time and provide music and videos to reduce stress and anxiety. For example, facial expression recognition technology can be used to analyze the elderly person's facial expressions with a camera to estimate their emotional state. Voice recognition technology can also be used to analyze the tone of voice and speaking style to estimate their emotional state. Furthermore, it is possible to analyze vital signs data, constantly monitor heart rate and blood pressure, and estimate their emotional state. This makes it possible to monitor the emotional state of the elderly person in real time and take appropriate measures to reduce stress and anxiety.

[0095] The lifestyle monitoring unit can monitor the usage of home appliances used by the elderly and issue alerts if any abnormalities are detected. For example, a smart plug can be used to constantly monitor the power consumption of home appliances and issue alerts if any abnormalities are detected. IoT technology can also be used to attach sensors to home appliances to constantly monitor their usage and issue alerts if any abnormalities are detected. Furthermore, data analysis algorithms can be used to learn from past usage data, detect abnormal usage patterns, and issue alerts. This makes it possible to detect abnormalities in home appliances used by the elderly early on and ensure their safety.

[0096] The problem identification unit can analyze the dietary content of elderly people, detect nutritional imbalances, and make suggestions for improvement. For example, it can take photos of meals and analyze the nutritional components using image recognition technology. For example, it can automatically calculate calories and nutrients from photos of meals. It can also input meal contents using a food record app and analyze nutritional balance. Furthermore, it can use an algorithm that analyzes dietary data to learn from past dietary data and detect nutritional imbalances. This allows the dietary content of elderly people to be analyzed, detect nutritional imbalances, and make suggestions for improvement, thereby helping to maintain their health.

[0097] The problem identification unit can analyze the elderly person's sleep patterns in detail and provide advice to improve sleep quality. For example, a wearable device can be used to constantly monitor heart rate and movement during sleep to analyze sleep quality. A smart mattress can also be used to analyze sleep quality by monitoring movement during sleep with sensors built into the mattress. Furthermore, an algorithm for analyzing sleep data can be used to learn past sleep data and provide advice to improve sleep quality. By analyzing the elderly person's sleep patterns and providing advice to improve sleep quality, health can be maintained.

[0098] The problem identification unit can use the emotion estimation function to detect loneliness and social isolation in elderly people and make suggestions to increase opportunities for communication. For example, facial expression recognition technology can be used to analyze the facial expressions of elderly people with a camera to estimate feelings of loneliness. Voice recognition technology can also be used to analyze tone of voice and speaking style to estimate feelings of loneliness. Furthermore, it is possible to analyze vital signs data and constantly monitor heart rate and blood pressure to estimate feelings of loneliness. This makes it possible to detect feelings of loneliness and social isolation in elderly people and provide psychological support by making suggestions to increase opportunities for communication.

[0099] The advice providing unit can provide personalized advice tailored to the elderly person's lifestyle rhythm. For example, a wearable device can be used to constantly monitor daily activity data and provide advice tailored to the elderly person's lifestyle rhythm. It is also possible to input daily activity details using a lifestyle record app and provide advice tailored to the elderly person's lifestyle rhythm. Furthermore, it is possible to use an algorithm that analyzes lifestyle data to learn from past lifestyle data and provide advice tailored to the elderly person's lifestyle rhythm. This makes it possible to provide personalized advice tailored to the elderly person's lifestyle rhythm, thereby improving their quality of life.

[0100] The advice providing unit can use the emotion estimation function to provide advice according to the emotional state of the elderly person and provide psychological support. For example, facial expression recognition technology can be used to analyze the facial expressions of the elderly person using a camera, and advice according to the emotional state can be provided. Voice recognition technology can also be used to analyze the tone of voice and speaking style, and advice according to the emotional state can be provided. Furthermore, it is possible to analyze vital data, constantly monitor heart rate and blood pressure, and provide advice according to the emotional state. In this way, advice according to the emotional state of the elderly person can be provided and psychological support can be provided, thereby maintaining mental health.

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

[0102] Step 1: The living situation monitoring unit monitors the elderly person's living situation. For example, it uses sensors and cameras to record the elderly person's movements and behaviors and analyzes the data. It also monitors daily movement patterns, meal frequency, sleep quality, etc., and issues an alert if any abnormalities are detected. Step 2: The problem identification unit analyzes the data acquired by the lifestyle monitoring unit to identify problems associated with the aging process. For example, it detects changes such as unsteady walking or a decrease in food intake. It also identifies problems based on daily movement data and health data. Step 3: The advice provider provides appropriate advice based on the problems identified by the problem identification unit. For example, if the user's walking is unstable, the advice provider may suggest installing handrails or using walking aids. The advice provider also generates advice based on data related to the problems. Step 4: The consultation suggestion unit makes a consultation suggestion to a helper or a housekeeping service based on the advice provided by the advice providing unit. For example, if daily housework becomes difficult, it suggests using a housekeeping service. In addition, the consultation suggestion is generated based on data on the living situation and problems.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

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

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

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

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

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

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

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

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

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

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

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

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Claims

1. a living situation monitoring department that monitors the living situation of the elderly; a problem identification unit that analyzes the data acquired by the living condition monitoring unit and identifies problems associated with the progression of aging; an advice providing unit that provides appropriate advice based on the problem identified by the problem identifying unit; a consultation suggestion unit that suggests consultation to a helper or a housekeeping service based on the advice provided by the advice providing unit. A system characterized by:

2. The living condition monitoring unit Analyzing changes in the elderly person's tone of voice and speaking style to detect changes in their emotions and health status 2. The system of claim 1.

3. The living condition monitoring unit The system monitors the elderly person's living environment and automatically adjusts temperature, humidity, and lighting.

2. The system of claim 1.

4. The living condition monitoring unit Monitor the elderly person's emotional state in real time and provide music and videos to reduce stress and anxiety.

2. The system of claim 1.

5. The living condition monitoring unit The behavior of the elderly person's pet is monitored, and the health condition and behavioral patterns of the pet are analyzed to be useful for the elderly person's life.

2. The system of claim 1.

6. The living condition monitoring unit Monitors the usage of home appliances used by the elderly and issues an alert if there is an abnormality.

2. The system of claim 1.

7. The living condition monitoring unit Suggesting hobbies and activities that the elderly enjoy and improving their quality of life 2. The system of claim 1.

Images