system

Abstract

The system according to this embodiment aims to collect and analyze information necessary for supporting the lives of elderly people, and to make and implement specific suggestions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information necessary for supporting the lives of the elderly. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The execution unit carries out the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in the life support for the elderly, collection, analysis of information, and execution of specific proposals are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to collect and analyze information necessary for the life support of the elderly, and make and execute specific proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information necessary for supporting the lives of elderly people. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. The execution unit carries out the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze information necessary for supporting the lives of elderly people, make concrete suggestions, and implement them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An autonomous AI agent system according to an embodiment of the present invention is a system that supports elderly people living alone so that they can live safely and comfortably. This autonomous AI agent system collects and analyzes information necessary for supporting the lives of the elderly, makes concrete suggestions, and implements the suggested content, thereby alleviating feelings of loneliness and anxiety among the elderly and realizing a society where they can live with peace of mind. For example, the autonomous AI agent system creates a rehabilitation plan, suggests necessary exercises and treatment methods, and automatically sends feedback to doctors and care staff according to the progress. In addition, the autonomous AI agent system performs more accurate emotion analysis by combining voice recognition, facial recognition, and physical condition data (blood pressure, pulse, etc.) and automatically suggests conversations that are appropriate to the user's state, such as, "You seem tired today. Shall I suggest some ways to relax?" Furthermore, the autonomous AI agent system comprehensively understands the living situation of the elderly, predicts shortages of supplies while at home, and automatically makes optimal purchase suggestions and orders. By utilizing drones, etc., necessary items can be delivered in real time even if the elderly person cannot physically go to a store. In addition, the autonomous AI agent system automatically suggests local events and community activities based on the elderly person's hobbies and interests, and adjusts participation methods and times. The system records the feedback and physical condition of elderly participants after events, and uses this information to inform future event proposals. This helps alleviate feelings of loneliness among the elderly and strengthens ties with the community. As a result, the autonomous AI agent system can automatically provide support for the elderly's daily lives, creating a society where they can live with peace of mind.

[0029] The autonomous AI agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information necessary for supporting the lives of elderly people. The collection unit can, for example, collect the content of speech spoken by elderly people using speech recognition technology. The collection unit can also collect facial expression data of elderly people using facial recognition technology. Furthermore, the collection unit can collect physical condition data (e.g., blood pressure, pulse, body temperature, etc.) using sensors. For example, the collection unit can analyze the content of speech spoken by elderly people in real time using speech recognition technology and extract important information. The collection unit can analyze the facial expression data of elderly people using facial recognition technology and estimate their emotional state. The collection unit can collect physical condition data using sensors and monitor their health status. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected speech data using natural language processing technology and extract important information. Furthermore, the analysis unit can analyze the collected facial data using image analysis technology and estimate their emotional state. Furthermore, the analysis unit can analyze the collected physical condition data using statistical analysis technology and evaluate their health status. For example, the analysis unit analyzes voice data using natural language processing technology to extract important information from the speech content of elderly people. The analysis unit can analyze facial data using image analysis technology to estimate the emotional state of elderly people. The analysis unit can analyze physical condition data using statistical analysis technology to evaluate the health status of elderly people. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose relaxation methods based on the results of emotional analysis. The proposal unit can also propose appropriate exercise and treatment methods based on the health status evaluation results. Furthermore, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit proposes relaxation methods based on the results of emotional analysis. The proposal unit can propose appropriate exercise and treatment methods based on the health status evaluation results. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The execution unit carries out the content proposed by the proposal unit. For example, the execution unit can propose or order the purchase of goods.Furthermore, the execution unit can deliver supplies using drones. In addition, the execution unit can record feedback and physical condition after the event. For example, the execution unit can suggest and order supplies. The execution unit can deliver supplies using drones. The execution unit can record feedback and physical condition after the event. As a result, the autonomous AI agent system according to this embodiment can automatically provide support for the lives of the elderly, realizing a society where they can live with peace of mind.

[0030] The data collection unit collects information necessary for supporting the lives of elderly people. Specifically, it can collect the content of elderly people's speech using speech recognition technology. Speech recognition technology analyzes what elderly people say on a daily basis in real time and extracts important keywords and phrases. For example, if an elderly person says, "I'm not feeling well today," the data collection unit immediately recognizes this information and records it as a sign of poor health. The data collection unit can also collect facial expression data of elderly people using facial recognition technology. Facial recognition technology analyzes the elderly person's facial expressions and estimates emotional states such as joy, sadness, anger, and surprise. For example, if an elderly person smiles, the data collection unit records that expression as joy, and conversely, if they frown, it records it as anxiety or confusion. Furthermore, the data collection unit can collect health data (e.g., blood pressure, pulse, body temperature) using sensors. These sensors are attached to the elderly person's body and collect data periodically. For example, a blood pressure monitor measures the elderly person's blood pressure, a pulse monitor records their heart rate, and a thermometer monitors fluctuations in body temperature. This allows the data collection unit to monitor the health status of elderly individuals in real time and respond immediately if any abnormalities are detected. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes the information collected by the collection department. Specifically, it can analyze collected audio data using natural language processing technology to extract important information. Natural language processing technology converts the speech content of elderly people into text data and extracts important information about their health status and living situation from it. For example, if an elderly person says, "Recently, I've been having trouble sleeping at night," the analysis department will record this information as a sign of a sleep disorder and suggest necessary countermeasures. The analysis department can also analyze collected facial data using image analysis technology to estimate emotional states. Image analysis technology analyzes the facial expressions of elderly people and detects changes in emotion. For example, if an elderly person frequently has a sad expression, the analysis department will record that emotional state and determine that psychological support is needed. Furthermore, the analysis department can analyze collected physical condition data using statistical analysis technology to evaluate health status. Statistical analysis technology analyzes data such as blood pressure, pulse rate, and body temperature and detects abnormal values ​​and trends. For example, if blood pressure rises sharply, the analysis department will record the data as an abnormality and recommend contacting a medical institution. This allows the analysis department to quickly and accurately analyze collected data, enabling them to understand the health and living conditions of elderly individuals in real time. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past health data, they can predict risk fluctuations in specific seasons or time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. Specifically, it can suggest relaxation methods based on the results of emotional analysis. For example, if an elderly person is experiencing stress, the proposal department can suggest deep breathing or meditation techniques to help them relax. The proposal department can also suggest appropriate exercise or treatment methods based on the results of health assessments. For example, if blood pressure is high, the proposal department can recommend walking or light stretching and advise the person to see a doctor. Furthermore, the proposal department can suggest events and community activities based on the elderly person's hobbies and interests. For example, if an elderly person is interested in music, the proposal department can provide information on local music events and concerts and encourage participation. In this way, the proposal department can suggest concrete actions to improve the quality of life for the elderly and support their health maintenance and social participation. In addition, the proposal department can monitor the effectiveness of the suggestions and modify them as needed. For example, it can regularly evaluate whether the suggested exercises are effective and suggest alternative exercises if they are not effective. The proposal department can also collect feedback from the elderly and continuously improve the accuracy and effectiveness of the suggestions. In this way, the proposal department can make flexible and effective suggestions that meet the needs of the elderly and improve their quality of life.

[0033] The implementation team carries out the proposals made by the proposal team. Specifically, they can propose and order the purchase of supplies. For example, they can order medicines and daily necessities needed by the elderly online and have them delivered to their homes. The implementation team can also deliver supplies using drones. Drones can deliver supplies quickly and efficiently, allowing the elderly to receive necessary supplies without leaving their homes. Furthermore, the implementation team can record feedback and physical condition after events. For example, they can record the feedback and changes in physical condition of elderly participants in events and incorporate this into future proposals. This ensures that the implementation team reliably carries out the proposed content and supports the lives of the elderly. In addition, the implementation team can monitor the effectiveness of the implemented content and modify it as needed. For example, they can regularly evaluate whether the proposed exercises are effective and implement a different exercise method if no effect is seen. The implementation team can also collect feedback from the elderly and continuously improve the accuracy and effectiveness of the implemented content. This allows the implementation team to carry out flexible and effective implementations tailored to the needs of the elderly, improving their quality of life. Furthermore, the implementation team can respond quickly in emergencies. For example, if an elderly person suddenly falls ill, the operational unit will immediately contact a medical institution and provide the necessary support. In this way, the operational unit can comprehensively support the lives of elderly people and provide an environment in which they can live with peace of mind.

[0034] The data collection unit can collect speech recognition, facial recognition, and health data. For example, the data collection unit can collect the content of elderly people's speech using speech recognition technology. The data collection unit can also collect facial expression data of elderly people using facial recognition technology. The data collection unit can also collect health data using sensors. For example, the data collection unit can analyze the content of elderly people's speech in real time using speech recognition technology and extract important information. The data collection unit can analyze the facial expression data of elderly people using facial recognition technology and estimate their emotional state. The data collection unit can collect health data with sensors and monitor their health status. By collecting speech recognition, facial recognition, and health data, more accurate information can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the speech data collected using speech recognition technology into a generating AI and have the generating AI perform the analysis of the speech data.

[0035] The analysis unit can perform sentiment analysis based on the collected data. For example, the analysis unit can analyze collected voice data using natural language processing technology to estimate emotional states. The analysis unit can also analyze collected facial data using image analysis technology to estimate emotional states. The analysis unit can also analyze collected physical condition data using statistical analysis technology to evaluate emotional states. For example, the analysis unit can analyze voice data using natural language processing technology to estimate emotional states from the content of elderly people's speech. The analysis unit can analyze facial data using image analysis technology to estimate the emotional states of elderly people. The analysis unit can analyze physical condition data using statistical analysis technology to evaluate the emotional states of elderly people. By performing sentiment analysis, it is possible to understand the emotional states of elderly people and provide appropriate support. Sentiment analysis is implemented using sentiment estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform sentiment analysis.

[0036] The suggestion unit can suggest relaxation methods based on the results of emotional analysis. The suggestion unit can, for example, suggest relaxation methods based on the results of emotional analysis. The suggestion unit can suggest appropriate relaxation methods based on the results of emotional analysis. The suggestion unit can suggest relaxation methods based on the results of emotional analysis. For example, the suggestion unit can suggest relaxation methods based on the results of emotional analysis. The suggestion unit can suggest appropriate relaxation methods based on the results of emotional analysis. The suggestion unit can suggest relaxation methods based on the results of emotional analysis. This makes it possible to reduce stress in the elderly by suggesting relaxation methods based on the results of emotional analysis. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the results of emotional analysis into a generating AI and have the generating AI execute suggestions for relaxation methods.

[0037] The execution unit can make purchase proposals and orders for supplies. The execution unit can, for example, make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. For example, the execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. This allows for support of the lives of the elderly by making purchase proposals and orders for supplies. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input purchase proposals and orders for supplies into a generating AI and have the generating AI execute the purchase proposals and orders.

[0038] The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. For example, the execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. This makes it possible to deliver necessary items to elderly people even if they are physically unable to go to stores, by delivering goods using drones. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input drone flight data into a generating AI and have the generating AI perform optimization of the delivery route.

[0039] The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. By proposing events and community activities based on the hobbies and interests of elderly people, it is possible to alleviate feelings of loneliness among the elderly and strengthen ties with the community. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the hobbies and interests of elderly people into a generating AI and have the generating AI execute proposals for events and community activities.

[0040] The execution unit can record post-event impressions and physical condition. The execution unit can, for example, record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. For example, the execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. By recording post-event impressions and physical condition, this information can be used to inform future proposals. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input post-event impressions and physical condition data into a generating AI and have the generating AI perform analysis to inform future proposals.

[0041] The data collection unit can learn the lifestyle rhythms of elderly individuals and automatically determine the optimal data collection timing. For example, the data collection unit can collect data related to meals at the time elderly individuals eat breakfast. The data collection unit can collect data related to exercise at the time elderly individuals take walks. The data collection unit can collect data related to sleep at the time elderly individuals go to bed. For example, the data collection unit can collect data related to meals at the time elderly individuals eat breakfast. The data collection unit can collect data related to exercise at the time elderly individuals take walks. The data collection unit can collect data related to sleep at the time elderly individuals go to bed. By determining the data collection timing according to the lifestyle rhythms of elderly individuals, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the lifestyle rhythm data of elderly individuals into a generating AI and have the generating AI determine the optimal data collection timing.

[0042] The data collection unit can dynamically change the priority of the data to be collected according to the living environment of the elderly person. For example, if the elderly person lives in a cold region, the data collection unit can prioritize the collection of data related to room temperature. If the elderly person lives in an urban area, the data collection unit can prioritize the collection of data related to noise. If the elderly person lives in a rural area, the data collection unit can prioritize the collection of data related to air quality. By changing the data priority according to the living environment of the elderly person, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's living environment data into a generating AI and have the generating AI dynamically change the priority of the data to be collected.

[0043] The data collection unit can customize the types of data it collects by referring to the elderly person's past behavioral history. For example, the data collection unit can prioritize collecting data on activities the elderly person has frequently engaged in in the past. The data collection unit can prioritize collecting data on areas of interest the elderly person has shown in the past. The data collection unit can avoid collecting data on activities the elderly person has avoided in the past. For example, the data collection unit can prioritize collecting data on activities the elderly person has frequently engaged in in the past. The data collection unit can prioritize collecting data on areas of interest the elderly person has shown in the past. The data collection unit can avoid collecting data on activities the elderly person has avoided in the past. This allows for the collection of more appropriate data by customizing the types of data based on the elderly person's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the elderly person's past behavioral history data into a generating AI and have the generating AI customize the types of data to be collected.

[0044] The data collection unit can improve the accuracy of data collection by collecting feedback from the elderly person's family and care staff. For example, the data collection unit can adjust the types of data to collect based on feedback from family members. The data collection unit can adjust the frequency of data collection based on feedback from care staff. The data collection unit can change the priority of data to collect based on feedback from family members and care staff. For example, the data collection unit can adjust the types of data to collect based on feedback from family members. The data collection unit can adjust the frequency of data collection based on feedback from care staff. The data collection unit can change the priority of data to collect based on feedback from family members and care staff. This improves the accuracy of data collection by collecting feedback from family members and care staff. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback data from family members and care staff into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0045] The analysis unit can monitor the health data of elderly individuals over the long term and detect abnormal values ​​early. For example, the analysis unit can monitor the blood pressure data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the pulse rate data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the body temperature data of elderly individuals over the long term and detect abnormal values. For example, the analysis unit can monitor the blood pressure data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the pulse rate data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the body temperature data of elderly individuals over the long term and detect abnormal values. In this way, abnormal values ​​can be detected early by monitoring health data over the long term. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the health data of elderly individuals into a generating AI and have the generating AI perform early detection of abnormal values.

[0046] The analysis unit can analyze the lifestyle patterns of elderly people and predict potential risks. For example, the analysis unit can analyze the eating patterns of elderly people and predict the risk of nutritional deficiencies. The analysis unit can analyze the exercise patterns of elderly people and predict the risk of inactivity. The analysis unit can analyze the sleep patterns of elderly people and predict the risk of sleep deprivation. For example, the analysis unit can analyze the eating patterns of elderly people and predict the risk of nutritional deficiencies. The analysis unit can analyze the exercise patterns of elderly people and predict the risk of inactivity. The analysis unit can analyze the sleep patterns of elderly people and predict the risk of sleep deprivation. In this way, potential risks can be predicted by analyzing lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input elderly people's lifestyle pattern data into a generating AI and have the generating AI perform predictions of potential risks.

[0047] The analysis unit can evaluate the current health status of elderly individuals by comparing it with their past health data. For example, the analysis unit can evaluate the current blood pressure status by comparing it with the elderly individual's past blood pressure data. The analysis unit can evaluate the current pulse rate status by comparing it with the elderly individual's past pulse rate data. The analysis unit can evaluate the current body temperature status by comparing it with the elderly individual's past body temperature data. For example, the analysis unit can evaluate the current blood pressure status by comparing it with the elderly individual's past blood pressure data. The analysis unit can evaluate the current pulse rate status by comparing it with the elderly individual's past pulse rate data. The analysis unit can evaluate the current body temperature status by comparing it with the elderly individual's past body temperature data. In this way, the current health status can be evaluated by comparing it with past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the elderly individual's past health data into a generating AI and have the generating AI perform the evaluation of the current health status.

[0048] The analysis department can analyze data on the living environment of elderly people and propose environmental improvements. For example, the analysis department can analyze temperature data of the elderly people's living environment and propose appropriate temperature management. The analysis department can analyze humidity data of the elderly people's living environment and propose appropriate humidity management. The analysis department can analyze noise data of the elderly people's living environment and propose appropriate noise countermeasures. For example, the analysis department can analyze temperature data of the elderly people's living environment and propose appropriate temperature management. The analysis department can analyze humidity data of the elderly people's living environment and propose appropriate humidity management. The analysis department can analyze noise data of the elderly people's living environment and propose appropriate noise countermeasures. This makes it possible to propose appropriate environmental improvements by analyzing living environment data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without using AI. For example, the analysis department can input data on the elderly people's living environment into a generating AI and have the generating AI execute environmental improvement proposals.

[0049] The suggestion unit can customize the proposed rehabilitation plan according to the health condition of the elderly person. For example, the suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's physical strength. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's health condition. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's progress. For example, the suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's physical strength. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's health condition. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's progress. This makes it possible to provide a more effective rehabilitation plan by proposing a rehabilitation plan that is appropriate to the elderly person's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the elderly person's health data into a generating AI and have the generating AI perform the customization of the rehabilitation plan.

[0050] The suggestion unit can optimize the proposed meal plan by taking into account the lifestyle habits of the elderly. For example, the suggestion unit can propose a meal plan that is tailored to the eating habits of the elderly. The suggestion unit can propose a meal plan that is tailored to the health condition of the elderly. The suggestion unit can propose a meal plan that takes into account the nutritional balance of the elderly. For example, the suggestion unit can propose a meal plan that is tailored to the eating habits of the elderly. The suggestion unit can propose a meal plan that is tailored to the health condition of the elderly. The suggestion unit can propose a meal plan that takes into account the nutritional balance of the elderly. In this way, by proposing a meal plan that is tailored to the lifestyle habits of the elderly, it is possible to support a healthier diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input lifestyle data of the elderly into a generating AI and have the generating AI perform the optimization of the meal plan.

[0051] The suggestion unit can personalize the entertainment content it suggests based on the hobbies and interests of elderly people. For example, the suggestion unit can suggest entertainment content that matches the hobbies of elderly people. The suggestion unit can suggest entertainment content that matches the interests of elderly people. The suggestion unit can suggest entertainment content based on the past entertainment history of elderly people. For example, the suggestion unit can suggest entertainment content that matches the hobbies of elderly people. The suggestion unit can suggest entertainment content that matches the interests of elderly people. The suggestion unit can suggest entertainment content based on the past entertainment history of elderly people. This makes it possible to provide more enjoyable content by suggesting entertainment content based on the hobbies and interests of elderly people. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the hobbies and interests of elderly people into a generating AI and have the generating AI perform the personalization of entertainment content.

[0052] The proposal department can collaborate with the elderly person's family and care staff to share the proposed solutions. For example, the proposal department can share the proposed solutions with the elderly person's family and receive feedback. The proposal department can share the proposed solutions with care staff and receive feedback. The proposal department can collaborate with the elderly person's family and care staff to optimize the proposed solutions. For example, the proposal department can share the proposed solutions with the elderly person's family and receive feedback. The proposal department can share the proposed solutions with care staff and receive feedback. The proposal department can collaborate with the elderly person's family and care staff to optimize the proposed solutions. This allows for more appropriate support by collaborating with family and care staff to share the proposed solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input feedback data from the elderly person's family and care staff into a generating AI and have the generating AI optimize the proposed solutions.

[0053] The execution unit can optimize the timing of its execution to match the elderly person's daily rhythm. For example, the execution unit can execute a meal plan to coincide with the time the elderly person eats breakfast. The execution unit can execute an exercise plan to coincide with the time the elderly person takes a walk. The execution unit can execute a relaxation method to coincide with the time the elderly person goes to bed. For example, the execution unit can execute a meal plan to coincide with the time the elderly person eats breakfast. The execution unit can execute an exercise plan to coincide with the time the elderly person takes a walk. The execution unit can execute a relaxation method to coincide with the time the elderly person goes to bed. By optimizing the timing of execution to match the elderly person's daily rhythm, more effective support becomes possible. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the execution timing.

[0054] The execution unit can customize the exercises and treatment methods to be performed according to the health condition of the elderly person. For example, the execution unit can execute an exercise plan according to the elderly person's physical strength. The execution unit can execute treatment methods according to the elderly person's health condition. The execution unit can execute an exercise plan according to the elderly person's progress. For example, the execution unit can execute an exercise plan according to the elderly person's physical strength. The execution unit can execute treatment methods according to the elderly person's health condition. The execution unit can execute an exercise plan according to the elderly person's progress. This makes it possible to provide more effective support by performing exercises and treatment methods according to the health condition of the elderly person. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the elderly person's health data into a generating AI and have the generating AI perform the customization of exercises and treatment methods.

[0055] The execution unit can adjust the actions it takes according to the elderly person's living environment. For example, if the elderly person lives in a cold region, the execution unit can manage the room temperature. If the elderly person lives in an urban area, the execution unit can implement noise reduction measures. If the elderly person lives in a rural area, the execution unit can manage the air quality. For example, if the elderly person lives in a cold region, the execution unit can manage the room temperature. If the elderly person lives in an urban area, the execution unit can implement noise reduction measures. If the elderly person lives in a rural area, the execution unit can manage the air quality. By adjusting the actions taken according to the elderly person's living environment, more appropriate support becomes possible. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input data on the elderly person's living environment into a generating AI and have the generating AI adjust the actions taken.

[0056] The execution unit can collaborate with the elderly person's family and care staff to share the details of the execution. For example, the execution unit can share the details of the execution with the elderly person's family and receive feedback. The execution unit can share the details of the execution with care staff and receive feedback. The execution unit can collaborate with the elderly person's family and care staff to optimize the execution. For example, the execution unit can share the details of the execution with the elderly person's family and receive feedback. The execution unit can share the details of the execution with care staff and receive feedback. The execution unit can collaborate with the elderly person's family and care staff to optimize the execution. This allows for more appropriate support by collaborating with family and care staff to share the details of the execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input feedback data from the elderly person's family and care staff into a generating AI and have the generating AI optimize the execution.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can learn the daily routines of elderly individuals and automatically determine the optimal timing for data collection. For example, it can collect data on meals at the time elderly individuals eat breakfast, collect data on exercise at the time elderly individuals take walks, and collect data on sleep at the time elderly individuals go to bed. By determining the timing of data collection according to the elderly individual's daily routine, more effective data collection becomes possible.

[0059] The analysis department can monitor the health data of elderly individuals over the long term and detect abnormal values ​​early. For example, it can monitor blood pressure data of elderly individuals over the long term and detect abnormal values. It can monitor pulse rate data of elderly individuals over the long term and detect abnormal values. It can monitor body temperature data of elderly individuals over the long term and detect abnormal values. In this way, abnormal values ​​can be detected early by monitoring health data over the long term.

[0060] The proposal department can customize the proposed rehabilitation plan according to the health condition of the elderly person. For example, it can propose a rehabilitation plan that is appropriate to the elderly person's physical strength, their health condition, and their progress. By proposing a rehabilitation plan that is appropriate to the elderly person's health condition, more effective rehabilitation becomes possible.

[0061] The execution unit can optimize the timing of its actions to match the elderly person's daily rhythm. For example, it can execute a meal plan to coincide with the time the elderly person eats breakfast, an exercise plan to coincide with the time the elderly person goes for a walk, and a relaxation method to coincide with the time the elderly person goes to bed. By optimizing the timing of execution to match the elderly person's daily rhythm, more effective support becomes possible.

[0062] The execution unit can customize the exercise and treatment methods to be performed according to the health condition of the elderly person. For example, it can execute an exercise plan that matches the elderly person's physical strength, execute treatment methods that match the elderly person's health condition, and execute an exercise plan that matches the elderly person's progress. This makes it possible to provide more effective support by executing exercise and treatment methods that match the health condition of the elderly person.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects information necessary for supporting the lives of elderly people. The data collection unit can collect the content of elderly people's speech using speech recognition technology and collect facial expression data using facial recognition technology. It can also collect health data (e.g., blood pressure, pulse, body temperature) using sensors. The data collection unit can analyze this data in real time, extract important information, estimate emotional states, and monitor health conditions. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected voice data using natural language processing technology and extracts important information. It can also analyze the collected facial data using image analysis technology to estimate emotional states. Furthermore, it can analyze the collected physical condition data using statistical analysis technology to evaluate health status. Step 3: The proposal department makes specific proposals based on the analysis results obtained by the analysis department. The proposal department can suggest relaxation methods based on the results of the emotional analysis, and suggest appropriate exercise or treatment methods based on the results of the health status assessment. They can also suggest events and community activities based on the hobbies and interests of the elderly. Step 4: The execution team implements the proposals made by the proposal team. The execution team can propose and order supplies and deliver them using drones. They can also record feedback and physical condition after the event.

[0065] (Example of form 2) An autonomous AI agent system according to an embodiment of the present invention is a system that supports elderly people living alone so that they can live safely and comfortably. This autonomous AI agent system collects and analyzes information necessary for supporting the lives of the elderly, makes concrete suggestions, and implements the suggested content, thereby alleviating feelings of loneliness and anxiety among the elderly and realizing a society where they can live with peace of mind. For example, the autonomous AI agent system creates a rehabilitation plan, suggests necessary exercises and treatment methods, and automatically sends feedback to doctors and care staff according to the progress. In addition, the autonomous AI agent system performs more accurate emotion analysis by combining voice recognition, facial recognition, and physical condition data (blood pressure, pulse, etc.) and automatically suggests conversations that are appropriate to the user's state, such as, "You seem tired today. Shall I suggest some ways to relax?" Furthermore, the autonomous AI agent system comprehensively understands the living situation of the elderly, predicts shortages of supplies while at home, and automatically makes optimal purchase suggestions and orders. By utilizing drones, etc., necessary items can be delivered in real time even if the elderly person cannot physically go to a store. In addition, the autonomous AI agent system automatically suggests local events and community activities based on the elderly person's hobbies and interests, and adjusts participation methods and times. The system records the feedback and physical condition of elderly participants after events, and uses this information to inform future event proposals. This helps alleviate feelings of loneliness among the elderly and strengthens ties with the community. As a result, the autonomous AI agent system can automatically provide support for the elderly's daily lives, creating a society where they can live with peace of mind.

[0066] The autonomous AI agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects information necessary for supporting the lives of elderly people. The collection unit can, for example, collect the content of speech spoken by elderly people using speech recognition technology. The collection unit can also collect facial expression data of elderly people using facial recognition technology. Furthermore, the collection unit can collect physical condition data (e.g., blood pressure, pulse, body temperature, etc.) using sensors. For example, the collection unit can analyze the content of speech spoken by elderly people in real time using speech recognition technology and extract important information. The collection unit can analyze the facial expression data of elderly people using facial recognition technology and estimate their emotional state. The collection unit can collect physical condition data using sensors and monitor their health status. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected speech data using natural language processing technology and extract important information. Furthermore, the analysis unit can analyze the collected facial data using image analysis technology and estimate their emotional state. Furthermore, the analysis unit can analyze the collected physical condition data using statistical analysis technology and evaluate their health status. For example, the analysis unit analyzes voice data using natural language processing technology to extract important information from the speech content of elderly people. The analysis unit can analyze facial data using image analysis technology to estimate the emotional state of elderly people. The analysis unit can analyze physical condition data using statistical analysis technology to evaluate the health status of elderly people. The proposal unit makes specific proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose relaxation methods based on the results of emotional analysis. The proposal unit can also propose appropriate exercise and treatment methods based on the health status evaluation results. Furthermore, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit proposes relaxation methods based on the results of emotional analysis. The proposal unit can propose appropriate exercise and treatment methods based on the health status evaluation results. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The execution unit carries out the content proposed by the proposal unit. For example, the execution unit can propose or order the purchase of goods.Furthermore, the execution unit can deliver supplies using drones. In addition, the execution unit can record feedback and physical condition after the event. For example, the execution unit can suggest and order supplies. The execution unit can deliver supplies using drones. The execution unit can record feedback and physical condition after the event. As a result, the autonomous AI agent system according to this embodiment can automatically provide support for the lives of the elderly, realizing a society where they can live with peace of mind.

[0067] The data collection unit collects information necessary for supporting the lives of elderly people. Specifically, it can collect the content of elderly people's speech using speech recognition technology. Speech recognition technology analyzes what elderly people say on a daily basis in real time and extracts important keywords and phrases. For example, if an elderly person says, "I'm not feeling well today," the data collection unit immediately recognizes this information and records it as a sign of poor health. The data collection unit can also collect facial expression data of elderly people using facial recognition technology. Facial recognition technology analyzes the elderly person's facial expressions and estimates emotional states such as joy, sadness, anger, and surprise. For example, if an elderly person smiles, the data collection unit records that expression as joy, and conversely, if they frown, it records it as anxiety or confusion. Furthermore, the data collection unit can collect health data (e.g., blood pressure, pulse, body temperature) using sensors. These sensors are attached to the elderly person's body and collect data periodically. For example, a blood pressure monitor measures the elderly person's blood pressure, a pulse monitor records their heart rate, and a thermometer monitors fluctuations in body temperature. This allows the data collection unit to monitor the health status of elderly individuals in real time and respond immediately if any abnormalities are detected. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis department analyzes the information collected by the collection department. Specifically, it can analyze collected audio data using natural language processing technology to extract important information. Natural language processing technology converts the speech content of elderly people into text data and extracts important information about their health status and living situation from it. For example, if an elderly person says, "Recently, I've been having trouble sleeping at night," the analysis department will record this information as a sign of a sleep disorder and suggest necessary countermeasures. The analysis department can also analyze collected facial data using image analysis technology to estimate emotional states. Image analysis technology analyzes the facial expressions of elderly people and detects changes in emotion. For example, if an elderly person frequently has a sad expression, the analysis department will record that emotional state and determine that psychological support is needed. Furthermore, the analysis department can analyze collected physical condition data using statistical analysis technology to evaluate health status. Statistical analysis technology analyzes data such as blood pressure, pulse rate, and body temperature and detects abnormal values ​​and trends. For example, if blood pressure rises sharply, the analysis department will record the data as an abnormality and recommend contacting a medical institution. This allows the analysis department to quickly and accurately analyze collected data, enabling them to understand the health and living conditions of elderly individuals in real time. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past health data, they can predict risk fluctuations in specific seasons or time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The proposal department makes specific suggestions based on the analysis results obtained by the analysis department. Specifically, it can suggest relaxation methods based on the results of emotional analysis. For example, if an elderly person is experiencing stress, the proposal department can suggest deep breathing or meditation techniques to help them relax. The proposal department can also suggest appropriate exercise or treatment methods based on the results of health assessments. For example, if blood pressure is high, the proposal department can recommend walking or light stretching and advise the person to see a doctor. Furthermore, the proposal department can suggest events and community activities based on the elderly person's hobbies and interests. For example, if an elderly person is interested in music, the proposal department can provide information on local music events and concerts and encourage participation. In this way, the proposal department can suggest concrete actions to improve the quality of life for the elderly and support their health maintenance and social participation. In addition, the proposal department can monitor the effectiveness of the suggestions and modify them as needed. For example, it can regularly evaluate whether the suggested exercises are effective and suggest alternative exercises if they are not effective. The proposal department can also collect feedback from the elderly and continuously improve the accuracy and effectiveness of the suggestions. In this way, the proposal department can make flexible and effective suggestions that meet the needs of the elderly and improve their quality of life.

[0070] The implementation team carries out the proposals made by the proposal team. Specifically, they can propose and order the purchase of supplies. For example, they can order medicines and daily necessities needed by the elderly online and have them delivered to their homes. The implementation team can also deliver supplies using drones. Drones can deliver supplies quickly and efficiently, allowing the elderly to receive necessary supplies without leaving their homes. Furthermore, the implementation team can record feedback and physical condition after events. For example, they can record the feedback and changes in physical condition of elderly participants in events and incorporate this into future proposals. This ensures that the implementation team reliably carries out the proposed content and supports the lives of the elderly. In addition, the implementation team can monitor the effectiveness of the implemented content and modify it as needed. For example, they can regularly evaluate whether the proposed exercises are effective and implement a different exercise method if no effect is seen. The implementation team can also collect feedback from the elderly and continuously improve the accuracy and effectiveness of the implemented content. This allows the implementation team to carry out flexible and effective implementations tailored to the needs of the elderly, improving their quality of life. Furthermore, the implementation team can respond quickly in emergencies. For example, if an elderly person suddenly falls ill, the operational unit will immediately contact a medical institution and provide the necessary support. In this way, the operational unit can comprehensively support the lives of elderly people and provide an environment in which they can live with peace of mind.

[0071] The data collection unit can collect speech recognition, facial recognition, and health data. For example, the data collection unit can collect the content of elderly people's speech using speech recognition technology. The data collection unit can also collect facial expression data of elderly people using facial recognition technology. The data collection unit can also collect health data using sensors. For example, the data collection unit can analyze the content of elderly people's speech in real time using speech recognition technology and extract important information. The data collection unit can analyze the facial expression data of elderly people using facial recognition technology and estimate their emotional state. The data collection unit can collect health data with sensors and monitor their health status. By collecting speech recognition, facial recognition, and health data, more accurate information can be obtained. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the speech data collected using speech recognition technology into a generating AI and have the generating AI perform the analysis of the speech data.

[0072] The analysis unit can perform sentiment analysis based on the collected data. For example, the analysis unit can analyze collected voice data using natural language processing technology to estimate emotional states. The analysis unit can also analyze collected facial data using image analysis technology to estimate emotional states. The analysis unit can also analyze collected physical condition data using statistical analysis technology to evaluate emotional states. For example, the analysis unit can analyze voice data using natural language processing technology to estimate emotional states from the content of elderly people's speech. The analysis unit can analyze facial data using image analysis technology to estimate the emotional states of elderly people. The analysis unit can analyze physical condition data using statistical analysis technology to evaluate the emotional states of elderly people. By performing sentiment analysis, it is possible to understand the emotional states of elderly people and provide appropriate support. Sentiment analysis is implemented using sentiment estimation functions, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform sentiment analysis.

[0073] The suggestion unit can suggest relaxation methods based on the results of emotional analysis. The suggestion unit can, for example, suggest relaxation methods based on the results of emotional analysis. The suggestion unit can suggest appropriate relaxation methods based on the results of emotional analysis. The suggestion unit can suggest relaxation methods based on the results of emotional analysis. For example, the suggestion unit can suggest relaxation methods based on the results of emotional analysis. The suggestion unit can suggest appropriate relaxation methods based on the results of emotional analysis. The suggestion unit can suggest relaxation methods based on the results of emotional analysis. This makes it possible to reduce stress in the elderly by suggesting relaxation methods based on the results of emotional analysis. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the results of emotional analysis into a generating AI and have the generating AI execute suggestions for relaxation methods.

[0074] The execution unit can make purchase proposals and orders for supplies. The execution unit can, for example, make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. For example, the execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. The execution unit can make purchase proposals and orders for supplies. This allows for support of the lives of the elderly by making purchase proposals and orders for supplies. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input purchase proposals and orders for supplies into a generating AI and have the generating AI execute the purchase proposals and orders.

[0075] The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. For example, the execution unit can deliver goods using drones. The execution unit can deliver goods using drones. The execution unit can deliver goods using drones. This makes it possible to deliver necessary items to elderly people even if they are physically unable to go to stores, by delivering goods using drones. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input drone flight data into a generating AI and have the generating AI perform optimization of the delivery route.

[0076] The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. For example, the proposal unit can propose events and community activities based on the hobbies and interests of elderly people. The proposal unit can propose events and community activities based on the hobbies and interests of elderly people. By proposing events and community activities based on the hobbies and interests of elderly people, it is possible to alleviate feelings of loneliness among the elderly and strengthen ties with the community. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the hobbies and interests of elderly people into a generating AI and have the generating AI execute proposals for events and community activities.

[0077] The execution unit can record post-event impressions and physical condition. The execution unit can, for example, record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. For example, the execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. The execution unit can record post-event impressions and physical condition. By recording post-event impressions and physical condition, this information can be used to inform future proposals. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input post-event impressions and physical condition data into a generating AI and have the generating AI perform analysis to inform future proposals.

[0078] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data related to relaxation. If the user is having fun, the data collection unit can prioritize collecting data related to entertainment. If the user is tired, the data collection unit can prioritize collecting data related to rest. For example, if the user is stressed, the data collection unit can prioritize collecting data related to relaxation. If the user is having fun, the data collection unit can prioritize collecting data related to entertainment. If the user is tired, the data collection unit can prioritize collecting data related to rest. This allows for the collection of more appropriate data by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the types of data to be collected.

[0079] The data collection unit can learn the lifestyle rhythms of elderly individuals and automatically determine the optimal data collection timing. For example, the data collection unit can collect data related to meals at the time elderly individuals eat breakfast. The data collection unit can collect data related to exercise at the time elderly individuals take walks. The data collection unit can collect data related to sleep at the time elderly individuals go to bed. For example, the data collection unit can collect data related to meals at the time elderly individuals eat breakfast. The data collection unit can collect data related to exercise at the time elderly individuals take walks. The data collection unit can collect data related to sleep at the time elderly individuals go to bed. By determining the data collection timing according to the lifestyle rhythms of elderly individuals, more effective data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the lifestyle rhythm data of elderly individuals into a generating AI and have the generating AI determine the optimal data collection timing.

[0080] The data collection unit can dynamically change the priority of the data to be collected according to the living environment of the elderly person. For example, if the elderly person lives in a cold region, the data collection unit can prioritize the collection of data related to room temperature. If the elderly person lives in an urban area, the data collection unit can prioritize the collection of data related to noise. If the elderly person lives in a rural area, the data collection unit can prioritize the collection of data related to air quality. By changing the data priority according to the living environment of the elderly person, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the elderly person's living environment data into a generating AI and have the generating AI dynamically change the priority of the data to be collected.

[0081] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect emotion data more frequently. If the user is relaxed, the data collection unit can reduce the frequency of emotion data collection. If the user is excited, the data collection unit can increase the frequency of emotion data collection. For example, if the user is stressed, the data collection unit will collect emotion data more frequently. If the user is relaxed, the data collection unit can reduce the frequency of emotion data collection. If the user is excited, the data collection unit can increase the frequency of emotion data collection. This allows for data collection at a more appropriate frequency by adjusting the data collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the frequency of data collection.

[0082] The data collection unit can customize the types of data it collects by referring to the elderly person's past behavioral history. For example, the data collection unit can prioritize collecting data on activities the elderly person has frequently engaged in in the past. The data collection unit can prioritize collecting data on areas of interest the elderly person has shown in the past. The data collection unit can avoid collecting data on activities the elderly person has avoided in the past. For example, the data collection unit can prioritize collecting data on activities the elderly person has frequently engaged in in the past. The data collection unit can prioritize collecting data on areas of interest the elderly person has shown in the past. The data collection unit can avoid collecting data on activities the elderly person has avoided in the past. This allows for the collection of more appropriate data by customizing the types of data based on the elderly person's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the elderly person's past behavioral history data into a generating AI and have the generating AI customize the types of data to be collected.

[0083] The data collection unit can improve the accuracy of data collection by collecting feedback from the elderly person's family and care staff. For example, the data collection unit can adjust the types of data to collect based on feedback from family members. The data collection unit can adjust the frequency of data collection based on feedback from care staff. The data collection unit can change the priority of data to collect based on feedback from family members and care staff. For example, the data collection unit can adjust the types of data to collect based on feedback from family members. The data collection unit can adjust the frequency of data collection based on feedback from care staff. The data collection unit can change the priority of data to collect based on feedback from family members and care staff. This improves the accuracy of data collection by collecting feedback from family members and care staff. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input feedback data from family members and care staff into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analysis related to stress reduction. If the user is relaxed, the analysis unit may prioritize analysis related to maintaining a relaxed state. If the user is excited, the analysis unit may prioritize analysis related to managing an excited state. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the analysis algorithm.

[0085] The analysis unit can monitor the health data of elderly individuals over the long term and detect abnormal values ​​early. For example, the analysis unit can monitor the blood pressure data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the pulse rate data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the body temperature data of elderly individuals over the long term and detect abnormal values. For example, the analysis unit can monitor the blood pressure data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the pulse rate data of elderly individuals over the long term and detect abnormal values. The analysis unit can monitor the body temperature data of elderly individuals over the long term and detect abnormal values. In this way, abnormal values ​​can be detected early by monitoring health data over the long term. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the health data of elderly individuals into a generating AI and have the generating AI perform early detection of abnormal values.

[0086] The analysis unit can analyze the lifestyle patterns of elderly people and predict potential risks. For example, the analysis unit can analyze the eating patterns of elderly people and predict the risk of nutritional deficiencies. The analysis unit can analyze the exercise patterns of elderly people and predict the risk of inactivity. The analysis unit can analyze the sleep patterns of elderly people and predict the risk of sleep deprivation. For example, the analysis unit can analyze the eating patterns of elderly people and predict the risk of nutritional deficiencies. The analysis unit can analyze the exercise patterns of elderly people and predict the risk of inactivity. The analysis unit can analyze the sleep patterns of elderly people and predict the risk of sleep deprivation. In this way, potential risks can be predicted by analyzing lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input elderly people's lifestyle pattern data into a generating AI and have the generating AI perform predictions of potential risks.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust how the analysis results are displayed.

[0088] The analysis unit can evaluate the current health status of elderly individuals by comparing it with their past health data. For example, the analysis unit can evaluate the current blood pressure status by comparing it with the elderly individual's past blood pressure data. The analysis unit can evaluate the current pulse rate status by comparing it with the elderly individual's past pulse rate data. The analysis unit can evaluate the current body temperature status by comparing it with the elderly individual's past body temperature data. For example, the analysis unit can evaluate the current blood pressure status by comparing it with the elderly individual's past blood pressure data. The analysis unit can evaluate the current pulse rate status by comparing it with the elderly individual's past pulse rate data. The analysis unit can evaluate the current body temperature status by comparing it with the elderly individual's past body temperature data. In this way, the current health status can be evaluated by comparing it with past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the elderly individual's past health data into a generating AI and have the generating AI perform the evaluation of the current health status.

[0089] The analysis department can analyze data on the living environment of elderly people and propose environmental improvements. For example, the analysis department can analyze temperature data of the elderly people's living environment and propose appropriate temperature management. The analysis department can analyze humidity data of the elderly people's living environment and propose appropriate humidity management. The analysis department can analyze noise data of the elderly people's living environment and propose appropriate noise countermeasures. For example, the analysis department can analyze temperature data of the elderly people's living environment and propose appropriate temperature management. The analysis department can analyze humidity data of the elderly people's living environment and propose appropriate humidity management. The analysis department can analyze noise data of the elderly people's living environment and propose appropriate noise countermeasures. This makes it possible to propose appropriate environmental improvements by analyzing living environment data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without using AI. For example, the analysis department can input data on the elderly people's living environment into a generating AI and have the generating AI execute environmental improvement proposals.

[0090] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can suggest ways to relax. If the user is relaxed, the suggestion unit can suggest enjoyable entertainment. If the user is tired, the suggestion unit can suggest ways to rest. For example, if the user is feeling stressed, the suggestion unit can suggest ways to relax. If the user is relaxed, the suggestion unit can suggest enjoyable entertainment. If the user is tired, the suggestion unit can suggest ways to rest. This allows for more appropriate suggestions by adjusting the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0091] The suggestion unit can customize the proposed rehabilitation plan according to the health condition of the elderly person. For example, the suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's physical strength. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's health condition. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's progress. For example, the suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's physical strength. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's health condition. The suggestion unit can propose a rehabilitation plan that is appropriate to the elderly person's progress. This makes it possible to provide a more effective rehabilitation plan by proposing a rehabilitation plan that is appropriate to the elderly person's health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the elderly person's health data into a generating AI and have the generating AI perform the customization of the rehabilitation plan.

[0092] The suggestion unit can optimize the proposed meal plan by taking into account the lifestyle habits of the elderly. For example, the suggestion unit can propose a meal plan that is tailored to the eating habits of the elderly. The suggestion unit can propose a meal plan that is tailored to the health condition of the elderly. The suggestion unit can propose a meal plan that takes into account the nutritional balance of the elderly. For example, the suggestion unit can propose a meal plan that is tailored to the eating habits of the elderly. The suggestion unit can propose a meal plan that is tailored to the health condition of the elderly. The suggestion unit can propose a meal plan that takes into account the nutritional balance of the elderly. In this way, by proposing a meal plan that is tailored to the lifestyle habits of the elderly, it is possible to support a healthier diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input lifestyle data of the elderly into a generating AI and have the generating AI perform the optimization of the meal plan.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit may prioritize suggestions for relaxation. If the user is relaxed, the suggestion unit may prioritize suggestions for entertainment. If the user is tired, the suggestion unit may prioritize suggestions for rest. For example, if the user is stressed, the suggestion unit may prioritize suggestions for relaxation. If the user is relaxed, the suggestion unit may prioritize suggestions for entertainment. If the user is tired, the suggestion unit may prioritize suggestions for rest. This allows for more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI determine the priority of proposals.

[0094] The suggestion unit can personalize the entertainment content it suggests based on the hobbies and interests of elderly people. For example, the suggestion unit can suggest entertainment content that matches the hobbies of elderly people. The suggestion unit can suggest entertainment content that matches the interests of elderly people. The suggestion unit can suggest entertainment content based on the past entertainment history of elderly people. For example, the suggestion unit can suggest entertainment content that matches the hobbies of elderly people. The suggestion unit can suggest entertainment content that matches the interests of elderly people. The suggestion unit can suggest entertainment content based on the past entertainment history of elderly people. This makes it possible to provide more enjoyable content by suggesting entertainment content based on the hobbies and interests of elderly people. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the hobbies and interests of elderly people into a generating AI and have the generating AI perform the personalization of entertainment content.

[0095] The proposal department can collaborate with the elderly person's family and care staff to share the proposed solutions. For example, the proposal department can share the proposed solutions with the elderly person's family and receive feedback. The proposal department can share the proposed solutions with care staff and receive feedback. The proposal department can collaborate with the elderly person's family and care staff to optimize the proposed solutions. For example, the proposal department can share the proposed solutions with the elderly person's family and receive feedback. The proposal department can share the proposed solutions with care staff and receive feedback. The proposal department can collaborate with the elderly person's family and care staff to optimize the proposed solutions. This allows for more appropriate support by collaborating with family and care staff to share the proposed solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input feedback data from the elderly person's family and care staff into a generating AI and have the generating AI optimize the proposed solutions.

[0096] The execution unit can estimate the user's emotions and adjust its actions based on the estimated emotions. For example, if the user is stressed, the execution unit can perform relaxation techniques. If the user is relaxed, the execution unit can perform enjoyable entertainment. If the user is tired, the execution unit can perform rest techniques. For example, if the user is stressed, the execution unit can perform relaxation techniques. If the user is relaxed, the execution unit can perform enjoyable entertainment. If the user is tired, the execution unit can perform rest techniques. This allows for more appropriate support by adjusting actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI adjust the actions.

[0097] The execution unit can optimize the timing of its execution to match the elderly person's daily rhythm. For example, the execution unit can execute a meal plan to coincide with the time the elderly person eats breakfast. The execution unit can execute an exercise plan to coincide with the time the elderly person takes a walk. The execution unit can execute a relaxation method to coincide with the time the elderly person goes to bed. For example, the execution unit can execute a meal plan to coincide with the time the elderly person eats breakfast. The execution unit can execute an exercise plan to coincide with the time the elderly person takes a walk. The execution unit can execute a relaxation method to coincide with the time the elderly person goes to bed. By optimizing the timing of execution to match the elderly person's daily rhythm, more effective support becomes possible. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the optimization of the execution timing.

[0098] The execution unit can customize the exercises and treatment methods to be performed according to the health condition of the elderly person. For example, the execution unit can execute an exercise plan according to the elderly person's physical strength. The execution unit can execute treatment methods according to the elderly person's health condition. The execution unit can execute an exercise plan according to the elderly person's progress. For example, the execution unit can execute an exercise plan according to the elderly person's physical strength. The execution unit can execute treatment methods according to the elderly person's health condition. The execution unit can execute an exercise plan according to the elderly person's progress. This makes it possible to provide more effective support by performing exercises and treatment methods according to the health condition of the elderly person. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the elderly person's health data into a generating AI and have the generating AI perform the customization of exercises and treatment methods.

[0099] The execution unit can estimate the user's emotions and determine the priority of actions based on the estimated emotions. For example, if the user is stressed, the execution unit may prioritize performing relaxation activities. If the user is relaxed, the execution unit may prioritize performing entertainment activities. If the user is tired, the execution unit may prioritize performing rest activities. For example, if the user is stressed, the execution unit may prioritize performing relaxation activities. If the user is relaxed, the execution unit may prioritize performing entertainment activities. If the user is tired, the execution unit may prioritize performing rest activities. This allows for more appropriate support by determining the priority of actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into the generating AI and have the generating AI determine the execution priorities.

[0100] The execution unit can adjust the actions it takes according to the elderly person's living environment. For example, if the elderly person lives in a cold region, the execution unit can manage the room temperature. If the elderly person lives in an urban area, the execution unit can implement noise reduction measures. If the elderly person lives in a rural area, the execution unit can manage the air quality. For example, if the elderly person lives in a cold region, the execution unit can manage the room temperature. If the elderly person lives in an urban area, the execution unit can implement noise reduction measures. If the elderly person lives in a rural area, the execution unit can manage the air quality. By adjusting the actions taken according to the elderly person's living environment, more appropriate support becomes possible. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input data on the elderly person's living environment into a generating AI and have the generating AI adjust the actions taken.

[0101] The execution unit can collaborate with the elderly person's family and care staff to share the details of the execution. For example, the execution unit can share the details of the execution with the elderly person's family and receive feedback. The execution unit can share the details of the execution with care staff and receive feedback. The execution unit can collaborate with the elderly person's family and care staff to optimize the execution. For example, the execution unit can share the details of the execution with the elderly person's family and receive feedback. The execution unit can share the details of the execution with care staff and receive feedback. The execution unit can collaborate with the elderly person's family and care staff to optimize the execution. This allows for more appropriate support by collaborating with family and care staff to share the details of the execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input feedback data from the elderly person's family and care staff into a generating AI and have the generating AI optimize the execution.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those estimates. For example, if the user is stressed, it can prioritize collecting data related to relaxation. If the user is having fun, it can prioritize collecting data related to entertainment. If the user is tired, it can prioritize collecting data related to rest. By adjusting the types of data collected based on the user's emotions, more relevant data can be collected.

[0104] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those estimates. For example, if the user is stressed, the analysis can prioritize stress reduction. If the user is relaxed, the analysis can prioritize maintaining that relaxed state. If the user is excited, the analysis can prioritize managing that excited state. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis becomes possible.

[0105] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is stressed, it can suggest ways to relax. If the user is relaxed, it can suggest enjoyable entertainment. If the user is tired, it can suggest ways to rest. By adjusting the suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0106] The execution unit can estimate the user's emotions and adjust the actions taken based on those emotions. For example, if the user is stressed, it can perform relaxation techniques. If the user is relaxed, it can perform enjoyable entertainment. If the user is tired, it can perform rest techniques. By adjusting the actions taken based on the user's emotions, more appropriate support becomes possible.

[0107] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, it can prioritize suggestions for relaxation. If the user is relaxed, it can prioritize suggestions for entertainment. If the user is tired, it can prioritize suggestions for rest. By prioritizing suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0108] The data collection unit can learn the daily routines of elderly individuals and automatically determine the optimal timing for data collection. For example, it can collect data on meals at the time elderly individuals eat breakfast, collect data on exercise at the time elderly individuals take walks, and collect data on sleep at the time elderly individuals go to bed. By determining the timing of data collection according to the elderly individual's daily routine, more effective data collection becomes possible.

[0109] The analysis department can monitor the health data of elderly individuals over the long term and detect abnormal values ​​early. For example, it can monitor blood pressure data of elderly individuals over the long term and detect abnormal values. It can monitor pulse rate data of elderly individuals over the long term and detect abnormal values. It can monitor body temperature data of elderly individuals over the long term and detect abnormal values. In this way, abnormal values ​​can be detected early by monitoring health data over the long term.

[0110] The proposal department can customize the proposed rehabilitation plan according to the health condition of the elderly person. For example, it can propose a rehabilitation plan that is appropriate to the elderly person's physical strength, their health condition, and their progress. By proposing a rehabilitation plan that is appropriate to the elderly person's health condition, more effective rehabilitation becomes possible.

[0111] The execution unit can optimize the timing of its actions to match the elderly person's daily rhythm. For example, it can execute a meal plan to coincide with the time the elderly person eats breakfast, an exercise plan to coincide with the time the elderly person goes for a walk, and a relaxation method to coincide with the time the elderly person goes to bed. By optimizing the timing of execution to match the elderly person's daily rhythm, more effective support becomes possible.

[0112] The execution unit can customize the exercise and treatment methods to be performed according to the health condition of the elderly person. For example, it can execute an exercise plan that matches the elderly person's physical strength, execute treatment methods that match the elderly person's health condition, and execute an exercise plan that matches the elderly person's progress. This makes it possible to provide more effective support by executing exercise and treatment methods that match the health condition of the elderly person.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects information necessary for supporting the lives of elderly people. The data collection unit can collect the content of elderly people's speech using speech recognition technology and collect facial expression data using facial recognition technology. It can also collect health data (e.g., blood pressure, pulse, body temperature) using sensors. The data collection unit can analyze this data in real time, extract important information, estimate emotional states, and monitor health conditions. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected voice data using natural language processing technology and extracts important information. It can also analyze the collected facial data using image analysis technology to estimate emotional states. Furthermore, it can analyze the collected physical condition data using statistical analysis technology to evaluate health status. Step 3: The proposal department makes specific proposals based on the analysis results obtained by the analysis department. The proposal department can suggest relaxation methods based on the results of the emotional analysis, and suggest appropriate exercise or treatment methods based on the results of the health status assessment. They can also suggest events and community activities based on the hobbies and interests of the elderly. Step 4: The execution team implements the proposals made by the proposal team. The execution team can propose and order supplies and deliver them using drones. They can also record feedback and physical condition after the event.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the facial expressions and speech of the elderly person using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects physical condition data using sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate emotional state and health state. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes relaxation methods, exercise, and treatment methods based on the analysis results. The execution unit is implemented in the control unit 46A of the smart device 14, for example, and executes the proposed content, such as suggesting and ordering the purchase of goods and making deliveries using drones. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the facial expressions and speech of the elderly person using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects physical condition data using sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate emotional state and health state. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes relaxation methods, exercise, and treatment methods based on the analysis results. The execution unit is implemented in the control unit 46A of the smart glasses 214, for example, and executes the proposed content, such as suggesting and ordering the purchase of goods and making deliveries using drones. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the facial expressions and speech of elderly people using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects physical condition data using sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate emotional and health states. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes relaxation methods, exercises, and treatment methods based on the analysis results. The execution unit is implemented in the control unit 46A of the headset terminal 314, for example, and executes the proposed content, such as suggesting or ordering the purchase of goods or making deliveries using drones. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and execution unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the facial expressions and speech of elderly people using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects physical condition data using sensors. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and evaluates emotional and health conditions. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which proposes relaxation methods, exercises, and treatment methods based on the analysis results. The execution unit is implemented in, for example, the control unit 46A of the robot 414, which executes the proposed content, such as proposing and ordering the purchase of goods and making deliveries using drones. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A collection department that collects information necessary for supporting the lives of the elderly, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes specific proposals, The system comprises an execution unit that executes the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Voice recognition, facial recognition, and health data collection. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Sentiment analysis is performed based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will suggest relaxation methods based on the results of your emotional analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Propose or order the purchase of supplies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Delivering supplies using drones The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose events and community activities based on the hobbies and interests of senior citizens. The system described in Appendix 1, characterized by the features described herein. (Note 8) The execution unit is, Record your impressions and physical condition after the event. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It learns the daily routines of elderly people and automatically determines the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The priority of data to be collected is dynamically changed according to the living environment of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Refer to the past behavioral history of elderly individuals to customize the types of data collected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We collect feedback from families of elderly individuals and care staff to improve the accuracy of data collection. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Long-term monitoring of health data for the elderly to detect abnormal values ​​early. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Analyze the lifestyle patterns of the elderly and predict potential risks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is The current health status of elderly individuals is assessed by comparing it with their past health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is We analyze data on the living environment of the elderly and propose improvements to that environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, We customize the proposed rehabilitation plan according to the health condition of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We optimize the proposed meal plan by taking into account the lifestyle habits of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, Personalize the entertainment content suggested based on the hobbies and interests of senior citizens. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We collaborate with the families of elderly individuals and care staff to share our proposals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, It estimates the user's emotions and adjusts actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, Optimize the timing of implementation to match the lifestyle rhythm of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, Customize the exercise and treatment methods to suit the health condition of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, The content of the program will be adjusted according to the living environment of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, We collaborate with the elderly's family and care staff to share the details of what we're doing. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information necessary for supporting the lives of the elderly, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes specific proposals, The system comprises an execution unit that executes the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Voice recognition, facial recognition, and health data collection. The system according to feature 1.

3. The aforementioned analysis unit is Sentiment analysis is performed based on the collected data. The system according to feature 1.

4. The aforementioned proposal section is, We will suggest relaxation methods based on the results of your emotional analysis. The system according to feature 1.

5. The execution unit is, Propose or order the purchase of supplies. The system according to feature 1.

6. The execution unit is, Delivering supplies using drones The system according to feature 1.

7. The aforementioned proposal section is, We propose events and community activities based on the hobbies and interests of senior citizens. The system according to feature 1.

8. The execution unit is, Record your impressions and physical condition after the event. The system according to feature 1.

Images