system

Abstract

The system according to this embodiment aims to appropriately understand the feelings of loneliness and health conditions of elderly people and to respond quickly. [Solution] The system according to the embodiment comprises an information gathering unit, an analysis unit, a notification unit, an emergency contact unit, and a shopping support unit. The information gathering unit collects information through conversations and actions with the elderly. The analysis unit analyzes the information collected by the information gathering unit to understand the degree of isolation and health status. The notification unit notifies family members and medical personnel based on the information understood by the analysis unit. The emergency contact unit contacts emergency services when an emergency occurs. The shopping support unit orders goods based on the shopping list.

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, there was a problem that it was difficult to appropriately grasp and quickly respond to the loneliness and health status of the elderly.

[0005] The system according to the embodiment aims to appropriately grasp and quickly respond to the loneliness and health status of the elderly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, an analysis unit, a notification unit, an emergency contact unit, and a shopping support unit. The information gathering unit collects information through conversations and actions with the elderly. The analysis unit analyzes the information collected by the information gathering unit to understand the degree of isolation and health status. The notification unit notifies family members and medical professionals based on the information obtained by the analysis unit. The emergency contact unit contacts emergency services in the event of an emergency. The shopping support unit orders goods based on the shopping list. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately understand the feelings of loneliness and health conditions of elderly people and respond quickly. [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 applied 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 receiving 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 receiving 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) The robot system according to an embodiment of the present invention is a system for appropriately detecting and promptly responding to feelings of loneliness and physical changes in elderly people in an aging society. This robot system adds advanced large-scale language model (LLM) functionality to existing robots, continuously understanding the degree of isolation and health status from daily conversations and actions with elderly people, and providing information to family members and medical professionals as needed. It also has a function to automatically contact emergency services in the event of an emergency. Furthermore, a shopping support function is added to overcome the situation of elderly people who are unable to shop and to serve as one solution to the problem of elderly people surrendering their driver's licenses. In this way, the robot supports the healthy and enjoyable lives of elderly people. For example, the robot continuously understands the degree of isolation and health status through daily conversations and actions with elderly people. In this process, the robot analyzes the content of the elderly person's statements and behavioral patterns to detect abnormalities. For example, if an elderly person behaves differently than usual or if there is a change in their health status, the robot records the information and notifies family members and medical professionals as needed. Next, in the event of an emergency, the robot automatically contacts emergency services. For example, if an elderly person falls or suddenly becomes ill, the robot detects the situation and quickly contacts emergency services. This allows elderly people to receive prompt and appropriate medical support. Furthermore, the robot is equipped with a shopping support function, assisting elderly people in purchasing daily necessities. For example, if an elderly person enters a shopping list into the robot, the robot will order the items online based on that list. The robot can also learn the elderly person's preferences and past purchase history and suggest appropriate products. This will help overcome the situation where elderly people are considered "shopping refugees," allowing them to continue living with peace of mind even after surrendering their driver's license. In this way, the robot system of the present invention continuously monitors the loneliness and health status of elderly people and provides information to family members and medical professionals as needed. In the event of an emergency, it will also quickly contact emergency services to ensure the safety of the elderly. Furthermore, by incorporating a shopping support function, it supports the daily lives of elderly people and allows them to continue living with peace of mind even after surrendering their driver's license. This makes it possible to support a healthy and enjoyable life for the elderly.This allows the robotic system to continuously monitor the loneliness and health status of elderly individuals and provide information to family members and medical professionals as needed. Furthermore, in the event of an emergency, it can quickly contact emergency services to ensure the safety of the elderly. Additionally, by incorporating a shopping support function, it assists with the daily lives of the elderly, allowing them to continue living with peace of mind even after surrendering their driver's licenses. This ultimately supports a healthy and enjoyable life for the elderly.

[0029] The robot system according to this embodiment comprises an information gathering unit, an analysis unit, a notification unit, an emergency contact unit, and a shopping support unit. The information gathering unit collects information through conversations and actions with the elderly. For example, the information gathering unit collects the content of the elderly person's statements and behavioral patterns. The information gathering unit can analyze the content of the elderly person's statements using speech recognition technology and detect the elderly person's behavioral patterns using behavioral sensors. For example, the information gathering unit records the actions that the elderly person performs on a daily basis and detects abnormal behavior. The information gathering unit can also analyze the elderly person's facial expressions and movements using a camera and estimate their emotions and health status. The analysis unit analyzes the information collected by the information gathering unit to understand the degree of isolation and health status. For example, the analysis unit evaluates the frequency of social contact and feelings of loneliness of the elderly person based on the collected information. The analysis unit can also analyze vital sign measurement data and evaluate the health status. For example, the analysis unit analyzes fluctuations in the elderly person's heart rate and blood pressure and detects abnormalities. The analysis unit can also analyze self-reported health status and perform a comprehensive health assessment. The notification unit notifies family members and medical professionals based on information gathered by the analysis unit. For example, the notification unit notifies family members and medical professionals via email or SMS if an abnormality is detected. The notification unit can also contact them directly by phone. For example, in an emergency, the notification unit will quickly contact family members and medical professionals to encourage appropriate action. The notification unit can also customize the content of notifications and provide information in a format that is easy for recipients to understand. The emergency contact unit contacts emergency services when an emergency occurs. For example, the emergency contact unit contacts emergency services if an elderly person falls or experiences a sudden illness. The emergency contact unit can detect falls using an acceleration sensor and determine an emergency using camera video analysis. For example, when the emergency contact unit detects a fall, it automatically contacts emergency services to encourage a quick response. The emergency contact unit can also detect sudden changes in vital signs and determine an emergency. The shopping support unit orders products based on a shopping list. For example, when an elderly person enters a shopping list, the shopping support unit orders products online based on that list.The shopping support unit can learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, the shopping support unit can suggest related products based on products that the elderly person has purchased in the past. The shopping support unit can also suggest seasonal products to support efficient shopping. As a result, the robot system according to the embodiment can continuously monitor the degree of isolation and health condition of elderly people, provide information to family members and medical professionals as needed, and respond quickly in emergencies. Some or all of the above processing in the shopping support unit may be performed using AI, for example, or without AI. For example, the shopping support unit can suggest products using an AI model that takes the preferences and past purchase history of elderly people as input and outputs appropriate products.

[0030] The information gathering unit collects information through conversations and actions with elderly individuals. Specifically, it analyzes the content of elderly individuals' speech using speech recognition technology and detects their behavioral patterns using behavioral sensors. For example, it can record the actions that elderly individuals perform on a daily basis and detect abnormal behavior. Speech recognition technology transcribes elderly individuals' speech into text in real time and analyzes its content. This allows for an understanding of their emotions, health status, and daily needs. Behavioral sensors, such as accelerometers and gyroscopes, record the movements of elderly individuals in detail and detect abnormal movements or falls. Furthermore, the information gathering unit can also use cameras to analyze the facial expressions and movements of elderly individuals and estimate their emotions and health status. Camera footage is analyzed to check for abnormalities, including facial expressions, body movements, and walking stability. This allows the information gathering unit to monitor the overall lives of elderly individuals and provide basic data for rapid response when abnormalities occur. In addition, the information gathering unit sends the collected data to a cloud server, making it accessible to the analysis and notification units. This allows the entire system to work together to protect the safety and health of elderly individuals.

[0031] The analysis unit analyzes information collected by the information gathering unit to understand the degree of isolation and health status. Specifically, it evaluates the frequency of social contact and feelings of loneliness among the elderly based on the collected information. For example, it analyzes conversation data collected using speech recognition technology to evaluate how much the elderly communicate with others. It also analyzes data obtained from behavioral sensors and cameras to estimate emotions and health status based on the elderly's behavioral patterns and changes in facial expressions. The analysis unit can also analyze vital sign measurement data to evaluate health status. For example, it analyzes fluctuations in heart rate and blood pressure to detect abnormalities. This allows the analysis unit to grasp the health status of the elderly in real time and respond quickly when abnormalities occur. Furthermore, the analysis unit can analyze self-reported health status to conduct a comprehensive health assessment. For example, it analyzes data in which the elderly report their daily physical condition and mood to understand long-term fluctuations in health status. This allows the analysis unit to comprehensively evaluate the health status of the elderly and provide information to take necessary measures. In addition, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to continuously monitor the health status of elderly individuals and detect abnormalities early.

[0032] The notification unit notifies family members and medical professionals based on information gathered by the analysis unit. Specifically, it notifies family members and medical professionals via email or SMS when an abnormality is detected. For example, if an abnormality is found in the health of an elderly person, the notification unit will quickly contact family members and medical professionals to encourage appropriate action. The notification unit can also contact people directly by telephone. For example, in an emergency, it will quickly contact family members and medical professionals by telephone and instruct them on the necessary actions. The notification unit can also customize the content of notifications and provide information in a format that is easy for recipients to understand. For example, it can summarize the content of notifications concisely and highlight important information to enable recipients to respond quickly. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can use not only email and SMS but also voice calls and app notifications to ensure that important information is delivered reliably. This allows the notification unit to provide information quickly and reliably to protect the safety and health of the elderly.

[0033] The emergency liaison unit contacts emergency services in the event of an emergency. Specifically, it contacts emergency services if an elderly person falls or experiences a sudden illness. The emergency liaison unit can detect falls using an acceleration sensor and determine the severity of an emergency using camera footage analysis. For example, upon detecting a fall, it automatically contacts emergency services to encourage a rapid response. The emergency liaison unit can also detect sudden changes in vital signs and determine if an emergency has occurred. For example, if it detects a sudden change in heart rate or blood pressure, it immediately contacts emergency services and instructs them on the necessary actions. Furthermore, the emergency liaison unit can simultaneously contact family members and medical professionals when an emergency occurs, sharing the situation. This allows the emergency liaison unit to ensure the safety of the elderly and support a swift and appropriate response. In addition, the emergency liaison unit can perform data analysis to predict the occurrence of emergencies. For example, based on past data, it can evaluate the risk of an emergency occurring under specific conditions and take preventative measures. This allows the emergency liaison unit to play a crucial role in continuously protecting the safety of the elderly.

[0034] The shopping support system orders products based on shopping lists. Specifically, when an elderly person enters a shopping list, the system orders the products online based on that list. The shopping support system can also learn the elderly person's preferences and past purchase history to suggest appropriate products. For example, it can suggest related products based on products the elderly person has purchased in the past. The shopping support system can also suggest seasonal products to support efficient shopping. For example, it can suggest products needed during seasonal changes or products tailored to specific events. Furthermore, the shopping support system can use AI to predict the elderly person's preferences and needs and suggest the most suitable products. For example, it can use an AI model to analyze the elderly person's past purchase history and preferences and predict products they are likely to purchase next. This allows the shopping support system to support the elderly person's shopping efficiently and conveniently. In addition, the shopping support system can track the delivery status of ordered products in real time and notify the elderly person. This allows the elderly person to wait for the arrival of their ordered products with peace of mind. The shopping support system provides important functions to support the lives of the elderly and can make everyday shopping more convenient.

[0035] The information gathering unit can collect the content of elderly people's speech and their behavioral patterns. For example, the information gathering unit can analyze the content of elderly people's speech using speech recognition technology and detect their behavioral patterns using behavioral sensors. For example, the information gathering unit can record the actions that elderly people perform on a daily basis and detect abnormal behavior. The information gathering unit can also analyze the facial expressions and movements of elderly people using a camera and estimate their emotions and health status. By collecting the content of elderly people's speech and their behavioral patterns, more accurate information can be obtained. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the content of elderly people's speech and their behavioral patterns as input and outputs the analysis results.

[0036] The analysis unit can analyze the collected information and detect anomalies. For example, the analysis unit can evaluate the frequency of social contact and feelings of loneliness in elderly individuals based on the collected information. The analysis unit can also analyze vital sign measurement data and evaluate health status. For example, the analysis unit can analyze fluctuations in the heart rate and blood pressure of elderly individuals and detect anomalies. Furthermore, the analysis unit can analyze self-reported health status and perform a comprehensive health assessment. This allows for a rapid response by analyzing the collected information and detecting anomalies. 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 analyze the information using an AI model that takes the collected information as input and outputs anomalies.

[0037] The notification unit can notify family members or medical professionals if an abnormality is detected. For example, the notification unit can notify family members or medical professionals via email or SMS if an abnormality is detected. The notification unit can also contact them directly by telephone. For example, in an emergency, the notification unit can quickly contact family members or medical professionals to encourage appropriate action. The notification unit can also customize the notification content and provide information in a format that is easy for recipients to understand. This enables a quick response by notifying family members or medical professionals when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can make notifications using an AI model that takes information on detected abnormalities as input and outputs notification content.

[0038] The emergency contact unit can contact emergency services in the event of a fall or sudden illness in an elderly person. For example, the emergency contact unit can contact emergency services if an elderly person falls or experiences a sudden illness. The emergency contact unit can detect falls using an acceleration sensor and determine the emergency situation using camera video analysis. For example, when a fall is detected, the emergency contact unit automatically contacts emergency services to encourage a rapid response. The emergency contact unit can also detect sudden changes in vital signs and determine the emergency situation. This allows for prompt contact with emergency services in the event of a fall or sudden illness in an elderly person, ensuring they receive appropriate medical assistance. Some or all of the above-described processes in the emergency contact unit may be performed using AI, or not. For example, the emergency contact unit can use an AI model that takes information about falls or sudden illnesses as input and outputs the content of the emergency contact to be sent to emergency services.

[0039] The shopping support unit can learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, when an elderly person enters a shopping list, the shopping support unit orders products online based on that list. The shopping support unit can also learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, the shopping support unit can suggest related products based on products that elderly people have purchased in the past. The shopping support unit can also suggest seasonal products to support efficient shopping. In this way, the convenience of shopping is improved by learning the preferences and past purchase history of elderly people and suggesting appropriate products. Some or all of the above processes in the shopping support unit may be performed using AI, for example, or not using AI. For example, the shopping support unit can suggest products using an AI model that takes the preferences and past purchase history of elderly people as input and outputs appropriate products.

[0040] The information gathering unit can analyze the past behavioral patterns of elderly individuals and select the most appropriate information gathering method. For example, if an elderly person has a habit of taking a walk every morning, the information gathering unit can initiate a conversation at that time to check on their health. If an elderly person watches a particular television program every day, the information gathering unit can also initiate a conversation after the program ends to ask for their opinion. If an elderly person often spends weekends with their family, the information gathering unit can also confirm their family plans before the weekend. By analyzing the elderly person's past behavioral patterns, more effective information gathering becomes possible. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can select an information gathering method using an AI model that takes the elderly person's past behavioral patterns as input and outputs the most appropriate information gathering method.

[0041] The information gathering unit can filter information based on the elderly person's living environment and daily routines during the information gathering process. For example, if an elderly person lives alone, the information gathering unit can assess their degree of isolation based on their daily routines and collect necessary information. If an elderly person owns a pet, the information gathering unit can also collect information about pet care and check their health status. If an elderly person regularly visits a doctor, the information gathering unit can also check their health status before and after their doctor's appointments. By filtering information based on the elderly person's living environment and daily routines, more relevant information can be collected. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can filter information using an AI model that takes the elderly person's living environment and daily routines as input and outputs filtered information.

[0042] The information gathering unit can prioritize collecting highly relevant information by considering the geographical location of the elderly person during information gathering. For example, if the elderly person is at home, the information gathering unit can collect information about events and services around their home. If the elderly person is out, the information gathering unit can also collect weather and traffic information for their destination. If the elderly person is traveling, the information gathering unit can also collect tourist information and emergency contact information for their travel destination. By considering the geographical location of the elderly person, more relevant information can be collected. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the geographical location of the elderly person as input and outputs highly relevant information.

[0043] The information gathering unit can analyze the social media activities of elderly individuals and collect relevant information during the information gathering process. For example, the information gathering unit can collect information related to their interests and concerns based on what elderly individuals share on social media. The information gathering unit can also analyze the posts of accounts that elderly individuals follow on social media and collect relevant information. The information gathering unit can also collect relevant information based on the activities of groups that elderly individuals participate in on social media. This allows for the collection of more relevant information by analyzing the social media activities of elderly individuals. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the social media activities of elderly individuals as input and outputs relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important information and provide specific results. For less important information, the analysis unit can perform a concise analysis and provide an overview. For information of moderate importance, the analysis unit can perform an analysis with an appropriate level of detail and provide balanced results. By adjusting the level of detail of the analysis based on the importance of the collected information, more effective analysis becomes possible. 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 adjust the level of detail of the analysis using an AI model that takes the importance of the collected information as input and outputs the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a medical data analysis algorithm to information about health status. The analysis unit can also apply a social network analysis algorithm to information about isolation levels. The analysis unit can also apply a behavioral analysis algorithm to information about behavioral patterns. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. 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 apply an analysis algorithm using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected information to grasp the latest situation. The analysis unit can also analyze the current situation while referring to past information. The analysis unit can also focus on analyzing information collected during a specific period to grasp changes within that period. This allows for a grasp of the latest situation by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the timing of information collection as input and outputs the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of information related to the health status of elderly people. The analysis unit may also prioritize the analysis of information related to the degree of isolation of elderly people. The analysis unit may also prioritize the analysis of information related to the behavioral patterns of elderly people. By adjusting the order of analysis based on the relevance of the information, more relevant information can be prioritized for analysis. 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 adjust the order of analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0048] The notification unit can adjust the level of detail in notifications based on the severity of the anomaly. For example, for highly severe anomalies, the notification unit can provide detailed notifications and propose specific countermeasures. For less severe anomalies, the notification unit can provide concise notifications and an overview. For moderately severe anomalies, the notification unit can provide notifications with a moderate level of detail, offering balanced information. By adjusting the level of detail in notifications based on the severity of the anomaly, more effective notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can adjust the level of detail in notifications using an AI model that takes the severity of the anomaly as input and outputs the level of detail in the notification.

[0049] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, the notification unit may prioritize notifying medical professionals for anomalies related to health status. It may also prioritize notifying family members for anomalies related to isolation. It may also notify appropriate stakeholders for anomalies related to behavioral patterns. This allows for more effective notifications by selecting the appropriate notification method according to the category of the anomaly. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can apply a notification method using an AI model that takes the category of the anomaly as input and outputs the notification method to be applied.

[0050] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit can prioritize notifications for recently occurring anomalies to understand the latest situation. The notification unit can also notify about the current situation while referring to past anomalies. The notification unit can also focus on notifying about anomalies that occurred during a specific period to understand changes within that period. This allows for understanding the latest situation by determining the priority of notifications based on when the anomaly occurred. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can determine the priority of notifications using an AI model that takes the time of anomaly occurrence as input and outputs the priority of notifications.

[0051] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifications of anomalies related to the health status of elderly people. The notification unit may also prioritize notifications of anomalies related to the degree of isolation of elderly people. The notification unit may also prioritize notifications of anomalies related to the behavioral patterns of elderly people. By adjusting the order of notifications based on the relevance of the anomalies, more relevant information can be prioritized. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may adjust the order of notifications using an AI model that takes the relevance of anomalies as input and outputs the order of notifications.

[0052] The Emergency Liaison Department can adjust the level of detail in emergency communications based on the severity of the emergency. For example, for high-severity emergencies, the Emergency Liaison Department may send emergency communications containing detailed information. For low-severity emergencies, the Emergency Liaison Department may send emergency communications containing concise information. For moderate-severity emergencies, the Emergency Liaison Department may send emergency communications with an appropriate level of detail. By adjusting the level of detail in communications based on the severity of the emergency, more effective emergency communications become possible. Some or all of the above processing in the Emergency Liaison Department may be performed using AI, for example, or not using AI. For example, the Emergency Liaison Department can adjust the level of detail in communications using an AI model that takes the severity of the emergency as input and outputs the level of detail in communications.

[0053] The emergency contact unit can apply different contact methods depending on the category of the emergency. For example, in the case of an emergency related to health status, the emergency contact unit may prioritize contacting medical personnel. In the case of an emergency related to isolation, the emergency contact unit may also prioritize contacting family members. In the case of an emergency related to behavioral patterns, the emergency contact unit may also contact the appropriate parties. This allows for more effective emergency communication by selecting the appropriate contact method according to the category of the emergency. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or not. For example, the emergency contact unit can apply a contact method using an AI model that takes the category of the emergency as input and outputs the contact method to be applied.

[0054] The emergency liaison department can determine the priority of communications based on when the emergency occurred. For example, the emergency liaison department can prioritize communications for recently occurring emergencies to grasp the latest situation. The emergency liaison department can also communicate the current situation while referring to past emergencies. The emergency liaison department can also focus communications on emergencies that occurred during a specific period to grasp changes within that period. This allows for grasping the latest situation by determining the priority of communications based on when the emergency occurred. Some or all of the above processes in the emergency liaison department may be performed using AI, for example, or not. For example, the emergency liaison department can determine the priority of communications using an AI model that takes the time the emergency occurred as input and outputs the priority of communications.

[0055] The emergency contact unit can adjust the order of communications based on the relevance of the emergencies. For example, the emergency contact unit may prioritize communications related to the health status of elderly individuals. It may also prioritize communications related to the degree of isolation of elderly individuals. It may also prioritize communications related to the behavioral patterns of elderly individuals. By adjusting the order of communications based on the relevance of the emergencies, more relevant information can be prioritized. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or not. For example, the emergency contact unit can adjust the order of communications using an AI model that takes the relevance of the emergencies as input and outputs the order of communications.

[0056] The shopping support department can analyze the elderly person's past purchase history to suggest the most suitable products when providing shopping assistance. For example, the shopping support department can suggest related products based on the elderly person's past purchases. The shopping support department can also suggest seasonal products based on the elderly person's past purchase history. The shopping support department can also analyze the elderly person's past purchase history to suggest the most efficient shopping list. In this way, by analyzing the elderly person's past purchase history, it is possible to suggest more appropriate products. Some or all of the above processes in the shopping support department may be performed using AI, for example, or not using AI. For example, the shopping support department can suggest products using an AI model that takes the elderly person's past purchase history as input and outputs the most suitable products.

[0057] The shopping support department can customize its product recommendations based on the elderly person's current living situation when providing shopping assistance. For example, if the elderly person lives alone, the shopping support department will suggest products necessary for daily life. If the elderly person lives with family, the shopping support department can also suggest products that meet the needs of the entire family. If the elderly person has a specific health condition, the shopping support department can also suggest products suitable for that condition. By customizing the product recommendations based on the elderly person's current living situation, more appropriate products can be suggested. Some or all of the above processing in the shopping support department may be performed using AI, for example, or without AI. For example, the shopping support department can customize its product recommendations using an AI model that takes the elderly person's current living situation as input and outputs product recommendation methods.

[0058] The shopping support unit can suggest the most suitable products when assisting elderly people with their shopping, taking into account their geographical location. For example, if an elderly person is at home, the unit can suggest products that can be purchased at stores near their home. If an elderly person is out, the unit can also suggest products that can be purchased at their destination. If an elderly person is traveling, the unit can also suggest products that can be purchased at their travel destination. By considering the elderly person's geographical location, the unit can suggest more appropriate products. Some or all of the above processing in the shopping support unit may be performed using AI, for example, or without AI. For example, the shopping support unit can suggest products using an AI model that takes the elderly person's geographical location as input and outputs the most suitable products.

[0059] The shopping support department can analyze the social media activity of elderly people during shopping assistance and propose methods for suggesting products. For example, the shopping support department can suggest products related to the interests of elderly people based on what they have shared on social media. The shopping support department can also analyze the content of posts from accounts that elderly people follow on social media and suggest related products. The shopping support department can also suggest related products based on the activities of groups that elderly people participate in on social media. In this way, by analyzing the social media activity of elderly people, more appropriate products can be suggested. Some or all of the above processes in the shopping support department may be performed using AI, for example, or not using AI. For example, the shopping support department can propose methods for suggesting products using an AI model that takes the social media activity of elderly people as input and outputs methods for suggesting products.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The robot system can also be equipped with an activity suggestion unit that proposes daily activities based on the hobbies and interests of elderly individuals. This unit analyzes past conversations and behavioral data of the elderly person and suggests activities related to their hobbies and interests. For example, if an elderly person is interested in gardening, it can provide information on how to grow plants according to the season and information on gardening events. If the elderly person enjoys music, it can also suggest information on concerts and music classes held in the neighborhood. Furthermore, if the elderly person enjoys reading, it can provide recommendations for new books and information on reading groups. This allows for an improvement in the quality of life by suggesting daily activities based on the elderly person's hobbies and interests.

[0062] The robotic system can also be equipped with a meal management unit to support the dietary management of elderly individuals. This unit proposes meal plans considering the health condition and nutritional balance of the elderly person. For example, if an elderly person has diabetes, it can suggest a low-carbohydrate meal plan. Similarly, if an elderly person has high blood pressure, it can suggest a low-sodium meal plan. Furthermore, if an elderly person needs to consume specific nutrients, it can provide recipes using ingredients containing those nutrients. This allows for support of dietary management tailored to the health condition of the elderly, contributing to their overall health maintenance.

[0063] The robotic system can also be equipped with an exercise support unit to assist elderly people with their exercise. This unit proposes exercise plans tailored to the elderly person's physical strength and health condition. For example, if an elderly person has joint pain, it can suggest exercises that put less strain on the joints. It can also suggest light strength training if the elderly person wants to maintain muscle strength. Furthermore, if the elderly person wants to improve their balance, it can suggest balance exercises. In this way, by supporting exercise according to the elderly person's health condition, it can contribute to maintaining and improving their physical fitness.

[0064] The robotic system can also be equipped with a sleep support unit to further assist the elderly in their sleep. This unit analyzes the elderly person's sleep patterns and makes suggestions to promote high-quality sleep. For example, if the elderly person frequently wakes up during the night, it can provide relaxing music or meditation guidance. If the elderly person has trouble falling asleep, it can suggest a bedtime routine to create a relaxing environment. Furthermore, if the elderly person wakes up early in the morning, it can suggest a morning routine to help them start their day smoothly. In this way, supporting the elderly person's sleep can contribute to maintaining their health.

[0065] The robot system can also be equipped with a social interaction support unit to further assist elderly individuals in their social interactions. This unit analyzes the frequency of elderly individuals' social contacts and suggests appropriate opportunities for interaction. For example, if an elderly person is isolated, it can suggest local community events or club activities. It can also support video calls and messaging with family members if they wish to connect with them. Furthermore, if an elderly person wishes to connect with friends, it can suggest opportunities for gatherings or online interactions with friends. This supports social interaction among the elderly, reducing feelings of isolation and improving their quality of life.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The information gathering unit collects information through conversations and interactions with elderly individuals. For example, it collects the content of elderly individuals' statements and behavioral patterns, analyzes their statements using speech recognition technology, and detects their behavioral patterns using behavioral sensors. It can also analyze the elderly individuals' facial expressions and movements using cameras to estimate their emotions and health status. Step 2: The analysis unit analyzes the information collected by the information collection unit to understand the degree of isolation and health status. For example, based on the collected information, it evaluates the frequency of social contact and feelings of loneliness in elderly individuals, and analyzes vital sign measurement data to assess their health status. It can also analyze fluctuations in heart rate and blood pressure to detect abnormalities. Step 3: The notification unit notifies family members and medical professionals based on the information gathered by the analysis unit. For example, if an abnormality is detected, notifications can be sent via email or SMS, and direct contact can also be made by phone. It is also possible to customize the notification content and provide information in a format that is easy for recipients to understand. Step 4: The emergency contact unit contacts emergency services when an emergency occurs. For example, they contact emergency services if an elderly person falls or experiences a sudden illness. They can use acceleration sensors to detect falls and camera video analysis to determine if an emergency has occurred. They can also detect sudden changes in vital signs to determine if an emergency has occurred. Step 5: The shopping support department orders items based on the shopping list. For example, when an elderly person enters a shopping list, the department orders items online based on that list. It can also learn the elderly person's preferences and past purchase history to suggest appropriate products. It can also suggest seasonal products to support efficient shopping.

[0068] (Example of form 2) The robot system according to an embodiment of the present invention is a system for appropriately detecting and promptly responding to feelings of loneliness and physical changes in elderly people in an aging society. This robot system adds advanced large-scale language model (LLM) functionality to existing robots, continuously understanding the degree of isolation and health status from daily conversations and actions with elderly people, and providing information to family members and medical professionals as needed. It also has a function to automatically contact emergency services in the event of an emergency. Furthermore, a shopping support function is added to overcome the situation of elderly people who are unable to shop and to serve as one solution to the problem of elderly people surrendering their driver's licenses. In this way, the robot supports the healthy and enjoyable lives of elderly people. For example, the robot continuously understands the degree of isolation and health status through daily conversations and actions with elderly people. In this process, the robot analyzes the content of the elderly person's statements and behavioral patterns to detect abnormalities. For example, if an elderly person behaves differently than usual or if there is a change in their health status, the robot records the information and notifies family members and medical professionals as needed. Next, in the event of an emergency, the robot automatically contacts emergency services. For example, if an elderly person falls or suddenly becomes ill, the robot detects the situation and quickly contacts emergency services. This allows elderly people to receive prompt and appropriate medical support. Furthermore, the robot is equipped with a shopping support function, assisting elderly people in purchasing daily necessities. For example, if an elderly person enters a shopping list into the robot, the robot will order the items online based on that list. The robot can also learn the elderly person's preferences and past purchase history and suggest appropriate products. This will help overcome the situation where elderly people are considered "shopping refugees," allowing them to continue living with peace of mind even after surrendering their driver's license. In this way, the robot system of the present invention continuously monitors the loneliness and health status of elderly people and provides information to family members and medical professionals as needed. In the event of an emergency, it will also quickly contact emergency services to ensure the safety of the elderly. Furthermore, by incorporating a shopping support function, it supports the daily lives of elderly people and allows them to continue living with peace of mind even after surrendering their driver's license. This makes it possible to support a healthy and enjoyable life for the elderly.This allows the robotic system to continuously monitor the loneliness and health status of elderly individuals and provide information to family members and medical professionals as needed. Furthermore, in the event of an emergency, it can quickly contact emergency services to ensure the safety of the elderly. Additionally, by incorporating a shopping support function, it assists with the daily lives of the elderly, allowing them to continue living with peace of mind even after surrendering their driver's licenses. This ultimately supports a healthy and enjoyable life for the elderly.

[0069] The robot system according to this embodiment comprises an information gathering unit, an analysis unit, a notification unit, an emergency contact unit, and a shopping support unit. The information gathering unit collects information through conversations and actions with the elderly. For example, the information gathering unit collects the content of the elderly person's statements and behavioral patterns. The information gathering unit can analyze the content of the elderly person's statements using speech recognition technology and detect the elderly person's behavioral patterns using behavioral sensors. For example, the information gathering unit records the actions that the elderly person performs on a daily basis and detects abnormal behavior. The information gathering unit can also analyze the elderly person's facial expressions and movements using a camera and estimate their emotions and health status. The analysis unit analyzes the information collected by the information gathering unit to understand the degree of isolation and health status. For example, the analysis unit evaluates the frequency of social contact and feelings of loneliness of the elderly person based on the collected information. The analysis unit can also analyze vital sign measurement data and evaluate the health status. For example, the analysis unit analyzes fluctuations in the elderly person's heart rate and blood pressure and detects abnormalities. The analysis unit can also analyze self-reported health status and perform a comprehensive health assessment. The notification unit notifies family members and medical professionals based on information gathered by the analysis unit. For example, the notification unit notifies family members and medical professionals via email or SMS if an abnormality is detected. The notification unit can also contact them directly by phone. For example, in an emergency, the notification unit will quickly contact family members and medical professionals to encourage appropriate action. The notification unit can also customize the content of notifications and provide information in a format that is easy for recipients to understand. The emergency contact unit contacts emergency services when an emergency occurs. For example, the emergency contact unit contacts emergency services if an elderly person falls or experiences a sudden illness. The emergency contact unit can detect falls using an acceleration sensor and determine an emergency using camera video analysis. For example, when the emergency contact unit detects a fall, it automatically contacts emergency services to encourage a quick response. The emergency contact unit can also detect sudden changes in vital signs and determine an emergency. The shopping support unit orders products based on a shopping list. For example, when an elderly person enters a shopping list, the shopping support unit orders products online based on that list.The shopping support unit can learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, the shopping support unit can suggest related products based on products that the elderly person has purchased in the past. The shopping support unit can also suggest seasonal products to support efficient shopping. As a result, the robot system according to the embodiment can continuously monitor the degree of isolation and health condition of elderly people, provide information to family members and medical professionals as needed, and respond quickly in emergencies. Some or all of the above processing in the shopping support unit may be performed using AI, for example, or without AI. For example, the shopping support unit can suggest products using an AI model that takes the preferences and past purchase history of elderly people as input and outputs appropriate products.

[0070] The information gathering unit collects information through conversations and actions with elderly individuals. Specifically, it analyzes the content of elderly individuals' speech using speech recognition technology and detects their behavioral patterns using behavioral sensors. For example, it can record the actions that elderly individuals perform on a daily basis and detect abnormal behavior. Speech recognition technology transcribes elderly individuals' speech into text in real time and analyzes its content. This allows for an understanding of their emotions, health status, and daily needs. Behavioral sensors, such as accelerometers and gyroscopes, record the movements of elderly individuals in detail and detect abnormal movements or falls. Furthermore, the information gathering unit can also use cameras to analyze the facial expressions and movements of elderly individuals and estimate their emotions and health status. Camera footage is analyzed to check for abnormalities, including facial expressions, body movements, and walking stability. This allows the information gathering unit to monitor the overall lives of elderly individuals and provide basic data for rapid response when abnormalities occur. In addition, the information gathering unit sends the collected data to a cloud server, making it accessible to the analysis and notification units. This allows the entire system to work together to protect the safety and health of elderly individuals.

[0071] The analysis unit analyzes information collected by the information gathering unit to understand the degree of isolation and health status. Specifically, it evaluates the frequency of social contact and feelings of loneliness among the elderly based on the collected information. For example, it analyzes conversation data collected using speech recognition technology to evaluate how much the elderly communicate with others. It also analyzes data obtained from behavioral sensors and cameras to estimate emotions and health status based on the elderly's behavioral patterns and changes in facial expressions. The analysis unit can also analyze vital sign measurement data to evaluate health status. For example, it analyzes fluctuations in heart rate and blood pressure to detect abnormalities. This allows the analysis unit to grasp the health status of the elderly in real time and respond quickly when abnormalities occur. Furthermore, the analysis unit can analyze self-reported health status to conduct a comprehensive health assessment. For example, it analyzes data in which the elderly report their daily physical condition and mood to understand long-term fluctuations in health status. This allows the analysis unit to comprehensively evaluate the health status of the elderly and provide information to take necessary measures. In addition, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to continuously monitor the health status of elderly individuals and detect abnormalities early.

[0072] The notification unit notifies family members and medical professionals based on information gathered by the analysis unit. Specifically, it notifies family members and medical professionals via email or SMS when an abnormality is detected. For example, if an abnormality is found in the health of an elderly person, the notification unit will quickly contact family members and medical professionals to encourage appropriate action. The notification unit can also contact people directly by telephone. For example, in an emergency, it will quickly contact family members and medical professionals by telephone and instruct them on the necessary actions. The notification unit can also customize the content of notifications and provide information in a format that is easy for recipients to understand. For example, it can summarize the content of notifications concisely and highlight important information to enable recipients to respond quickly. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can use not only email and SMS but also voice calls and app notifications to ensure that important information is delivered reliably. This allows the notification unit to provide information quickly and reliably to protect the safety and health of the elderly.

[0073] The emergency liaison unit contacts emergency services in the event of an emergency. Specifically, it contacts emergency services if an elderly person falls or experiences a sudden illness. The emergency liaison unit can detect falls using an acceleration sensor and determine the severity of an emergency using camera footage analysis. For example, upon detecting a fall, it automatically contacts emergency services to encourage a rapid response. The emergency liaison unit can also detect sudden changes in vital signs and determine if an emergency has occurred. For example, if it detects a sudden change in heart rate or blood pressure, it immediately contacts emergency services and instructs them on the necessary actions. Furthermore, the emergency liaison unit can simultaneously contact family members and medical professionals when an emergency occurs, sharing the situation. This allows the emergency liaison unit to ensure the safety of the elderly and support a swift and appropriate response. In addition, the emergency liaison unit can perform data analysis to predict the occurrence of emergencies. For example, based on past data, it can evaluate the risk of an emergency occurring under specific conditions and take preventative measures. This allows the emergency liaison unit to play a crucial role in continuously protecting the safety of the elderly.

[0074] The shopping support system orders products based on shopping lists. Specifically, when an elderly person enters a shopping list, the system orders the products online based on that list. The shopping support system can also learn the elderly person's preferences and past purchase history to suggest appropriate products. For example, it can suggest related products based on products the elderly person has purchased in the past. The shopping support system can also suggest seasonal products to support efficient shopping. For example, it can suggest products needed during seasonal changes or products tailored to specific events. Furthermore, the shopping support system can use AI to predict the elderly person's preferences and needs and suggest the most suitable products. For example, it can use an AI model to analyze the elderly person's past purchase history and preferences and predict products they are likely to purchase next. This allows the shopping support system to support the elderly person's shopping efficiently and conveniently. In addition, the shopping support system can track the delivery status of ordered products in real time and notify the elderly person. This allows the elderly person to wait for the arrival of their ordered products with peace of mind. The shopping support system provides important functions to support the lives of the elderly and can make everyday shopping more convenient.

[0075] The information gathering unit can collect the content of elderly people's speech and their behavioral patterns. For example, the information gathering unit can analyze the content of elderly people's speech using speech recognition technology and detect their behavioral patterns using behavioral sensors. For example, the information gathering unit can record the actions that elderly people perform on a daily basis and detect abnormal behavior. The information gathering unit can also analyze the facial expressions and movements of elderly people using a camera and estimate their emotions and health status. By collecting the content of elderly people's speech and their behavioral patterns, more accurate information can be obtained. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the content of elderly people's speech and their behavioral patterns as input and outputs the analysis results.

[0076] The analysis unit can analyze the collected information and detect anomalies. For example, the analysis unit can evaluate the frequency of social contact and feelings of loneliness in elderly individuals based on the collected information. The analysis unit can also analyze vital sign measurement data and evaluate health status. For example, the analysis unit can analyze fluctuations in the heart rate and blood pressure of elderly individuals and detect anomalies. Furthermore, the analysis unit can analyze self-reported health status and perform a comprehensive health assessment. This allows for a rapid response by analyzing the collected information and detecting anomalies. 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 analyze the information using an AI model that takes the collected information as input and outputs anomalies.

[0077] The notification unit can notify family members or medical professionals if an abnormality is detected. For example, the notification unit can notify family members or medical professionals via email or SMS if an abnormality is detected. The notification unit can also contact them directly by telephone. For example, in an emergency, the notification unit can quickly contact family members or medical professionals to encourage appropriate action. The notification unit can also customize the notification content and provide information in a format that is easy for recipients to understand. This enables a quick response by notifying family members or medical professionals when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can make notifications using an AI model that takes information on detected abnormalities as input and outputs notification content.

[0078] The emergency contact unit can contact emergency services in the event of a fall or sudden illness in an elderly person. For example, the emergency contact unit can contact emergency services if an elderly person falls or experiences a sudden illness. The emergency contact unit can detect falls using an acceleration sensor and determine the emergency situation using camera video analysis. For example, when a fall is detected, the emergency contact unit automatically contacts emergency services to encourage a rapid response. The emergency contact unit can also detect sudden changes in vital signs and determine the emergency situation. This allows for prompt contact with emergency services in the event of a fall or sudden illness in an elderly person, ensuring they receive appropriate medical assistance. Some or all of the above-described processes in the emergency contact unit may be performed using AI, or not. For example, the emergency contact unit can use an AI model that takes information about falls or sudden illnesses as input and outputs the content of the emergency contact to be sent to emergency services.

[0079] The shopping support unit can learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, when an elderly person enters a shopping list, the shopping support unit orders products online based on that list. The shopping support unit can also learn the preferences and past purchase history of elderly people and suggest appropriate products. For example, the shopping support unit can suggest related products based on products that elderly people have purchased in the past. The shopping support unit can also suggest seasonal products to support efficient shopping. In this way, the convenience of shopping is improved by learning the preferences and past purchase history of elderly people and suggesting appropriate products. Some or all of the above processes in the shopping support unit may be performed using AI, for example, or not using AI. For example, the shopping support unit can suggest products using an AI model that takes the preferences and past purchase history of elderly people as input and outputs appropriate products.

[0080] The information gathering unit can estimate the emotions of elderly individuals and adjust the content and timing of conversations based on the estimated emotions. For example, if an elderly individual is feeling stressed, the information gathering unit can select relaxing topics and slow down the pace of the conversation. If an elderly individual is enjoying themselves, the information gathering unit can also provide interesting topics and liven up the conversation. If an elderly individual is tired, the information gathering unit can select short conversations and include content that encourages rest. By adjusting the content and timing of conversations according to the emotions of elderly individuals, more appropriate communication becomes possible. 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 information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can adjust conversations using an AI model that takes elderly individuals' emotion data as input and outputs conversation content and timing.

[0081] The information gathering unit can analyze the past behavioral patterns of elderly individuals and select the most appropriate information gathering method. For example, if an elderly person has a habit of taking a walk every morning, the information gathering unit can initiate a conversation at that time to check on their health. If an elderly person watches a particular television program every day, the information gathering unit can also initiate a conversation after the program ends to ask for their opinion. If an elderly person often spends weekends with their family, the information gathering unit can also confirm their family plans before the weekend. By analyzing the elderly person's past behavioral patterns, more effective information gathering becomes possible. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can select an information gathering method using an AI model that takes the elderly person's past behavioral patterns as input and outputs the most appropriate information gathering method.

[0082] The information gathering unit can filter information based on the elderly person's living environment and daily routines during the information gathering process. For example, if an elderly person lives alone, the information gathering unit can assess their degree of isolation based on their daily routines and collect necessary information. If an elderly person owns a pet, the information gathering unit can also collect information about pet care and check their health status. If an elderly person regularly visits a doctor, the information gathering unit can also check their health status before and after their doctor's appointments. By filtering information based on the elderly person's living environment and daily routines, more relevant information can be collected. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can filter information using an AI model that takes the elderly person's living environment and daily routines as input and outputs filtered information.

[0083] The information gathering unit can estimate the emotions of elderly individuals and determine the priority of information to collect based on the estimated emotions. For example, if an elderly individual is feeling anxious, the information gathering unit may prioritize collecting information related to their health. If an elderly individual is enjoying themselves, the information gathering unit may also prioritize collecting information related to their hobbies and interests. If an elderly individual is tired, the information gathering unit may also prioritize collecting information related to rest. By prioritizing information based on the emotions of elderly individuals, more important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 information gathering unit may be performed using AI, or not using AI. For example, the information gathering unit may determine the priority of information using an AI model that takes elderly individuals' emotion data as input and outputs information priorities.

[0084] The information gathering unit can prioritize collecting highly relevant information by considering the geographical location of the elderly person during information gathering. For example, if the elderly person is at home, the information gathering unit can collect information about events and services around their home. If the elderly person is out, the information gathering unit can also collect weather and traffic information for their destination. If the elderly person is traveling, the information gathering unit can also collect tourist information and emergency contact information for their travel destination. By considering the geographical location of the elderly person, more relevant information can be collected. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the geographical location of the elderly person as input and outputs highly relevant information.

[0085] The information gathering unit can analyze the social media activities of elderly individuals and collect relevant information during the information gathering process. For example, the information gathering unit can collect information related to their interests and concerns based on what elderly individuals share on social media. The information gathering unit can also analyze the posts of accounts that elderly individuals follow on social media and collect relevant information. The information gathering unit can also collect relevant information based on the activities of groups that elderly individuals participate in on social media. This allows for the collection of more relevant information by analyzing the social media activities of elderly individuals. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can collect information using an AI model that takes the social media activities of elderly individuals as input and outputs relevant information.

[0086] The analysis unit can estimate the emotions of elderly individuals and adjust the presentation of the analysis based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit can present the analysis results in a clear and concise manner. If an elderly individual is relaxed, the analysis unit can also provide detailed analysis results. If an elderly individual is agitated, the analysis unit can also provide visually appealing analysis results. By adjusting the presentation of the analysis based on the emotions of the elderly individual, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, 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 adjust the presentation of the analysis using an AI model that takes elderly individuals' emotional data as input and outputs the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important information and provide specific results. For less important information, the analysis unit can perform a concise analysis and provide an overview. For information of moderate importance, the analysis unit can perform an analysis with an appropriate level of detail and provide balanced results. By adjusting the level of detail of the analysis based on the importance of the collected information, more effective analysis becomes possible. 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 adjust the level of detail of the analysis using an AI model that takes the importance of the collected information as input and outputs the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a medical data analysis algorithm to information about health status. The analysis unit can also apply a social network analysis algorithm to information about isolation levels. The analysis unit can also apply a behavioral analysis algorithm to information about behavioral patterns. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. 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 apply an analysis algorithm using an AI model that takes the category of information as input and outputs the analysis algorithm to be applied.

[0089] The analysis unit can estimate the emotions of elderly individuals and determine the priority of analysis based on the estimated emotions. For example, if an elderly individual is feeling anxious, the analysis unit may prioritize analysis related to their health status. If an elderly individual is enjoying themselves, the analysis unit may also prioritize analysis related to their hobbies and interests. If an elderly individual is tired, the analysis unit may also prioritize analysis related to rest. By determining the priority of analysis based on the emotions of elderly individuals, more important information can be prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, 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 may determine the priority of analysis using an AI model that takes elderly individuals' emotion data as input and outputs the priority of analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit can prioritize the analysis of recently collected information to grasp the latest situation. The analysis unit can also analyze the current situation while referring to past information. The analysis unit can also focus on analyzing information collected during a specific period to grasp changes within that period. This allows for a grasp of the latest situation by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that takes the timing of information collection as input and outputs the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of information related to the health status of elderly people. The analysis unit may also prioritize the analysis of information related to the degree of isolation of elderly people. The analysis unit may also prioritize the analysis of information related to the behavioral patterns of elderly people. By adjusting the order of analysis based on the relevance of the information, more relevant information can be prioritized for analysis. 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 adjust the order of analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0092] The notification unit can estimate the emotions of elderly individuals and adjust the way notifications are presented based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit will present a notification in a reassuring manner. If an elderly individual is relaxed, the notification unit may also present a notification containing detailed information. If an elderly individual is agitated, the notification unit may also present a visually appealing notification. By adjusting the way notifications are presented based on the emotions of elderly individuals, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can adjust the way notifications are presented using an AI model that takes elderly individuals' emotion data as input and outputs notification presentation methods.

[0093] The notification unit can adjust the level of detail in notifications based on the severity of the anomaly. For example, for highly severe anomalies, the notification unit can provide detailed notifications and propose specific countermeasures. For less severe anomalies, the notification unit can provide concise notifications and an overview. For moderately severe anomalies, the notification unit can provide notifications with a moderate level of detail, offering balanced information. By adjusting the level of detail in notifications based on the severity of the anomaly, more effective notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can adjust the level of detail in notifications using an AI model that takes the severity of the anomaly as input and outputs the level of detail in the notification.

[0094] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, the notification unit may prioritize notifying medical professionals for anomalies related to health status. It may also prioritize notifying family members for anomalies related to isolation. It may also notify appropriate stakeholders for anomalies related to behavioral patterns. This allows for more effective notifications by selecting the appropriate notification method according to the category of the anomaly. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can apply a notification method using an AI model that takes the category of the anomaly as input and outputs the notification method to be applied.

[0095] The notification unit can estimate the emotions of elderly individuals and determine the priority of notifications based on the estimated emotions. For example, if an elderly individual is feeling anxious, the notification unit will prioritize notifications related to their health. If an elderly individual is enjoying themselves, the notification unit may also prioritize notifications related to their hobbies and interests. If an elderly individual is tired, the notification unit may also prioritize notifications related to rest. This allows for prioritizing more important information by determining notification priorities based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI, or not using AI. For example, the notification unit may determine the priority of notifications using an AI model that takes elderly individuals' emotion data as input and outputs notification priorities.

[0096] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit can prioritize notifications for recently occurring anomalies to understand the latest situation. The notification unit can also notify about the current situation while referring to past anomalies. The notification unit can also focus on notifying about anomalies that occurred during a specific period to understand changes within that period. This allows for understanding the latest situation by determining the priority of notifications based on when the anomaly occurred. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can determine the priority of notifications using an AI model that takes the time of anomaly occurrence as input and outputs the priority of notifications.

[0097] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifications of anomalies related to the health status of elderly people. The notification unit may also prioritize notifications of anomalies related to the degree of isolation of elderly people. The notification unit may also prioritize notifications of anomalies related to the behavioral patterns of elderly people. By adjusting the order of notifications based on the relevance of the anomalies, more relevant information can be prioritized. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may adjust the order of notifications using an AI model that takes the relevance of anomalies as input and outputs the order of notifications.

[0098] The emergency contact unit can estimate the emotions of elderly individuals and adjust the method of emergency contact based on the estimated emotions. For example, if an elderly individual is feeling anxious, the emergency contact unit will make an emergency contact in a reassuring manner. If an elderly individual is relaxed, the emergency contact unit may also make an emergency contact that includes detailed information. If an elderly individual is agitated, the emergency contact unit may also make a quick and concise emergency contact. This allows for more appropriate emergency contact by adjusting the method of emergency contact based on the emotions of the elderly individual. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 emergency contact unit may be performed using AI, for example, or not using AI. For example, the emergency contact unit can adjust the method of emergency contact using an AI model that takes elderly individual emotion data as input and outputs an emergency contact method.

[0099] The Emergency Liaison Department can adjust the level of detail in emergency communications based on the severity of the emergency. For example, for high-severity emergencies, the Emergency Liaison Department may send emergency communications containing detailed information. For low-severity emergencies, the Emergency Liaison Department may send emergency communications containing concise information. For moderate-severity emergencies, the Emergency Liaison Department may send emergency communications with an appropriate level of detail. By adjusting the level of detail in communications based on the severity of the emergency, more effective emergency communications become possible. Some or all of the above processing in the Emergency Liaison Department may be performed using AI, for example, or not using AI. For example, the Emergency Liaison Department can adjust the level of detail in communications using an AI model that takes the severity of the emergency as input and outputs the level of detail in communications.

[0100] The emergency contact unit can apply different contact methods depending on the category of the emergency. For example, in the case of an emergency related to health status, the emergency contact unit may prioritize contacting medical personnel. In the case of an emergency related to isolation, the emergency contact unit may also prioritize contacting family members. In the case of an emergency related to behavioral patterns, the emergency contact unit may also contact the appropriate parties. This allows for more effective emergency communication by selecting the appropriate contact method according to the category of the emergency. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or not. For example, the emergency contact unit can apply a contact method using an AI model that takes the category of the emergency as input and outputs the contact method to be applied.

[0101] The emergency contact unit can estimate the emotions of elderly individuals and determine the priority of emergency communications based on those estimated emotions. For example, if an elderly individual is feeling anxious, the emergency contact unit will prioritize emergency communications related to their health. If an elderly individual is enjoying themselves, the emergency contact unit may also prioritize emergency communications related to their hobbies and interests. If an elderly individual is tired, the emergency contact unit may also prioritize emergency communications related to rest. By prioritizing emergency communications based on the emotions of elderly individuals, more important information can be communicated preferentially. 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 emergency contact unit may be performed using AI, for example, or not using AI. For example, the emergency contact unit can determine the priority of emergency communications using an AI model that takes elderly individuals' emotion data as input and outputs the priority of emergency communications.

[0102] The emergency liaison department can determine the priority of communications based on when the emergency occurred. For example, the emergency liaison department can prioritize communications for recently occurring emergencies to grasp the latest situation. The emergency liaison department can also communicate the current situation while referring to past emergencies. The emergency liaison department can also focus communications on emergencies that occurred during a specific period to grasp changes within that period. This allows for grasping the latest situation by determining the priority of communications based on when the emergency occurred. Some or all of the above processes in the emergency liaison department may be performed using AI, for example, or not. For example, the emergency liaison department can determine the priority of communications using an AI model that takes the time the emergency occurred as input and outputs the priority of communications.

[0103] The emergency contact unit can adjust the order of communications based on the relevance of the emergencies. For example, the emergency contact unit may prioritize communications related to the health status of elderly individuals. It may also prioritize communications related to the degree of isolation of elderly individuals. It may also prioritize communications related to the behavioral patterns of elderly individuals. By adjusting the order of communications based on the relevance of the emergencies, more relevant information can be prioritized. Some or all of the above processing in the emergency contact unit may be performed using AI, for example, or not. For example, the emergency contact unit can adjust the order of communications using an AI model that takes the relevance of the emergencies as input and outputs the order of communications.

[0104] The shopping support unit can estimate the emotions of elderly people and adjust its shopping support methods based on the estimated emotions. For example, if an elderly person is feeling anxious, the shopping support unit can provide shopping support in a way that provides reassurance. If an elderly person is relaxed, the shopping support unit can also provide shopping support that includes detailed information. If an elderly person is agitated, the shopping support unit can also provide shopping support quickly and concisely. By adjusting the shopping support methods based on the emotions of elderly people, more appropriate shopping support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 shopping support unit may be performed using AI, for example, or not using AI. For example, the shopping support unit can adjust its shopping support methods using an AI model that takes elderly people's emotion data as input and outputs shopping support methods.

[0105] The shopping support department can analyze the elderly person's past purchase history to suggest the most suitable products when providing shopping assistance. For example, the shopping support department can suggest related products based on the elderly person's past purchases. The shopping support department can also suggest seasonal products based on the elderly person's past purchase history. The shopping support department can also analyze the elderly person's past purchase history to suggest the most efficient shopping list. In this way, by analyzing the elderly person's past purchase history, it is possible to suggest more appropriate products. Some or all of the above processes in the shopping support department may be performed using AI, for example, or not using AI. For example, the shopping support department can suggest products using an AI model that takes the elderly person's past purchase history as input and outputs the most suitable products.

[0106] The shopping support department can customize its product recommendations based on the elderly person's current living situation when providing shopping assistance. For example, if the elderly person lives alone, the shopping support department will suggest products necessary for daily life. If the elderly person lives with family, the shopping support department can also suggest products that meet the needs of the entire family. If the elderly person has a specific health condition, the shopping support department can also suggest products suitable for that condition. By customizing the product recommendations based on the elderly person's current living situation, more appropriate products can be suggested. Some or all of the above processing in the shopping support department may be performed using AI, for example, or without AI. For example, the shopping support department can customize its product recommendations using an AI model that takes the elderly person's current living situation as input and outputs product recommendation methods.

[0107] The shopping support unit can estimate the emotions of elderly people and determine the priority of shopping support based on the estimated emotions. For example, if an elderly person is feeling anxious, the shopping support unit will prioritize suggesting items necessary for daily life. If an elderly person is enjoying themselves, the shopping support unit may also prioritize suggesting items related to their hobbies and interests. If an elderly person is tired, the shopping support unit may also prioritize suggesting items related to rest. By determining the priority of shopping support based on the emotions of elderly people, more important items can be suggested preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 shopping support unit may be performed using AI, for example, or without AI. For example, the shopping support unit can determine the priority of shopping support using an AI model that takes elderly people's emotion data as input and outputs the priority of shopping support.

[0108] The shopping support unit can suggest the most suitable products when assisting elderly people with their shopping, taking into account their geographical location. For example, if an elderly person is at home, the unit can suggest products that can be purchased at stores near their home. If an elderly person is out, the unit can also suggest products that can be purchased at their destination. If an elderly person is traveling, the unit can also suggest products that can be purchased at their travel destination. By considering the elderly person's geographical location, the unit can suggest more appropriate products. Some or all of the above processing in the shopping support unit may be performed using AI, for example, or without AI. For example, the shopping support unit can suggest products using an AI model that takes the elderly person's geographical location as input and outputs the most suitable products.

[0109] The shopping support department can analyze the social media activity of elderly people during shopping assistance and propose methods for suggesting products. For example, the shopping support department can suggest products related to the interests of elderly people based on what they have shared on social media. The shopping support department can also analyze the content of posts from accounts that elderly people follow on social media and suggest related products. The shopping support department can also suggest related products based on the activities of groups that elderly people participate in on social media. In this way, by analyzing the social media activity of elderly people, more appropriate products can be suggested. Some or all of the above processes in the shopping support department may be performed using AI, for example, or not using AI. For example, the shopping support department can propose methods for suggesting products using an AI model that takes the social media activity of elderly people as input and outputs methods for suggesting products.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The robot system can also be equipped with an activity suggestion unit that proposes daily activities based on the hobbies and interests of elderly individuals. This unit analyzes past conversations and behavioral data of the elderly person and suggests activities related to their hobbies and interests. For example, if an elderly person is interested in gardening, it can provide information on how to grow plants according to the season and information on gardening events. If the elderly person enjoys music, it can also suggest information on concerts and music classes held in the neighborhood. Furthermore, if the elderly person enjoys reading, it can provide recommendations for new books and information on reading groups. This allows for an improvement in the quality of life by suggesting daily activities based on the elderly person's hobbies and interests.

[0112] The robot system can also be equipped with a relaxation suggestion unit that estimates the emotions of elderly individuals and proposes relaxation methods based on those emotions. If an elderly person is feeling stressed, the relaxation suggestion unit can provide relaxing music or meditation guidance. For example, if an elderly person is feeling anxious, it can play relaxing music and provide guidance on meditation and deep breathing. If an elderly person is tired, it can also suggest videos of light stretching or yoga. Furthermore, if an elderly person is relaxed, it can provide nature sounds or images of scenery to create an even more relaxing environment. In this way, by suggesting relaxation methods based on the emotions of elderly individuals, the system can support their physical and mental health.

[0113] The robotic system can also be equipped with a meal management unit to support the dietary management of elderly individuals. This unit proposes meal plans considering the health condition and nutritional balance of the elderly person. For example, if an elderly person has diabetes, it can suggest a low-carbohydrate meal plan. Similarly, if an elderly person has high blood pressure, it can suggest a low-sodium meal plan. Furthermore, if an elderly person needs to consume specific nutrients, it can provide recipes using ingredients containing those nutrients. This allows for support of dietary management tailored to the health condition of the elderly, contributing to their overall health maintenance.

[0114] The robot system can also be equipped with an entertainment provider that estimates the emotions of elderly people and provides entertainment based on those estimated emotions. If the elderly person is enjoying themselves, the entertainment provider will suggest interesting movies or television programs. For example, if the elderly person is smiling, it will suggest comedy movies or variety shows. It can also suggest emotionally moving movies or dramas if the elderly person is moved. Furthermore, if the elderly person is relaxed, it can provide relaxing music or nature videos. This allows for the provision of entertainment based on the emotions of elderly people, adding enjoyment to their daily lives.

[0115] The robotic system can also be equipped with an exercise support unit to assist elderly people with their exercise. This unit proposes exercise plans tailored to the elderly person's physical strength and health condition. For example, if an elderly person has joint pain, it can suggest exercises that put less strain on the joints. It can also suggest light strength training if the elderly person wants to maintain muscle strength. Furthermore, if the elderly person wants to improve their balance, it can suggest balance exercises. In this way, by supporting exercise according to the elderly person's health condition, it can contribute to maintaining and improving their physical fitness.

[0116] The robot system may also include a communication adjustment unit that estimates the emotions of elderly individuals and adjusts the communication method based on those estimated emotions. If the elderly individual is stressed, the communication adjustment unit will speak in a gentle tone and choose relaxing topics. For example, if the elderly individual is feeling anxious, it will offer reassuring topics and proceed at a slow pace. If the elderly individual is enjoying themselves, it can offer interesting topics and encourage active conversation. Furthermore, if the elderly individual is tired, it can choose shorter conversations and include content that encourages rest. This allows for more appropriate communication by adjusting the communication method according to the elderly individual's emotions.

[0117] The robotic system can also be equipped with a sleep support unit to further assist the elderly in their sleep. This unit analyzes the elderly person's sleep patterns and makes suggestions to promote high-quality sleep. For example, if the elderly person frequently wakes up during the night, it can provide relaxing music or meditation guidance. If the elderly person has trouble falling asleep, it can suggest a bedtime routine to create a relaxing environment. Furthermore, if the elderly person wakes up early in the morning, it can suggest a morning routine to help them start their day smoothly. In this way, supporting the elderly person's sleep can contribute to maintaining their health.

[0118] The robotic system may also include a routine adjustment unit that estimates the emotions of elderly individuals and adjusts their daily routines based on those estimates. If an elderly person is feeling stressed, the routine adjustment unit prioritizes relaxing activities. For example, if an elderly person is feeling anxious, it may increase the time they spend listening to relaxing music. If an elderly person is enjoying themselves, it may increase activities related to their hobbies and interests. Furthermore, if an elderly person is tired, it may increase rest time and provide a relaxing environment. In this way, by adjusting daily routines based on the emotions of elderly individuals, it can support a more comfortable life.

[0119] The robot system can also be equipped with a social interaction support unit to further assist elderly individuals in their social interactions. This unit analyzes the frequency of elderly individuals' social contacts and suggests appropriate opportunities for interaction. For example, if an elderly person is isolated, it can suggest local community events or club activities. It can also support video calls and messaging with family members if they wish to connect with them. Furthermore, if an elderly person wishes to connect with friends, it can suggest opportunities for gatherings or online interactions with friends. This supports social interaction among the elderly, reducing feelings of isolation and improving their quality of life.

[0120] The robot system may also include a health management adjustment unit that estimates the emotions of elderly individuals and adjusts health management methods based on those estimated emotions. If an elderly person is experiencing stress, the health management adjustment unit will suggest relaxing health management methods. For example, if an elderly person is feeling anxious, it may suggest relaxing exercises or meditation. If an elderly person is enjoying themselves, it can also suggest enjoyable exercises or activities. Furthermore, if an elderly person is feeling tired, it can suggest health management methods that prioritize rest. This allows for more effective health management by adjusting health management methods based on the elderly person's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The information gathering unit collects information through conversations and interactions with elderly individuals. For example, it collects the content of elderly individuals' statements and behavioral patterns, analyzes their statements using speech recognition technology, and detects their behavioral patterns using behavioral sensors. It can also analyze the elderly individuals' facial expressions and movements using cameras to estimate their emotions and health status. Step 2: The analysis unit analyzes the information collected by the information collection unit to understand the degree of isolation and health status. For example, based on the collected information, it evaluates the frequency of social contact and feelings of loneliness in elderly individuals, and analyzes vital sign measurement data to assess their health status. It can also analyze fluctuations in heart rate and blood pressure to detect abnormalities. Step 3: The notification unit notifies family members and medical professionals based on the information gathered by the analysis unit. For example, if an abnormality is detected, notifications can be sent via email or SMS, and direct contact can also be made by phone. It is also possible to customize the notification content and provide information in a format that is easy for recipients to understand. Step 4: The emergency contact unit contacts emergency services when an emergency occurs. For example, they contact emergency services if an elderly person falls or experiences a sudden illness. They can use acceleration sensors to detect falls and camera video analysis to determine if an emergency has occurred. They can also detect sudden changes in vital signs to determine if an emergency has occurred. Step 5: The shopping support department orders items based on the shopping list. For example, when an elderly person enters a shopping list, the department orders items online based on that list. It can also learn the elderly person's preferences and past purchase history to suggest appropriate products. It can also suggest seasonal products to support efficient shopping.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the information gathering unit, analysis unit, notification unit, emergency contact unit, and shopping support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and microphone 38B of the smart device 14 to collect the elderly person's speech content and behavioral patterns, and the control unit 46A analyzes them. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and grasps the elderly person's degree of isolation and health status based on the collected information. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies family members and medical personnel if an abnormality is detected. The emergency contact unit is implemented in the specific processing unit 46A of the smart device 14, for example, and contacts emergency services when an emergency occurs. The shopping support unit is implemented in the specific processing unit 46A of the smart device 14, for example, and orders goods online based on the elderly person's shopping list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the information gathering unit, analysis unit, notification unit, emergency contact unit, and shopping support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the elderly person's speech content and behavioral patterns, and the control unit 46A analyzes them. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and grasps the elderly person's degree of isolation and health status based on the collected information. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and notifies family members and medical personnel if an abnormality is detected. The emergency contact unit is implemented in the control unit 46A of the smart glasses 214, for example, and contacts emergency services when an emergency occurs. The shopping support unit is implemented in the control unit 46A of the smart glasses 214, for example, and orders goods online based on the elderly person's shopping list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the information gathering unit, analysis unit, notification unit, emergency contact unit, and shopping support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the elderly person's speech content and behavioral patterns, and the control unit 46A analyzes them. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and grasps the elderly person's degree of isolation and health status based on the collected information. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and notifies family members and medical personnel if an abnormality is detected. The emergency contact unit is implemented by, for example, the control unit 46A of the headset terminal 314, and contacts emergency services when an emergency occurs. The shopping support unit is implemented by, for example, the control unit 46A of the headset terminal 314, and orders goods online based on the elderly person's shopping list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the information gathering unit, analysis unit, notification unit, emergency contact unit, and shopping support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the information gathering unit uses the camera 42 and microphone 238 of the robot 414 to collect the elderly person's statements and behavioral patterns, and the control unit 46A analyzes them. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and based on the collected information, it grasps the elderly person's degree of isolation and health status. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12, and notifies family members and medical personnel if an abnormality is detected. The emergency contact unit is implemented in the control unit 46A of the robot 414, and contacts emergency services when an emergency occurs. The shopping support unit is implemented in the control unit 46A of the robot 414, and orders goods online based on the elderly person's shopping list. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The information gathering department collects information through conversations and interactions with the elderly, The information collected by the aforementioned information collection unit is analyzed by an analysis unit to understand the degree of isolation and health status, A notification unit that notifies family members and medical professionals based on the information obtained by the analysis unit, The emergency liaison department contacts emergency services in the event of an emergency, It includes a shopping support unit that orders products based on a shopping list. A system characterized by the following features. (Note 2) The aforementioned information gathering unit, Collect information on what elderly people say and their behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If an abnormality is detected, family members and medical professionals will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned emergency liaison department, If an elderly person falls or experiences a sudden illness, contact emergency services. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shopping support department, It learns the preferences and past purchase history of elderly people and suggests appropriate products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, The system estimates the emotions of elderly individuals and adjusts the content and timing of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, Analyze the past behavioral patterns of elderly individuals and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, filtering is performed based on the living environment and daily routines of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, The system estimates the emotions of older adults and prioritizes the information to be collected based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information gathering unit, When gathering information, prioritize collecting highly relevant information by considering the geographical location of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, When gathering information, analyze the social media activities of elderly people and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of elderly people and adjust the representation of the analysis based on the estimated emotions of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of elderly individuals and determines the priority of analysis based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates the emotions of elderly individuals and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, adjust the level of detail in the notification based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a notification is sent, different notification methods will be applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The system estimates the emotions of older adults and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When a notification is sent, the notification priority is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emergency liaison department, The system estimates the emotions of elderly individuals and adjusts emergency contact methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned emergency liaison department, When making an emergency call, adjust the level of detail in the call based on the severity of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned emergency liaison department, When making an emergency call, different contact methods will be applied depending on the category of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned emergency liaison department, The system estimates the emotions of elderly individuals and prioritizes emergency contacts based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned emergency liaison department, When making an emergency contact, prioritize the contact based on when the emergency occurred. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned emergency liaison department, When making an emergency call, the order of contacts will be adjusted based on the relevance of the emergency. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shopping support department, The system estimates the emotions of elderly people and adjusts shopping assistance methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shopping support department, When assisting elderly people with their shopping, we analyze their past purchase history to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shopping support department, When providing shopping assistance, customize the methods of suggesting products based on the elderly person's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shopping support department, The system estimates the emotions of elderly people and determines the priority of shopping assistance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shopping support department, When providing shopping assistance, we suggest the most suitable products considering the geographical location of elderly customers. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shopping support department, When providing shopping assistance, we analyze the social media activity of elderly people and propose methods for suggesting products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. The information gathering department collects information through conversations and interactions with the elderly, The information collected by the aforementioned information collection unit is analyzed by an analysis unit to understand the degree of isolation and health status, A notification unit that notifies family members and medical professionals based on the information obtained by the analysis unit, The emergency liaison department contacts emergency services in the event of an emergency, It includes a shopping support unit that orders products based on a shopping list. A system characterized by the following features.

2. The aforementioned information gathering unit, Collect information on what elderly people say and their behavioral patterns. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to detect anomalies. The system according to feature 1.

4. The aforementioned notification unit, If an abnormality is detected, family members and medical professionals will be notified. The system according to feature 1.

5. The aforementioned emergency liaison department, If an elderly person falls or experiences a sudden illness, contact emergency services. The system according to feature 1.

6. The aforementioned shopping support department, It learns the preferences and past purchase history of elderly people and suggests appropriate products. The system according to feature 1.

7. The aforementioned information gathering unit, The system estimates the emotions of elderly individuals and adjusts the content and timing of conversations based on those estimated emotions. The system according to feature 1.

8. The aforementioned information gathering unit, Analyze the past behavioral patterns of elderly individuals and select the most suitable information gathering method. The system according to feature 1.

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