System

Abstract

An object of the system according to the embodiment is to efficiently grasp a health condition and a mental and physical condition in a daily life of an elderly person and share the health condition and the mental and physical condition with a family member and a person in charge of a facility.SOLUTION: A system includes a pet robot, a data collection part, a data analysis part, and a sharing part. The data collection unit collects data through conversations and actions with the elderly person. The data analysis unit analyzes the data collected by the data collection unit. The sharing unit shares a result analyzed by the data analysis unit with a family member or a person in charge of the facility.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to efficiently grasp the health status and physical and mental condition of elderly people in their daily lives and share this information with their families and facility staff.

[0005] The system according to the embodiment aims to efficiently grasp the health condition and physical and mental state of elderly people in their daily lives and share this information with their families and facility staff. [Means for solving the problem]

[0006] The system according to the embodiment includes a pet robot, a data collection unit, a data analysis unit, and a sharing unit. The data collection unit collects data through conversations with and actions of the elderly person. The data analysis unit analyzes the data collected by the data collection unit. The sharing unit shares the results of the analysis by the data analysis unit with family members and facility staff. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the health condition and physical and mental state of elderly people in their daily lives and share this information with their families and facility staff. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The elderly support system according to an embodiment of the present invention utilizes a pet robot and AI to support the daily lives of the elderly, detect illnesses early, promote health, and share information on the physical and mental condition of the elderly with family members and facility staff. This allows the elderly support system to enrich the lives of the elderly and enable them to live with peace of mind.

[0029] An elderly support system according to an embodiment includes a pet robot, a data collection unit, a data analysis unit, and a sharing unit. The pet robot collects data through conversations and behaviors with the elderly. For example, the pet robot asks questions such as "How are you feeling today?" and "How have you been feeling lately?" and collects responses from the elderly. The pet robot also monitors the elderly's behavior and collects data such as walking speed, meal frequency, and sleep patterns. The data collection unit collects data collected by the pet robot. For example, the data collection unit transmits the conversation data and behavior data collected by the pet robot to the data collection unit. The data analysis unit analyzes the data collected by the data collection unit. For example, AI detects mood swings and changes in physical condition from the content of conversations and finds abnormal patterns from behavioral data. The AI ​​comprehensively evaluates this information and performs early detection of illness. The sharing unit shares the results of the analysis by the data analysis unit with family members and facility staff. For example, the sharing unit reports the elderly's health and physical and mental conditions in real time via a dedicated app or email. This allows the elderly support system to support the daily lives of the elderly, detect illness early, promote health, and share information about their physical and mental condition with family members and facility staff.

[0030] The data collection unit can also monitor the elderly person's living environment (room temperature, humidity, lighting, etc.) using sensors and collect that data. For example, the data collection unit monitors the room temperature using a temperature sensor installed in the pet robot and collects data. For example, it issues a warning if the room temperature is not appropriate. The data collection unit also measures the humidity in the room using a humidity sensor in the pet robot and collects data. For example, it prompts dehumidification if the humidity is too high. The data collection unit also monitors the lighting conditions in the room using a lighting sensor in the pet robot and collects data. For example, it suggests adjusting the lighting if the lighting is too dim. This makes it possible to monitor the elderly person's living environment and maintain an appropriate environment.

[0031] The data collection unit can analyze the dietary content of an elderly person using image recognition technology and evaluate the nutritional balance. For example, the data collection unit uses a pet robot to take a photo of the elderly person's meal with a camera and identify the ingredients using image recognition technology. For example, the data collection unit analyzes the dietary content and evaluates the nutritional balance. The data collection unit also analyzes images of the meal by the pet robot and calculates calorie and nutrient intake. For example, it evaluates the calorie, vitamin, and mineral intake of the meal. The data collection unit also builds a system in which the pet robot sends images of the meal to the cloud and a professional nutritionist analyzes the data. For example, it makes suggestions for improving the diet based on the nutritionist's advice. This makes it possible to analyze the dietary content of an elderly person and evaluate the nutritional balance.

[0032] The data collection unit generates questions based on the hobbies and interests of the elderly, thereby improving the quality of conversation. For example, the data collection unit allows the pet robot to generate questions based on the hobbies of the elderly to promote conversation. For example, it asks questions such as, "What book have you read recently?" or "What is your favorite music?" The data collection unit also allows the pet robot to provide related topics based on the elderly's interests. For example, it provides the latest information on gardening, a hobby. The data collection unit also allows the pet robot to analyze the elderly's past conversation history, predict topics that the elderly will be interested in, and generate questions based on those topics. For example, it asks questions about travel destinations that the elderly have previously discussed. This improves the quality of conversation with the elderly.

[0033] The data collection unit can use a sensor to detect the actions that the elderly person takes toward the pet robot and collect the reactions as data. For example, the data collection unit can be equipped with a tactile sensor in the pet robot to detect the elderly person's stroking actions. For example, the data collection unit can collect the number of strokes and strength of strokes as data. The data collection unit can also use a pressure sensor to detect the elderly person's touching actions in the pet robot and collect the data. For example, it can record changes in the frequency of touching and pressure. The data collection unit can also use a motion sensor to detect the elderly person's actions in the pet robot and collect the data. For example, it can analyze the speed and direction of the stroking action. This makes it possible to collect data on the actions that the elderly person takes toward the pet robot.

[0034] The data analysis unit can estimate stress levels from the content of elderly people's conversations and analyze long-term fluctuations in stress. For example, the data analysis unit uses AI to analyze the content of elderly people's conversations and estimate stress levels. For example, it detects negative words and changes in tone. The data analysis unit also uses AI to accumulate elderly people's conversation data and analyze long-term fluctuations in stress. For example, it compares stress levels with past data to evaluate changes in stress levels. The data analysis unit also uses AI to analyze the content of elderly people's conversations and identify the causes of stress. For example, it determines whether specific topics or situations cause stress. This makes it possible to estimate elderly people's stress levels and analyze long-term fluctuations.

[0035] The data analysis unit analyzes the behavioral patterns of elderly people as time-series data, enabling early detection of abnormal behavioral patterns. For example, the data analysis unit uses AI to analyze the behavioral data of elderly people over time and detect abnormal behavioral patterns. For example, it detects abnormally slow walking speeds compared to normal walking speeds. The data analysis unit also uses AI to analyze the behavioral patterns of elderly people and detect signs of abnormal behavior early. For example, it detects changes in eating frequency and sleep patterns. The data analysis unit also uses AI to accumulate the behavioral data of elderly people and monitor abnormal behavioral patterns over the long term. For example, it compares the data with past data to evaluate abnormal changes. This allows the analysis of the behavioral patterns of elderly people and early detection of abnormal behavior.

[0036] The data analysis unit can analyze the sleep data of the elderly and identify sleep quality and abnormal sleep patterns. In the data analysis unit, for example, AI analyzes the sleep data of the elderly and evaluates sleep quality. For example, it analyzes the ratio of deep sleep to light sleep. In addition, AI analyzes the sleep patterns of the elderly and identifies abnormal sleep patterns. For example, it detects patterns of frequent waking up in the middle of the night. In addition, AI accumulates the sleep data of the elderly and analyzes long-term fluctuations in sleep quality. For example, it compares the data with past data to evaluate changes in sleep quality. In this way, it is possible to analyze the sleep data of the elderly and identify sleep quality and abnormal patterns.

[0037] When analyzing the health condition of an elderly person, the data analysis unit can also refer to past medical data and family health history to perform a more accurate analysis. For example, the data analysis unit uses AI to refer to the elderly person's past medical data and analyze their current health condition. For example, the analysis is performed based on past medical history and treatment history. The data analysis unit also uses AI to refer to the health history of the elderly person's family and perform an analysis that takes genetic risks into account. For example, it evaluates the risk of diseases that are common in the family. The data analysis unit also uses AI to integrate the elderly person's past medical data and family health history to perform a more accurate analysis of their health condition. For example, it performs an analysis by combining multiple data sources. This allows for a more accurate analysis of the elderly person's health condition.

[0038] The data analysis unit can be programmed to analyze the elderly person's daily rhythm and, if an abnormality is detected, encourage the Pet Robot to take appropriate action. For example, the data analysis unit uses AI to analyze the elderly person's daily rhythm and, if an abnormality is detected, encourage the Pet Robot to take appropriate action. For example, if meal times are irregular, encourage the Pet Robot to eat. The data analysis unit also uses AI to monitor the elderly person's daily rhythm and, if an abnormality is detected, have the Pet Robot provide a reminder. For example, notifying them when it's time to take medicine. The data analysis unit also uses AI to analyze the elderly person's daily rhythm and, if an abnormality is detected, have the Pet Robot propose an action plan. For example, if there is a lack of exercise, suggest taking a walk. In this way, the elderly person's daily rhythm can be analyzed and, if an abnormality is detected, appropriate action can be encouraged.

[0039] The shared unit can visualize the collected data and provide it to family members and facility staff in the form of graphs and charts. For example, the shared unit converts data collected by AI into graphs and charts and provides them to family members and facility staff. For example, it displays fluctuations in health status in a line graph. The shared unit also visualizes the data collected by AI, allowing family members and facility staff to intuitively understand it. For example, it displays meal frequency and sleep patterns in a pie chart. The shared unit also provides the data collected by AI in dashboard format, allowing family members and facility staff to grasp the situation in real time. For example, they can check the overall health status at a glance. This makes it possible to visualize the collected data and provide it to family members and facility staff.

[0040] The shared unit can introduce encryption technology for privacy protection when sharing data. For example, the shared unit encrypts data collected by the AI ​​and shares it securely with family members and facility personnel. For example, it uses an encryption protocol when transmitting data. The shared unit also introduces encryption technology for privacy protection when the AI ​​shares data. For example, it applies an encryption algorithm when saving data. The shared unit also strengthens access control when the AI ​​shares data, ensuring that only authenticated users can access the data. For example, it introduces two-factor authentication. This makes it possible to introduce encryption technology for privacy protection when sharing data.

[0041] When data is shared, the sharing unit can enable family members and facility staff to receive advice tailored to the elderly person's situation. For example, the sharing unit provides family members and facility staff with advice tailored to the elderly person's situation based on data collected by AI. For example, it may suggest dietary improvements or exercise. When data is shared by AI, the sharing unit also enables family members and facility staff to receive specific action plans tailored to the elderly person's situation. For example, it may suggest a health management schedule. When data is shared by AI, the sharing unit also enables family members and facility staff to receive expert advice tailored to the elderly person's situation. For example, it may provide advice that reflects the opinions of a doctor or nutritionist. This enables family members and facility staff to receive advice tailored to the elderly person's situation.

[0042] The sharing unit adds a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care. For example, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care. For example, it may link data with medical institutions and nursing care facilities. Furthermore, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care plans. For example, it may integrate data on rehabilitation and nutritional management. Furthermore, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to share data in real time. For example, it may link data to enable rapid response in emergencies. This may add a function for linking with other elderly care systems, making it possible to provide comprehensive care.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The elderly support system can further include a voice recognition unit. The voice recognition unit can analyze the elderly's speech and detect specific keywords and phrases. For example, it can detect urgent words such as "ouch" or "help" and immediately notify family members or facility staff. The voice recognition unit can also analyze the elderly's speech patterns to detect early decline in cognitive function. For example, it can monitor word choice and speech fluency, and if abnormalities are detected, it can prompt a specialist for diagnosis. Furthermore, the voice recognition unit can analyze the elderly's speech and generate conversation content based on their hobbies and interests. For example, it can provide topics related to recent news and hobbies, improving the quality of the conversation.

[0045] The data collection unit can further include a biosensor that monitors the elderly person's heart rate and blood pressure. The biosensor measures the elderly person's heart rate and blood pressure in real time and immediately notifies family members or facility staff if an abnormality is detected. For example, if the heart rate suddenly increases or blood pressure becomes abnormally high, emergency response will be initiated. The biosensor can also accumulate long-term data and analyze fluctuations in health status. For example, it can evaluate fluctuations in heart rate and blood pressure by comparing them with past data and predict health risks. Furthermore, the biosensor can suggest exercise and relaxation techniques. For example, it can suggest light exercise if the heart rate is stable, and provide relaxation methods if stress levels are high.

[0046] The data collection unit can further include sensors that monitor the elderly person's sleeping environment. The sleep environment sensor monitors room temperature, humidity, and lighting conditions and makes suggestions to maintain an appropriate sleeping environment. For example, if the room temperature is too high, it recommends turning on the air conditioner, and if the humidity is too low, it suggests increasing the humidity. The sleep environment sensor can also monitor lighting brightness and sound levels and make adjustments to provide a comfortable sleeping environment. For example, if the lighting is too bright, it will suggest dimming it, and if the noise is too loud, it will provide a quiet environment. The sleep environment sensor can also accumulate long-term data and evaluate sleep quality. For example, it can compare it with past data to analyze fluctuations in sleep quality and suggest improvement measures.

[0047] The data collection unit can further include an acceleration sensor that monitors the elderly person's physical activity. The acceleration sensor can analyze the elderly person's walking and exercise patterns and detect lack of exercise or abnormal movements. For example, if the walking speed slows or the risk of falling increases, it can notify family members or facility staff. The acceleration sensor can also record the amount of exercise and suggest an exercise plan to maintain health. For example, if the daily amount of exercise is low, it can suggest light exercise or stretching. Furthermore, the acceleration sensor can accumulate long-term data and analyze fluctuations in exercise patterns. For example, it can compare the fluctuations in exercise amount with past data and predict health risks.

[0048] The data collection unit can further be equipped with a function to monitor the social connections of elderly people. For example, the robotic pet can record the frequency of elderly people's telephone and video calls and make suggestions to prevent social isolation. For example, if there has been a long period of loss of contact, it can encourage contact with family and friends. The data collection unit can also collect data on how often elderly people go out and the events they attend and make suggestions to promote social activity. For example, it can suggest that they participate in local events or hobby clubs. Furthermore, the data collection unit can accumulate long-term data and analyze changes in social connections. For example, it can compare changes in social activity with past data and predict the risk of isolation.

[0049] The data analysis unit can estimate stress levels from the content of elderly people's conversations and analyze long-term fluctuations in stress. For example, the data analysis unit uses AI to analyze the content of elderly people's conversations and estimate stress levels. For example, it detects negative words and changes in tone. The data analysis unit also uses AI to accumulate elderly people's conversation data and analyze long-term fluctuations in stress. For example, it compares stress levels with past data to evaluate changes in stress levels. The data analysis unit also uses AI to analyze the content of elderly people's conversations and identify the causes of stress. For example, it determines whether specific topics or situations cause stress. This makes it possible to estimate elderly people's stress levels and analyze long-term fluctuations.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: PetRobo collects data through conversations and behaviors with the elderly. For example, PetRobo asks questions such as "How are you feeling today?" and "How have you been feeling lately?" and collects the elderly's responses. PetRobo also monitors the elderly's behavior and collects data such as walking speed, eating frequency, and sleep patterns. Step 2: The data collection unit collects data collected by the pet robot. For example, conversation data and behavior data collected by the pet robot are transmitted to the data collection unit. Step 3: The data analysis unit analyzes the data collected by the data collection unit. For example, the AI ​​can detect mood swings and changes in physical condition from the content of conversations, and find abnormal patterns from behavioral data. The AI ​​then comprehensively assesses this information and performs early detection of illness. Step 4: The sharing unit shares the results of the analysis by the data analysis unit with family members and facility staff. For example, the system reports the elderly person's health and physical and mental condition in real time via a dedicated app or email.

[0052] (Example 2) The elderly support system according to an embodiment of the present invention utilizes a pet robot and AI to support the daily lives of the elderly, detect illnesses early, promote health, and share information on the physical and mental condition of the elderly with family members and facility staff. This allows the elderly support system to enrich the lives of the elderly and enable them to live with peace of mind.

[0053] An elderly support system according to an embodiment includes a pet robot, a data collection unit, a data analysis unit, and a sharing unit. The pet robot collects data through conversations and behaviors with the elderly. For example, the pet robot asks questions such as "How are you feeling today?" and "How have you been feeling lately?" and collects responses from the elderly. The pet robot also monitors the elderly's behavior and collects data such as walking speed, meal frequency, and sleep patterns. The data collection unit collects data collected by the pet robot. For example, the data collection unit transmits the conversation data and behavior data collected by the pet robot to the data collection unit. The data analysis unit analyzes the data collected by the data collection unit. For example, AI detects mood swings and changes in physical condition from the content of conversations and finds abnormal patterns from behavioral data. The AI ​​comprehensively evaluates this information and performs early detection of illness. The sharing unit shares the results of the analysis by the data analysis unit with family members and facility staff. For example, the sharing unit reports the elderly's health and physical and mental conditions in real time via a dedicated app or email. This allows the elderly support system to support the daily lives of the elderly, detect illness early, promote health, and share information about their physical and mental condition with family members and facility staff.

[0054] The data collection unit can analyze the elderly person's tone of voice and facial expression and record changes in emotion in real time using an emotion estimation function. For example, the data collection unit allows the pet robot to analyze the elderly person's tone of voice and record changes in emotion in real time. For example, it analyzes the pitch, speed, and intonation of the voice to estimate emotions such as joy or sadness. The data collection unit also allows the pet robot to capture the elderly person's facial expression with a camera and estimate emotions using facial expression analysis technology. For example, it analyzes changes in facial expression such as smiling or furrowing the brow and records changes in emotion. The data collection unit also allows the pet robot to simultaneously analyze both the elderly person's voice and facial expression to more accurately estimate changes in emotion. For example, it comprehensively determines emotions from a combination of tone of voice and facial expression. This allows changes in the elderly person's emotions to be recorded in real time, enabling more accurate health monitoring.

[0055] The data collection unit can also monitor the elderly person's living environment (room temperature, humidity, lighting, etc.) using sensors and collect that data. For example, the data collection unit monitors the room temperature using a temperature sensor installed in the pet robot and collects data. For example, it issues a warning if the room temperature is not appropriate. The data collection unit also measures the humidity in the room using a humidity sensor in the pet robot and collects data. For example, it prompts dehumidification if the humidity is too high. The data collection unit also monitors the lighting conditions in the room using a lighting sensor in the pet robot and collects data. For example, it suggests adjusting the lighting if the lighting is too dim. This makes it possible to monitor the elderly person's living environment and maintain an appropriate environment.

[0056] The data collection unit can analyze the dietary content of an elderly person using image recognition technology and evaluate the nutritional balance. For example, the data collection unit uses a pet robot to take a photo of the elderly person's meal with a camera and identify the ingredients using image recognition technology. For example, the data collection unit analyzes the dietary content and evaluates the nutritional balance. The data collection unit also analyzes images of the meal by the pet robot and calculates calorie and nutrient intake. For example, it evaluates the calorie, vitamin, and mineral intake of the meal. The data collection unit also builds a system in which the pet robot sends images of the meal to the cloud and a professional nutritionist analyzes the data. For example, it makes suggestions for improving the diet based on the nutritionist's advice. This makes it possible to analyze the dietary content of an elderly person and evaluate the nutritional balance.

[0057] The data collection unit generates questions based on the hobbies and interests of the elderly, thereby improving the quality of conversation. For example, the data collection unit allows the pet robot to generate questions based on the hobbies of the elderly to promote conversation. For example, it asks questions such as, "What book have you read recently?" or "What is your favorite music?" The data collection unit also allows the pet robot to provide related topics based on the elderly's interests. For example, it provides the latest information on gardening, a hobby. The data collection unit also allows the pet robot to analyze the elderly's past conversation history, predict topics that the elderly will be interested in, and generate questions based on those topics. For example, it asks questions about travel destinations that the elderly have previously discussed. This improves the quality of conversation with the elderly.

[0058] The data collection unit can use a sensor to detect the actions that the elderly person takes toward the pet robot and collect the reactions as data. For example, the data collection unit can be equipped with a tactile sensor in the pet robot to detect the elderly person's stroking actions. For example, the data collection unit can collect the number of strokes and strength of strokes as data. The data collection unit can also use a pressure sensor to detect the elderly person's touching actions in the pet robot and collect the data. For example, it can record changes in the frequency of touching and pressure. The data collection unit can also use a motion sensor to detect the elderly person's actions in the pet robot and collect the data. For example, it can analyze the speed and direction of the stroking action. This makes it possible to collect data on the actions that the elderly person takes toward the pet robot.

[0059] The data collection unit can use the emotion estimation function to analyze the emotions of the elderly when interacting with the pet robot and automatically generate conversation content to elicit positive emotions. For example, the data collection unit allows the pet robot to analyze the elderly's emotions in real time and generate conversation content to elicit positive emotions. For example, it provides words of encouragement and fun topics. The data collection unit also allows the pet robot to accumulate emotional data of the elderly and suggest optimal conversation content based on past data. For example, it re-provides topics that the elderly enjoyed in previous conversations. The data collection unit also allows the pet robot to analyze changes in the elderly's emotions and generate conversation content to alleviate negative emotions. For example, it provides relaxing topics and jokes. In this way, conversation content to elicit positive emotions in the elderly can be automatically generated.

[0060] The data analysis unit can estimate stress levels from the content of elderly people's conversations and analyze long-term fluctuations in stress. For example, the data analysis unit uses AI to analyze the content of elderly people's conversations and estimate stress levels. For example, it detects negative words and changes in tone. The data analysis unit also uses AI to accumulate elderly people's conversation data and analyze long-term fluctuations in stress. For example, it compares stress levels with past data to evaluate changes in stress levels. The data analysis unit also uses AI to analyze the content of elderly people's conversations and identify the causes of stress. For example, it determines whether specific topics or situations cause stress. This makes it possible to estimate elderly people's stress levels and analyze long-term fluctuations.

[0061] The data analysis unit analyzes the behavioral patterns of elderly people as time-series data, enabling early detection of abnormal behavioral patterns. For example, the data analysis unit uses AI to analyze the behavioral data of elderly people over time and detect abnormal behavioral patterns. For example, it detects abnormally slow walking speeds compared to normal walking speeds. The data analysis unit also uses AI to analyze the behavioral patterns of elderly people and detect signs of abnormal behavior early. For example, it detects changes in eating frequency and sleep patterns. The data analysis unit also uses AI to accumulate the behavioral data of elderly people and monitor abnormal behavioral patterns over the long term. For example, it compares the data with past data to evaluate abnormal changes. This allows the analysis of the behavioral patterns of elderly people and early detection of abnormal behavior.

[0062] The data analysis unit can analyze the sleep data of the elderly and identify sleep quality and abnormal sleep patterns. In the data analysis unit, for example, AI analyzes the sleep data of the elderly and evaluates sleep quality. For example, it analyzes the ratio of deep sleep to light sleep. In addition, AI analyzes the sleep patterns of the elderly and identifies abnormal sleep patterns. For example, it detects patterns of frequent waking up in the middle of the night. In addition, AI accumulates the sleep data of the elderly and analyzes long-term fluctuations in sleep quality. For example, it compares the data with past data to evaluate changes in sleep quality. In this way, it is possible to analyze the sleep data of the elderly and identify sleep quality and abnormal patterns.

[0063] When analyzing the health condition of an elderly person, the data analysis unit can also refer to past medical data and family health history to perform a more accurate analysis. For example, the data analysis unit uses AI to refer to the elderly person's past medical data and analyze their current health condition. For example, the analysis is performed based on past medical history and treatment history. The data analysis unit also uses AI to refer to the health history of the elderly person's family and perform an analysis that takes genetic risks into account. For example, it evaluates the risk of diseases that are common in the family. The data analysis unit also uses AI to integrate the elderly person's past medical data and family health history to perform a more accurate analysis of their health condition. For example, it performs an analysis by combining multiple data sources. This allows for a more accurate analysis of the elderly person's health condition.

[0064] The data analysis unit uses the emotion estimation function to analyze the relationship between an elderly person's emotional fluctuations and their health condition, and can evaluate the impact of emotional changes on their health. For example, the data analysis unit uses AI to analyze the elderly person's emotional data and evaluate the relationship between emotional fluctuations and their health condition. For example, it analyzes the impact of stress on their health. The data analysis unit also uses AI to integrate the elderly person's emotional data and their health data and evaluate the impact of emotional changes on their health. For example, it analyzes the impact of emotional fluctuations on their physical condition. The data analysis unit also uses AI to monitor the elderly person's emotional data over the long term and analyze the relationship between emotional fluctuations and their health condition. For example, it evaluates the impact of emotional changes on the risk of disease. This makes it possible to analyze the relationship between an elderly person's emotional fluctuations and their health condition, and evaluate the impact of emotional changes on their health.

[0065] The data analysis unit can be programmed to analyze the elderly person's daily rhythm and, if an abnormality is detected, encourage the Pet Robot to take appropriate action. For example, the data analysis unit uses AI to analyze the elderly person's daily rhythm and, if an abnormality is detected, encourage the Pet Robot to take appropriate action. For example, if meal times are irregular, encourage the Pet Robot to eat. The data analysis unit also uses AI to monitor the elderly person's daily rhythm and, if an abnormality is detected, have the Pet Robot provide a reminder. For example, notifying them when it's time to take medicine. The data analysis unit also uses AI to analyze the elderly person's daily rhythm and, if an abnormality is detected, have the Pet Robot propose an action plan. For example, if there is a lack of exercise, suggest taking a walk. In this way, the elderly person's daily rhythm can be analyzed and, if an abnormality is detected, appropriate action can be encouraged.

[0066] The shared unit can visualize the collected data and provide it to family members and facility staff in the form of graphs and charts. For example, the shared unit converts data collected by AI into graphs and charts and provides them to family members and facility staff. For example, it displays fluctuations in health status in a line graph. The shared unit also visualizes the data collected by AI, allowing family members and facility staff to intuitively understand it. For example, it displays meal frequency and sleep patterns in a pie chart. The shared unit also provides the data collected by AI in dashboard format, allowing family members and facility staff to grasp the situation in real time. For example, they can check the overall health status at a glance. This makes it possible to visualize the collected data and provide it to family members and facility staff.

[0067] The shared unit can introduce encryption technology for privacy protection when sharing data. For example, the shared unit encrypts data collected by the AI ​​and shares it securely with family members and facility personnel. For example, it uses an encryption protocol when transmitting data. The shared unit also introduces encryption technology for privacy protection when the AI ​​shares data. For example, it applies an encryption algorithm when saving data. The shared unit also strengthens access control when the AI ​​shares data, ensuring that only authenticated users can access the data. For example, it introduces two-factor authentication. This makes it possible to introduce encryption technology for privacy protection when sharing data.

[0068] When family members or facility staff receive the data, the sharing unit uses an emotion estimation function to analyze their reactions and improve the feedback. For example, the sharing unit uses AI to analyze the emotional reactions of family members and facility staff when they receive the data and improve the feedback. For example, if there are a lot of positive reactions, it will emphasize that content. The sharing unit also uses AI to monitor the emotional reactions of family members and facility staff in real time and adjust the content of the feedback. For example, if there are a lot of negative reactions, it will add a detailed explanation. The sharing unit also uses AI to accumulate emotional data of family members and facility staff and suggest the optimal feedback method based on past data. For example, it will adjust the tone of the feedback according to changes in emotions. This makes it possible to analyze the reactions of family members and facility staff and improve the feedback.

[0069] When data is shared, the sharing unit can enable family members and facility staff to receive advice tailored to the elderly person's situation. For example, the sharing unit provides family members and facility staff with advice tailored to the elderly person's situation based on data collected by AI. For example, it may suggest dietary improvements or exercise. When data is shared by AI, the sharing unit also enables family members and facility staff to receive specific action plans tailored to the elderly person's situation. For example, it may suggest a health management schedule. When data is shared by AI, the sharing unit also enables family members and facility staff to receive expert advice tailored to the elderly person's situation. For example, it may provide advice that reflects the opinions of a doctor or nutritionist. This enables family members and facility staff to receive advice tailored to the elderly person's situation.

[0070] The sharing unit adds a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care. For example, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care. For example, it may link data with medical institutions and nursing care facilities. Furthermore, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to provide comprehensive care plans. For example, it may integrate data on rehabilitation and nutritional management. Furthermore, AI may add a function for linking with other elderly care systems to the data sharing platform, making it possible to share data in real time. For example, it may link data to enable rapid response in emergencies. This may add a function for linking with other elderly care systems, making it possible to provide comprehensive care.

[0071] The shared unit uses the emotion estimation function to analyze how family members and facility staff feel about the elderly person's situation and can make suggestions to improve the quality of care. For example, the shared unit uses the emotion estimation function to analyze how family members and facility staff feel about the elderly person's situation. For example, it evaluates feelings of relief and anxiety. The shared unit also uses the AI ​​to make specific suggestions to improve the quality of care based on the emotional data of family members and facility staff. For example, it suggests improving communication and strengthening the support system. The shared unit also uses the AI ​​to accumulate emotion estimation data and propose optimal care methods based on past data. For example, it adjusts the care plan according to changes in emotions. This makes it possible to analyze the emotions of family members and facility staff and make suggestions to improve the quality of care.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The elderly support system can further include a voice recognition unit. The voice recognition unit can analyze the elderly's speech and detect specific keywords and phrases. For example, it can detect urgent words such as "ouch" or "help" and immediately notify family members or facility staff. The voice recognition unit can also analyze the elderly's speech patterns to detect early decline in cognitive function. For example, it can monitor word choice and speech fluency, and if abnormalities are detected, it can prompt a specialist for diagnosis. Furthermore, the voice recognition unit can analyze the elderly's speech and generate conversation content based on their hobbies and interests. For example, it can provide topics related to recent news and hobbies, improving the quality of the conversation.

[0074] The data collection unit can further include a biosensor that monitors the elderly person's heart rate and blood pressure. The biosensor measures the elderly person's heart rate and blood pressure in real time and immediately notifies family members or facility staff if an abnormality is detected. For example, if the heart rate suddenly increases or blood pressure becomes abnormally high, emergency response will be initiated. The biosensor can also accumulate long-term data and analyze fluctuations in health status. For example, it can evaluate fluctuations in heart rate and blood pressure by comparing them with past data and predict health risks. Furthermore, the biosensor can suggest exercise and relaxation techniques. For example, it can suggest light exercise if the heart rate is stable, and provide relaxation methods if stress levels are high.

[0075] The data collection unit can further include sensors that monitor the elderly person's sleeping environment. The sleep environment sensor monitors room temperature, humidity, and lighting conditions and makes suggestions to maintain an appropriate sleeping environment. For example, if the room temperature is too high, it recommends turning on the air conditioner, and if the humidity is too low, it suggests increasing the humidity. The sleep environment sensor can also monitor lighting brightness and sound levels and make adjustments to provide a comfortable sleeping environment. For example, if the lighting is too bright, it will suggest dimming it, and if the noise is too loud, it will provide a quiet environment. The sleep environment sensor can also accumulate long-term data and evaluate sleep quality. For example, it can compare it with past data to analyze fluctuations in sleep quality and suggest improvement measures.

[0076] The data collection unit can further include an acceleration sensor that monitors the elderly person's physical activity. The acceleration sensor can analyze the elderly person's walking and exercise patterns and detect lack of exercise or abnormal movements. For example, if the walking speed slows or the risk of falling increases, it can notify family members or facility staff. The acceleration sensor can also record the amount of exercise and suggest an exercise plan to maintain health. For example, if the daily amount of exercise is low, it can suggest light exercise or stretching. Furthermore, the acceleration sensor can accumulate long-term data and analyze fluctuations in exercise patterns. For example, it can compare the fluctuations in exercise amount with past data and predict health risks.

[0077] The data collection unit can further be equipped with a function to monitor the social connections of elderly people. For example, the robotic pet can record the frequency of elderly people's telephone and video calls and make suggestions to prevent social isolation. For example, if there has been a long period of loss of contact, it can encourage contact with family and friends. The data collection unit can also collect data on how often elderly people go out and the events they attend and make suggestions to promote social activity. For example, it can suggest that they participate in local events or hobby clubs. Furthermore, the data collection unit can accumulate long-term data and analyze changes in social connections. For example, it can compare changes in social activity with past data and predict the risk of isolation.

[0078] The data collection unit can use the emotion estimation function to analyze the emotions of the elderly when interacting with the pet robot and automatically generate conversation content to elicit positive emotions. For example, the data collection unit allows the pet robot to analyze the elderly's emotions in real time and generate conversation content to elicit positive emotions. For example, it provides words of encouragement and fun topics. The data collection unit also allows the pet robot to accumulate emotional data of the elderly and suggest optimal conversation content based on past data. For example, it re-provides topics that the elderly enjoyed in previous conversations. The data collection unit also allows the pet robot to analyze changes in the elderly's emotions and generate conversation content to alleviate negative emotions. For example, it provides relaxing topics and jokes. In this way, conversation content to elicit positive emotions in the elderly can be automatically generated.

[0079] The data analysis unit uses the emotion estimation function to analyze the relationship between an elderly person's emotional fluctuations and their health condition, and can evaluate the impact of emotional changes on their health. For example, the data analysis unit uses AI to analyze the elderly person's emotional data and evaluate the relationship between emotional fluctuations and their health condition. For example, it analyzes the impact of stress on their health. The data analysis unit also uses AI to integrate the elderly person's emotional data and their health data and evaluate the impact of emotional changes on their health. For example, it analyzes the impact of emotional fluctuations on their physical condition. The data analysis unit also uses AI to monitor the elderly person's emotional data over the long term and analyze the relationship between emotional fluctuations and their health condition. For example, it evaluates the impact of emotional changes on the risk of disease. This makes it possible to analyze the relationship between an elderly person's emotional fluctuations and their health condition, and evaluate the impact of emotional changes on their health.

[0080] The shared unit uses the emotion estimation function to analyze how family members and facility staff feel about the elderly person's situation and can make suggestions to improve the quality of care. For example, the shared unit uses the emotion estimation function to analyze how family members and facility staff feel about the elderly person's situation. For example, it evaluates feelings of relief and anxiety. The shared unit also uses the AI ​​to make specific suggestions to improve the quality of care based on the emotional data of family members and facility staff. For example, it suggests improving communication and strengthening the support system. The shared unit also uses the AI ​​to accumulate emotion estimation data and propose optimal care methods based on past data. For example, it adjusts the care plan according to changes in emotions. This makes it possible to analyze the emotions of family members and facility staff and make suggestions to improve the quality of care.

[0081] When family members or facility staff receive the data, the sharing unit uses an emotion estimation function to analyze their reactions and improve the feedback. For example, the sharing unit uses AI to analyze the emotional reactions of family members and facility staff when they receive the data and improve the feedback. For example, if there are a lot of positive reactions, it will emphasize that content. The sharing unit also uses AI to monitor the emotional reactions of family members and facility staff in real time and adjust the content of the feedback. For example, if there are a lot of negative reactions, it will add a detailed explanation. The sharing unit also uses AI to accumulate emotional data of family members and facility staff and suggest the optimal feedback method based on past data. For example, it will adjust the tone of the feedback according to changes in emotions. This makes it possible to analyze the reactions of family members and facility staff and improve the feedback.

[0082] The data analysis unit can estimate stress levels from the content of elderly people's conversations and analyze long-term fluctuations in stress. For example, the data analysis unit uses AI to analyze the content of elderly people's conversations and estimate stress levels. For example, it detects negative words and changes in tone. The data analysis unit also uses AI to accumulate elderly people's conversation data and analyze long-term fluctuations in stress. For example, it compares stress levels with past data to evaluate changes in stress levels. The data analysis unit also uses AI to analyze the content of elderly people's conversations and identify the causes of stress. For example, it determines whether specific topics or situations cause stress. This makes it possible to estimate elderly people's stress levels and analyze long-term fluctuations.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: PetRobo collects data through conversations and behaviors with the elderly. For example, PetRobo asks questions such as "How are you feeling today?" and "How have you been feeling lately?" and collects the elderly's responses. PetRobo also monitors the elderly's behavior and collects data such as walking speed, eating frequency, and sleep patterns. Step 2: The data collection unit collects data collected by the pet robot. For example, conversation data and behavior data collected by the pet robot are transmitted to the data collection unit. Step 3: The data analysis unit analyzes the data collected by the data collection unit. For example, the AI ​​can detect mood swings and changes in physical condition from the content of conversations, and find abnormal patterns from behavioral data. The AI ​​then comprehensively assesses this information and performs early detection of illness. Step 4: The sharing unit shares the results of the analysis by the data analysis unit with family members and facility staff. For example, the system reports the elderly person's health and physical and mental condition in real time via a dedicated app or email.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Pet robots and A data collection department collects data through conversations and behaviors with the elderly. a data analysis unit that analyzes the data collected by the data collection unit; a sharing unit that shares the results of the analysis by the data analysis unit with family members and facility personnel. A system characterized by:

2. The data collection unit Analyze the elderly person's tone of voice and facial expressions and record emotional changes in real time.

2. The system of claim 1.

3. The data collection unit The elderly person's living environment (room temperature, humidity, lighting, etc.) will be monitored using sensors, and that data will also be collected.

2. The system of claim 1.

4. The data collection unit Analyze the dietary content of the elderly person using image recognition technology and evaluate their nutritional balance 2. The system of claim 1.

5. The data collection unit Generate questions based on the elderly person's hobbies and interests to improve the quality of the conversation 2. The system of claim 1.

6. The data collection unit The sensor detects the elderly person's actions toward the pet robot and collects their reactions as data.

2. The system of claim 1.

7. The data collection unit The emotions felt by the elderly when they interact with the pet robot are analyzed, and conversation content designed to elicit positive emotions is automatically generated.

2. The system of claim 1.

8. The data analysis unit Estimate the stress level of the elderly person from their conversations and analyze long-term stress fluctuations.

2. The system of claim 1.

Images