system

Abstract

The system according to this embodiment aims to effectively monitor and manage the health of elderly people. [Solution] The system according to this embodiment comprises a sensor unit, a speaking unit, an exercise promotion unit, a questioning unit, and a reporting unit. The sensor unit detects the presence of an elderly person. The speaking unit speaks to the elderly person based on the information detected by the sensor unit. The exercise promotion unit encourages the elderly person who has been spoken to by the speaking unit to exercise. The questioning unit asks the elderly person who has been spoken to by the speaking unit questions to prevent cognitive decline. The reporting unit summarizes the chat exchange with the parent and provides a report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to effectively monitor and manage the health of the elderly.

[0005] The system according to the embodiment aims to effectively monitor and manage the health of the elderly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sensor unit, a speaking unit, an exercise promotion unit, a questioning unit, and a reporting unit. The sensor unit detects the presence of an elderly person. The speaking unit speaks to the elderly person based on the information detected by the sensor unit. The exercise promotion unit encourages the elderly person who has been spoken to by the speaking unit to exercise. The questioning unit asks the elderly person who has been spoken to by the speaking unit questions to prevent cognitive decline. The reporting unit summarizes the chat exchange with the parent and provides a report. [Effects of the Invention]

[0007] The system according to this embodiment can effectively monitor and manage the health of elderly people. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The proactive AI chat speaker system according to an embodiment of the present invention is a system that supports the monitoring and health management of the elderly. Unlike conventional AI speakers that provide information and functions in response to user inquiries, this system supports the monitoring and health management of the elderly by initiating conversations. Specifically, when a sensor detects the presence of an elderly person, the AI ​​chat speaker proactively initiates a conversation. For example, it may ask questions such as, "How are you feeling today?", "Is there any information you would like to know?", or "Is there any music you would like to listen to?". This allows the elderly person to obtain the necessary information without having to ask for it themselves. Next, the AI ​​chat speaker encourages the elderly person to do simple exercises. For example, it may give instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Let's do some light squats." This promotes physical activity in the elderly person and contributes to maintaining their health. Furthermore, the AI ​​chat speaker asks questions for cognitive prevention. For example, it may ask questions such as, "What month and day is it today?", "When is your birthday?", or "What are your daughter's / son's birthdays?". This stimulates the cognitive function of the elderly person and helps prevent dementia. The AI ​​chat speaker analyzes and judges the user's situation and initiates conversations at a frequency that is comfortable for the user. Furthermore, the system summarizes chat interactions with parents and provides a report via email or other means once a day. This provides peace of mind to users in the caregiving generation who want to keep an eye on their parents who live far away. Users register basic information about their parents and pre-define how they should interact with them. The generating AI creates an interaction plan based on this information, and after the system is put into operation, it provides a status report once a day as a "Parent Report." In this way, the proactive AI chat speaker system can support monitoring and health management of the elderly.

[0029] The proactive AI chat speaker system according to this embodiment comprises a sensor unit, a speaking unit, an exercise promotion unit, a questioning unit, and a reporting unit. The sensor unit detects the presence of an elderly person. The sensor unit can detect the elderly person's movements using, for example, an infrared sensor or an ultrasonic sensor. The sensor unit can also detect the elderly person's posture and movements using a camera. For example, the sensor unit uses an infrared sensor to detect the elderly person's movements and confirm their presence. An ultrasonic sensor uses the reflection of sound waves to pinpoint the elderly person's location. A camera uses image analysis technology to detect the elderly person's posture and movements. The speaking unit speaks to the elderly person based on the information detected by the sensor unit. The speaking unit asks questions such as, for example, "How are you feeling today?", "Is there any information you'd like to know?", or "Is there any music you'd like to listen to?". The speaking unit generates appropriate questions for the elderly person using generative AI. For example, the generative AI can generate appropriate questions based on the elderly person's past response history. The exercise promotion unit encourages the elderly person who has been spoken to by the speaking unit to exercise. The exercise promotion unit gives instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Try doing some light squats." The exercise promotion unit uses generative AI to suggest appropriate exercises to the elderly. For example, the generative AI can suggest appropriate exercises based on the elderly person's health condition and exercise history. The questioning unit asks cognitive prevention questions to the elderly person who is spoken to by the speaking unit. For example, the questioning unit asks questions such as, "What month and day is it today?", "When is your birthday?", or "What is your daughter's / son's birthday?". The questioning unit uses generative AI to generate appropriate questions for the elderly person. For example, the generative AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. The reporting unit summarizes the chat exchange with the parent and provides a report. The reporting unit uses generative AI to summarize the chat content and provides a report once a day via email or other means. For example, the generative AI analyzes the chat content, extracts important information, and generates a report. In this way, the proactive AI chat speaker system according to this embodiment can support monitoring and health management of the elderly.

[0030] The sensor unit detects the presence of elderly individuals. For example, the sensor unit can detect the movements of elderly individuals using infrared sensors or ultrasonic sensors. Specifically, infrared sensors emit infrared light and detect the reflection to sense the movement of elderly individuals. This allows for real-time monitoring of the elderly person's movement and activities within a room. Ultrasonic sensors emit sound waves and receive the reflection to pinpoint the elderly person's location. This allows for accurate location tracking without being affected by furniture or obstacles. Furthermore, cameras can be used to detect the elderly person's posture and movements. Cameras use image analysis technology to analyze the elderly person's posture and movements in real time, detecting falls and abnormal movements. For example, a camera can analyze an elderly person's movements, such as standing up from a chair or walking, to determine if the movements are normal. This allows the sensor unit to ensure the safety of elderly individuals and respond quickly if an abnormality occurs. The sensor unit can also transmit this data to the cloud, allowing for centralized management of the elderly person's condition in collaboration with other departments. This enables the sensor unit to accurately detect the presence of elderly individuals and improve the overall reliability and safety of the system.

[0031] The speaking unit speaks to the elderly based on information detected by the sensor unit. The speaking unit asks questions such as, "How are you feeling today?", "Is there any information you'd like to know?", and "Is there any music you'd like to listen to?". The speaking unit uses generative AI to generate appropriate questions for the elderly. The generative AI can generate appropriate questions based on the elderly person's past response history. Specifically, the generative AI analyzes the elderly person's past conversation data and generates questions based on each elderly person's interests and concerns. For example, for an elderly person who has shown interest in music in the past, it might ask, "What kind of music would you like to listen to today?" The generative AI can also ask health-related questions based on the elderly person's health condition and daily activities. For example, it might ask questions such as, "How have you been feeling lately?", and "Are you eating properly?" to check the elderly person's health condition. This allows the speaking unit to facilitate communication with the elderly and reduce feelings of loneliness. Furthermore, the speaking unit can analyze the elderly person's responses in real time and provide appropriate feedback. This allows the speaking unit to support daily monitoring and health management through dialogue with the elderly person.

[0032] The Exercise Promotion Unit encourages exercise in elderly individuals who have been spoken to by the Communication Unit. The Exercise Promotion Unit provides instructions such as, "Try standing up from your chair," "Try walking down the corridor," or "Try some light squats." The Exercise Promotion Unit uses Generative AI to suggest appropriate exercises for elderly individuals. The Generative AI can suggest appropriate exercises based on the elderly individual's health condition and exercise history. Specifically, the Generative AI analyzes the elderly individual's past exercise data and health checkup results to generate an optimal exercise program for each individual. For example, for an elderly individual with knee problems, it suggests exercises that put less strain on the knees and recommends exercises to improve cardiopulmonary function. Furthermore, the Generative AI can monitor the progress of exercise in real time and provide appropriate feedback. For example, it monitors the elderly individual's heart rate and respiratory rate during exercise to ensure that they are not being subjected to excessive strain. This allows the Exercise Promotion Unit to support elderly individuals in exercising safely and maintaining their health. In addition, the Exercise Promotion Unit can accumulate elderly individuals' exercise data and use it for long-term health management. This allows the Exercise Promotion Unit to support elderly individuals in maintaining their health and establishing exercise habits.

[0033] The questioning unit asks cognitive prevention questions to elderly individuals who have been spoken to by the speaking unit. For example, the questioning unit might ask questions such as, "What month and day is it today?", "When is your birthday?", or "What are your daughter's / son's birthdays?". The questioning unit uses a generative AI to generate appropriate questions for the elderly. The generative AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. Specifically, the generative AI analyzes the elderly person's past answer data and generates questions that help maintain and improve cognitive function. For example, if an elderly person has previously failed to answer questions about dates or days of the week accurately, the same question will be asked again to train their cognitive function. The generative AI can also ask questions based on the elderly person's interests and concerns. For example, it can ask questions about hobbies or family to stimulate the elderly person's memory. In this way, the questioning unit can support the maintenance and improvement of the elderly person's cognitive function. Furthermore, the questioning unit can analyze the elderly person's answers in real time and provide appropriate feedback. In this way, the questioning unit can effectively train cognitive function through dialogue with the elderly person.

[0034] The reporting department summarizes chat conversations with parents and provides reports. The reporting department uses generative AI to summarize chat content and provides a report once a day via email or other means. Specifically, the generative AI analyzes the chat content, extracts important information, and generates a report. For example, it summarizes the elderly person's health status, daily activities, and exercise progress, and reports this to the parents. The generative AI uses natural language processing technology to analyze chat content and automatically extract important information. This allows the reporting department to quickly and accurately report the elderly person's condition to the parents. Furthermore, the reporting department can collect feedback from parents and continuously improve the report content. For example, it can adjust the report content to prioritize information that the parents are particularly interested in. The reporting department can also reliably transmit information using multiple communication methods. For example, it provides reports not only via email but also via SMS and a dedicated app. This allows the reporting department to quickly and reliably report the elderly person's condition to the parents, providing them with peace of mind.

[0035] The sensor unit can analyze the elderly person's past behavioral patterns and select the optimal sensor placement. For example, the sensor unit can identify rooms or places frequently used by the elderly person and concentrate sensors there. For example, the sensor unit can analyze the elderly person's movement patterns and place sensors along their movement routes. For example, the sensor unit can consider the elderly person's activity times and increase the sensitivity of the sensors during specific time periods. This enables effective monitoring by optimally placing sensors based on the elderly person's behavioral patterns. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly person's behavioral data into a generating AI and have the generating AI execute the optimal sensor placement.

[0036] The sensor unit can monitor the health status of elderly individuals and issue alerts if abnormalities are detected. For example, the sensor unit can monitor the heart rate and respiratory rate of elderly individuals and issue alerts if abnormalities are detected. For example, the sensor unit can detect falls by elderly individuals and immediately issue alerts. For example, the sensor unit can monitor the body temperature and blood pressure of elderly individuals and issue alerts if abnormalities are detected. This allows for a rapid response by monitoring the health status of elderly individuals and issuing alerts when abnormalities are detected. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input elderly individuals' health data into a generating AI and have the generating AI perform abnormality detection.

[0037] The sensor unit can monitor the living environment of elderly people and collect data in response to changes in the environment. For example, the sensor unit can monitor changes in room temperature and humidity and collect data if an abnormality is detected. For example, the sensor unit can monitor the brightness of lighting and sound levels and collect data in response to changes in the environment. For example, the sensor unit can monitor changes in furniture arrangement and room layout and collect data. This allows for appropriate responses by collecting data in response to changes in the living environment of elderly people. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input living environment data into a generating AI and cause the generating AI to perform data collection in response to changes in the environment.

[0038] The sensor unit can monitor the activity level of elderly individuals and notify them if their activity level declines. For example, the sensor unit can monitor the distance walked and the number of steps taken by elderly individuals and notify them if their activity level declines. For example, the sensor unit can monitor the amount of time elderly individuals spend sitting and notify them if they sit for extended periods. For example, the sensor unit can monitor the frequency of exercise by elderly individuals and notify them if their exercise level decreases. This allows for early intervention by monitoring the activity level of elderly individuals and notifying them if their activity level declines. Some or all of the above-described processes in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly individual's activity data into a generating AI and have the generating AI detect a decline in activity level and issue a notification.

[0039] The speaking unit can select the optimal way to speak by referring to the elderly person's past response history. For example, the speaking unit may speak based on topics the elderly person has liked in the past. For example, the speaking unit may reproduce a speaking style that the elderly person responded well to in the past. For example, the speaking unit may avoid topics that the elderly person has avoided in the past. This enables effective communication by selecting the optimal way to speak based on the elderly person's past response history. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's response history data into a generating AI and have the generating AI execute the optimal way to speak.

[0040] The speaking unit can adjust the frequency of conversations based on the elderly person's current health condition. For example, if the elderly person is healthy, the speaking unit will set the frequency of conversations low. For example, if the elderly person is unwell, the speaking unit will set the frequency of conversations high. For example, if the elderly person is tired, the speaking unit will set the frequency of conversations to a moderate level. This allows for appropriate support by adjusting the frequency of conversations according to the elderly person's health condition. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's health data into a generating AI and have the generating AI determine the frequency of conversations.

[0041] The speaking unit can select the optimal speaking time considering the elderly person's daily rhythm. For example, if the elderly person is a morning person, the speaking unit will speak to them in the morning. For example, if the elderly person is a night owl, the speaking unit will speak to them in the evening. For example, the speaking unit will speak to them at an appropriate time according to the elderly person's daily rhythm. This enables effective communication by speaking to the elderly person according to their daily rhythm. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI execute the optimal speaking time.

[0042] The conversational unit can select topics based on the elderly person's hobbies and interests. For example, the conversational unit might talk about the elderly person's favorite music. For example, the conversational unit might talk about news that the elderly person is interested in. For example, the conversational unit might offer topics related to the elderly person's hobbies. This allows for engaging communication by selecting topics based on the elderly person's hobbies and interests. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input data on the elderly person's hobbies and interests into a generating AI and have the generating AI generate the most suitable topics.

[0043] The exercise promotion unit can provide an optimal exercise plan by referring to the elderly person's past exercise history. For example, the exercise promotion unit provides an optimal exercise plan based on the exercise the elderly person has performed in the past. For example, the exercise promotion unit suggests effective exercises based on the elderly person's exercise history. For example, the exercise promotion unit analyzes the elderly person's exercise history and provides an appropriate exercise plan. This makes effective exercise possible by providing an optimal exercise plan based on the elderly person's past exercise history. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input the elderly person's exercise history data into a generating AI and have the generating AI execute an optimal exercise plan.

[0044] The exercise promotion unit can adjust the exercise intensity based on the elderly person's current physical condition. For example, if the elderly person is healthy, the exercise promotion unit will set the exercise intensity high. For example, if the elderly person is unwell, the exercise promotion unit will set the exercise intensity low. For example, if the elderly person is tired, the exercise promotion unit will set the exercise intensity to a moderate level. This allows for exercise that is not strenuous by adjusting the exercise intensity according to the elderly person's physical condition. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without using AI. For example, the exercise promotion unit can input the elderly person's physical condition data into a generating AI and have the generating AI execute the exercise intensity.

[0045] The exercise promotion unit can suggest the optimal exercise location considering the living environment of the elderly. For example, if the elderly exercise at home, the exercise promotion unit can suggest a suitable location such as the living room or garden. If the elderly exercise outside, the exercise promotion unit can suggest a suitable location such as a park or plaza. The exercise promotion unit suggests an appropriate exercise location according to the elderly's living environment. By suggesting an exercise location that matches the elderly's living environment, effective exercise becomes possible. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input data on the elderly's living environment into a generating AI and have the generating AI determine the optimal exercise location.

[0046] The exercise promotion unit can select the type of exercise based on the elderly person's preferences. For example, the exercise promotion unit may suggest exercises set to music that the elderly person likes. For example, the exercise promotion unit may suggest exercises that the elderly person is interested in. For example, the exercise promotion unit may select an appropriate exercise type according to the elderly person's preferences. This makes it possible to provide exercises that are interesting to the elderly person by selecting the type of exercise based on their preferences. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input the elderly person's preference data into a generating AI and have the generating AI execute the optimal exercise type.

[0047] The questioning unit can select the optimal questioning method by referring to the elderly person's past response history. For example, the questioning unit may ask questions based on question formats that the elderly person has preferred in the past. For example, the questioning unit may reproduce the content of questions that the elderly person responded well to in the past. For example, the questioning unit may ask questions while avoiding the content of questions that the elderly person has avoided in the past. In this way, by selecting the optimal questioning method based on the elderly person's past response history, effective questioning becomes possible. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit may input the elderly person's response history data into a generating AI and have the generating AI execute the optimal questioning method.

[0048] The questioning unit can adjust the difficulty level of questions based on the elderly person's current cognitive state. For example, if the elderly person has high cognitive function, the questioning unit will ask difficult questions. For example, if the elderly person has declining cognitive function, the questioning unit will ask easy questions. For example, the questioning unit will ask questions of an appropriate difficulty level according to the elderly person's cognitive state. This makes it possible to ask questions of an appropriate difficulty level by adjusting the difficulty level according to the elderly person's cognitive state. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the elderly person's cognitive state into a generating AI and have the generating AI determine the difficulty level of the questions.

[0049] The questioning unit can select the optimal questioning time considering the elderly person's daily rhythm. For example, if the elderly person is a morning person, the questioning unit will ask questions in the morning. For example, if the elderly person is a night owl, the questioning unit will ask questions in the evening. For example, the questioning unit will ask questions at an appropriate time according to the elderly person's daily rhythm. This makes it possible to ask questions effectively by tailoring the questions to the elderly person's daily rhythm. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI execute the optimal questioning time.

[0050] The questioning unit can select question themes based on the interests and concerns of elderly individuals. For example, the questioning unit may ask questions about news that the elderly person is interested in. For example, the questioning unit may ask questions related to the elderly person's hobbies. For example, the questioning unit may select appropriate question themes to match the elderly person's interests. This makes it possible to ask questions that will pique the elderly person's interest by selecting question themes based on their interests and concerns. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the elderly person's interests and concerns into a generating AI and have the generating AI execute the optimal question themes.

[0051] The reporting unit can select the optimal report format by referring to the elderly person's past chat history. The reporting unit can, for example, create a report based on the report format the elderly person preferred in the past. The reporting unit can, for example, select an effective report format from the elderly person's past chat history. The reporting unit can, for example, analyze the elderly person's past chat history and select an appropriate report format. This ensures that an effective report is created by selecting the optimal report format based on the elderly person's past chat history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's chat history data into a generating AI and have the generating AI execute the optimal report format.

[0052] The reporting unit can adjust the level of detail in the report based on the elderly person's current health status. For example, if the elderly person is healthy, the reporting unit will set the level of detail to low. For example, if the elderly person is unwell, the reporting unit will set the level of detail to high. For example, if the elderly person is tired, the reporting unit will set the level of detail to medium. In this way, by adjusting the level of detail in the report according to the elderly person's health status, a report with an appropriate level of detail is created. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's health data into a generating AI and have the generating AI determine the level of detail in the report.

[0053] The reporting unit can select the optimal report transmission time considering the elderly person's lifestyle rhythm. For example, if the elderly person is a morning person, the reporting unit will send the report in the morning. For example, if the elderly person is a night owl, the reporting unit will send the report in the evening. For example, the reporting unit will send the report at an appropriate time according to the elderly person's lifestyle rhythm. This ensures that effective reports are created by sending reports according to the elderly person's lifestyle rhythm. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's lifestyle rhythm data into a generating AI and have the generating AI execute the optimal report transmission time.

[0054] The reporting unit can customize the content of reports based on the needs of the elderly person's family. For example, the reporting unit creates reports based on the information requested by the elderly person's family. For example, the reporting unit customizes the content of reports to suit the needs of the elderly person's family. For example, the reporting unit creates reports that include information of interest to the elderly person's family. By customizing the content of reports based on the needs of the elderly person's family, more appropriate reports are created. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input data on the elderly person's family's needs into a generating AI and have the generating AI execute the report content.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The sensor unit can monitor the elderly person's daily rhythm and adjust its sensitivity according to changes in the rhythm. For example, if the elderly person wakes up earlier than their usual waking time, the sensor sensitivity can be set higher to detect the abnormality early. Conversely, if they stay up later than their usual bedtime, the sensor sensitivity can be set lower to avoid an overreaction. This allows for adjustment of the sensor sensitivity according to the elderly person's daily rhythm, resulting in more appropriate monitoring. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the sensitivity adjustment.

[0057] The exercise promotion unit can provide an optimal exercise plan by referring to the elderly person's past exercise history. For example, it can provide an optimal exercise plan based on the exercise the elderly person has performed in the past. It can suggest effective exercises based on the elderly person's exercise history. It analyzes the elderly person's exercise history and provides an appropriate exercise plan. This makes effective exercise possible by providing an optimal exercise plan based on the elderly person's past exercise history. Some or all of the above processing in the exercise promotion unit may be performed using AI or not. For example, the exercise promotion unit can input the elderly person's exercise history data into a generating AI and have the generating AI execute an optimal exercise plan.

[0058] The sensor unit can monitor the living environment of elderly people and collect data in response to changes in the environment. For example, it can monitor changes in room temperature and humidity and collect data if an abnormality is detected. It can also monitor the brightness of lighting and sound levels and collect data in response to changes in the environment. It can monitor changes in furniture arrangement and room layout and collect data. This allows for appropriate responses by collecting data in response to changes in the living environment of elderly people. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data on the living environment into a generating AI and have the generating AI perform data collection in response to changes in the environment.

[0059] The speaking unit can select the optimal way to speak to an elderly person by referring to their past response history. For example, it may initiate a conversation based on topics the elderly person has enjoyed in the past. It may reproduce speaking styles that the elderly person responded well to in the past. It may avoid topics the elderly person has avoided in the past. By selecting the optimal way to speak based on the elderly person's past response history, effective communication becomes possible. Some or all of the above processing in the speaking unit may be performed using AI or not. For example, the speaking unit can input the elderly person's response history data into a generating AI and have the generating AI execute the optimal way to speak.

[0060] The exercise promotion unit can adjust the exercise intensity based on the elderly person's current physical condition. For example, if the elderly person is healthy, the exercise intensity is set high. If the elderly person is unwell, the exercise intensity is set low. If the elderly person is tired, the exercise intensity is set to moderate. This allows for exercise that is not strenuous by adjusting the exercise intensity according to the elderly person's physical condition. Some or all of the above processing in the exercise promotion unit may be performed using AI or not. For example, the exercise promotion unit can input the elderly person's physical condition data into a generating AI and have the generating AI execute the exercise intensity.

[0061] The questioning unit can adjust the difficulty level of questions based on the elderly person's current cognitive state. For example, if the elderly person has high cognitive function, difficult questions will be asked. If the elderly person has declining cognitive function, easy questions will be asked. The system will ask questions of appropriate difficulty according to the elderly person's cognitive state. This allows for questions of appropriate difficulty to be asked according to the elderly person's cognitive state. Some or all of the above processing in the questioning unit may be performed using AI or not. For example, the questioning unit can input data on the elderly person's cognitive state into a generating AI and have the generating AI determine the difficulty level of the questions.

[0062] The reporting unit can customize the content of reports based on the needs of the elderly person's family. For example, it can create reports based on the information the elderly person's family requests. It can customize the content of reports to suit the elderly person's family's needs. It can create reports that include information that the elderly person's family is interested in. By customizing the content of reports based on the elderly person's family's needs, more appropriate reports can be created. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input data on the elderly person's family's needs into a generating AI and have the generating AI execute the report content.

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

[0064] Step 1: The sensor unit detects the presence of an elderly person. The sensor unit can detect the movement of an elderly person using infrared sensors or ultrasonic sensors. It can also detect the posture and movements of an elderly person using a camera. For example, an infrared sensor detects the movement of an elderly person and confirms their presence. An ultrasonic sensor uses the reflection of sound waves to pinpoint the location of an elderly person. A camera uses image analysis technology to detect the posture and movements of an elderly person. Step 2: The speaking unit speaks to the elderly person based on the information detected by the sensor unit. For example, it may ask questions such as, "How are you feeling today?", "Is there any information you would like to know?", or "Is there any music you would like to listen to?". The speaking unit uses a generative AI to generate appropriate questions for the elderly person. The generative AI can generate appropriate questions based on the elderly person's past response history. Step 3: The exercise promotion unit encourages exercise in elderly individuals who have been spoken to by the conversation unit. For example, it gives instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Try doing some light squats." The exercise promotion unit uses generative AI to suggest appropriate exercises for elderly individuals. The generative AI can suggest appropriate exercises based on the elderly individual's health condition and exercise history. Step 4: The questioning unit asks cognitive prevention questions to the elderly person who has been spoken to by the speaking unit. For example, it may ask questions such as, "What month and day is it today?", "When is your birthday?", or "When is your daughter's / son's birthday?". The questioning unit uses a generation AI to generate appropriate questions for the elderly person. The generation AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. Step 5: The reporting department summarizes the chat interactions with parents and provides a report. The reporting department uses a generation AI to summarize the chat content and provides a report once a day via email or other means. The generation AI analyzes the chat content, extracts important information, and generates the report.

[0065] (Example of form 2) The proactive AI chat speaker system according to an embodiment of the present invention is a system that supports the monitoring and health management of the elderly. Unlike conventional AI speakers that provide information and functions in response to user inquiries, this system supports the monitoring and health management of the elderly by initiating conversations. Specifically, when a sensor detects the presence of an elderly person, the AI ​​chat speaker proactively initiates a conversation. For example, it may ask questions such as, "How are you feeling today?", "Is there any information you would like to know?", or "Is there any music you would like to listen to?". This allows the elderly person to obtain the necessary information without having to ask for it themselves. Next, the AI ​​chat speaker encourages the elderly person to do simple exercises. For example, it may give instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Let's do some light squats." This promotes physical activity in the elderly person and contributes to maintaining their health. Furthermore, the AI ​​chat speaker asks questions for cognitive prevention. For example, it may ask questions such as, "What month and day is it today?", "When is your birthday?", or "What are your daughter's / son's birthdays?". This stimulates the cognitive function of the elderly person and helps prevent dementia. The AI ​​chat speaker analyzes and judges the user's situation and initiates conversations at a frequency that is comfortable for the user. Furthermore, the system summarizes chat interactions with parents and provides a report via email or other means once a day. This provides peace of mind to users in the caregiving generation who want to keep an eye on their parents who live far away. Users register basic information about their parents and pre-define how they should interact with them. The generating AI creates an interaction plan based on this information, and after the system is put into operation, it provides a status report once a day as a "Parent Report." In this way, the proactive AI chat speaker system can support monitoring and health management of the elderly.

[0066] The proactive AI chat speaker system according to this embodiment comprises a sensor unit, a speaking unit, an exercise promotion unit, a questioning unit, and a reporting unit. The sensor unit detects the presence of an elderly person. The sensor unit can detect the elderly person's movements using, for example, an infrared sensor or an ultrasonic sensor. The sensor unit can also detect the elderly person's posture and movements using a camera. For example, the sensor unit uses an infrared sensor to detect the elderly person's movements and confirm their presence. An ultrasonic sensor uses the reflection of sound waves to pinpoint the elderly person's location. A camera uses image analysis technology to detect the elderly person's posture and movements. The speaking unit speaks to the elderly person based on the information detected by the sensor unit. The speaking unit asks questions such as, for example, "How are you feeling today?", "Is there any information you'd like to know?", or "Is there any music you'd like to listen to?". The speaking unit generates appropriate questions for the elderly person using generative AI. For example, the generative AI can generate appropriate questions based on the elderly person's past response history. The exercise promotion unit encourages the elderly person who has been spoken to by the speaking unit to exercise. The exercise promotion unit gives instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Try doing some light squats." The exercise promotion unit uses generative AI to suggest appropriate exercises to the elderly. For example, the generative AI can suggest appropriate exercises based on the elderly person's health condition and exercise history. The questioning unit asks cognitive prevention questions to the elderly person who is spoken to by the speaking unit. For example, the questioning unit asks questions such as, "What month and day is it today?", "When is your birthday?", or "What is your daughter's / son's birthday?". The questioning unit uses generative AI to generate appropriate questions for the elderly person. For example, the generative AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. The reporting unit summarizes the chat exchange with the parent and provides a report. The reporting unit uses generative AI to summarize the chat content and provides a report once a day via email or other means. For example, the generative AI analyzes the chat content, extracts important information, and generates a report. In this way, the proactive AI chat speaker system according to this embodiment can support monitoring and health management of the elderly.

[0067] The sensor unit detects the presence of elderly individuals. For example, the sensor unit can detect the movements of elderly individuals using infrared sensors or ultrasonic sensors. Specifically, infrared sensors emit infrared light and detect the reflection to sense the movement of elderly individuals. This allows for real-time monitoring of the elderly person's movement and activities within a room. Ultrasonic sensors emit sound waves and receive the reflection to pinpoint the elderly person's location. This allows for accurate location tracking without being affected by furniture or obstacles. Furthermore, cameras can be used to detect the elderly person's posture and movements. Cameras use image analysis technology to analyze the elderly person's posture and movements in real time, detecting falls and abnormal movements. For example, a camera can analyze an elderly person's movements, such as standing up from a chair or walking, to determine if the movements are normal. This allows the sensor unit to ensure the safety of elderly individuals and respond quickly if an abnormality occurs. The sensor unit can also transmit this data to the cloud, allowing for centralized management of the elderly person's condition in collaboration with other departments. This enables the sensor unit to accurately detect the presence of elderly individuals and improve the overall reliability and safety of the system.

[0068] The speaking unit speaks to the elderly based on information detected by the sensor unit. The speaking unit asks questions such as, "How are you feeling today?", "Is there any information you'd like to know?", and "Is there any music you'd like to listen to?". The speaking unit uses generative AI to generate appropriate questions for the elderly. The generative AI can generate appropriate questions based on the elderly person's past response history. Specifically, the generative AI analyzes the elderly person's past conversation data and generates questions based on each elderly person's interests and concerns. For example, for an elderly person who has shown interest in music in the past, it might ask, "What kind of music would you like to listen to today?" The generative AI can also ask health-related questions based on the elderly person's health condition and daily activities. For example, it might ask questions such as, "How have you been feeling lately?", and "Are you eating properly?" to check the elderly person's health condition. This allows the speaking unit to facilitate communication with the elderly and reduce feelings of loneliness. Furthermore, the speaking unit can analyze the elderly person's responses in real time and provide appropriate feedback. This allows the speaking unit to support daily monitoring and health management through dialogue with the elderly person.

[0069] The Exercise Promotion Unit encourages exercise in elderly individuals who have been spoken to by the Communication Unit. The Exercise Promotion Unit provides instructions such as, "Try standing up from your chair," "Try walking down the corridor," or "Try some light squats." The Exercise Promotion Unit uses Generative AI to suggest appropriate exercises for elderly individuals. The Generative AI can suggest appropriate exercises based on the elderly individual's health condition and exercise history. Specifically, the Generative AI analyzes the elderly individual's past exercise data and health checkup results to generate an optimal exercise program for each individual. For example, for an elderly individual with knee problems, it suggests exercises that put less strain on the knees and recommends exercises to improve cardiopulmonary function. Furthermore, the Generative AI can monitor the progress of exercise in real time and provide appropriate feedback. For example, it monitors the elderly individual's heart rate and respiratory rate during exercise to ensure that they are not being subjected to excessive strain. This allows the Exercise Promotion Unit to support elderly individuals in exercising safely and maintaining their health. In addition, the Exercise Promotion Unit can accumulate elderly individuals' exercise data and use it for long-term health management. This allows the Exercise Promotion Unit to support elderly individuals in maintaining their health and establishing exercise habits.

[0070] The questioning unit asks cognitive prevention questions to elderly individuals who have been spoken to by the speaking unit. For example, the questioning unit might ask questions such as, "What month and day is it today?", "When is your birthday?", or "What are your daughter's / son's birthdays?". The questioning unit uses a generative AI to generate appropriate questions for the elderly. The generative AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. Specifically, the generative AI analyzes the elderly person's past answer data and generates questions that help maintain and improve cognitive function. For example, if an elderly person has previously failed to answer questions about dates or days of the week accurately, the same question will be asked again to train their cognitive function. The generative AI can also ask questions based on the elderly person's interests and concerns. For example, it can ask questions about hobbies or family to stimulate the elderly person's memory. In this way, the questioning unit can support the maintenance and improvement of the elderly person's cognitive function. Furthermore, the questioning unit can analyze the elderly person's answers in real time and provide appropriate feedback. In this way, the questioning unit can effectively train cognitive function through dialogue with the elderly person.

[0071] The reporting department summarizes chat conversations with parents and provides reports. The reporting department uses generative AI to summarize chat content and provides a report once a day via email or other means. Specifically, the generative AI analyzes the chat content, extracts important information, and generates a report. For example, it summarizes the elderly person's health status, daily activities, and exercise progress, and reports this to the parents. The generative AI uses natural language processing technology to analyze chat content and automatically extract important information. This allows the reporting department to quickly and accurately report the elderly person's condition to the parents. Furthermore, the reporting department can collect feedback from parents and continuously improve the report content. For example, it can adjust the report content to prioritize information that the parents are particularly interested in. The reporting department can also reliably transmit information using multiple communication methods. For example, it provides reports not only via email but also via SMS and a dedicated app. This allows the reporting department to quickly and reliably report the elderly person's condition to the parents, providing them with peace of mind.

[0072] The sensor unit can estimate the emotions of elderly individuals and adjust the sensor's sensitivity based on the estimated emotions. For example, if an elderly person is stressed, the sensor unit can set the sensor's sensitivity low to avoid excessive reactions. For example, if an elderly person is relaxed, the sensor unit can set the sensor's sensitivity high to detect even subtle movements. For example, if an elderly person is anxious, the sensor unit can set the sensor's sensitivity to a moderate level to maintain an appropriate response. In this way, by adjusting the sensor's sensitivity according to the elderly person's emotions, a more appropriate response can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly person's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0073] The sensor unit can analyze the elderly person's past behavioral patterns and select the optimal sensor placement. For example, the sensor unit can identify rooms or places frequently used by the elderly person and concentrate sensors there. For example, the sensor unit can analyze the elderly person's movement patterns and place sensors along their movement routes. For example, the sensor unit can consider the elderly person's activity times and increase the sensitivity of the sensors during specific time periods. This enables effective monitoring by optimally placing sensors based on the elderly person's behavioral patterns. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly person's behavioral data into a generating AI and have the generating AI execute the optimal sensor placement.

[0074] The sensor unit can monitor the health status of elderly individuals and issue alerts if abnormalities are detected. For example, the sensor unit can monitor the heart rate and respiratory rate of elderly individuals and issue alerts if abnormalities are detected. For example, the sensor unit can detect falls by elderly individuals and immediately issue alerts. For example, the sensor unit can monitor the body temperature and blood pressure of elderly individuals and issue alerts if abnormalities are detected. This allows for a rapid response by monitoring the health status of elderly individuals and issuing alerts when abnormalities are detected. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input elderly individuals' health data into a generating AI and have the generating AI perform abnormality detection.

[0075] The sensor unit can estimate the emotions of elderly individuals and adjust the frequency of data collection based on the estimated emotions. For example, if an elderly person is relaxed, the sensor unit can set a low data collection frequency to reduce the burden. For example, if an elderly person is active, the sensor unit can set a high data collection frequency to collect detailed data. For example, if an elderly person is feeling anxious, the sensor unit can set a medium data collection frequency to collect appropriate data. In this way, by adjusting the data collection frequency according to the emotions of elderly individuals, detailed data can be collected while reducing the burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly person's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The sensor unit can monitor the living environment of elderly people and collect data in response to changes in the environment. For example, the sensor unit can monitor changes in room temperature and humidity and collect data if an abnormality is detected. For example, the sensor unit can monitor the brightness of lighting and sound levels and collect data in response to changes in the environment. For example, the sensor unit can monitor changes in furniture arrangement and room layout and collect data. This allows for appropriate responses by collecting data in response to changes in the living environment of elderly people. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input living environment data into a generating AI and cause the generating AI to perform data collection in response to changes in the environment.

[0077] The sensor unit can monitor the activity level of elderly individuals and notify them if their activity level declines. For example, the sensor unit can monitor the distance walked and the number of steps taken by elderly individuals and notify them if their activity level declines. For example, the sensor unit can monitor the amount of time elderly individuals spend sitting and notify them if they sit for extended periods. For example, the sensor unit can monitor the frequency of exercise by elderly individuals and notify them if their exercise level decreases. This allows for early intervention by monitoring the activity level of elderly individuals and notifying them if their activity level declines. Some or all of the above-described processes in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the elderly individual's activity data into a generating AI and have the generating AI detect a decline in activity level and issue a notification.

[0078] The speaking unit can estimate the emotions of elderly individuals and adjust the content of its conversation based on those estimated emotions. For example, if an elderly person is relaxed, the speaking unit will speak in a calm tone. If an elderly person is stressed, the speaking unit will offer words of encouragement. If an elderly person is agitated, the speaking unit will provide topics to calm them down. By adjusting the content of the conversation according to the elderly person's emotions, more appropriate communication becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the speaking unit may be performed using AI, or not using AI. For example, the speaking unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0079] The speaking unit can select the optimal way to speak by referring to the elderly person's past response history. For example, the speaking unit may speak based on topics the elderly person has liked in the past. For example, the speaking unit may reproduce a speaking style that the elderly person responded well to in the past. For example, the speaking unit may avoid topics that the elderly person has avoided in the past. This enables effective communication by selecting the optimal way to speak based on the elderly person's past response history. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's response history data into a generating AI and have the generating AI execute the optimal way to speak.

[0080] The speaking unit can adjust the frequency of conversations based on the elderly person's current health condition. For example, if the elderly person is healthy, the speaking unit will set the frequency of conversations low. For example, if the elderly person is unwell, the speaking unit will set the frequency of conversations high. For example, if the elderly person is tired, the speaking unit will set the frequency of conversations to a moderate level. This allows for appropriate support by adjusting the frequency of conversations according to the elderly person's health condition. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's health data into a generating AI and have the generating AI determine the frequency of conversations.

[0081] The speaking unit can estimate the emotions of elderly people and adjust the timing of its communication based on the estimated emotions. For example, if the elderly person is relaxed, the speaking unit will speak at a calm time. For example, if the elderly person is stressed, the speaking unit will offer words of encouragement at an appropriate time. For example, if the elderly person is agitated, the speaking unit will speak at a time to calm them down. By adjusting the timing of communication according to the elderly person's emotions, more effective communication becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the speaking unit may be performed using AI, for example, or not using AI. For example, the speaking unit can input the elderly person's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The speaking unit can select the optimal speaking time considering the elderly person's daily rhythm. For example, if the elderly person is a morning person, the speaking unit will speak to them in the morning. For example, if the elderly person is a night owl, the speaking unit will speak to them in the evening. For example, the speaking unit will speak to them at an appropriate time according to the elderly person's daily rhythm. This enables effective communication by speaking to the elderly person according to their daily rhythm. Some or all of the above processing in the speaking unit may be performed using AI, for example, or without AI. For example, the speaking unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI execute the optimal speaking time.

[0083] The conversational unit can select topics based on the elderly person's hobbies and interests. For example, the conversational unit might talk about the elderly person's favorite music. For example, the conversational unit might talk about news that the elderly person is interested in. For example, the conversational unit might offer topics related to the elderly person's hobbies. This allows for engaging communication by selecting topics based on the elderly person's hobbies and interests. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input data on the elderly person's hobbies and interests into a generating AI and have the generating AI generate the most suitable topics.

[0084] The exercise promotion unit can estimate the emotions of elderly individuals and adjust the type of exercise based on the estimated emotions. For example, if an elderly person is relaxed, the exercise promotion unit may suggest light stretching. If an elderly person is stressed, the exercise promotion unit may suggest exercises with a relaxing effect. If an elderly person is agitated, the exercise promotion unit may suggest exercises to calm them down. By adjusting the type of exercise according to the elderly person's emotions, the unit can suggest more appropriate exercises. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0085] The exercise promotion unit can provide an optimal exercise plan by referring to the elderly person's past exercise history. For example, the exercise promotion unit provides an optimal exercise plan based on the exercise the elderly person has performed in the past. For example, the exercise promotion unit suggests effective exercises based on the elderly person's exercise history. For example, the exercise promotion unit analyzes the elderly person's exercise history and provides an appropriate exercise plan. This makes effective exercise possible by providing an optimal exercise plan based on the elderly person's past exercise history. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input the elderly person's exercise history data into a generating AI and have the generating AI execute an optimal exercise plan.

[0086] The exercise promotion unit can adjust the exercise intensity based on the elderly person's current physical condition. For example, if the elderly person is healthy, the exercise promotion unit will set the exercise intensity high. For example, if the elderly person is unwell, the exercise promotion unit will set the exercise intensity low. For example, if the elderly person is tired, the exercise promotion unit will set the exercise intensity to a moderate level. This allows for exercise that is not strenuous by adjusting the exercise intensity according to the elderly person's physical condition. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without using AI. For example, the exercise promotion unit can input the elderly person's physical condition data into a generating AI and have the generating AI execute the exercise intensity.

[0087] The exercise promotion unit can estimate the emotions of elderly individuals and adjust the timing of exercises based on the estimated emotions. For example, if an elderly individual is relaxed, the exercise promotion unit will suggest exercises at a calming time. For example, if an elderly individual is stressed, the exercise promotion unit will suggest relaxing exercises at an appropriate time. For example, if an elderly individual is agitated, the exercise promotion unit will suggest exercises at a time to calm them down. By adjusting the timing of exercises according to the emotions of elderly individuals, more effective exercise becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0088] The exercise promotion unit can suggest the optimal exercise location considering the living environment of the elderly. For example, if the elderly exercise at home, the exercise promotion unit can suggest a suitable location such as the living room or garden. If the elderly exercise outside, the exercise promotion unit can suggest a suitable location such as a park or plaza. The exercise promotion unit suggests an appropriate exercise location according to the elderly's living environment. By suggesting an exercise location that matches the elderly's living environment, effective exercise becomes possible. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input data on the elderly's living environment into a generating AI and have the generating AI determine the optimal exercise location.

[0089] The exercise promotion unit can select the type of exercise based on the elderly person's preferences. For example, the exercise promotion unit may suggest exercises set to music that the elderly person likes. For example, the exercise promotion unit may suggest exercises that the elderly person is interested in. For example, the exercise promotion unit may select an appropriate exercise type according to the elderly person's preferences. This makes it possible to provide exercises that are interesting to the elderly person by selecting the type of exercise based on their preferences. Some or all of the above processing in the exercise promotion unit may be performed using AI, for example, or without AI. For example, the exercise promotion unit can input the elderly person's preference data into a generating AI and have the generating AI execute the optimal exercise type.

[0090] The questioning unit can estimate the emotions of elderly individuals and adjust the content of the questions based on the estimated emotions. For example, if an elderly person is relaxed, the questioning unit will ask gentle questions. If an elderly person is stressed, the questioning unit will ask encouraging questions. If an elderly person is agitated, the questioning unit will ask calming questions. By adjusting the content of the questions according to the emotions of the elderly person, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0091] The questioning unit can select the optimal questioning method by referring to the elderly person's past response history. For example, the questioning unit may ask questions based on question formats that the elderly person has preferred in the past. For example, the questioning unit may reproduce the content of questions that the elderly person responded well to in the past. For example, the questioning unit may ask questions while avoiding the content of questions that the elderly person has avoided in the past. In this way, by selecting the optimal questioning method based on the elderly person's past response history, effective questioning becomes possible. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit may input the elderly person's response history data into a generating AI and have the generating AI execute the optimal questioning method.

[0092] The questioning unit can adjust the difficulty level of questions based on the elderly person's current cognitive state. For example, if the elderly person has high cognitive function, the questioning unit will ask difficult questions. For example, if the elderly person has declining cognitive function, the questioning unit will ask easy questions. For example, the questioning unit will ask questions of an appropriate difficulty level according to the elderly person's cognitive state. This makes it possible to ask questions of an appropriate difficulty level by adjusting the difficulty level according to the elderly person's cognitive state. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the elderly person's cognitive state into a generating AI and have the generating AI determine the difficulty level of the questions.

[0093] The questioning unit can estimate the emotions of elderly individuals and adjust the timing of questions based on the estimated emotions. For example, if an elderly person is relaxed, the questioning unit will ask questions at a calm timing. If an elderly person is stressed, the questioning unit will ask encouraging questions at an appropriate time. If an elderly person is agitated, the questioning unit will ask questions at a time to calm them down. By adjusting the timing of questions according to the emotions of the elderly person, more effective questioning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using AI, for example, or not using AI. For example, the questioning unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0094] The questioning unit can select the optimal questioning time considering the elderly person's daily rhythm. For example, if the elderly person is a morning person, the questioning unit will ask questions in the morning. For example, if the elderly person is a night owl, the questioning unit will ask questions in the evening. For example, the questioning unit will ask questions at an appropriate time according to the elderly person's daily rhythm. This makes it possible to ask questions effectively by tailoring the questions to the elderly person's daily rhythm. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI execute the optimal questioning time.

[0095] The questioning unit can select question themes based on the interests and concerns of elderly individuals. For example, the questioning unit may ask questions about news that the elderly person is interested in. For example, the questioning unit may ask questions related to the elderly person's hobbies. For example, the questioning unit may select appropriate question themes to match the elderly person's interests. This makes it possible to ask questions that will pique the elderly person's interest by selecting question themes based on their interests and concerns. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the elderly person's interests and concerns into a generating AI and have the generating AI execute the optimal question themes.

[0096] The reporting unit can estimate the emotions of elderly individuals and adjust the content of the report based on the estimated emotions. For example, if an elderly individual is relaxed, the reporting unit will create a report in a calm tone. For example, if an elderly individual is stressed, the reporting unit will create a report that includes words of encouragement. For example, if an elderly individual is agitated, the reporting unit will create a report that includes calming content. By adjusting the content of the report according to the emotions of the elderly individual, a more appropriate report can be created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0097] The reporting unit can select the optimal report format by referring to the elderly person's past chat history. The reporting unit can, for example, create a report based on the report format the elderly person preferred in the past. The reporting unit can, for example, select an effective report format from the elderly person's past chat history. The reporting unit can, for example, analyze the elderly person's past chat history and select an appropriate report format. This ensures that an effective report is created by selecting the optimal report format based on the elderly person's past chat history. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's chat history data into a generating AI and have the generating AI execute the optimal report format.

[0098] The reporting unit can adjust the level of detail in the report based on the elderly person's current health status. For example, if the elderly person is healthy, the reporting unit will set the level of detail to low. For example, if the elderly person is unwell, the reporting unit will set the level of detail to high. For example, if the elderly person is tired, the reporting unit will set the level of detail to medium. In this way, by adjusting the level of detail in the report according to the elderly person's health status, a report with an appropriate level of detail is created. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's health data into a generating AI and have the generating AI determine the level of detail in the report.

[0099] The reporting unit can estimate the emotions of elderly individuals and adjust the timing of report transmission based on the estimated emotions. For example, if an elderly individual is relaxed, the reporting unit will send a report at a calm time. For example, if an elderly individual is stressed, the reporting unit will send a report containing words of encouragement at an appropriate time. For example, if an elderly individual is agitated, the reporting unit will send a report at a time to calm them down. By adjusting the timing of report transmission according to the emotions of the elderly individual, reports are sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0100] The reporting unit can select the optimal report transmission time considering the elderly person's lifestyle rhythm. For example, if the elderly person is a morning person, the reporting unit will send the report in the morning. For example, if the elderly person is a night owl, the reporting unit will send the report in the evening. For example, the reporting unit will send the report at an appropriate time according to the elderly person's lifestyle rhythm. This ensures that effective reports are created by sending reports according to the elderly person's lifestyle rhythm. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the elderly person's lifestyle rhythm data into a generating AI and have the generating AI execute the optimal report transmission time.

[0101] The reporting unit can customize the content of reports based on the needs of the elderly person's family. For example, the reporting unit creates reports based on the information requested by the elderly person's family. For example, the reporting unit customizes the content of reports to suit the needs of the elderly person's family. For example, the reporting unit creates reports that include information of interest to the elderly person's family. By customizing the content of reports based on the needs of the elderly person's family, more appropriate reports are created. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input data on the elderly person's family's needs into a generating AI and have the generating AI execute the report content.

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

[0103] The sensor unit can monitor the elderly person's daily rhythm and adjust its sensitivity according to changes in the rhythm. For example, if the elderly person wakes up earlier than their usual waking time, the sensor sensitivity can be set higher to detect the abnormality early. Conversely, if they stay up later than their usual bedtime, the sensor sensitivity can be set lower to avoid an overreaction. This allows for adjustment of the sensor sensitivity according to the elderly person's daily rhythm, resulting in more appropriate monitoring. Some or all of the above processing in the sensor unit may be performed using AI, or it may be performed without AI. For example, the sensor unit can input the elderly person's daily rhythm data into a generating AI and have the generating AI perform the sensitivity adjustment.

[0104] The speaking unit can estimate the emotions of elderly individuals and adjust the content of the conversation based on the estimated emotions. For example, if an elderly person is relaxed, it will speak in a calm tone. If an elderly person is stressed, it will offer words of encouragement. If an elderly person is agitated, it will provide topics to calm them down. This allows for more appropriate communication by adjusting the content of the conversation according to the elderly person's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the speaking unit may be performed using AI or not. For example, the speaking unit can input the elderly person's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The exercise promotion unit can provide an optimal exercise plan by referring to the elderly person's past exercise history. For example, it can provide an optimal exercise plan based on the exercise the elderly person has performed in the past. It can suggest effective exercises based on the elderly person's exercise history. It analyzes the elderly person's exercise history and provides an appropriate exercise plan. This makes effective exercise possible by providing an optimal exercise plan based on the elderly person's past exercise history. Some or all of the above processing in the exercise promotion unit may be performed using AI or not. For example, the exercise promotion unit can input the elderly person's exercise history data into a generating AI and have the generating AI execute an optimal exercise plan.

[0106] The questioning unit can estimate the emotions of elderly individuals and adjust the content of the questions based on the estimated emotions. For example, if an elderly person is relaxed, gentle questions will be asked. If an elderly person is stressed, encouraging questions will be asked. If an elderly person is agitated, calming questions will be asked. By adjusting the content of the questions according to the emotions of the elderly person, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the questioning unit may be performed using AI or not. For example, the questioning unit can input the elderly person's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The reporting unit can estimate the emotions of elderly individuals and adjust the report content based on the estimated emotions. For example, if an elderly person is relaxed, the report will be written in a calm tone. If an elderly person is stressed, the report will include words of encouragement. If an elderly person is agitated, the report will include calming content. By adjusting the report content according to the elderly person's emotions, a more appropriate report can be produced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input facial expression data of elderly individuals into a generative AI and have the generative AI perform emotion estimation.

[0108] The sensor unit can monitor the living environment of elderly people and collect data in response to changes in the environment. For example, it can monitor changes in room temperature and humidity and collect data if an abnormality is detected. It can also monitor the brightness of lighting and sound levels and collect data in response to changes in the environment. It can monitor changes in furniture arrangement and room layout and collect data. This allows for appropriate responses by collecting data in response to changes in the living environment of elderly people. Some or all of the above processing in the sensor unit may be performed using AI or not. For example, the sensor unit can input data on the living environment into a generating AI and have the generating AI perform data collection in response to changes in the environment.

[0109] The speaking unit can select the optimal way to speak to an elderly person by referring to their past response history. For example, it may initiate a conversation based on topics the elderly person has enjoyed in the past. It may reproduce speaking styles that the elderly person responded well to in the past. It may avoid topics the elderly person has avoided in the past. By selecting the optimal way to speak based on the elderly person's past response history, effective communication becomes possible. Some or all of the above processing in the speaking unit may be performed using AI or not. For example, the speaking unit can input the elderly person's response history data into a generating AI and have the generating AI execute the optimal way to speak.

[0110] The exercise promotion unit can adjust the exercise intensity based on the elderly person's current physical condition. For example, if the elderly person is healthy, the exercise intensity is set high. If the elderly person is unwell, the exercise intensity is set low. If the elderly person is tired, the exercise intensity is set to moderate. This allows for exercise that is not strenuous by adjusting the exercise intensity according to the elderly person's physical condition. Some or all of the above processing in the exercise promotion unit may be performed using AI or not. For example, the exercise promotion unit can input the elderly person's physical condition data into a generating AI and have the generating AI execute the exercise intensity.

[0111] The questioning unit can adjust the difficulty level of questions based on the elderly person's current cognitive state. For example, if the elderly person has high cognitive function, difficult questions will be asked. If the elderly person has declining cognitive function, easy questions will be asked. The system will ask questions of appropriate difficulty according to the elderly person's cognitive state. This allows for questions of appropriate difficulty to be asked according to the elderly person's cognitive state. Some or all of the above processing in the questioning unit may be performed using AI or not. For example, the questioning unit can input data on the elderly person's cognitive state into a generating AI and have the generating AI determine the difficulty level of the questions.

[0112] The reporting unit can customize the content of reports based on the needs of the elderly person's family. For example, it can create reports based on the information the elderly person's family requests. It can customize the content of reports to suit the elderly person's family's needs. It can create reports that include information that the elderly person's family is interested in. By customizing the content of reports based on the elderly person's family's needs, more appropriate reports can be created. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input data on the elderly person's family's needs into a generating AI and have the generating AI execute the report content.

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

[0114] Step 1: The sensor unit detects the presence of an elderly person. The sensor unit can detect the movement of an elderly person using infrared sensors or ultrasonic sensors. It can also detect the posture and movements of an elderly person using a camera. For example, an infrared sensor detects the movement of an elderly person and confirms their presence. An ultrasonic sensor uses the reflection of sound waves to pinpoint the location of an elderly person. A camera uses image analysis technology to detect the posture and movements of an elderly person. Step 2: The speaking unit speaks to the elderly person based on the information detected by the sensor unit. For example, it may ask questions such as, "How are you feeling today?", "Is there any information you would like to know?", or "Is there any music you would like to listen to?". The speaking unit uses a generative AI to generate appropriate questions for the elderly person. The generative AI can generate appropriate questions based on the elderly person's past response history. Step 3: The exercise promotion unit encourages exercise in elderly individuals who have been spoken to by the conversation unit. For example, it gives instructions such as, "Try standing up from your chair," "Try walking down the hallway," or "Try doing some light squats." The exercise promotion unit uses generative AI to suggest appropriate exercises for elderly individuals. The generative AI can suggest appropriate exercises based on the elderly individual's health condition and exercise history. Step 4: The questioning unit asks cognitive prevention questions to the elderly person who has been spoken to by the speaking unit. For example, it may ask questions such as, "What month and day is it today?", "When is your birthday?", or "When is your daughter's / son's birthday?". The questioning unit uses a generation AI to generate appropriate questions for the elderly person. The generation AI can generate appropriate questions based on the elderly person's cognitive state and past answer history. Step 5: The reporting department summarizes the chat interactions with parents and provides a report. The reporting department uses a generation AI to summarize the chat content and provides a report once a day via email or other means. The generation AI analyzes the chat content, extracts important information, and generates the report.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0118] Each of the multiple elements described above, including the sensor unit, speaking unit, exercise promotion unit, questioning unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit detects the presence of an elderly person using the camera 42 or infrared sensor of the smart device 14. The speaking unit is implemented by the control unit 46A of the smart device 14 and generates appropriate questions for the elderly person. The exercise promotion unit is implemented by the control unit 46A of the smart device 14 and encourages the elderly person to exercise. The questioning unit is implemented by the control unit 46A of the smart device 14 and asks the elderly person questions to prevent cognitive decline. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a report summarizing the chat exchange with the parent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the sensor unit, speaking unit, exercise promotion unit, questioning unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit detects the presence of an elderly person using the camera 42 or infrared sensor of the smart glasses 214. The speaking unit is implemented by the control unit 46A of the smart glasses 214 and generates appropriate questions for the elderly person. The exercise promotion unit is implemented by the control unit 46A of the smart glasses 214 and encourages exercise for the elderly person. The questioning unit is implemented by the control unit 46A of the smart glasses 214 and asks questions to prevent cognitive decline for the elderly person. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a report summarizing the chat exchange with the parent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

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

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

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the sensor unit, speaking unit, exercise promotion unit, questioning unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit detects the presence of an elderly person using the camera 42 or infrared sensor of the headset terminal 314. The speaking unit is implemented by the control unit 46A of the headset terminal 314 and generates appropriate questions for the elderly person. The exercise promotion unit is implemented by the control unit 46A of the headset terminal 314 and encourages the elderly person to exercise. The questioning unit is implemented by the control unit 46A of the headset terminal 314 and asks the elderly person questions to prevent cognitive decline. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a report summarizing the chat exchange with the parent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the sensor unit, speaking unit, exercise promotion unit, questioning unit, and reporting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the sensor unit detects the presence of an elderly person using the camera 42 or infrared sensor of the robot 414. The speaking unit is implemented by the control unit 46A of the robot 414 and generates appropriate questions for the elderly person. The exercise promotion unit is implemented by the control unit 46A of the robot 414 and encourages the elderly person to exercise. The questioning unit is implemented by the control unit 46A of the robot 414 and asks the elderly person questions to prevent cognitive decline. The reporting unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a report summarizing the chat exchange with the parent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0186] (Note 1) A sensor unit that detects the presence of elderly people, A speaking unit that speaks to the elderly based on the information detected by the aforementioned sensor unit, The exercise promotion unit encourages exercise in elderly people who are spoken to by the aforementioned speaking unit, The aforementioned speaking unit has a questioning unit that asks cognitive prevention questions to the elderly person who has been spoken to by the speaking unit, It includes a reporting department that summarizes chat interactions with parents and provides reports. A system characterized by the following features. (Note 2) The aforementioned sensor unit is The system estimates the emotions of elderly individuals and adjusts the sensor sensitivity based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sensor unit is Analyze the past behavioral patterns of elderly individuals to select the optimal sensor placement. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sensor unit is It monitors the health status of elderly people and issues alerts if abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned sensor unit is The system estimates the emotions of elderly individuals and adjusts the frequency of sensor data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sensor unit is Monitor the living environment of elderly people and collect data in response to changes in the environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is Monitor the activity levels of elderly individuals and notify them if their activity levels decline. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned speaking unit is, The system estimates the emotions of elderly people and adjusts the content of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned speaking unit is, The system selects the most appropriate way to communicate with elderly individuals by referring to their past response history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned speaking unit is, Adjust the frequency of conversations based on the elderly person's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned speaking unit is, The system estimates the emotions of elderly people and adjusts the timing of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned speaking unit is, We select the optimal time to talk to elderly people, taking into account their daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned speaking unit is, Select topics based on the hobbies and interests of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned exercise promotion unit is The system estimates the emotions of older adults and adjusts the type of exercise based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned exercise promotion unit is We provide an optimal exercise plan by referring to the past exercise history of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned exercise promotion unit is Adjust the intensity of exercise based on the elderly person's current physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned exercise promotion unit is The system estimates the emotions of elderly individuals and adjusts the timing of exercise based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned exercise promotion unit is We propose the most suitable exercise location considering the living environment of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned exercise promotion unit is Select the type of exercise based on the preferences of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question section is, The system estimates the emotions of older adults and adjusts the content of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question section is, Select the most suitable questioning method by referring to the past response history of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question section is, The difficulty level of the questions is adjusted based on the current cognitive state of the elderly participant. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question section is, The system estimates the emotions of elderly individuals and adjusts the timing of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned question section is, We select the optimal question time considering the daily routines of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question section is, Select question topics based on the interests and concerns of elderly people. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report section is, The system estimates the emotions of elderly individuals and adjusts the report content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report section is, Select the most suitable report format by referring to the elderly person's past chat history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report section is, Adjust the level of detail in the report based on the current health status of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned report section is, The system estimates the emotions of elderly individuals and adjusts the timing of report submissions based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned report section is, The optimal report transmission time is selected considering the daily routines of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned report section is, Customize the report content based on the needs of the elderly person's family. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A sensor unit that detects the presence of elderly people, A speaking unit that speaks to the elderly based on the information detected by the aforementioned sensor unit, The exercise promotion unit encourages exercise in elderly people who are spoken to by the aforementioned speaking unit, The aforementioned speaking unit has a questioning unit that asks cognitive prevention questions to the elderly person who has been spoken to by the speaking unit, It includes a reporting department that summarizes chat interactions with parents and provides reports. A system characterized by the following features.

2. The aforementioned sensor unit is The system estimates the emotions of elderly individuals and adjusts the sensor sensitivity based on the estimated emotions. The system according to feature 1.

3. The aforementioned sensor unit is Analyze the past behavioral patterns of elderly individuals to select the optimal sensor placement. The system according to feature 1.

4. The aforementioned sensor unit is It monitors the health status of elderly people and issues alerts if abnormalities are detected. The system according to feature 1.

5. The aforementioned sensor unit is The system estimates the emotions of elderly individuals and adjusts the frequency of sensor data collection based on the estimated emotions. The system according to feature 1.

6. The aforementioned sensor unit is Monitor the living environment of elderly people and collect data in response to changes in the environment. The system according to feature 1.

7. The aforementioned sensor unit is Monitor the activity levels of elderly individuals and notify them if their activity levels decline. The system according to feature 1.

8. The aforementioned speaking unit is, The system estimates the emotions of elderly people and adjusts the content of conversations based on those estimated emotions. The system according to feature 1.

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