System

Abstract

An object of the system according to the embodiment is to reduce a sense of loneliness of a solitary elderly person, stimulate a cognitive function, and quickly respond to an emergency.SOLUTION: A system according to an embodiment includes an interaction unit, a cognitive function stimulator, and an emergency responder. The interaction unit interacts with the elderly person. The cognitive function stimulator stimulates the cognitive function through the interaction performed by the interaction unit. The emergency response unit responds to the emergency situation detected by the interaction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately reduced the sense of loneliness felt by elderly people living alone, maintained their cognitive functions, or provided rapid responses to emergencies, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the sense of loneliness felt by elderly people living alone, stimulate cognitive function, and respond quickly to emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a cognitive function stimulation unit, and an emergency response unit. The dialogue unit dialogues with the elderly. The cognitive function stimulation unit stimulates cognitive function through dialogue performed by the dialogue unit. The emergency response unit responds to an emergency detected by the dialogue unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the sense of loneliness of elderly people living alone, stimulate cognitive function, and respond quickly to emergency situations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The monitoring system according to the embodiment of the present invention is a system that enables elderly people living alone to communicate with AI, thereby reducing their sense of loneliness and allowing them to live their lives while maintaining their dignity.

[0029] A monitoring system according to an embodiment includes a dialogue unit, a cognitive function stimulation unit, and an emergency response unit. The dialogue unit dialogues with the elderly. For example, the generation AI understands what the elderly is saying and generates an appropriate response. The generation AI listens to what the elderly is saying, asking questions such as, "What did you do today?" and "How are you feeling lately?" The generation AI receives the elderly's story and questions as input and generates an appropriate response. The cognitive function stimulation unit stimulates cognitive function through dialogue with the dialogue unit. For example, the generation AI poses quizzes and puzzles, and activates the elderly's brain by having the elderly answer them. The generation AI also helps evoke memories by eliciting stories and memories from the elderly's past. The generation AI receives quiz and puzzle questions or the elderly's past stories as input and generates an appropriate response. The emergency response unit responds to emergencies detected by the dialogue unit. For example, if the elderly says, "Help me," the generation AI automatically contacts family members or caregivers. The AI ​​generator also constantly monitors the elderly person's health condition and automatically issues an alert if an abnormality is detected. The AI ​​generator receives the elderly person's comments about an emergency and data on their health condition as input, and responds appropriately. As a result, the monitoring system according to the embodiment reduces the elderly person's sense of loneliness, stimulates their cognitive functions, and enables a rapid response to emergencies.

[0030] The dialogue unit can learn the elderly person's past dialogue history and provide topics based on their hobbies and interests. For example, the dialogue unit's generation AI analyzes the elderly person's past dialogue history and identifies their hobbies and interests. For example, it provides topics that interest the elderly person based on themes that have been frequently discussed in the past. The generation AI receives the elderly person's past dialogue history as input and provides appropriate topics based on that. This makes it possible to have a dialogue based on the elderly person's individual hobbies and interests.

[0031] The cognitive function stimulation unit can evaluate the cognitive function of the elderly and provide an individually optimized cognitive training program. The cognitive function stimulation unit, for example, constructs a system in which a generation AI evaluates the cognitive function of the elderly and provides an individually optimized cognitive training program. For example, it provides training to improve memory and attention. The generation AI receives data evaluating the elderly's cognitive function as input and provides an appropriate training program based on that data. This makes it possible to provide optimal training according to the elderly's cognitive function.

[0032] The cognitive function stimulation unit can analyze the lifestyle habit data of the elderly and suggest lifestyle habits that have a positive effect on cognitive function. For example, the cognitive function stimulation unit constructs a system in which a generation AI collects lifestyle habit data of the elderly and suggests lifestyle habits that have a positive effect on cognitive function. For example, it suggests appropriate sleep time and meal contents. The generation AI receives the elderly's lifestyle habit data as input and suggests appropriate lifestyle habits based on that. This makes it possible to suggest ways to improve cognitive function based on the elderly's lifestyle habits.

[0033] The emergency response department can monitor the elderly person's living environment with sensors and issue an alert if it detects a fall or abnormal behavior. For example, the emergency response department will build a system in which the generation AI monitors the elderly person's living environment with sensors and issues an alert if it detects a fall or abnormal behavior. For example, it will use pressure sensors installed on the floor. The generation AI will receive data from the sensors as input and issue an appropriate alert based on that data. This will allow the system to monitor the elderly person's living environment and respond quickly if it detects a fall or abnormal behavior.

[0034] The emergency response department can work with the elderly's neighbors and local volunteers to build a system where they can receive prompt assistance in the event of an emergency. For example, the emergency response department could develop a system where the generative AI works with the elderly's neighbors and local volunteers to build a system where they can receive prompt assistance in the event of an emergency. For example, it could create an emergency contact list. The generative AI would receive the elderly's emergency as input and provide appropriate assistance based on that. This would build a system where the elderly can receive prompt assistance in the event of an emergency.

[0035] The emergency response department can periodically report the health status of the elderly person to their family or medical institution, promoting preventive care. The emergency response department, for example, builds a system in which a generative AI periodically reports the health status of the elderly person to their family or medical institution. For example, it automatically generates a health report once a week. The generative AI receives the elderly person's health status as input and makes an appropriate report based on that. This allows the elderly person's health status to be periodically reported, promoting preventive care.

[0036] The dialogue unit can provide advice on caring for pets and plants to the elderly, improving their quality of life. For example, the dialogue unit constructs a system in which the generation AI provides information on caring for pets and plants to the elderly. For example, it provides advice on pet health management and when to water plants. The generation AI receives information on caring for the elderly's pets and plants as input and provides appropriate advice based on that information. This allows the system to provide advice on caring for pets and plants to the elderly, improving their quality of life.

[0037] The dialogue unit can manage the elderly's diet and exercise records and support healthy lifestyle habits. For example, the dialogue unit can build a system in which the generation AI manages the elderly's diet and exercise records and supports healthy lifestyle habits. For example, it records the content of meals and the amount of exercise and provides health advice. The generation AI receives the elderly's diet and exercise records as input and provides appropriate advice based on them. This can support the elderly's healthy lifestyle habits.

[0038] The dialogue unit can monitor the elderly person's medication status and send reminders at the appropriate time. For example, the dialogue unit will build a system in which the generation AI monitors the elderly person's medication status and sends reminders at the appropriate time. For example, it will record the type of medication and the time it is taken and set reminders. The generation AI will receive the elderly person's medication status as input and send appropriate reminders based on that. This will allow the elderly person's medication to be managed appropriately.

[0039] The dialogue unit can suggest daily activities based on the elderly's hobbies and interests, improving their quality of life. The dialogue unit, for example, builds a system in which the generation AI suggests daily activities based on the elderly's hobbies and interests. For example, it suggests activities such as handicrafts and gardening. The generation AI receives the elderly's hobbies and interests as input and suggests appropriate activities based on them. This makes it possible to suggest daily activities to improve the elderly's quality of life.

[0040] The dialogue unit can support the elderly with housework and shopping, reducing the burden of daily life. The dialogue unit, for example, builds a system in which the generation AI supports the elderly with housework and shopping. For example, it creates shopping lists and manages housework schedules. The generation AI receives information about the elderly's housework and shopping as input and provides appropriate support based on that information. This provides support to reduce the burden of daily life for the elderly.

[0041] The dialogue unit can record the elderly person's health condition and daily activities in detail and report them to family members and caregivers. For example, the dialogue unit can build a system in which a generation AI records the elderly person's health condition and daily activities in detail and reports them to family members and caregivers. For example, it automatically generates daily health data and activity records. The generation AI receives the elderly person's health condition and daily activities as input and makes appropriate reports based on that. This allows the elderly person's health condition and daily activities to be recorded in detail and reported to family members and caregivers.

[0042] The dialogue unit can automatically set up video calls with family members and caregivers so that the elderly person does not feel lonely. For example, the dialogue unit constructs a system in which the generation AI automatically sets up video calls with family members and caregivers. For example, it automatically sets up a schedule for regular video calls. The generation AI receives the elderly person's video call setting information as input and makes appropriate settings based on that. This makes it possible to automatically set up video calls with family members and caregivers so that the elderly person does not feel lonely.

[0043] The dialogue unit can share the elderly person's health data with family members and caregivers, and jointly formulate a care plan. For example, the dialogue unit builds a system in which the generation AI shares the elderly person's health data with family members and caregivers, and jointly formulates a care plan. For example, the health data is stored and shared in the cloud. The generation AI receives the elderly person's health data as input and formulates an appropriate care plan based on that data. This allows the elderly person's health data to be shared, and a care plan to be jointly formulated with family members and caregivers.

[0044] The dialogue unit can remotely monitor the elderly person's living environment and report the situation to family members and caregivers in real time. The dialogue unit, for example, builds a system in which a generation AI remotely monitors the elderly person's living environment and reports the situation to family members and caregivers in real time. For example, the living environment is monitored using cameras and sensors. The generation AI receives data on the elderly person's living environment as input and makes appropriate reports based on that data. This allows the elderly person's living environment to be remotely monitored and the situation to be reported to family members and caregivers in real time.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The monitoring system can further include a health management unit. The health management unit can regularly monitor the elderly person's vital signs and notify a medical institution if an abnormality is detected. For example, it can measure blood pressure and heart rate and automatically contact a medical institution if an abnormal value is detected. The health management unit can also manage the elderly person's diet and exercise records to support healthy lifestyle habits. For example, it can record the contents of meals and the amount of exercise and provide health advice. This makes it possible to constantly understand the elderly person's health condition and provide appropriate care.

[0047] The monitoring system can also be equipped with a reminder unit. The reminder unit can monitor the elderly person's medication status and send reminders at appropriate times. For example, it can record the type of medication and the time it is taken and set reminders. The reminder unit can also manage the elderly person's schedules and important events and send notifications at appropriate times. For example, it can remind the elderly person of medical appointments or scheduled video calls with family members. This helps the elderly person remember important plans.

[0048] The monitoring system can further include an entertainment unit. The entertainment unit can provide content based on the elderly's hobbies and interests to improve their quality of life. For example, it can recommend music or movies that the elderly likes. The entertainment unit can also provide games and activities that the elderly can enjoy. For example, it can suggest ideas for puzzle games or crafts. This allows the elderly to find enjoyment in their daily lives and spend their time more fulfillingly.

[0049] The monitoring system may further include a communication unit. The communication unit may support the elderly person in easily contacting family and friends. For example, it may provide a function for easily making calls using voice recognition. The communication unit may also help the elderly person interact with friends through social networking sites. For example, it may support sending messages and sharing photos. This may help the elderly person to avoid feeling lonely and maintain connections with family and friends.

[0050] The monitoring system can further include a learning support unit. The learning support unit can help the elderly learn new knowledge and skills. For example, it can introduce online courses or hobby classes. The learning support unit can also provide information on areas that interest the elderly. For example, it can recommend articles or videos on history or science. This allows the elderly to satisfy their intellectual curiosity and spend their time in a fulfilling way.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The dialogue unit converses with the elderly person. For example, the generation AI understands what the elderly person is saying and generates an appropriate response. The generation AI listens to what the elderly person is saying, asking questions such as "What did you do today?" and "How are you feeling these days?" The generation AI receives the elderly person's words and questions as input and generates an appropriate response. Step 2: The cognitive function stimulation unit stimulates cognitive functions through dialogue conducted by the dialogue unit. For example, the generation AI presents quizzes and puzzles to the elderly, activating their brains as they answer them. The generation AI also helps evoke memories by eliciting stories and memories from the elderly's past. The generation AI receives quiz and puzzle questions and stories from the elderly's past as input, and generates appropriate responses. Step 3: The emergency response unit responds to the emergency detected by the dialogue unit. For example, if an elderly person says "help me," the generation AI automatically contacts their family or caregivers. The generation AI also constantly monitors the elderly person's health condition and automatically issues an alert if an abnormality is detected. The generation AI receives the elderly person's emergency statement and health condition data as input and responds appropriately.

[0053] (Example 2) The monitoring system according to the embodiment of the present invention is a system that enables elderly people living alone to communicate with AI, thereby reducing their sense of loneliness and allowing them to live their lives while maintaining their dignity.

[0054] A monitoring system according to an embodiment includes a dialogue unit, a cognitive function stimulation unit, and an emergency response unit. The dialogue unit dialogues with the elderly. For example, the generation AI understands what the elderly is saying and generates an appropriate response. The generation AI listens to what the elderly is saying, asking questions such as, "What did you do today?" and "How are you feeling lately?" The generation AI receives the elderly's story and questions as input and generates an appropriate response. The cognitive function stimulation unit stimulates cognitive function through dialogue with the dialogue unit. For example, the generation AI poses quizzes and puzzles, and activates the elderly's brain by having the elderly answer them. The generation AI also helps evoke memories by eliciting stories and memories from the elderly's past. The generation AI receives quiz and puzzle questions or the elderly's past stories as input and generates an appropriate response. The emergency response unit responds to emergencies detected by the dialogue unit. For example, if the elderly says, "Help me," the generation AI automatically contacts family members or caregivers. The AI ​​generator also constantly monitors the elderly person's health condition and automatically issues an alert if an abnormality is detected. The AI ​​generator receives the elderly person's comments about an emergency and data on their health condition as input, and responds appropriately. As a result, the monitoring system according to the embodiment reduces the elderly person's sense of loneliness, stimulates their cognitive functions, and enables a rapid response to emergencies.

[0055] The dialogue unit can analyze the elderly person's tone of voice and speaking style, infer their emotional state, and adjust the response accordingly. For example, the dialogue unit uses a generation AI to analyze the elderly person's tone of voice and speaking style in real time to infer their emotional state. For example, it analyzes the pitch, speed, and intonation of the voice, and if the elderly person is sad, it will offer words of comfort. The generation AI receives the elderly person's tone of voice and speaking style as input and generates an appropriate response. This makes it possible to provide an appropriate response according to the elderly person's emotional state.

[0056] The dialogue unit can learn the elderly person's past dialogue history and provide topics based on their hobbies and interests. For example, the dialogue unit's generation AI analyzes the elderly person's past dialogue history and identifies their hobbies and interests. For example, it provides topics that interest the elderly person based on themes that have been frequently discussed in the past. The generation AI receives the elderly person's past dialogue history as input and provides appropriate topics based on that. This makes it possible to have a dialogue based on the elderly person's individual hobbies and interests.

[0057] The dialogue unit uses the emotion estimation function to analyze the emotions of the elderly when they speak in real time, and can engage in dialogue that draws out positive emotions. For example, the dialogue unit uses the emotion estimation function to build a system that analyzes the emotions of the elderly when they speak in real time. For example, it analyzes the content and tone of what the elderly says and engages in dialogue that draws out positive emotions. The generation AI receives the content and tone of what the elderly says as input and generates an appropriate response. This makes it possible to engage in dialogue that draws out positive emotions from the elderly.

[0058] The cognitive function stimulation unit can evaluate the cognitive function of the elderly and provide an individually optimized cognitive training program. The cognitive function stimulation unit, for example, constructs a system in which a generation AI evaluates the cognitive function of the elderly and provides an individually optimized cognitive training program. For example, it provides training to improve memory and attention. The generation AI receives data evaluating the elderly's cognitive function as input and provides an appropriate training program based on that data. This makes it possible to provide optimal training according to the elderly's cognitive function.

[0059] The cognitive function stimulation unit can analyze the lifestyle habit data of the elderly and suggest lifestyle habits that have a positive effect on cognitive function. For example, the cognitive function stimulation unit constructs a system in which a generation AI collects lifestyle habit data of the elderly and suggests lifestyle habits that have a positive effect on cognitive function. For example, it suggests appropriate sleep time and meal contents. The generation AI receives the elderly's lifestyle habit data as input and suggests appropriate lifestyle habits based on that. This makes it possible to suggest ways to improve cognitive function based on the elderly's lifestyle habits.

[0060] The cognitive function stimulation unit uses the emotion estimation function to provide cognitive training that the elderly can enjoy, encouraging continued participation. The cognitive function stimulation unit, for example, uses the emotion estimation function to build a system that provides cognitive training that the elderly can enjoy. For example, it provides game-style training. The generation AI receives the elderly's emotional state as input and provides appropriate training based on that. This allows the elderly to enjoy participating in cognitive training.

[0061] The emergency response department can monitor the elderly person's living environment with sensors and issue an alert if it detects a fall or abnormal behavior. For example, the emergency response department will build a system in which the generation AI monitors the elderly person's living environment with sensors and issues an alert if it detects a fall or abnormal behavior. For example, it will use pressure sensors installed on the floor. The generation AI will receive data from the sensors as input and issue an appropriate alert based on that data. This will allow the system to monitor the elderly person's living environment and respond quickly if it detects a fall or abnormal behavior.

[0062] The emergency response department uses the emotion estimation function to engage in conversations that will relax the elderly when they feel anxious or stressed, thereby preventing emergencies from occurring. For example, the emergency response department uses the emotion estimation function to build a system that engages in conversations that will relax the elderly when they feel anxious or stressed. For example, it could provide relaxing topics or music. The generation AI receives the elderly's emotional state as input and engages in appropriate conversations based on that. This reduces the elderly's anxiety and stress and prevents emergencies from occurring.

[0063] The emergency response department can work with the elderly's neighbors and local volunteers to build a system where they can receive prompt assistance in the event of an emergency. For example, the emergency response department could develop a system where the generative AI works with the elderly's neighbors and local volunteers to build a system where they can receive prompt assistance in the event of an emergency. For example, it could create an emergency contact list. The generative AI would receive the elderly's emergency as input and provide appropriate assistance based on that. This would build a system where the elderly can receive prompt assistance in the event of an emergency.

[0064] The emergency response department can periodically report the health status of the elderly person to their family or medical institution, promoting preventive care. The emergency response department, for example, builds a system in which a generative AI periodically reports the health status of the elderly person to their family or medical institution. For example, it automatically generates a health report once a week. The generative AI receives the elderly person's health status as input and makes an appropriate report based on that. This allows the elderly person's health status to be periodically reported, promoting preventive care.

[0065] The emergency response department can use the emotion estimation function to instantly hold a dialogue that gives a sense of security to the elderly when they sense an emergency. The emergency response department, for example, uses the emotion estimation function to build a system that instantly holds a dialogue that gives a sense of security to the elderly when they sense an emergency. For example, it can provide calming words or music. The generation AI receives the elderly's emotional state as input and holds an appropriate dialogue based on that. This makes it possible to instantly hold a dialogue that gives a sense of security to the elderly when they sense an emergency.

[0066] The dialogue unit can provide advice on caring for pets and plants to the elderly, improving their quality of life. For example, the dialogue unit constructs a system in which the generation AI provides information on caring for pets and plants to the elderly. For example, it provides advice on pet health management and when to water plants. The generation AI receives information on caring for the elderly's pets and plants as input and provides appropriate advice based on that information. This allows the system to provide advice on caring for pets and plants to the elderly, improving their quality of life.

[0067] The dialogue unit can use the emotion estimation function to recommend music and movies that the elderly like, thereby increasing their emotional satisfaction. For example, the dialogue unit uses the emotion estimation function to build a system that recommends music and movies that the elderly like. For example, it analyzes the elderly's emotional state and recommends relaxing music and movies. The generation AI receives the elderly's emotional state as input and recommends appropriate music and movies based on that. This can increase the elderly's emotional satisfaction.

[0068] The dialogue unit can manage the elderly's diet and exercise records and support healthy lifestyle habits. For example, the dialogue unit can build a system in which the generation AI manages the elderly's diet and exercise records and supports healthy lifestyle habits. For example, it records the content of meals and the amount of exercise and provides health advice. The generation AI receives the elderly's diet and exercise records as input and provides appropriate advice based on them. This can support the elderly's healthy lifestyle habits.

[0069] The dialogue unit can monitor the elderly person's medication status and send reminders at the appropriate time. For example, the dialogue unit will build a system in which the generation AI monitors the elderly person's medication status and sends reminders at the appropriate time. For example, it will record the type of medication and the time it is taken and set reminders. The generation AI will receive the elderly person's medication status as input and send appropriate reminders based on that. This will allow the elderly person's medication to be managed appropriately.

[0070] The dialogue unit can use the emotion estimation function to provide advice to reduce the stress that the elderly feel in their daily lives. For example, the dialogue unit uses the emotion estimation function to build a system that provides advice to reduce the stress that the elderly feel in their daily lives. For example, it provides advice on relaxation methods and stress management. The generation AI receives the elderly's emotional state as input and provides appropriate advice based on that. This makes it possible to provide advice to reduce the stress that the elderly feel in their daily lives.

[0071] The dialogue unit can suggest daily activities based on the elderly's hobbies and interests, improving their quality of life. The dialogue unit, for example, builds a system in which the generation AI suggests daily activities based on the elderly's hobbies and interests. For example, it suggests activities such as handicrafts and gardening. The generation AI receives the elderly's hobbies and interests as input and suggests appropriate activities based on them. This makes it possible to suggest daily activities to improve the elderly's quality of life.

[0072] The dialogue unit can support the elderly with housework and shopping, reducing the burden of daily life. The dialogue unit, for example, builds a system in which the generation AI supports the elderly with housework and shopping. For example, it creates shopping lists and manages housework schedules. The generation AI receives information about the elderly's housework and shopping as input and provides appropriate support based on that information. This provides support to reduce the burden of daily life for the elderly.

[0073] The dialogue unit can record the elderly person's health condition and daily activities in detail and report them to family members and caregivers. For example, the dialogue unit can build a system in which a generation AI records the elderly person's health condition and daily activities in detail and reports them to family members and caregivers. For example, it automatically generates daily health data and activity records. The generation AI receives the elderly person's health condition and daily activities as input and makes appropriate reports based on that. This allows the elderly person's health condition and daily activities to be recorded in detail and reported to family members and caregivers.

[0074] The dialogue unit can automatically set up video calls with family members and caregivers so that the elderly person does not feel lonely. For example, the dialogue unit constructs a system in which the generation AI automatically sets up video calls with family members and caregivers. For example, it automatically sets up a schedule for regular video calls. The generation AI receives the elderly person's video call setting information as input and makes appropriate settings based on that. This makes it possible to automatically set up video calls with family members and caregivers so that the elderly person does not feel lonely.

[0075] The dialogue unit uses the emotion estimation function to analyze the emotions felt by the elderly when communicating with family members and caregivers, and can promote positive interactions. For example, the dialogue unit uses the emotion estimation function to build a system that analyzes the emotions felt by the elderly when communicating with family members and caregivers. For example, it promotes positive interactions based on emotion scores. The generation AI receives the elderly's emotional state as input and promotes appropriate interactions based on that. This allows for positive interactions to be promoted when the elderly communicate with family members and caregivers.

[0076] The dialogue unit can share the elderly person's health data with family members and caregivers, and jointly formulate a care plan. For example, the dialogue unit builds a system in which the generation AI shares the elderly person's health data with family members and caregivers, and jointly formulates a care plan. For example, the health data is stored and shared in the cloud. The generation AI receives the elderly person's health data as input and formulates an appropriate care plan based on that data. This allows the elderly person's health data to be shared, and a care plan to be jointly formulated with family members and caregivers.

[0077] The dialogue unit can remotely monitor the elderly person's living environment and report the situation to family members and caregivers in real time. The dialogue unit, for example, builds a system in which a generation AI remotely monitors the elderly person's living environment and reports the situation to family members and caregivers in real time. For example, the living environment is monitored using cameras and sensors. The generation AI receives data on the elderly person's living environment as input and makes appropriate reports based on that data. This allows the elderly person's living environment to be remotely monitored and the situation to be reported to family members and caregivers in real time.

[0078] The dialogue unit can use the emotion estimation function to make suggestions to increase the sense of security that the elderly feel when interacting with family members and caregivers. For example, the dialogue unit uses the emotion estimation function to build a system that makes suggestions to increase the sense of security that the elderly feel when interacting with family members and caregivers. For example, it conducts dialogue that elicits positive emotions. The generation AI receives the elderly's emotional state as input and makes appropriate suggestions based on that. This allows it to make suggestions to increase the sense of security that the elderly feel when interacting with family members and caregivers.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The monitoring system can further include a health management unit. The health management unit can regularly monitor the elderly person's vital signs and notify a medical institution if an abnormality is detected. For example, it can measure blood pressure and heart rate and automatically contact a medical institution if an abnormal value is detected. The health management unit can also manage the elderly person's diet and exercise records to support healthy lifestyle habits. For example, it can record the contents of meals and the amount of exercise and provide health advice. This makes it possible to constantly understand the elderly person's health condition and provide appropriate care.

[0081] The monitoring system can also be equipped with a reminder unit. The reminder unit can monitor the elderly person's medication status and send reminders at appropriate times. For example, it can record the type of medication and the time it is taken and set reminders. The reminder unit can also manage the elderly person's schedules and important events and send notifications at appropriate times. For example, it can remind the elderly person of medical appointments or scheduled video calls with family members. This helps the elderly person remember important plans.

[0082] The monitoring system can further include an entertainment unit. The entertainment unit can provide content based on the elderly's hobbies and interests to improve their quality of life. For example, it can recommend music or movies that the elderly likes. The entertainment unit can also provide games and activities that the elderly can enjoy. For example, it can suggest ideas for puzzle games or crafts. This allows the elderly to find enjoyment in their daily lives and spend their time more fulfillingly.

[0083] The monitoring system may further include a communication unit. The communication unit may support the elderly person in easily contacting family and friends. For example, it may provide a function for easily making calls using voice recognition. The communication unit may also help the elderly person interact with friends through social networking sites. For example, it may support sending messages and sharing photos. This may help the elderly person to avoid feeling lonely and maintain connections with family and friends.

[0084] The monitoring system can further include a learning support unit. The learning support unit can help the elderly learn new knowledge and skills. For example, it can introduce online courses or hobby classes. The learning support unit can also provide information on areas that interest the elderly. For example, it can recommend articles or videos on history or science. This allows the elderly to satisfy their intellectual curiosity and spend their time in a fulfilling way.

[0085] The monitoring system can also use the emotion estimation function to recommend music and movies based on the elderly person's emotional state. For example, if the elderly person wants to relax, it can recommend calming music and movies. The emotion estimation function can also be used to provide advice on relaxation methods and stress management if the elderly person is feeling stressed. This allows the system to provide appropriate content and advice according to the elderly person's emotional state, thereby increasing their emotional satisfaction.

[0086] The monitoring system can also use its emotion estimation function to provide advice to reduce the stress that the elderly feel in their daily lives. For example, it can provide advice on relaxation methods and stress management. The emotion estimation function can also be used to engage in conversations that help the elderly relax when they feel anxious or stressed. For example, it can provide relaxing topics or music. This can reduce the stress that the elderly feel in their daily lives and give them a sense of security.

[0087] The monitoring system can also use the emotion estimation function to analyze the emotions felt by the elderly when communicating with family members and caregivers, and promote positive interactions. For example, it can promote positive interactions based on emotion scores. The emotion estimation function can also be used to make suggestions to increase the sense of security felt by the elderly when communicating with family members and caregivers. For example, it can conduct dialogue that elicits positive emotions. This can promote positive interactions and increase the sense of security felt by the elderly when communicating with family members and caregivers.

[0088] The monitoring system can also use its emotion estimation function to instantly provide a conversation that gives a sense of security to the elderly when they sense an emergency. For example, it can provide soothing words or music. The emotion estimation function can also be used to provide a conversation that relaxes the elderly when they feel anxious or stressed. For example, it can provide relaxing topics or music. This makes it possible to instantly provide a conversation that gives a sense of security to the elderly when they sense an emergency.

[0089] The monitoring system can also use its emotion estimation function to provide advice to reduce the stress that the elderly feel in their daily lives. For example, it can provide advice on relaxation methods and stress management. The emotion estimation function can also be used to engage in conversations that help the elderly relax when they feel anxious or stressed. For example, it can provide relaxing topics or music. This can reduce the stress that the elderly feel in their daily lives and give them a sense of security.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The dialogue unit converses with the elderly person. For example, the generation AI understands what the elderly person is saying and generates an appropriate response. The generation AI listens to what the elderly person is saying, asking questions such as "What did you do today?" and "How are you feeling these days?" The generation AI receives the elderly person's words and questions as input and generates an appropriate response. Step 2: The cognitive function stimulation unit stimulates cognitive functions through dialogue conducted by the dialogue unit. For example, the generation AI presents quizzes and puzzles to the elderly, activating their brains as they answer them. The generation AI also helps evoke memories by eliciting stories and memories from the elderly's past. The generation AI receives quiz and puzzle questions and stories from the elderly's past as input, and generates appropriate responses. Step 3: The emergency response unit responds to the emergency detected by the dialogue unit. For example, if an elderly person says "help me," the generation AI automatically contacts their family or caregivers. The generation AI also constantly monitors the elderly person's health condition and automatically issues an alert if an abnormality is detected. The generation AI receives the elderly person's emergency statement and health condition data as input and responds appropriately.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A dialogue section where the characters talk to the elderly, a cognitive function stimulation unit that stimulates cognitive function through the dialogue performed by the dialogue unit; an emergency response unit that responds to an emergency detected by the dialogue unit A system characterized by:

2. The dialogue unit Analyze the old man's tone of voice and speaking style to estimate his emotional state and tailor responses accordingly 2. The system of claim 1.

3. The cognitive function stimulation unit assessing the cognitive function of the elderly person and providing an individually optimized cognitive training program; 2. The system of claim 1.

4. The cognitive function stimulation unit Providing cognitive training that the elderly can enjoy and encourage continued participation 2. The system of claim 1.

5. The emergency response department: When the elderly person feels anxious or stressed, the elderly person is given a relaxing conversation to prevent an emergency from occurring.

2. The system of claim 1.

6. The emergency response department: Regularly report the health status of the elderly to their families and medical institutions, and promote preventive care.

2. The system of claim 1.

7. The dialogue unit Providing advice to reduce the stress experienced by the elderly in their daily lives 2. The system of claim 1.

8. The dialogue unit Analyze the emotions felt by the elderly when communicating with their family and caregivers, and promote positive interactions.

2. The system of claim 1.

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