system

Abstract

An object of a system according to an embodiment is to reduce a burden on a care worker through communication with a dementia patient.SOLUTION: A system according to an embodiment includes a robot equipped with a generative AI, a camera, a speaker, a microphone, a sensor, and an ICT technology. The generation AI analyzes the patient speech and behavior and generates an appropriate response. The camera and the microphone collect actions and statements of the patient. The sensor acquires physical data. ICT techniques analyze the collected information to help improve the service of the caregiver.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 had the problem of not providing sufficient support in terms of communication with dementia patients or reducing the burden on caregivers.

[0005] The system according to the embodiment aims to reduce the burden on caregivers through communication with dementia patients. [Means for solving the problem]

[0006] The system according to the embodiment comprises a robot equipped with a generative AI, a camera, a speaker, a microphone, sensors, and ICT technology. The generative AI analyzes the patient's words and actions and generates an appropriate response. The camera and microphone collect the patient's actions and words. The sensors acquire physical data. The ICT technology analyzes the collected data to support caregivers in improving their services. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on caregivers through communication with dementia patients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The care support system according to an embodiment of the present invention uses a robot equipped with generative AI to communicate with dementia patients, reducing the burden on caregivers. This system is equipped with a camera, speaker, and microphone, and can autonomously communicate naturally using generative AI. This allows the care support system to achieve natural communication with dementia patients and reduce the burden on caregivers.

[0029] A care support system according to an embodiment includes a generation AI, a camera, a speaker, a microphone, a sensor, and ICT technology. The generation AI analyzes a patient's comments and behavior and generates an appropriate response. For example, when a patient asks, "What day is it today?", the generation AI responds, "Today is ____ day." The generation AI can also analyze the patient's behavior and provide appropriate instructions and advice. The camera and microphone collect the patient's actions and comments. For example, the camera monitors the patient's facial expressions and movements, and the microphone records the patient's comments. This allows the patient's condition to be understood in real time. The sensor acquires the patient's physical data. For example, the sensor measures data such as heart rate, body temperature, and blood pressure. This allows the patient's health condition to be constantly monitored. ICT technology analyzes the collected data to support caregivers in improving their services. For example, the system analyzes the patient's behavioral patterns and health condition and suggests appropriate care methods to caregivers. Furthermore, the system can improve the quality of services by formulating care plans based on the data. As a result, the care support system according to an embodiment enables natural communication with dementia patients and reduces the burden on caregivers.

[0030] Generative AI can learn a patient's past conversation history and generate responses based on the patient's individual preferences and interests. For example, generative AI can learn a patient's past conversation history and generate responses based on the patient's individual preferences and interests. For example, it can remember information about the patient's previously mentioned hobbies or favorite foods and advance the conversation based on that. Generative AI can also analyze a patient's past conversation history and evaluate their level of interest in specific topics. Based on that information, generative AI can select topics that are likely to interest the patient and advance the conversation. Generative AI can also learn a patient's past conversation history and memorize information about specific events and anniversaries. For example, it can remember the patient's birthday or wedding anniversary and provide topics related to those days. This allows for more natural communication by generating responses based on the patient's individual preferences and interests.

[0031] The generative AI supports multiple languages ​​and can communicate naturally with patients who speak different languages. The generative AI supports multiple languages ​​and can communicate naturally with patients who speak different languages. For example, the conversation can proceed by switching between languages ​​such as Japanese, English, and Chinese. The generative AI can also automatically recognize the patient's native language and generate a response in that language. For example, if a patient speaks in Spanish, the generative AI will respond in Spanish. The generative AI also supports multiple languages ​​and can communicate naturally with patients who speak different languages. For example, if a patient asks a question in French, the generative AI will respond in French. This enables natural communication with patients who speak different languages.

[0032] Generative AI can communicate using music and video to pique the patient's interest. Generative AI can communicate using music and video to pique the patient's interest. For example, it can play music that the patient likes and offer topics related to that music. Generative AI can also communicate using video to pique the patient's interest. For example, it can play a scene from a movie that the patient likes and lead a conversation about that scene. Generative AI can also communicate using music and video to pique the patient's interest. For example, it can play a documentary video that the patient might be interested in and lead a conversation about the content. In this way, it is possible to pique the patient's interest through communication using music and video.

[0033] The camera can analyze video data, learn the patient's movement patterns, and detect abnormalities. For example, the camera analyzes video data and learns the patient's movement patterns. For example, it records daily movement patterns and sends an alert if abnormal movement occurs. The camera also analyzes video data and learns the patient's movement patterns. For example, it detects movements that pose a high risk of falls and notifies the caregiver. The camera also analyzes video data and learns the patient's movement patterns to detect abnormalities. For example, it detects movements that differ from normal walking patterns and reports the abnormality. In this way, the camera can learn the patient's movement patterns and detect abnormalities, enabling a prompt response.

[0034] The microphone can analyze the audio data and detect changes or abnormalities in the patient's voice. For example, the microphone analyzes the audio data and detects changes in the patient's voice. For example, it analyzes changes in the tone or speed of the voice and reports any abnormalities. The microphone also analyzes the audio data and detects abnormalities in the patient's voice. For example, it sends an alert if the voice becomes hoarse or if the patient suddenly shouts loudly. The microphone also analyzes the audio data and detects changes or abnormalities in the patient's voice. For example, it detects changes in the voice that differ from normal conversation patterns and reports any abnormalities. This makes it possible to respond quickly by detecting changes or abnormalities in the patient's voice.

[0035] Cameras and microphones can monitor a patient's sleep state and detect abnormalities. For example, cameras and microphones monitor a patient's sleep state. For example, they record the number of times the patient turns over in bed and the sound of snoring to detect abnormal patterns. Cameras and microphones can also analyze video and audio data during sleep to detect abnormalities. For example, they can send an alert if the patient wakes up frequently during the night or if an abnormal sound is heard. Cameras and microphones can also monitor a patient's sleep state and detect abnormalities. For example, they can detect movements or sounds that differ from normal sleep patterns and report the abnormality. This makes it possible to monitor a patient's sleep state and detect abnormalities, enabling rapid response.

[0036] Cameras and microphones can monitor a patient's eating state and evaluate their nutritional status. For example, cameras and microphones monitor a patient's eating state. For example, they can record the amount of food eaten and the speed at which it is eaten to evaluate nutritional status. Cameras and microphones can also analyze video and audio data during meals to evaluate nutritional status. For example, they can analyze eating frequency and the amount of food left over to evaluate nutritional status. Cameras and microphones can also monitor a patient's eating state and evaluate nutritional status. For example, they can send an alert if there is a deviation from the patient's normal eating pattern. This makes it possible to monitor a patient's eating state and evaluate their nutritional status, enabling appropriate care.

[0037] The sensor can measure a patient's breathing pattern and detect abnormalities. For example, the sensor measures the patient's breathing pattern. For example, it records the rhythm and depth of breathing and detects abnormal patterns. The sensor also analyzes the breathing pattern in real time and detects abnormalities. For example, it sends an alert if breathing becomes shallow or suddenly stops. The sensor also measures a patient's breathing pattern and detects abnormalities. For example, it reports an abnormality if the breathing pattern differs from the normal breathing pattern. This makes it possible to respond quickly by measuring the patient's breathing pattern and detecting abnormalities.

[0038] The sensor can measure the patient's activity level and analyze the patient's daily life pattern. The sensor, for example, measures the patient's activity level. For example, it records the number of steps taken and the distance traveled and analyzes the patient's daily life pattern. The sensor also analyzes the activity level data in real time to understand the patient's daily life pattern. For example, it records changes in the patient's daily activity level and detects abnormalities. The sensor also measures the patient's activity level and analyzes the patient's daily life pattern. For example, it reports an abnormality if the patient's activity level differs from the normal activity pattern. In this way, by measuring the patient's activity level and analyzing the patient's daily life pattern, appropriate care can be provided.

[0039] The sensor can measure the patient's sleep quality and make suggestions for improvement. The sensor, for example, measures the patient's sleep quality. For example, it records the heart rate and breathing pattern during sleep to evaluate the quality of sleep. The sensor also analyzes the sleep data in real time and makes suggestions for improvement. For example, if the quality of sleep is declining, it provides advice for improvement. The sensor also measures the patient's sleep quality and makes suggestions for improvement. For example, if the sleep pattern differs from the normal sleep pattern, it makes suggestions for improvement. In this way, by measuring the patient's sleep quality and making suggestions for improvement, it is possible to provide a better sleeping environment.

[0040] The sensor can measure the patient's food intake and evaluate their nutritional status. The sensor, for example, measures the patient's food intake. For example, it records the amount of food eaten and calories eaten and evaluates the nutritional status. The sensor also analyzes the food data in real time and evaluates the nutritional status. For example, if the food intake decreases, it detects a decline in the nutritional status. The sensor also measures the patient's food intake and evaluates the nutritional status. For example, if the nutritional status differs from the patient's normal eating pattern, it evaluates the nutritional status. In this way, by measuring the patient's food intake and evaluating the nutritional status, appropriate nutritional management becomes possible.

[0041] ICT technology can analyze patient behavior patterns based on collected data and develop algorithms that detect abnormalities early. For example, ICT technology uses collected data to analyze patient behavior patterns. For example, it records daily behavior patterns and sends an alert if abnormal behavior occurs. ICT technology can also analyze behavior patterns in real time and develop algorithms that detect abnormalities early. For example, it can report an abnormality if the behavior differs from the normal behavior pattern. ICT technology can also use collected data to analyze patient behavior patterns and develop algorithms that detect abnormalities early. For example, it can detect behavior that poses a high risk of falls and notify caregivers. This makes it possible to analyze patient behavior patterns and detect abnormalities early, enabling rapid response.

[0042] ICT technology can be used to build a system that predicts a patient's health condition based on collected data and suggests preventive care. ICT technology, for example, uses collected data to predict a patient's health condition. For example, it analyzes changes in heart rate and body temperature to predict a deterioration in health condition. ICT technology can also build a system that predicts health condition and suggests preventive care. For example, if a deterioration in health condition is predicted, it can suggest an appropriate care method. ICT technology can also use collected data to build a system that predicts a patient's health condition and suggests preventive care. For example, if the patient's health condition differs from normal, it can suggest preventive care. In this way, by predicting a patient's health condition and suggesting preventive care, it becomes possible to maintain health.

[0043] ICT technology can perform comparative analysis with other patients based on collected data and create individual care plans. ICT technology, for example, uses collected data to perform comparative analysis with other patients. For example, it compares patients with patients of the same age group or health condition and creates an individual care plan. ICT technology can also build a system that performs comparative analysis and creates individual care plans. For example, it can suggest the optimal care method based on data from other patients. ICT technology can also use collected data to perform comparative analysis with other patients and create individual care plans. For example, it can suggest an individual care plan if a patient's health condition differs from normal. This allows for comparative analysis with other patients and the creation of an individual care plan, making it possible to provide more appropriate care.

[0044] ICT technology can evaluate the work efficiency of care workers based on collected data and make suggestions for improvement. ICT technology, for example, uses collected data to evaluate the work efficiency of care workers. For example, it records the time and effort spent on work and suggests more efficient methods. ICT technology can also build a system that evaluates work efficiency and makes suggestions for improvement. For example, it can make suggestions to reduce waste in work. ICT technology can also use collected data to evaluate the work efficiency of care workers and make suggestions for improvement. For example, it can suggest more efficient methods if the work pattern differs from the usual pattern. In this way, by evaluating the work efficiency of care workers and making suggestions for improvement, it becomes possible to improve work efficiency.

[0045] The caregiver monitoring function can develop a system that analyzes the caregiver's movements and suggests efficient care methods. The caregiver monitoring function, for example, can develop a system that analyzes the caregiver's movements and suggests efficient care methods. For example, the caregiver's movement patterns can be recorded and an efficient method can be suggested. The caregiver monitoring function can also analyze movement data in real time and suggest efficient care methods. For example, if the caregiver's movements are wasted, an efficient method can be suggested. The caregiver monitoring function can also develop a system that analyzes the caregiver's movements and suggests efficient care methods. For example, if the movements differ from the normal movement pattern, an efficient method can be suggested. In this way, the quality of care can be improved by analyzing the caregiver's movements and suggesting an efficient care method.

[0046] The caregiver monitoring function can develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. The caregiver monitoring function, for example, can develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. For example, the caregiver's movement patterns can be recorded and their level of fatigue evaluated. The caregiver monitoring function can also analyze movement data in real time and evaluate their level of fatigue. For example, the level of fatigue can be evaluated if the caregiver's movements become slow. The caregiver monitoring function can also develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. For example, the level of fatigue can be evaluated if the movement patterns differ from the normal pattern. In this way, the burden on the caregiver can be reduced by analyzing the caregiver's movements and evaluating their level of fatigue.

[0047] The caregiver monitoring function records the caregiver's work and can propose an efficient work schedule. For example, a system is developed for recording the caregiver's work and proposing an efficient work schedule. For example, the time and effort spent on work is recorded and an efficient schedule is proposed. The caregiver monitoring function also analyzes work data in real time and proposes an efficient work schedule. For example, a schedule is proposed to reduce waste in work. For example, a system is developed for recording the caregiver's work and proposing an efficient work schedule. For example, an efficient schedule is proposed if the work deviates from the normal work pattern. In this way, the burden on the caregiver can be reduced by recording the caregiver's work and proposing an efficient work schedule.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The care support system can also be equipped with environmental sensors that monitor the patient's living environment. For example, it measures room temperature, humidity, and lighting brightness and makes adjustments to maintain a comfortable living environment for the patient. The environmental sensors can also monitor air quality and automatically control air purifiers and humidifiers as needed. Furthermore, the environmental sensors can adjust lighting brightness and color temperature to match the patient's daily rhythm, providing a more natural living environment. This can optimize the patient's living environment and support a comfortable life.

[0050] The care support system can also be equipped with a nutrition management unit that manages the patient's diet. For example, it records the patient's diet and evaluates nutritional balance. The nutrition management unit can also suggest appropriate meal menus based on the patient's preferences and allergy information. Furthermore, the nutrition management unit can monitor food intake and suggest nutritional supplements as needed. This allows the patient's nutritional status to be properly managed and health maintenance to be supported.

[0051] The care support system can further include an exercise management unit that manages the patient's exercise. For example, it can record the amount of exercise the patient does and suggest an appropriate exercise program. The exercise management unit can also provide an exercise menu based on the patient's physical strength and health condition. Furthermore, the exercise management unit can monitor the patient's heart rate and calories burned during exercise and evaluate the effectiveness of the exercise. This can support the patient's exercise habits and promote health maintenance.

[0052] The care support system can also be equipped with a medication management unit that manages the patient's medication. For example, it can record the patient's medication schedule and prompt the patient to take their medication at the appropriate time. The medication management unit can also evaluate the effectiveness and side effects of medication based on the patient's medication history. Furthermore, the medication management unit can send an alert if the patient forgets to take their medication. This allows for appropriate medication management of patients and supports health maintenance.

[0053] The care support system can also be equipped with a community function to promote patients' social participation. For example, it can provide a platform where patients can interact with other patients and caregivers online. The community function can also suggest online events and workshops that patients can participate in. Furthermore, the community function can create groups based on patients' interests and hobbies and promote interactions. This can promote patients' social participation and reduce their sense of isolation.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The generative AI analyzes the patient's words and actions and generates an appropriate response. For example, if the patient asks, "What day is it today?", the generative AI will respond, "Today is ____ day." The generative AI can also analyze the patient's behavior and provide appropriate instructions and advice. Step 2: The camera and microphone collect the patient's behavior and speech. For example, the camera monitors the patient's facial expressions and movements, and the microphone records the patient's speech. This allows the patient's condition to be understood in real time. Step 3: The sensors acquire the patient's physical data. For example, they measure heart rate, body temperature, blood pressure, and other data. This allows the patient's health to be constantly monitored. Step 4: ICT technology analyzes the collected data to help caregivers improve their services. For example, it can analyze a patient's behavioral patterns and health status to suggest appropriate care methods to caregivers. It can also improve the quality of services by creating care plans based on the data.

[0056] (Example 2) The care support system according to an embodiment of the present invention uses a robot equipped with generative AI to communicate with dementia patients, reducing the burden on caregivers. This system is equipped with a camera, speaker, and microphone, and can autonomously communicate naturally using generative AI. This allows the care support system to achieve natural communication with dementia patients and reduce the burden on caregivers.

[0057] A care support system according to an embodiment includes a generation AI, a camera, a speaker, a microphone, a sensor, and ICT technology. The generation AI analyzes a patient's comments and behavior and generates an appropriate response. For example, when a patient asks, "What day is it today?", the generation AI responds, "Today is ____ day." The generation AI can also analyze the patient's behavior and provide appropriate instructions and advice. The camera and microphone collect the patient's actions and comments. For example, the camera monitors the patient's facial expressions and movements, and the microphone records the patient's comments. This allows the patient's condition to be understood in real time. The sensor acquires the patient's physical data. For example, the sensor measures data such as heart rate, body temperature, and blood pressure. This allows the patient's health condition to be constantly monitored. ICT technology analyzes the collected data to support caregivers in improving their services. For example, the system analyzes the patient's behavioral patterns and health condition and suggests appropriate care methods to caregivers. Furthermore, the system can improve the quality of services by formulating care plans based on the data. As a result, the care support system according to an embodiment enables natural communication with dementia patients and reduces the burden on caregivers.

[0058] Generative AI can learn a patient's past conversation history and generate responses based on the patient's individual preferences and interests. For example, generative AI can learn a patient's past conversation history and generate responses based on the patient's individual preferences and interests. For example, it can remember information about the patient's previously mentioned hobbies or favorite foods and advance the conversation based on that. Generative AI can also analyze a patient's past conversation history and evaluate their level of interest in specific topics. Based on that information, generative AI can select topics that are likely to interest the patient and advance the conversation. Generative AI can also learn a patient's past conversation history and memorize information about specific events and anniversaries. For example, it can remember the patient's birthday or wedding anniversary and provide topics related to those days. This allows for more natural communication by generating responses based on the patient's individual preferences and interests.

[0059] The generation AI can analyze the tone and rate of a patient's voice and generate a response that matches the patient's emotional state. For example, the generation AI can analyze the patient's tone and rate of voice in real time to estimate their emotional state. Based on this information, the generation AI can provide a gentle response if the patient is relaxed, or words of encouragement if the patient is nervous. The generation AI can also analyze the patient's tone and rate of voice to detect changes in emotion. For example, if the patient suddenly raises their voice, the generation AI can generate a calming response. The generation AI can also analyze the patient's tone and rate of voice to generate a response that matches their emotional state. For example, if the patient speaks slowly, the generation AI will also respond at a slower pace. This allows for more appropriate communication by generating responses that match the patient's emotional state.

[0060] The generative AI can use its emotion estimation function to estimate a patient's emotions in real time and generate a response that corresponds to their emotions. For example, the generative AI uses its emotion estimation function to estimate emotions in real time from the patient's facial expressions and tone of voice. Based on this information, the generative AI provides words of comfort if the patient is sad, and words of empathy if the patient is happy. The generative AI can also estimate a patient's emotions in real time and generate a response that corresponds to their emotions. For example, if the patient is feeling anxious, the generative AI provides information to reassure them. The generative AI can also use its emotion estimation function to estimate a patient's emotions in real time and generate a response that corresponds to their emotions. For example, if the patient is angry, the generative AI will respond calmly and choose words to calm the situation. This allows for more effective communication by estimating a patient's emotions in real time and generating appropriate responses.

[0061] The generative AI supports multiple languages ​​and can communicate naturally with patients who speak different languages. The generative AI supports multiple languages ​​and can communicate naturally with patients who speak different languages. For example, the conversation can proceed by switching between languages ​​such as Japanese, English, and Chinese. The generative AI can also automatically recognize the patient's native language and generate a response in that language. For example, if a patient speaks in Spanish, the generative AI will respond in Spanish. The generative AI also supports multiple languages ​​and can communicate naturally with patients who speak different languages. For example, if a patient asks a question in French, the generative AI will respond in French. This enables natural communication with patients who speak different languages.

[0062] Generative AI can communicate using music and video to pique the patient's interest. Generative AI can communicate using music and video to pique the patient's interest. For example, it can play music that the patient likes and offer topics related to that music. Generative AI can also communicate using video to pique the patient's interest. For example, it can play a scene from a movie that the patient likes and lead a conversation about that scene. Generative AI can also communicate using music and video to pique the patient's interest. For example, it can play a documentary video that the patient might be interested in and lead a conversation about the content. In this way, it is possible to pique the patient's interest through communication using music and video.

[0063] The generation AI can use its emotion estimation function to automatically select and provide music and videos that correspond to the patient's emotions. For example, the generation AI uses its emotion estimation function to automatically select and provide music that corresponds to the patient's emotions. For example, if the patient is relaxed, it will play calm music, and if the patient is excited, it will play soothing music. The generation AI can also estimate the patient's emotions in real time and automatically select and provide videos that correspond to those emotions. For example, if the patient is sad, it will play an uplifting video. The generation AI can also use its emotion estimation function to automatically select and provide music and videos that correspond to the patient's emotions. For example, if the patient is happy, it will play a video that further enhances those emotions. This allows for more appropriate care by providing music and videos that correspond to the patient's emotions.

[0064] The camera can analyze video data, learn the patient's movement patterns, and detect abnormalities. For example, the camera analyzes video data and learns the patient's movement patterns. For example, it records daily movement patterns and sends an alert if abnormal movement occurs. The camera also analyzes video data and learns the patient's movement patterns. For example, it detects movements that pose a high risk of falls and notifies the caregiver. The camera also analyzes video data and learns the patient's movement patterns to detect abnormalities. For example, it detects movements that differ from normal walking patterns and reports the abnormality. In this way, the camera can learn the patient's movement patterns and detect abnormalities, enabling a prompt response.

[0065] The microphone can analyze the audio data and detect changes or abnormalities in the patient's voice. For example, the microphone analyzes the audio data and detects changes in the patient's voice. For example, it analyzes changes in the tone or speed of the voice and reports any abnormalities. The microphone also analyzes the audio data and detects abnormalities in the patient's voice. For example, it sends an alert if the voice becomes hoarse or if the patient suddenly shouts loudly. The microphone also analyzes the audio data and detects changes or abnormalities in the patient's voice. For example, it detects changes in the voice that differ from normal conversation patterns and reports any abnormalities. This makes it possible to respond quickly by detecting changes or abnormalities in the patient's voice.

[0066] The emotion estimation function can infer emotions from a patient's facial expression and tone of voice and detect abnormalities. The emotion estimation function infers emotions from, for example, a patient's facial expression and tone of voice. For example, if a patient suddenly looks sad, it detects an abnormality and notifies the caregiver. The emotion estimation function also analyzes a patient's facial expression and tone of voice to detect changes in emotion. For example, it sends an alert if the patient's emotional state differs from their normal emotional state. The emotion estimation function also infers emotions from a patient's facial expression and tone of voice and detects abnormalities. For example, it reports an abnormality if a patient suddenly becomes angry. In this way, by inferring a patient's emotions and detecting abnormalities, a prompt response is possible.

[0067] Cameras and microphones can monitor a patient's sleep state and detect abnormalities. For example, cameras and microphones monitor a patient's sleep state. For example, they record the number of times the patient turns over in bed and the sound of snoring to detect abnormal patterns. Cameras and microphones can also analyze video and audio data during sleep to detect abnormalities. For example, they can send an alert if the patient wakes up frequently during the night or if an abnormal sound is heard. Cameras and microphones can also monitor a patient's sleep state and detect abnormalities. For example, they can detect movements or sounds that differ from normal sleep patterns and report the abnormality. This makes it possible to monitor a patient's sleep state and detect abnormalities, enabling rapid response.

[0068] Cameras and microphones can monitor a patient's eating state and evaluate their nutritional status. For example, cameras and microphones monitor a patient's eating state. For example, they can record the amount of food eaten and the speed at which it is eaten to evaluate nutritional status. Cameras and microphones can also analyze video and audio data during meals to evaluate nutritional status. For example, they can analyze eating frequency and the amount of food left over to evaluate nutritional status. Cameras and microphones can also monitor a patient's eating state and evaluate nutritional status. For example, they can send an alert if there is a deviation from the patient's normal eating pattern. This makes it possible to monitor a patient's eating state and evaluate their nutritional status, enabling appropriate care.

[0069] The emotion estimation function can monitor the emotional state of a patient and record changes in emotion. The emotion estimation function, for example, monitors the emotional state of a patient. For example, it records daily changes in emotion and detects abnormal emotional states. The emotion estimation function also analyzes the patient's facial expressions and tone of voice and records changes in emotion. For example, it sends an alert if the emotional state differs from the normal emotional state. The emotion estimation function also monitors the patient's emotional state and records changes in emotion. For example, it reports an abnormality if the patient suddenly looks sad. In this way, by monitoring the patient's emotional state and recording changes in emotion, appropriate care can be provided.

[0070] The sensor can measure a patient's breathing pattern and detect abnormalities. For example, the sensor measures the patient's breathing pattern. For example, it records the rhythm and depth of breathing and detects abnormal patterns. The sensor also analyzes the breathing pattern in real time and detects abnormalities. For example, it sends an alert if breathing becomes shallow or suddenly stops. The sensor also measures a patient's breathing pattern and detects abnormalities. For example, it reports an abnormality if the breathing pattern differs from the normal breathing pattern. This makes it possible to respond quickly by measuring the patient's breathing pattern and detecting abnormalities.

[0071] The sensor can measure the patient's activity level and analyze the patient's daily life pattern. The sensor, for example, measures the patient's activity level. For example, it records the number of steps taken and the distance traveled and analyzes the patient's daily life pattern. The sensor also analyzes the activity level data in real time to understand the patient's daily life pattern. For example, it records changes in the patient's daily activity level and detects abnormalities. The sensor also measures the patient's activity level and analyzes the patient's daily life pattern. For example, it reports an abnormality if the patient's activity level differs from the normal activity pattern. In this way, by measuring the patient's activity level and analyzing the patient's daily life pattern, appropriate care can be provided.

[0072] The emotion estimation function can analyze a patient's physical data and emotional state by associating them with each other. For example, the emotion estimation function associates a patient's physical data with their emotional state and analyzes them. For example, it associates changes in heart rate or body temperature with changes in emotions. The emotion estimation function also analyzes a patient's physical data and emotional state in real time to detect abnormalities. For example, it detects changes in emotions when the heart rate suddenly increases. The emotion estimation function also analyzes a patient's physical data and emotional state by associating them with each other. For example, it reports an abnormality when the physical data differs from normal physical data. This allows for a more comprehensive health assessment by associating and analyzing a patient's physical data with their emotional state.

[0073] The sensor can measure the patient's sleep quality and make suggestions for improvement. The sensor, for example, measures the patient's sleep quality. For example, it records the heart rate and breathing pattern during sleep to evaluate the quality of sleep. The sensor also analyzes the sleep data in real time and makes suggestions for improvement. For example, if the quality of sleep is declining, it provides advice for improvement. The sensor also measures the patient's sleep quality and makes suggestions for improvement. For example, if the sleep pattern differs from the normal sleep pattern, it makes suggestions for improvement. In this way, by measuring the patient's sleep quality and making suggestions for improvement, it is possible to provide a better sleeping environment.

[0074] The sensor can measure the patient's food intake and evaluate their nutritional status. The sensor, for example, measures the patient's food intake. For example, it records the amount of food eaten and calories eaten and evaluates the nutritional status. The sensor also analyzes the food data in real time and evaluates the nutritional status. For example, if the food intake decreases, it detects a decline in the nutritional status. The sensor also measures the patient's food intake and evaluates the nutritional status. For example, if the nutritional status differs from the patient's normal eating pattern, it evaluates the nutritional status. In this way, by measuring the patient's food intake and evaluating the nutritional status, appropriate nutritional management becomes possible.

[0075] The emotion estimation function correlates and analyzes a patient's emotional state with their physical data, enabling a comprehensive assessment of their health condition. The emotion estimation function, for example, correlates and analyzes a patient's emotional state with their physical data. For example, it correlates changes in heart rate and body temperature with changes in emotions. The emotion estimation function also analyzes a patient's emotional state and physical data in real time to comprehensively assess their health condition. For example, it detects a change in emotion when their heart rate suddenly increases. The emotion estimation function also correlates and analyzes a patient's emotional state with their physical data to comprehensively assess their health condition. For example, it reports an abnormality if the data differs from normal physical data. This allows for more appropriate care by correlating and analyzing a patient's emotional state with their physical data and comprehensively assessing their health condition.

[0076] ICT technology can analyze patient behavior patterns based on collected data and develop algorithms that detect abnormalities early. For example, ICT technology uses collected data to analyze patient behavior patterns. For example, it records daily behavior patterns and sends an alert if abnormal behavior occurs. ICT technology can also analyze behavior patterns in real time and develop algorithms that detect abnormalities early. For example, it can report an abnormality if the behavior differs from the normal behavior pattern. ICT technology can also use collected data to analyze patient behavior patterns and develop algorithms that detect abnormalities early. For example, it can detect behavior that poses a high risk of falls and notify caregivers. This makes it possible to analyze patient behavior patterns and detect abnormalities early, enabling rapid response.

[0077] ICT technology can be used to build a system that predicts a patient's health condition based on collected data and suggests preventive care. ICT technology, for example, uses collected data to predict a patient's health condition. For example, it analyzes changes in heart rate and body temperature to predict a deterioration in health condition. ICT technology can also build a system that predicts health condition and suggests preventive care. For example, if a deterioration in health condition is predicted, it can suggest an appropriate care method. ICT technology can also use collected data to build a system that predicts a patient's health condition and suggests preventive care. For example, if the patient's health condition differs from normal, it can suggest preventive care. In this way, by predicting a patient's health condition and suggesting preventive care, it becomes possible to maintain health.

[0078] The emotion estimation function can analyze the emotional state of a patient and suggest a care method according to the emotion. The emotion estimation function, for example, analyzes the emotional state of a patient. For example, it records daily changes in emotions and suggests a care method according to the emotion. The emotion estimation function also analyzes the emotional state of a patient in real time and suggests a care method according to the emotion. For example, if the patient is sad, it provides words of comfort. The emotion estimation function also analyzes the emotional state of a patient and suggests a care method according to the emotion. For example, if the patient is happy, it suggests a care method that will further enhance that emotion. In this way, by analyzing the emotional state of a patient and suggesting a care method according to the emotion, more appropriate care is possible.

[0079] ICT technology can perform comparative analysis with other patients based on collected data and create individual care plans. ICT technology, for example, uses collected data to perform comparative analysis with other patients. For example, it compares patients with patients of the same age group or health condition and creates an individual care plan. ICT technology can also build a system that performs comparative analysis and creates individual care plans. For example, it can suggest the optimal care method based on data from other patients. ICT technology can also use collected data to perform comparative analysis with other patients and create individual care plans. For example, it can suggest an individual care plan if a patient's health condition differs from normal. This allows for comparative analysis with other patients and the creation of an individual care plan, making it possible to provide more appropriate care.

[0080] ICT technology can evaluate the work efficiency of care workers based on collected data and make suggestions for improvement. ICT technology, for example, uses collected data to evaluate the work efficiency of care workers. For example, it records the time and effort spent on work and suggests more efficient methods. ICT technology can also build a system that evaluates work efficiency and makes suggestions for improvement. For example, it can make suggestions to reduce waste in work. ICT technology can also use collected data to evaluate the work efficiency of care workers and make suggestions for improvement. For example, it can suggest more efficient methods if the work pattern differs from the usual pattern. In this way, by evaluating the work efficiency of care workers and making suggestions for improvement, it becomes possible to improve work efficiency.

[0081] The emotion estimation function can analyze the emotional state of a caregiver and make suggestions for stress reduction. The emotion estimation function, for example, analyzes the emotional state of a caregiver. For example, it records daily changes in emotions and makes suggestions for stress reduction. The emotion estimation function also analyzes the emotional state of a caregiver in real time and makes suggestions for stress reduction. For example, if the caregiver is tired, it suggests relaxation methods. The emotion estimation function also analyzes the emotional state of a caregiver and makes suggestions for stress reduction. For example, if the caregiver is feeling stressed, it provides advice for stress reduction. In this way, by analyzing the emotional state of a caregiver and making suggestions for stress reduction, it is possible to reduce the burden on the caregiver.

[0082] The caregiver monitoring function can develop a system that analyzes the caregiver's movements and suggests efficient care methods. The caregiver monitoring function, for example, can develop a system that analyzes the caregiver's movements and suggests efficient care methods. For example, the caregiver's movement patterns can be recorded and an efficient method can be suggested. The caregiver monitoring function can also analyze movement data in real time and suggest efficient care methods. For example, if the caregiver's movements are wasted, an efficient method can be suggested. The caregiver monitoring function can also develop a system that analyzes the caregiver's movements and suggests efficient care methods. For example, if the movements differ from the normal movement pattern, an efficient method can be suggested. In this way, the quality of care can be improved by analyzing the caregiver's movements and suggesting an efficient care method.

[0083] The caregiver monitoring function can detect a state of stress by analyzing the tone and speed of the caregiver's voice. For example, the caregiver monitoring function can analyze the tone and speed of the caregiver's voice in real time to detect a state of stress. For example, if the voice becomes rough, stress is detected and an alert is sent. The caregiver monitoring function can also analyze the tone and speed of the voice to detect a state of stress. For example, stress is detected if the voice tone differs from the normal tone. The caregiver monitoring function can also analyze the tone and speed of the caregiver's voice to detect a state of stress. For example, stress is detected if the voice suddenly becomes faster and an alert is sent. In this way, by analyzing the tone and speed of the caregiver's voice and detecting a state of stress, the burden on the caregiver can be reduced.

[0084] The emotion estimation function can monitor the caregiver's emotional state in real time and make suggestions for stress reduction. The emotion estimation function, for example, monitors the caregiver's emotional state in real time. For example, it records daily changes in emotions and makes suggestions for stress reduction. The emotion estimation function also analyzes the caregiver's emotional state in real time and makes suggestions for stress reduction. For example, if the caregiver is tired, it suggests relaxation methods. The emotion estimation function also monitors the caregiver's emotional state in real time and makes suggestions for stress reduction. For example, if the caregiver is feeling stressed, it provides advice for stress reduction. In this way, by monitoring the caregiver's emotional state in real time and making suggestions for stress reduction, it is possible to reduce the burden on the caregiver.

[0085] The caregiver monitoring function can develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. The caregiver monitoring function, for example, can develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. For example, the caregiver's movement patterns can be recorded and their level of fatigue evaluated. The caregiver monitoring function can also analyze movement data in real time and evaluate their level of fatigue. For example, the level of fatigue can be evaluated if the caregiver's movements become slow. The caregiver monitoring function can also develop a system that analyzes the caregiver's movements and evaluates their level of fatigue. For example, the level of fatigue can be evaluated if the movement patterns differ from the normal pattern. In this way, the burden on the caregiver can be reduced by analyzing the caregiver's movements and evaluating their level of fatigue.

[0086] The caregiver monitoring function records the caregiver's work and can propose an efficient work schedule. For example, a system is developed for recording the caregiver's work and proposing an efficient work schedule. For example, the time and effort spent on work is recorded and an efficient schedule is proposed. The caregiver monitoring function also analyzes work data in real time and proposes an efficient work schedule. For example, a schedule is proposed to reduce waste in work. For example, a system is developed for recording the caregiver's work and proposing an efficient work schedule. For example, an efficient schedule is proposed if the work deviates from the normal work pattern. In this way, the burden on the caregiver can be reduced by recording the caregiver's work and proposing an efficient work schedule.

[0087] The emotion estimation function can monitor the emotional state of the caregiver and suggest a relaxation method according to the emotion. The emotion estimation function, for example, monitors the emotional state of the caregiver. For example, it records daily changes in emotions and suggests a relaxation method according to the emotion. The emotion estimation function also analyzes the emotional state of the caregiver in real time and suggests a relaxation method according to the emotion. For example, if the caregiver is tired, it suggests a relaxation method. The emotion estimation function also monitors the emotional state of the caregiver and suggests a relaxation method according to the emotion. For example, if the caregiver is feeling stressed, it suggests a relaxation method. In this way, by monitoring the emotional state of the caregiver and suggesting a relaxation method according to the emotion, it is possible to reduce the burden on the caregiver.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The care support system can also be equipped with environmental sensors that monitor the patient's living environment. For example, it measures room temperature, humidity, and lighting brightness and makes adjustments to maintain a comfortable living environment for the patient. The environmental sensors can also monitor air quality and automatically control air purifiers and humidifiers as needed. Furthermore, the environmental sensors can adjust lighting brightness and color temperature to match the patient's daily rhythm, providing a more natural living environment. This can optimize the patient's living environment and support a comfortable life.

[0090] The care support system can also be equipped with a nutrition management unit that manages the patient's diet. For example, it records the patient's diet and evaluates nutritional balance. The nutrition management unit can also suggest appropriate meal menus based on the patient's preferences and allergy information. Furthermore, the nutrition management unit can monitor food intake and suggest nutritional supplements as needed. This allows the patient's nutritional status to be properly managed and health maintenance to be supported.

[0091] The care support system can further include an exercise management unit that manages the patient's exercise. For example, it can record the amount of exercise the patient does and suggest an appropriate exercise program. The exercise management unit can also provide an exercise menu based on the patient's physical strength and health condition. Furthermore, the exercise management unit can monitor the patient's heart rate and calories burned during exercise and evaluate the effectiveness of the exercise. This can support the patient's exercise habits and promote health maintenance.

[0092] The care support system can also be equipped with a medication management unit that manages the patient's medication. For example, it can record the patient's medication schedule and prompt the patient to take their medication at the appropriate time. The medication management unit can also evaluate the effectiveness and side effects of medication based on the patient's medication history. Furthermore, the medication management unit can send an alert if the patient forgets to take their medication. This allows for appropriate medication management of patients and supports health maintenance.

[0093] The care support system can also be equipped with a community function to promote patients' social participation. For example, it can provide a platform where patients can interact with other patients and caregivers online. The community function can also suggest online events and workshops that patients can participate in. Furthermore, the community function can create groups based on patients' interests and hobbies and promote interactions. This can promote patients' social participation and reduce their sense of isolation.

[0094] The care support system can also analyze the patient's emotional state and suggest relaxation methods according to the emotion. For example, if the patient is feeling stressed, it can provide relaxation music or a meditation guide. It can also suggest aromatherapy or massage depending on the patient's emotional state. It can also analyze the patient's emotional state and provide advice on creating an environment where the patient can relax. This makes it possible to properly manage the patient's emotional state and support stress reduction.

[0095] The care support system can also analyze the patient's emotional state and provide entertainment tailored to the patient's emotions. For example, if the patient is sad, it can suggest uplifting movies or TV shows. It can also provide fun games and activities based on the patient's emotional state. Furthermore, it can analyze the patient's emotional state and suggest reading or art activities that will help the patient relax. This allows the system to appropriately manage the patient's emotional state and support a change of mood through entertainment.

[0096] The care support system can also analyze the patient's emotional state and suggest an exercise program that matches their emotions. For example, if the patient is feeling stressed, it can suggest yoga or stretching, which has a relaxing effect. It can also provide dance or exercise to release energy depending on the patient's emotional state. Furthermore, it can analyze the patient's emotional state and suggest outdoor activities or sports that the patient can enjoy. This makes it possible to appropriately manage the patient's emotional state and support stress reduction through exercise.

[0097] The care support system can also analyze the patient's emotional state and suggest a meal menu that corresponds to that emotion. For example, if the patient is feeling stressed, it can suggest a menu that uses ingredients that have a relaxing effect. It can also provide a nutritionally balanced menu that replenishes energy depending on the patient's emotional state. It can also analyze the patient's emotional state and suggest desserts and snacks that the patient will enjoy. This makes it possible to appropriately manage the patient's emotional state and support a change of mood through meals.

[0098] The care support system can also analyze the patient's emotional state and suggest communication methods appropriate to the emotion. For example, if the patient is sad, it can provide words of comfort or messages of sympathy. It can also provide words of encouragement or positive messages depending on the patient's emotional state. Furthermore, it can analyze the patient's emotional state and suggest conversation topics or topics that will help the patient relax. This makes it possible to appropriately manage the patient's emotional state and support a change of mood through communication.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The generative AI analyzes the patient's words and actions and generates an appropriate response. For example, if the patient asks, "What day is it today?", the generative AI will respond, "Today is ____ day." The generative AI can also analyze the patient's behavior and provide appropriate instructions and advice. Step 2: The camera and microphone collect the patient's behavior and speech. For example, the camera monitors the patient's facial expressions and movements, and the microphone records the patient's speech. This allows the patient's condition to be understood in real time. Step 3: The sensors acquire the patient's physical data. For example, they measure heart rate, body temperature, blood pressure, and other data. This allows the patient's health to be constantly monitored. Step 4: ICT technology analyzes the collected data to help caregivers improve their services. For example, it can analyze a patient's behavioral patterns and health status to suggest appropriate care methods to caregivers. It can also improve the quality of services by creating care plans based on the data.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

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

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

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0133] The data processing system 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

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

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] 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]

[0168] 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 robot equipped with generative AI, A camera and With a speaker, With a microphone, A sensor, Equipped with ICT technology, The generated AI is Analyze the patient's words and actions, generate appropriate responses, The camera and the microphone Collecting patient behavior and statements The sensor Acquire physical data The ICT technology is: Analyzing collected data to help caregivers improve their services A system characterized by:

2. The generated AI is Learns the patient's past conversation history and generates responses based on the patient's individual preferences and interests 2. The system of claim 1.

3. The generated AI is Analyzing the tone and rate of the patient's voice and generating a response according to the patient's emotional state.

2. The system of claim 1.

4. The generated AI is Estimating the patient's emotion in real time and generating a response according to the emotion 2. The system of claim 1.

5. The generated AI is Supports multiple languages, allowing natural communication with patients who speak different languages 2. The system of claim 1.

6. The generated AI is Communicate using music and video to attract the patient's interest 2. The system of claim 1.

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