System

Abstract

An object of the system according to the embodiment is to reduce the burden on the care assistant and to address the individual needs of the care receiver.SOLUTION: A system according to an embodiment includes a care plan generator, a record generator, a conversation generator, a recreation generator, an emotion analyzer, and a feedback unit. The care plan generator generates a care plan in response to an instruction from the supporter. The record creation unit creates a daily nursing care record. The conversation generation unit generates a conversation that matches the individual needs and preferences of the care receiver. The recreation generation unit generates recreation in accordance with individual needs and preferences of the care-receiver. The emotion analysis component analyzes the emotions of the care receiver. The feedback unit feeds back daily nursing care contents.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 placing a heavy burden on caregivers and making it difficult to meet the individual needs of those in need of care.

[0005] The system according to the embodiment aims to reduce the burden on caregivers and to meet the individual needs of those requiring care. [Means for solving the problem]

[0006] The system according to the embodiment includes a care plan creation unit, a record creation unit, a conversation generation unit, a recreation generation unit, an emotion analysis unit, and a feedback unit. The care plan creation unit creates a care plan in response to instructions from a supporter. The record creation unit creates a daily care record. The conversation generation unit generates conversations tailored to the individual needs and preferences of the care recipient. The recreation generation unit generates recreation tailored to the individual needs and preferences of the care recipient. The emotion analysis unit analyzes the emotions of the care recipient. The feedback unit provides feedback on the daily care content. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on caregivers and respond to the individual needs of those in need of care. [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 utilizes generative AI to reduce the burden on caregivers and provide optimal care for individuals requiring care. This solves problems such as a shortage of caregivers, the need to provide personalized care, and the inability to provide 24-hour supervision, and can alleviate the problem of reduced conversation time and brain deterioration among individuals requiring care.

[0029] A care support system according to an embodiment includes a generation AI, a care plan creation unit, a record creation unit, a conversation generation unit, a recreation generation unit, an emotion analysis unit, and a feedback unit. The generation AI includes a care plan creation unit that creates a care plan in response to instructions from a supporter. For example, the care plan creation unit provides the generation AI with information about the health condition and daily activities of a care recipient as input information, and the generation AI generates an optimal care plan based on the information. The generation AI also includes a record creation unit that creates daily care records. For example, the record creation unit automatically creates daily care records, reducing the burden on the supporter. The generation AI includes a conversation generation unit that generates conversations tailored to the individual needs and preferences of the care recipient. For example, the conversation generation unit provides the generation AI with the care recipient's past conversation history, hobbies, and interests as input information, and the generation AI generates appropriate conversation content based on the input information. The generation AI includes a recreation generation unit that generates recreation activities tailored to the individual needs and preferences of the care recipient. For example, the recreation generation unit provides the care recipient's hobbies and interests as input information, and the generation AI generates an appropriate recreation plan based on the input information. The generation AI includes an emotion analysis unit that analyzes the emotions of the care recipient. For example, the emotion analysis unit analyzes the facial expressions and tone of voice of the care recipient and adjusts the response based on those emotions. The generation AI also includes a feedback unit that provides feedback on the daily care content. For example, the feedback unit provides the generation AI with daily care records and the care recipient's reactions as input information, and the generation AI adjusts the next day's care plan based on that information. This allows the care support system according to the embodiment to reduce the burden on the supporter and provide optimal care individually to the care recipient. For example, even if the supporter cannot monitor the care recipient 24 hours a day, the generation AI and the robot work together to monitor the care recipient and provide appropriate care. Furthermore, by responding in accordance with the care recipient's emotions, the satisfaction of the care recipient can be improved.

[0030] The care plan creation unit can analyze vital sign data in real time and dynamically adjust the care plan based on that data. For example, the generation AI in the care plan creation unit monitors the heart rate and blood pressure of the person requiring care in real time and immediately adjusts the care plan if an abnormality is detected. For example, if the heart rate suddenly rises, the plan will be changed to one that encourages rest. The care plan creation unit also collects vital sign data of the person requiring care 24 hours a day, and the generation AI analyzes that data to optimize the daily care plan. For example, it adjusts the nighttime care content taking into account nighttime blood pressure fluctuations. The care plan creation unit also uses the generation AI to analyze the vital sign data of the person requiring care and generate a long-term health management plan. For example, it suggests regular exercise and dietary improvements to maintain health. This allows the care plan to be dynamically adjusted according to the health condition of the person requiring care.

[0031] The care plan creation unit can analyze medical history and generate a long-term health management plan. For example, the generation AI in the care plan creation unit analyzes the past medical records and prescription history of the care recipient and generates a long-term health management plan. For example, it suggests preventive care based on the past medical history. The care plan creation unit also evaluates individual health risks based on the past medical history of the care recipient and creates an appropriate care plan. For example, if there is a high risk of diabetes, it will strengthen dietary management. The care plan creation unit also analyzes the past medical history of the care recipient and suggests schedules for regular health checks and tests. For example, it recommends regular blood tests and electrocardiograms. This makes it possible to generate a long-term health management plan based on the past medical history of the care recipient.

[0032] The care plan creation unit can analyze dietary content and nutritional status and generate a care plan that takes nutritional balance into consideration. For example, the generation AI in the care plan creation unit analyzes the dietary content of the person requiring care and generates a care plan that takes nutritional balance into consideration. For example, it proposes meal menus that make up for vitamin and mineral deficiencies. The care plan creation unit also regularly monitors the nutritional status of the person requiring care, and the generation AI adjusts the care plan based on that data. For example, it adjusts the amount and content of meals according to weight fluctuations. The care plan creation unit also analyzes the dietary content of the person requiring care and suggests taking supplements if there is a deficiency of a specific nutrient. For example, it recommends supplements to make up for a calcium deficiency. This makes it possible to generate a care plan that takes nutritional balance into consideration for the person requiring care.

[0033] The care plan creation unit can analyze the communication history and generate a care plan to strengthen social ties. For example, in the care plan creation unit, the generation AI analyzes the communication history of the care recipient with family and friends and generates a care plan to strengthen social ties. For example, the generation AI suggests a schedule for regular video calls. In addition, in the care plan creation unit, the generation AI suggests activities to strengthen social ties based on the communication history of the care recipient. For example, the generation AI recommends joint activities with friends or joining a hobby club. In addition, in the care plan creation unit, the generation AI analyzes the communication history of the care recipient and generates a care plan to reduce feelings of isolation. For example, the generation AI suggests participation in local events and gatherings. In this way, a care plan can be generated to strengthen the social ties of the care recipient.

[0034] The conversation generation unit analyzes the conversation history and learns reactions to specific topics, thereby generating more personalized conversations. For example, the conversation generation unit uses a generation AI to analyze the past conversation history of the person requiring care and learn reactions to specific topics. For example, it prioritizes incorporating topics that interest the person requiring care into the conversation. The conversation generation unit also generates personalized conversation content based on the past conversation history of the person requiring care. For example, it suggests conversations about movies and music that the person requiring care likes. The conversation generation unit also uses a generation AI to analyze the conversation history of the person requiring care and learn reactions to specific topics, thereby improving the quality of the conversation. For example, it selects topics that will relax the person requiring care. This makes it possible to generate personalized conversations based on the past conversation history of the person requiring care.

[0035] The recreation generation unit generates quizzes and games on specific themes based on the hobbies and interests, thereby activating the brain. For example, the recreation generation unit uses a generation AI to analyze the hobbies and interests of the care recipient and generate quizzes on specific themes based on the analysis. For example, the recreation generation unit suggests a quiz on history, which the care recipient likes. The recreation generation unit also generates games for activating the brain based on the interests of the care recipient. For example, the recreation generation unit suggests puzzles and crossword puzzles. The recreation generation unit also generates recreations on specific themes based on the hobbies and interests of the care recipient. For example, the recreation generation AI suggests dances to music that the care recipient likes. In this way, quizzes and games for activating the brain can be generated based on the hobbies and interests of the care recipient.

[0036] The conversation generation unit can generate conversation content that corresponds to different cultures and languages ​​based on the cultural background and language. In the conversation generation unit, for example, the generation AI analyzes the cultural background of the care recipient and generates conversation content that corresponds to different cultures based on that. For example, if the care recipient is from a foreign country, it suggests topics related to that culture. In addition, the conversation generation unit generates conversation content based on the language of the care recipient. For example, if the care recipient speaks English, it suggests conversations in English. In addition, the conversation generation unit generates recreation that corresponds to different cultures and languages ​​by taking the cultural background and language of the care recipient into consideration. For example, it suggests foreign music and movies that the care recipient likes. In this way, conversation content that corresponds to different cultures and languages ​​can be generated based on the cultural background and language of the care recipient.

[0037] The recreation generation unit can generate appropriate recreation according to the physical condition. For example, the recreation generation unit generates recreation that does not depend on vision by using a generation AI that takes into account the visual impairment of the person requiring care. For example, it proposes activities that use music or audio guides. The recreation generation unit also generates recreation that does not depend on hearing by using a generation AI that takes into account the hearing impairment of the person requiring care. For example, it proposes visual puzzles or painting activities. The recreation generation unit also analyzes the physical condition of the person requiring care and generates recreation accordingly. For example, it proposes light physical activities that are suited to the person's athletic ability. This makes it possible to generate appropriate recreation according to the physical condition of the person requiring care.

[0038] The emotion analysis unit can analyze the emotional state and suggest appropriate meals and drinks according to emotional fluctuations. For example, the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient and suggests appropriate meals based on the results. For example, if the emotions are unstable, it will suggest meals that have a relaxing effect. The emotion analysis unit also suggests appropriate drinks based on the emotional state of the care recipient. For example, if the emotions are elevated, it will suggest chamomile tea. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the food and drink suggestions according to emotional fluctuations. For example, if the emotions are unstable, it will suggest meals that will give a sense of security. This makes it possible to suggest appropriate meals and drinks according to the emotional state of the care recipient.

[0039] The emotion analysis unit can analyze the emotional state and suggest appropriate exercises or physical activities according to emotional fluctuations. For example, the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient and suggests appropriate exercises based on the results. For example, if the emotions are unstable, it will suggest yoga, which has a relaxing effect. The emotion analysis unit also suggests appropriate physical activities based on the emotional state of the care recipient. For example, if the emotions are elevated, it will suggest light jogging. The emotion analysis unit also suggests the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient in real time and adjusts the suggested exercises or physical activities according to emotional fluctuations. For example, if the emotions are unstable, it will suggest stretching, which has a relaxing effect. This makes it possible to suggest appropriate exercises or physical activities according to the emotional state of the care recipient.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The care support system may further include a sleep analysis unit that analyzes the sleep patterns of the care recipient. For example, the sleep analysis unit collects the sleep data of the care recipient, and the generation AI analyzes the data to evaluate the quality of the sleep. For example, the sleep analysis unit monitors the depth of sleep and the frequency of interruptions and suggests an appropriate sleep environment. The sleep analysis unit also allows the generation AI to adjust the daytime activity plan based on the care recipient's sleep pattern. For example, if the care recipient's nighttime sleep is insufficient, the generation AI reduces daytime activities. The sleep analysis unit also allows the generation AI to analyze the care recipient's sleep data and propose a long-term sleep improvement plan. For example, the generation AI may recommend establishing a regular sleep rhythm or relaxation techniques. This can improve the quality of the care recipient's sleep and improve their overall health.

[0042] The care support system can further include an exercise analysis unit that analyzes the exercise data of the care recipient. For example, the exercise analysis unit monitors the amount and type of daily exercise of the care recipient, and the generation AI analyzes that data to optimize the exercise plan. For example, if a lack of exercise is detected, light exercise will be suggested. The movement analysis unit also allows the generation AI to create an individual exercise plan based on the care recipient's exercise data. For example, it may recommend stretching to increase joint flexibility. The movement analysis unit also allows the generation AI to analyze the care recipient's exercise data and support the establishment of long-term exercise habits. For example, it may suggest regular walking or a fitness program. This helps improve the care recipient's exercise habits and maintain their health.

[0043] The care support system can further include a hobby analysis unit that delves deeper into the hobbies and interests of the care recipient. For example, the hobby analysis unit collects data on the care recipient's past hobbies and interests, and the generation AI analyzes that data to suggest new hobbies and activities. For example, the system provides support for the care recipient to resume hobbies that they previously enjoyed. In addition, the hobby analysis unit uses the generation AI to suggest new hobbies and activities based on the care recipient's hobby data. For example, it recommends new hobbies such as handicrafts or gardening. In addition, the hobby analysis unit uses the generation AI to analyze the care recipient's hobby data and suggest activities to strengthen social connections through hobbies. For example, it recommends participation in hobby clubs or group activities. This can improve the quality of life of the care recipient and strengthen social connections.

[0044] The care support system may further include a stress analysis unit that analyzes the stress level of the care recipient. For example, the stress analysis unit collects physiological and behavioral data of the care recipient, and the generating AI analyzes the data to assess the stress level. For example, the stress analysis unit monitors the heart rate and electrodermal activity, and suggests relaxation techniques if stress is high. The stress analysis unit also generates a stress management plan based on the stress data of the care recipient. For example, the generating AI recommends regular relaxation sessions and stress reduction activities. The stress analysis unit also analyzes the stress data of the care recipient and suggests a long-term stress management strategy. For example, the generating AI identifies the causes of stress and takes measures to address them. This reduces the stress level of the care recipient and improves their overall health.

[0045] The care support system can further include an environmental analysis unit that analyzes the environmental data of the care recipient. For example, the environmental analysis unit collects data on the care recipient's living environment, and the generating AI analyzes that data to optimize the environment. For example, it monitors room temperature, humidity, and lighting conditions and makes suggestions for maintaining a comfortable environment. The environmental analysis unit also allows the generating AI to create an environmental improvement plan based on the care recipient's environmental data. For example, it recommends using an air purifier or adjusting the lighting appropriately. The environmental analysis unit also allows the generating AI to analyze the care recipient's environmental data and propose a long-term environmental management strategy. For example, it recommends seasonal environmental adjustments and regular environmental checks. This allows the care recipient's living environment to be optimized and a comfortable life to be supported.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The care plan creation unit creates a care plan in response to instructions from the caregiver. For example, the care recipient's health condition and daily activities are provided as input information to the generation AI, which then generates an optimal care plan based on that information. Step 2: The record creation unit creates daily care records. For example, the record creation unit automatically creates daily care records, thereby reducing the burden on the caregiver. Step 3: The conversation generation unit generates conversations tailored to the individual needs and preferences of the care recipient. For example, the care recipient's past conversation history, hobbies, and interests are provided as input information to the generation AI, which then generates appropriate conversation content based on that information. Step 4: The recreation generation unit generates recreation tailored to the individual needs and preferences of the care recipient. For example, the care recipient's hobbies and interests are provided as input information to the generation AI, which then generates an appropriate recreation plan based on that information. Step 5: The emotion analysis unit analyzes the emotions of the person requiring care. For example, it analyzes the person's facial expressions and tone of voice, and changes the response according to their emotions. Step 6: The feedback unit provides daily care information to the AI ​​generator. For example, the AI ​​generator provides daily care records and the care recipient's reactions as input information, and the AI ​​generator adjusts the care plan for the next day based on that information.

[0048] (Example 2) The care support system according to an embodiment of the present invention utilizes generative AI to reduce the burden on caregivers and provide optimal care for individuals requiring care. This solves problems such as a shortage of caregivers, the need to provide personalized care, and the inability to provide 24-hour supervision, and can alleviate the problem of reduced conversation time and brain deterioration among individuals requiring care.

[0049] A care support system according to an embodiment includes a generation AI, a care plan creation unit, a record creation unit, a conversation generation unit, a recreation generation unit, an emotion analysis unit, and a feedback unit. The generation AI includes a care plan creation unit that creates a care plan in response to instructions from a supporter. For example, the care plan creation unit provides the generation AI with information about the health condition and daily activities of a care recipient as input information, and the generation AI generates an optimal care plan based on the information. The generation AI also includes a record creation unit that creates daily care records. For example, the record creation unit automatically creates daily care records, reducing the burden on the supporter. The generation AI includes a conversation generation unit that generates conversations tailored to the individual needs and preferences of the care recipient. For example, the conversation generation unit provides the generation AI with the care recipient's past conversation history, hobbies, and interests as input information, and the generation AI generates appropriate conversation content based on the input information. The generation AI includes a recreation generation unit that generates recreation activities tailored to the individual needs and preferences of the care recipient. For example, the recreation generation unit provides the care recipient's hobbies and interests as input information, and the generation AI generates an appropriate recreation plan based on the input information. The generation AI includes an emotion analysis unit that analyzes the emotions of the care recipient. For example, the emotion analysis unit analyzes the facial expressions and tone of voice of the care recipient and adjusts the response based on those emotions. The generation AI also includes a feedback unit that provides feedback on the daily care content. For example, the feedback unit provides the generation AI with daily care records and the care recipient's reactions as input information, and the generation AI adjusts the next day's care plan based on that information. This allows the care support system according to the embodiment to reduce the burden on the supporter and provide optimal care individually to the care recipient. For example, even if the supporter cannot monitor the care recipient 24 hours a day, the generation AI and the robot work together to monitor the care recipient and provide appropriate care. Furthermore, by responding in accordance with the care recipient's emotions, the satisfaction of the care recipient can be improved.

[0050] The care plan creation unit can analyze vital sign data in real time and dynamically adjust the care plan based on that data. For example, the generation AI in the care plan creation unit monitors the heart rate and blood pressure of the person requiring care in real time and immediately adjusts the care plan if an abnormality is detected. For example, if the heart rate suddenly rises, the plan will be changed to one that encourages rest. The care plan creation unit also collects vital sign data of the person requiring care 24 hours a day, and the generation AI analyzes that data to optimize the daily care plan. For example, it adjusts the nighttime care content taking into account nighttime blood pressure fluctuations. The care plan creation unit also uses the generation AI to analyze the vital sign data of the person requiring care and generate a long-term health management plan. For example, it suggests regular exercise and dietary improvements to maintain health. This allows the care plan to be dynamically adjusted according to the health condition of the person requiring care.

[0051] The care plan creation unit can analyze medical history and generate a long-term health management plan. For example, the generation AI in the care plan creation unit analyzes the past medical records and prescription history of the care recipient and generates a long-term health management plan. For example, it suggests preventive care based on the past medical history. The care plan creation unit also evaluates individual health risks based on the past medical history of the care recipient and creates an appropriate care plan. For example, if there is a high risk of diabetes, it will strengthen dietary management. The care plan creation unit also analyzes the past medical history of the care recipient and suggests schedules for regular health checks and tests. For example, it recommends regular blood tests and electrocardiograms. This makes it possible to generate a long-term health management plan based on the past medical history of the care recipient.

[0052] The emotion analysis unit can analyze the emotional state in real time and adjust the care plan according to emotional fluctuations. For example, the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient in real time and adjusts the care plan according to emotional fluctuations. For example, if stress is high, the plan will be changed to one that encourages relaxation. The emotion analysis unit also uses an emotion estimation function to generate a care plan that takes into account the emotional state of the care recipient. For example, if positive emotions are strong, active recreation will be suggested. The emotion analysis unit also analyzes the emotional state of the care recipient using the generation AI and dynamically adjusts the care plan according to emotional fluctuations. For example, if emotions are unstable, care that provides a sense of security will be prioritized. This makes it possible to adjust the care plan according to the emotional state of the care recipient.

[0053] The care plan creation unit can analyze dietary content and nutritional status and generate a care plan that takes nutritional balance into consideration. For example, the generation AI in the care plan creation unit analyzes the dietary content of the person requiring care and generates a care plan that takes nutritional balance into consideration. For example, it proposes meal menus that make up for vitamin and mineral deficiencies. The care plan creation unit also regularly monitors the nutritional status of the person requiring care, and the generation AI adjusts the care plan based on that data. For example, it adjusts the amount and content of meals according to weight fluctuations. The care plan creation unit also analyzes the dietary content of the person requiring care and suggests taking supplements if there is a deficiency of a specific nutrient. For example, it recommends supplements to make up for a calcium deficiency. This makes it possible to generate a care plan that takes nutritional balance into consideration for the person requiring care.

[0054] The care plan creation unit can analyze the communication history and generate a care plan to strengthen social ties. For example, in the care plan creation unit, the generation AI analyzes the communication history of the care recipient with family and friends and generates a care plan to strengthen social ties. For example, the generation AI suggests a schedule for regular video calls. In addition, in the care plan creation unit, the generation AI suggests activities to strengthen social ties based on the communication history of the care recipient. For example, the generation AI recommends joint activities with friends or joining a hobby club. In addition, in the care plan creation unit, the generation AI analyzes the communication history of the care recipient and generates a care plan to reduce feelings of isolation. For example, the generation AI suggests participation in local events and gatherings. In this way, a care plan can be generated to strengthen the social ties of the care recipient.

[0055] The emotion analysis unit can provide feedback on the care plan to family and friends based on the emotional state. For example, the emotion analysis unit uses an emotion estimation function to analyze the emotional state of the care recipient and feeds the results back to the family and friends. For example, if the care recipient is feeling anxious, the emotion analysis unit suggests a response that will reassure the family. The emotion analysis unit also uses the generative AI to analyze the emotional state of the care recipient and provides feedback on the care plan to the family and friends. For example, if the positive emotion is strong, the emotion analysis unit suggests fun activities to do with the family. The emotion analysis unit also uses the emotion estimation function to suggest improvements to the care plan for family and friends based on the emotional state of the care recipient. For example, if the emotion is unstable, the emotion analysis unit suggests ways to support the family. This makes it possible to provide feedback on the care plan to family and friends based on the emotional state of the care recipient.

[0056] The conversation generation unit analyzes the conversation history and learns reactions to specific topics, thereby generating more personalized conversations. For example, the conversation generation unit uses a generation AI to analyze the past conversation history of the person requiring care and learn reactions to specific topics. For example, it prioritizes incorporating topics that interest the person requiring care into the conversation. The conversation generation unit also generates personalized conversation content based on the past conversation history of the person requiring care. For example, it suggests conversations about movies and music that the person requiring care likes. The conversation generation unit also uses a generation AI to analyze the conversation history of the person requiring care and learn reactions to specific topics, thereby improving the quality of the conversation. For example, it selects topics that will relax the person requiring care. This makes it possible to generate personalized conversations based on the past conversation history of the person requiring care.

[0057] The recreation generation unit generates quizzes and games on specific themes based on the hobbies and interests, thereby activating the brain. For example, the recreation generation unit uses a generation AI to analyze the hobbies and interests of the care recipient and generate quizzes on specific themes based on the analysis. For example, the recreation generation unit suggests a quiz on history, which the care recipient likes. The recreation generation unit also generates games for activating the brain based on the interests of the care recipient. For example, the recreation generation unit suggests puzzles and crossword puzzles. The recreation generation unit also generates recreations on specific themes based on the hobbies and interests of the care recipient. For example, the recreation generation AI suggests dances to music that the care recipient likes. In this way, quizzes and games for activating the brain can be generated based on the hobbies and interests of the care recipient.

[0058] The conversation generation unit uses the emotion estimation function to generate conversation content according to the emotional state and can adjust the tone and content of the conversation according to emotional fluctuations. For example, the conversation generation unit uses the emotion estimation function to analyze the emotional state of the care recipient and generate conversation content based on the results. For example, if the care recipient is depressed, it suggests words of encouragement. The conversation generation unit also uses the generation AI to analyze the emotional state of the care recipient in real time and adjust the tone and content of the conversation according to emotional fluctuations. For example, if the emotion is elevated, it suggests a fun topic. The conversation generation unit also uses the emotion estimation function to generate conversation content according to the emotional state of the care recipient and improve the quality of the conversation according to emotional fluctuations. For example, if the emotion is unstable, it suggests conversation that provides a sense of security. In this way, conversation content according to the emotional state of the care recipient can be generated and the tone and content of the conversation can be adjusted according to emotional fluctuations.

[0059] The conversation generation unit can generate conversation content that corresponds to different cultures and languages ​​based on the cultural background and language. In the conversation generation unit, for example, the generation AI analyzes the cultural background of the care recipient and generates conversation content that corresponds to different cultures based on that. For example, if the care recipient is from a foreign country, it suggests topics related to that culture. In addition, the conversation generation unit generates conversation content based on the language of the care recipient. For example, if the care recipient speaks English, it suggests conversations in English. In addition, the conversation generation unit generates recreation that corresponds to different cultures and languages ​​by taking the cultural background and language of the care recipient into consideration. For example, it suggests foreign music and movies that the care recipient likes. In this way, conversation content that corresponds to different cultures and languages ​​can be generated based on the cultural background and language of the care recipient.

[0060] The recreation generation unit can generate appropriate recreation according to the physical condition. For example, the recreation generation unit generates recreation that does not depend on vision by using a generation AI that takes into account the visual impairment of the person requiring care. For example, it proposes activities that use music or audio guides. The recreation generation unit also generates recreation that does not depend on hearing by using a generation AI that takes into account the hearing impairment of the person requiring care. For example, it proposes visual puzzles or painting activities. The recreation generation unit also analyzes the physical condition of the person requiring care and generates recreation accordingly. For example, it proposes light physical activities that are suited to the person's athletic ability. This makes it possible to generate appropriate recreation according to the physical condition of the person requiring care.

[0061] The conversation generation unit can use the emotion estimation function to suggest conversation content with family and friends based on the emotional state, thereby strengthening emotional ties. For example, the conversation generation unit uses the emotion estimation function to analyze the emotional state of the care recipient and suggest conversation content with family and friends based on the results. For example, if the care recipient is feeling lonely, it suggests talking about happy memories with family. In addition, the conversation generation unit uses the generation AI to analyze the emotional state of the care recipient in real time and adjust the conversation content with family and friends according to emotional fluctuations. For example, if the care recipient is emotionally excited, it suggests happy topics. In addition, the conversation generation unit uses the emotion estimation function to suggest conversation content to strengthen emotional ties with family and friends based on the emotional state of the care recipient. For example, if the care recipient is emotionally unstable, it suggests conversation that will give a sense of security. In this way, conversation content with family and friends can be suggested based on the emotional state of the care recipient, thereby strengthening emotional ties.

[0062] The emotion analysis unit analyzes the emotional state in real time and can dynamically adjust the care plan according to emotional fluctuations. For example, the generation AI in the emotion analysis unit monitors the emotional state of the care recipient in real time and adjusts the care plan according to emotional fluctuations. For example, if the emotional state is unstable, the plan may be changed to one that promotes relaxation. The emotion analysis unit also collects the emotional state of the care recipient 24 hours a day, and the generation AI analyzes that data to optimize the daily care plan. For example, it adjusts the nighttime care content taking into account emotional fluctuations at night. The emotion analysis unit also uses the generation AI to analyze the emotional state of the care recipient and generate a long-term emotion management plan. For example, it suggests regular emotion checks and relaxation activities to maintain the emotional state. This allows the care plan to be dynamically adjusted according to the emotional state of the care recipient.

[0063] The emotion analysis unit can analyze the emotional state and suggest appropriate music or videos depending on emotional fluctuations. For example, the emotion analysis unit uses a generation AI to analyze the emotional state of the care recipient and suggest appropriate music based on the results. For example, if the care recipient wants to relax, it will suggest relaxation music. The emotion analysis unit also uses a generation AI to suggest appropriate videos based on the emotional state of the care recipient. For example, if the care recipient is feeling emotional, it will suggest enjoyable movies or videos. The emotion analysis unit also uses a generation AI to analyze the emotional state of the care recipient in real time and adjust the suggested music and videos depending on emotional fluctuations. For example, if the emotion is unstable, it will suggest videos that give a sense of security. This makes it possible to suggest appropriate music and videos depending on the emotional state of the care recipient.

[0064] The emotion analysis unit can use the emotion estimation function to suggest relaxation techniques according to the emotional state. For example, the emotion analysis unit uses the emotion estimation function to analyze the emotional state of the person requiring care and suggest relaxation techniques based on the results. For example, if the person is emotionally unstable, it suggests deep breathing. The emotion analysis unit also uses the generative AI to analyze the emotional state of the person requiring care in real time and adjusts relaxation techniques according to emotional fluctuations. For example, if the person is emotionally elevated, it suggests meditation. The emotion analysis unit also uses the emotion estimation function to suggest relaxation techniques according to the emotional state of the person requiring care and improves the quality of relaxation according to emotional fluctuations. For example, if the person is emotionally unstable, it suggests relaxation music. This makes it possible to suggest relaxation techniques according to the emotional state of the person requiring care.

[0065] The emotion analysis unit can analyze the emotional state and suggest appropriate meals and drinks according to emotional fluctuations. For example, the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient and suggests appropriate meals based on the results. For example, if the emotions are unstable, it will suggest meals that have a relaxing effect. The emotion analysis unit also suggests appropriate drinks based on the emotional state of the care recipient. For example, if the emotions are elevated, it will suggest chamomile tea. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the food and drink suggestions according to emotional fluctuations. For example, if the emotions are unstable, it will suggest meals that will give a sense of security. This makes it possible to suggest appropriate meals and drinks according to the emotional state of the care recipient.

[0066] The emotion analysis unit can analyze the emotional state and suggest appropriate exercises or physical activities according to emotional fluctuations. For example, the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient and suggests appropriate exercises based on the results. For example, if the emotions are unstable, it will suggest yoga, which has a relaxing effect. The emotion analysis unit also suggests appropriate physical activities based on the emotional state of the care recipient. For example, if the emotions are elevated, it will suggest light jogging. The emotion analysis unit also suggests the generation AI in the emotion analysis unit analyzes the emotional state of the care recipient in real time and adjusts the suggested exercises or physical activities according to emotional fluctuations. For example, if the emotions are unstable, it will suggest stretching, which has a relaxing effect. This makes it possible to suggest appropriate exercises or physical activities according to the emotional state of the care recipient.

[0067] The emotion analysis unit can use the emotion estimation function to suggest emotional support for family and friends based on the emotional state. For example, the emotion analysis unit uses the emotion estimation function to analyze the emotional state of the care recipient and suggest emotional support for family and friends based on the results. For example, if the care recipient is feeling anxious, the emotion analysis unit suggests a response that will reassure the family. The emotion analysis unit also uses the generative AI to analyze the emotional state of the care recipient in real time and adjust the support content for family and friends according to emotional fluctuations. For example, if the emotion recipient is feeling excited, the emotion analysis unit suggests fun activities. The emotion analysis unit also uses the emotion estimation function to suggest areas for improving emotional support for family and friends based on the emotional state of the care recipient. For example, if the emotion recipient is emotionally unstable, the emotion analysis unit suggests methods of support for family. In this way, emotional support for family and friends can be suggested based on the emotional state of the care recipient.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The care support system may further include a sleep analysis unit that analyzes the sleep patterns of the care recipient. For example, the sleep analysis unit collects the sleep data of the care recipient, and the generation AI analyzes the data to evaluate the quality of the sleep. For example, the sleep analysis unit monitors the depth of sleep and the frequency of interruptions and suggests an appropriate sleep environment. The sleep analysis unit also allows the generation AI to adjust the daytime activity plan based on the care recipient's sleep pattern. For example, if the care recipient's nighttime sleep is insufficient, the generation AI reduces daytime activities. The sleep analysis unit also allows the generation AI to analyze the care recipient's sleep data and propose a long-term sleep improvement plan. For example, the generation AI may recommend establishing a regular sleep rhythm or relaxation techniques. This can improve the quality of the care recipient's sleep and improve their overall health.

[0070] The care support system can further include an exercise analysis unit that analyzes the exercise data of the care recipient. For example, the exercise analysis unit monitors the amount and type of daily exercise of the care recipient, and the generation AI analyzes that data to optimize the exercise plan. For example, if a lack of exercise is detected, light exercise will be suggested. The movement analysis unit also allows the generation AI to create an individual exercise plan based on the care recipient's exercise data. For example, it may recommend stretching to increase joint flexibility. The movement analysis unit also allows the generation AI to analyze the care recipient's exercise data and support the establishment of long-term exercise habits. For example, it may suggest regular walking or a fitness program. This helps improve the care recipient's exercise habits and maintain their health.

[0071] The care support system can further include a hobby analysis unit that delves deeper into the hobbies and interests of the care recipient. For example, the hobby analysis unit collects data on the care recipient's past hobbies and interests, and the generation AI analyzes that data to suggest new hobbies and activities. For example, the system provides support for the care recipient to resume hobbies that they previously enjoyed. In addition, the hobby analysis unit uses the generation AI to suggest new hobbies and activities based on the care recipient's hobby data. For example, it recommends new hobbies such as handicrafts or gardening. In addition, the hobby analysis unit uses the generation AI to analyze the care recipient's hobby data and suggest activities to strengthen social connections through hobbies. For example, it recommends participation in hobby clubs or group activities. This can improve the quality of life of the care recipient and strengthen social connections.

[0072] The care support system may further include a stress analysis unit that analyzes the stress level of the care recipient. For example, the stress analysis unit collects physiological and behavioral data of the care recipient, and the generating AI analyzes the data to assess the stress level. For example, the stress analysis unit monitors the heart rate and electrodermal activity, and suggests relaxation techniques if stress is high. The stress analysis unit also generates a stress management plan based on the stress data of the care recipient. For example, the generating AI recommends regular relaxation sessions and stress reduction activities. The stress analysis unit also analyzes the stress data of the care recipient and suggests a long-term stress management strategy. For example, the generating AI identifies the causes of stress and takes measures to address them. This reduces the stress level of the care recipient and improves their overall health.

[0073] The care support system can further include an environmental analysis unit that analyzes the environmental data of the care recipient. For example, the environmental analysis unit collects data on the living environment of the care recipient, and the generating AI analyzes that data to optimize the environment. For example, it monitors room temperature, humidity, and lighting conditions and makes suggestions for maintaining a comfortable environment. The environmental analysis unit also allows the generating AI to create an environmental improvement plan based on the environmental data of the care recipient. For example, it recommends using an air purifier or adjusting the lighting appropriately. The environmental analysis unit also allows the generating AI to analyze the environmental data of the care recipient and propose a long-term environmental management strategy. For example, it recommends seasonal environmental adjustments and regular environmental checks. This allows the living environment of the care recipient to be optimized and support a comfortable life.

[0074] The care support system can also suggest appropriate aromatherapy based on the emotional state of the care recipient. For example, the emotion analysis unit analyzes the emotional state of the care recipient and, based on the results, suggests an aroma with a relaxing effect. For example, if the person is emotionally unstable, it may recommend a lavender aroma. The emotion analysis unit also allows the generation AI to create an aromatherapy plan based on the emotional state of the care recipient. For example, if the person is emotionally elevated, it may suggest a chamomile aroma. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the aromatherapy suggestions according to emotional fluctuations. For example, if the person is emotionally unstable, it may suggest an aroma with a relaxing effect. This allows the system to suggest aromatherapy that suits the emotional state of the care recipient, thereby stabilizing their emotions.

[0075] The care support system can also suggest appropriate pet therapy based on the emotional state of the care recipient. For example, the emotion analysis unit analyzes the emotional state of the care recipient and suggests pet therapy based on the results. For example, if the care recipient is emotionally unstable, it may recommend interacting with a dog or cat. The emotion analysis unit also allows the generation AI to create a pet therapy plan based on the emotional state of the care recipient. For example, if the care recipient is emotionally elevated, it may suggest playing time with a pet. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the pet therapy suggestions according to emotional fluctuations. For example, if the care recipient is emotionally unstable, it may suggest interacting with a pet that has a relaxing effect. This allows the system to suggest pet therapy that suits the care recipient's emotional state, thereby stabilizing their emotions.

[0076] The care support system can also suggest appropriate art therapy based on the emotional state of the care recipient. For example, the emotion analysis unit analyzes the emotional state of the care recipient and suggests art therapy based on the results. For example, if the person is emotionally unstable, it may recommend drawing activities. The emotion analysis unit also uses the generative AI to create an art therapy plan based on the emotional state of the care recipient. For example, if the person is emotionally elevated, it may suggest colorful art activities. The emotion analysis unit also uses the generative AI to analyze the emotional state of the care recipient in real time and adjust the art therapy suggestions according to emotional fluctuations. For example, if the person is emotionally unstable, it may suggest art activities that have a relaxing effect. This allows the system to suggest art therapy that suits the emotional state of the care recipient, helping to stabilize their emotions.

[0077] The care support system can also suggest appropriate music therapy based on the emotional state of the care recipient. For example, the emotion analysis unit analyzes the emotional state of the care recipient and suggests music therapy based on the results. For example, if the emotional state is unstable, classical music may be recommended. The emotion analysis unit also allows the generation AI to create a music therapy plan based on the emotional state of the care recipient. For example, if the emotional state is elevated, pop music may be suggested. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the music therapy suggestions according to emotional fluctuations. For example, if the emotional state is unstable, music with a relaxing effect may be suggested. This allows the system to suggest music therapy that suits the emotional state of the care recipient, helping to stabilize their emotions.

[0078] The care support system can also suggest appropriate physical therapy based on the emotional state of the care recipient. For example, the emotion analysis unit analyzes the emotional state of the care recipient and suggests physical therapy based on the results. For example, if the care recipient is emotionally unstable, yoga, which has a relaxing effect, is recommended. The emotion analysis unit also allows the generation AI to create a physical therapy plan based on the emotional state of the care recipient. For example, if the emotion is elevated, light exercise is suggested. The emotion analysis unit also allows the generation AI to analyze the emotional state of the care recipient in real time and adjust the physical therapy suggestions according to emotional fluctuations. For example, if the emotion is unstable, stretching with a relaxing effect is suggested. This makes it possible to suggest physical therapy that suits the emotional state of the care recipient, thereby stabilizing their emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The care plan creation unit creates a care plan based on instructions from the caregiver. For example, the care recipient's health condition and daily activities are provided as input information to the generation AI, which then generates an optimal care plan based on that information. Step 2: The record creation unit creates daily care records. For example, the record creation unit automatically creates daily care records, thereby reducing the burden on the caregiver. Step 3: The conversation generation unit generates conversations tailored to the individual needs and preferences of the care recipient. For example, the care recipient's past conversation history, hobbies, and interests are provided as input information to the generation AI, which then generates appropriate conversation content based on that information. Step 4: The recreation generation unit generates recreation tailored to the individual needs and preferences of the care recipient. For example, the care recipient's hobbies and interests are provided as input information to the generation AI, which then generates an appropriate recreation plan based on that information. Step 5: The emotion analysis unit analyzes the emotions of the person requiring care. For example, it analyzes the person's facial expressions and tone of voice, and changes the response according to their emotions. Step 6: The feedback unit provides daily care information to the AI ​​generator. For example, the AI ​​generator provides daily care records and the care recipient's reactions as input information, and the AI ​​generator adjusts the care plan for the next day based on that information.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. Equipped with generative AI, The generated AI is a care plan creation department that creates a care plan in response to instructions from a supporter; A record-keeping department that creates daily nursing care records; a conversation generation unit that generates conversations tailored to the individual needs and preferences of the care recipient; a recreation generation unit that generates recreation tailored to the individual needs and preferences of the care recipient; an emotion analysis unit that analyzes the emotions of the person requiring care; A feedback unit that provides feedback on daily care content. A system characterized by:

2. The care plan creation unit Analyzing vital signs data in real time and dynamically adjusting the care plan based thereon 2. The system of claim 1.

3. The care plan creation unit Analyze medical history and generate long-term health management plans 2. The system of claim 1.

4. The emotion analysis unit Analyze emotional states in real time and adjust the care plan according to emotional fluctuations 2. The system of claim 1.

5. The care plan creation unit Analyze dietary content and nutritional status, and generate the care plan that takes nutritional balance into consideration.

2. The system of claim 1.

6. The care plan creation unit Analyzing communication history and generating a care plan to strengthen social connections 2. The system of claim 1.

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