System

Abstract

An object of a system according to an embodiment is to efficiently manage nursing care services.SOLUTION: A system includes a health monitoring part, a social activity support part, a drug management part, and a nursing care record creation part. The health monitoring unit monitors a health condition of the cared person. The social activity support unit supports a social activity of the cared person. The medication management unit performs medication management of the cared person. The nursing care record creation part creates a nursing care record.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 that the burden of caregiving work is heavy and it is difficult to manage efficiently.

[0005] The system according to the embodiment aims to efficiently manage nursing care services. [Means for solving the problem]

[0006] The system according to the embodiment includes a health monitoring unit, a social activity support unit, a drug management unit, and a care record creation unit. The health monitoring unit monitors the health status of the care recipient. The social activity support unit supports the social activities of the care recipient. The drug management unit manages the drugs of the care recipient. The care record creation unit creates care records. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage nursing care services. [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 nursing care service proxy system according to an embodiment of the present invention is a system that monitors the health status of a care recipient, supports social activities, manages medication, and creates care records, thereby improving the efficiency and quality of nursing care services.

[0029] The nursing care service proxy system according to the embodiment includes a health monitoring unit, a social activity support unit, a medication management unit, and a care record creation unit. The health monitoring unit monitors the health status of the care recipient. For example, a wearable device is used to collect vital signs such as heart rate, blood pressure, and body temperature, and AI analyzes this data. If an abnormality is detected, the health monitoring unit can notify care staff or a medical institution. The social activity support unit supports the social activities of the care recipient. For example, AI interacts with the care recipient and promotes social activities through daily conversations and games. The social activity support unit can also suggest appropriate activities based on the care recipient's hobbies and interests. The medication management unit manages the care recipient's medication. For example, AI can remind the care recipient to take their medication and instruct them to take the correct medication at the correct time. The medication management unit can manage medication inventory and suggest refills as needed. The care record creation unit automatically creates care records. For example, AI can record the care recipient's health status and daily activities so that care staff can review them later. In addition, the care record creation unit can analyze the care records and suggest improvements to the care plan. As a result, the care service proxy system according to the embodiment can improve the efficiency and quality of care services. For example, health monitoring can detect abnormalities early and take appropriate measures. Furthermore, support for social activities can maintain the mental health of the care recipient. Furthermore, medication management can prevent medication errors, and the creation of care records can improve the care plan.

[0030] The health monitoring unit can collect vital signs such as heart rate, blood pressure, and body temperature using a wearable device, and AI can analyze this data. The health monitoring unit can, for example, collect vital signs such as heart rate, blood pressure, and body temperature using a wearable device, and AI can analyze this data. For example, the AI ​​can analyze the collected vital sign data such as heart rate, blood pressure, and body temperature to generate an individual health prediction model. For example, it can predict future cardiovascular disease risk based on past data. The AI ​​can also generate an individual health prediction model based on the vital sign data of the care recipient to predict future diabetes risk. For example, it can analyze fluctuations in blood sugar levels to assess risk. The AI ​​can also generate an individual health prediction model based on the collected vital sign data to predict future respiratory disease risk. For example, it can analyze respiratory rate and oxygen saturation data to assess risk. This allows for detailed monitoring of the care recipient's health condition.

[0031] The health monitoring unit can generate an individual health prediction model based on vital sign data and predict future health risks. For example, the health monitoring unit analyzes vital sign data such as heart rate, blood pressure, and body temperature collected by AI to generate an individual health prediction model. For example, it predicts future risk of cardiovascular disease based on past data. The AI ​​can also generate an individual health prediction model based on the vital sign data of the care recipient to predict future risk of diabetes. For example, it can analyze fluctuations in blood sugar levels and assess risk. The AI ​​can also generate an individual health prediction model based on vital sign data collected by AI to predict future risk of respiratory disease. For example, it can analyze data on respiratory rate and oxygen saturation and assess risk. This allows future health risks to be predicted and addressed early.

[0032] The health monitoring unit can also collect dietary and exercise data to assess the overall health condition. For example, the health monitoring unit uses AI to record the dietary content of the care recipient and evaluate the nutritional balance. For example, it analyzes the calories and nutrients in meals to assess the health condition. The AI ​​also collects the care recipient's exercise data and evaluates the amount and intensity of exercise. For example, it analyzes the number of steps and exercise time to assess the health condition. The AI ​​also integrates the care recipient's dietary and exercise data to assess the overall health condition. For example, it analyzes the balance between diet and exercise and evaluates health risks. This makes it possible to assess the overall health condition of the care recipient.

[0033] The health monitoring unit can use AI to generate personalized health advice based on health monitoring data and provide it to the care recipient. The health monitoring unit generates personalized health advice based on vital sign data collected by AI, for example. For example, it analyzes heart rate and blood pressure data and provides advice on exercise and diet. The AI ​​also generates personalized health advice based on the health data of the care recipient. For example, it analyzes sleep data and provides advice on improving sleep. The AI ​​also generates personalized health advice based on the health data it collects and provides it to the care recipient. For example, it analyzes stress levels and provides advice on stress management. This makes it possible to provide personalized health advice to the care recipient.

[0034] The health monitoring unit can store the collected data in the cloud, allowing remote medical professionals to access it in real time. For example, the health monitoring unit stores vital sign data collected by the AI ​​in the cloud, allowing remote medical professionals to access it in real time. For example, heart rate and blood pressure data is uploaded to the cloud. The AI ​​also stores the care recipient's health data in the cloud, allowing remote medical professionals to access it in real time. For example, blood sugar level and body temperature data is uploaded to the cloud. The AI ​​also stores the health data collected by the AI ​​in the cloud, allowing remote medical professionals to access it in real time. For example, sleep data and exercise data are uploaded to the cloud. This allows remote medical professionals to access the data in real time.

[0035] The social activity support unit can analyze past conversation data of the care recipient and learn and adapt to the individual conversation style. In the social activity support unit, for example, AI analyzes past conversation data of the care recipient and learns the individual conversation style. For example, it analyzes and adapts to the tone of the conversation and the choice of topics. The AI ​​also learns and adapts the individual conversation style based on the past conversation data of the care recipient. For example, it adjusts the content of the conversation based on the preferences and interests of the care recipient. The AI ​​also analyzes past conversation data of the care recipient and learns and adapts to the individual conversation style. For example, it learns the way the care recipient speaks and expresses themselves to achieve natural conversation. This makes it possible to adapt an individual conversation style to the care recipient.

[0036] The social activity support unit records the social activity history of the care recipient and can suggest new activities at the optimal timing. In the social activity support unit, for example, AI records the social activity history of the care recipient and suggests new activities at the optimal timing. For example, it analyzes the frequency and content of past activities and suggests new activities at the appropriate timing. In addition, AI suggests new activities at the optimal timing based on the social activity history of the care recipient. For example, it selects activities based on the interests and preferences of the care recipient. In addition, AI records the social activity history of the care recipient and suggests new activities at the optimal timing. For example, it analyzes the success rate and satisfaction of past activities and adjusts the content of the suggestions. In this way, new activities can be suggested to the care recipient at the optimal timing.

[0037] The social activity support unit can support communication between the care recipient's family and friends and automate the sending and receiving of video calls and messages. In the social activity support unit, for example, AI automates video calls with the care recipient's family and friends and supports communication. For example, it schedules regular video calls and automatically starts the calls. In addition, AI automates the sending and receiving of messages with the care recipient's family and friends and supports communication. For example, it automatically sends and replies to regular messages. In addition, AI supports communication between the care recipient's family and friends and automates the sending and receiving of video calls and messages. For example, it reminds the care recipient of important events and anniversaries and promotes communication. In this way, it can support communication between the care recipient's family and friends.

[0038] The social activity support unit can suggest online communities and events based on the hobbies and interests of the care recipient. In the social activity support unit, for example, AI analyzes the hobbies and interests of the care recipient and suggests appropriate online communities. For example, it introduces forums and groups related to hobbies. Furthermore, AI suggests online events that the care recipient can participate in based on their interests. For example, it introduces webinars and virtual workshops. Furthermore, AI analyzes the hobbies and interests of the care recipient and suggests online communities and events. For example, it introduces online classes and seminars related to hobbies. In this way, it is possible to suggest appropriate online communities and events based on the hobbies and interests of the care recipient.

[0039] The drug management unit can analyze the history of drug allergies and side effects of the care recipient and automatically warn of risky drugs. For example, AI can analyze the drug allergy history of the care recipient and automatically warn of risky drugs. For example, it can identify drugs that may cause allergic reactions. AI can also analyze the side effect history of the care recipient and automatically warn of risky drugs. For example, it can identify drugs that have caused side effects in the past and issue a warning. AI can also analyze the history of drug allergies and side effects of the care recipient and automatically warn of risky drugs. For example, it can assess the risks when prescribing a new drug and issue a warning. This makes it possible to automatically warn care recipients of risky drugs.

[0040] The medication management unit can monitor the effects of medication and notify medical professionals if the medication is ineffective or if side effects occur. For example, the medication management unit uses AI to monitor the effects of medication administered to the care recipient and notify medical professionals if the medication is ineffective. For example, a notification is sent if blood pressure does not decrease. The AI ​​can also monitor the side effects of medication administered to the care recipient and notify medical professionals if side effects occur. For example, a notification is sent if a rash or nausea occurs. The AI ​​can also monitor the effects and side effects of medication and notify medical professionals if an abnormality is detected. For example, a notification is sent if the medication is ineffective or if side effects occur. This allows the medication effects and side effects to be monitored and medical professionals to be notified if an abnormality is detected.

[0041] The medication management unit can store medication administration histories in the cloud, allowing medical professionals to access them in real time. For example, the medication management unit uses AI to store the medication administration history of the care recipient in the cloud, allowing medical professionals to access it in real time. For example, data on administration times and dosages is uploaded to the cloud. The AI ​​also stores the medication administration history in the cloud, allowing medical professionals to access it in real time. For example, data on drug effects and side effects is uploaded to the cloud. The AI ​​also stores the medication administration history of the care recipient in the cloud, allowing medical professionals to access it in real time. For example, drug inventory status and the need for refills is uploaded to the cloud. This allows medical professionals to access the medication administration history in real time.

[0042] The medication management unit can optimize the timing of medication intake based on the diet and lifestyle habits of the care recipient. In the medication management unit, for example, AI analyzes the dietary data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of meals. In addition, AI analyzes the lifestyle data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of sleep patterns. In addition, AI integrates the dietary and lifestyle data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of exercise. In this way, it is possible to optimize the timing of medication intake based on the dietary and lifestyle habits of the care recipient.

[0043] The care record creation unit can automatically analyze the care records and suggest improvements to the care plan. In the care record creation unit, for example, AI automatically analyzes the care records and suggests improvements to the care plan. For example, it analyzes the health condition and activity data of the care recipient and identifies areas for improvement. In addition, AI suggests improvements to the care plan based on the care records. For example, it analyzes the diet and exercise data of the care recipient and identifies areas for improvement. In addition, AI automatically analyzes the care records and suggests improvements to the care plan. For example, it analyzes the emotional state and social activity data of the care recipient and identifies areas for improvement. In this way, improvements to the care plan can be automatically suggested.

[0044] The care record creation unit can summarize the care records using natural language processing technology so that they can be quickly checked by care staff. In the care record creation unit, for example, AI summarizes the care records using natural language processing technology so that they can be quickly checked by care staff. For example, it converts long records into short summaries. AI also summarizes the care records so that they can be quickly checked by care staff. For example, it highlights important information and abnormalities. AI also summarizes the care records using natural language processing technology so that they can be quickly checked by care staff. For example, it converts daily records into weekly or monthly summaries. This allows the care records to be quickly checked.

[0045] The care record creation unit can store the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. In the care record creation unit, for example, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, daily records may be uploaded to the cloud. Also, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, health status and activity data may be uploaded to the cloud. Also, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, emotional status and social activity data may be uploaded to the cloud. This allows care staff and medical professionals in remote locations to access the care records in real time.

[0046] The care record creation unit can convert care records into visual notes or mind maps to make them easier to understand visually. In the care record creation unit, for example, AI can convert care records into visual notes to make them easier to understand visually. For example, important information can be displayed using diagrams and icons. AI can also convert care records into mind maps to make them easier to understand visually. For example, related information can be displayed as nodes and connected by links. AI can also convert care records into visual notes or mind maps to make them easier to understand visually. For example, daily records can be visually organized to make it easier to grasp the overall picture. This makes care records easier to understand visually.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The care service proxy system can further include a voice recognition unit. The voice recognition unit can analyze the voice of the care recipient and understand everyday conversations and instructions. For example, if the care recipient says, "I want a drink of water," the voice recognition unit understands the instruction and notifies the care staff. The voice recognition unit can also analyze the voice data of the care recipient and detect changes in health status. For example, it can detect signs of stress or fatigue based on changes in tone of voice and speaking style. Furthermore, the voice recognition unit can learn individual conversation styles based on the voice data of the care recipient and realize natural conversations. This facilitates communication with the care recipient and improves the quality of care.

[0049] The caregiving service agent system can further include an environmental monitoring unit. The environmental monitoring unit can monitor the living environment of the care recipient and maintain a comfortable environment. For example, it can monitor room temperature and humidity and control air conditioners and humidifiers to keep them within appropriate ranges. The environmental monitoring unit can also monitor air quality and activate air purifiers as needed. Furthermore, the environmental monitoring unit can monitor lighting brightness and sound levels and adjust them to maintain the comfort of the care recipient. This allows the care recipient to live in a comfortable environment.

[0050] The caregiving service proxy system can further include a nutrition management unit. The nutrition management unit can record the dietary content of the care recipient and evaluate the nutritional balance. For example, it can analyze the calories and nutrients in meals and evaluate the health condition. The nutrition management unit can also provide individual nutritional advice based on the care recipient's dietary history. For example, if a specific nutrient is lacking, it can suggest ingredients and recipes to supplement it. Furthermore, the nutrition management unit can predict future health risks and suggest preventive measures based on the care recipient's dietary data. This helps maintain the care recipient's health and prevent illness.

[0051] The caregiving service agent system can further include an exercise support unit. The exercise support unit can collect exercise data from the care recipient and propose an appropriate exercise plan. For example, it can analyze the number of steps and exercise time and propose exercises to address lack of exercise. The exercise support unit can also provide individual exercise advice based on the care recipient's exercise history. For example, it can propose exercises to strengthen specific muscles. Furthermore, the exercise support unit can predict future health risks and propose preventive measures based on the care recipient's exercise data. This helps maintain the care recipient's health and prevent illness.

[0052] The caregiving task proxy system can further include a sleep management unit. The sleep management unit can collect sleep data of the care recipient and evaluate the quality of sleep. For example, it can analyze sleep duration and sleep depth to evaluate sleep quality. The sleep management unit can also provide individual sleep advice based on the care recipient's sleep history. For example, it can suggest improvements to the sleep environment and relaxation methods. Furthermore, the sleep management unit can predict future health risks and suggest preventive measures based on the care recipient's sleep data. This helps maintain the care recipient's health and prevent illness.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The health monitoring unit monitors the health of the care recipient. For example, a wearable device can be used to collect vital signs such as heart rate, blood pressure, and body temperature, and the AI ​​can analyze this data. If an abnormality is detected, the health monitoring unit can notify care staff or a medical institution. Step 2: The social activity support module supports the care recipient's social activities. For example, the AI ​​can interact with the care recipient and encourage social activities through everyday conversations and games. The social activity support module can also suggest appropriate activities based on the care recipient's hobbies and interests. Step 3: The Medication Management Department manages the care recipient's medication. For example, AI can remind them to take their medication and instruct them to take the right medication at the right time. The Medication Management Department can also manage medication inventory and suggest refills as needed. Step 4: The care record creation unit automatically creates care records. For example, AI can record the health status and daily activities of the care recipient so that care staff can review them later. The care record creation unit can also analyze the care records and suggest improvements to the care plan.

[0055] (Example 2) The nursing care service proxy system according to an embodiment of the present invention is a system that monitors the health status of a care recipient, supports social activities, manages medication, and creates care records, thereby improving the efficiency and quality of nursing care services.

[0056] The nursing care service proxy system according to the embodiment includes a health monitoring unit, a social activity support unit, a medication management unit, and a care record creation unit. The health monitoring unit monitors the health status of the care recipient. For example, a wearable device is used to collect vital signs such as heart rate, blood pressure, and body temperature, and AI analyzes this data. If an abnormality is detected, the health monitoring unit can notify care staff or a medical institution. The social activity support unit supports the social activities of the care recipient. For example, AI interacts with the care recipient and promotes social activities through daily conversations and games. The social activity support unit can also suggest appropriate activities based on the care recipient's hobbies and interests. The medication management unit manages the care recipient's medication. For example, AI can remind the care recipient to take their medication and instruct them to take the correct medication at the correct time. The medication management unit can manage medication inventory and suggest refills as needed. The care record creation unit automatically creates care records. For example, AI can record the care recipient's health status and daily activities so that care staff can review them later. In addition, the care record creation unit can analyze the care records and suggest improvements to the care plan. As a result, the care service proxy system according to the embodiment can improve the efficiency and quality of care services. For example, health monitoring can detect abnormalities early and take appropriate measures. Furthermore, support for social activities can maintain the mental health of the care recipient. Furthermore, medication management can prevent medication errors, and the creation of care records can improve the care plan.

[0057] The health monitoring unit can collect vital signs such as heart rate, blood pressure, and body temperature using a wearable device, and AI can analyze this data. The health monitoring unit can, for example, collect vital signs such as heart rate, blood pressure, and body temperature using a wearable device, and AI can analyze this data. For example, the AI ​​can analyze the collected vital sign data such as heart rate, blood pressure, and body temperature to generate an individual health prediction model. For example, it can predict future cardiovascular disease risk based on past data. The AI ​​can also generate an individual health prediction model based on the vital sign data of the care recipient to predict future diabetes risk. For example, it can analyze fluctuations in blood sugar levels to assess risk. The AI ​​can also generate an individual health prediction model based on the collected vital sign data to predict future respiratory disease risk. For example, it can analyze respiratory rate and oxygen saturation data to assess risk. This allows for detailed monitoring of the care recipient's health condition.

[0058] The health monitoring unit can generate an individual health prediction model based on vital sign data and predict future health risks. For example, the health monitoring unit analyzes vital sign data such as heart rate, blood pressure, and body temperature collected by AI to generate an individual health prediction model. For example, it predicts future risk of cardiovascular disease based on past data. The AI ​​can also generate an individual health prediction model based on the vital sign data of the care recipient to predict future risk of diabetes. For example, it can analyze fluctuations in blood sugar levels and assess risk. The AI ​​can also generate an individual health prediction model based on vital sign data collected by AI to predict future risk of respiratory disease. For example, it can analyze data on respiratory rate and oxygen saturation and assess risk. This allows future health risks to be predicted and addressed early.

[0059] The health monitoring unit can also collect dietary and exercise data to assess the overall health condition. For example, the health monitoring unit uses AI to record the dietary content of the care recipient and evaluate the nutritional balance. For example, it analyzes the calories and nutrients in meals to assess the health condition. The AI ​​also collects the care recipient's exercise data and evaluates the amount and intensity of exercise. For example, it analyzes the number of steps and exercise time to assess the health condition. The AI ​​also integrates the care recipient's dietary and exercise data to assess the overall health condition. For example, it analyzes the balance between diet and exercise and evaluates health risks. This makes it possible to assess the overall health condition of the care recipient.

[0060] The health monitoring unit uses the emotion estimation function to monitor the stress level of the care recipient and make suggestions for stress reduction. For example, the health monitoring unit uses AI to analyze the facial expressions and voice of the care recipient to estimate the stress level. For example, it calculates a stress score based on facial expressions and tone of voice. The AI ​​also analyzes the vital sign data of the care recipient to estimate the stress level. For example, it calculates a stress score based on fluctuations in heart rate and blood pressure. The AI ​​also monitors the stress level of the care recipient and makes suggestions for stress reduction. For example, it suggests relaxation exercises or hobby activities. In this way, the stress level of the care recipient can be monitored and appropriate suggestions can be made.

[0061] The health monitoring unit can use AI to generate personalized health advice based on health monitoring data and provide it to the care recipient. The health monitoring unit generates personalized health advice based on vital sign data collected by AI, for example. For example, it analyzes heart rate and blood pressure data and provides advice on exercise and diet. The AI ​​also generates personalized health advice based on the health data of the care recipient. For example, it analyzes sleep data and provides advice on improving sleep. The AI ​​also generates personalized health advice based on the health data it collects and provides it to the care recipient. For example, it analyzes stress levels and provides advice on stress management. This makes it possible to provide personalized health advice to the care recipient.

[0062] The health monitoring unit can store the collected data in the cloud, allowing remote medical professionals to access it in real time. For example, the health monitoring unit stores vital sign data collected by the AI ​​in the cloud, allowing remote medical professionals to access it in real time. For example, heart rate and blood pressure data is uploaded to the cloud. The AI ​​also stores the care recipient's health data in the cloud, allowing remote medical professionals to access it in real time. For example, blood sugar level and body temperature data is uploaded to the cloud. The AI ​​also stores the health data collected by the AI ​​in the cloud, allowing remote medical professionals to access it in real time. For example, sleep data and exercise data are uploaded to the cloud. This allows remote medical professionals to access the data in real time.

[0063] The health monitoring unit can use the emotion estimation function to propose a health management plan according to the emotional state of the care recipient. For example, the health monitoring unit uses AI to analyze the emotional state of the care recipient and propose a health management plan according to the emotional state. For example, if the stress level is high, it proposes relaxation exercises. The AI ​​also monitors the emotional state of the care recipient and proposes a health management plan according to the emotional state. For example, if the emotional state is unstable, it proposes mental health care. The AI ​​also analyzes the emotional state of the care recipient and proposes a health management plan according to the emotional state. For example, if the emotional state is positive, it proposes exercise or social activities. In this way, it is possible to propose a health management plan according to the emotional state of the care recipient.

[0064] The social activity support unit can analyze past conversation data of the care recipient and learn and adapt to the individual conversation style. In the social activity support unit, for example, AI analyzes past conversation data of the care recipient and learns the individual conversation style. For example, it analyzes and adapts to the tone of the conversation and the choice of topics. The AI ​​also learns and adapts the individual conversation style based on the past conversation data of the care recipient. For example, it adjusts the content of the conversation based on the preferences and interests of the care recipient. The AI ​​also analyzes past conversation data of the care recipient and learns and adapts to the individual conversation style. For example, it learns the way the care recipient speaks and expresses themselves to achieve natural conversation. This makes it possible to adapt an individual conversation style to the care recipient.

[0065] The social activity support unit records the social activity history of the care recipient and can suggest new activities at the optimal timing. In the social activity support unit, for example, AI records the social activity history of the care recipient and suggests new activities at the optimal timing. For example, it analyzes the frequency and content of past activities and suggests new activities at the appropriate timing. In addition, AI suggests new activities at the optimal timing based on the social activity history of the care recipient. For example, it selects activities based on the interests and preferences of the care recipient. In addition, AI records the social activity history of the care recipient and suggests new activities at the optimal timing. For example, it analyzes the success rate and satisfaction of past activities and adjusts the content of the suggestions. In this way, new activities can be suggested to the care recipient at the optimal timing.

[0066] The social activity support unit uses the emotion estimation function to adjust the content of the dialogue according to the emotional state of the care recipient, thereby eliciting positive emotions. In the social activity support unit, for example, AI analyzes the emotional state of the care recipient and adjusts the content of the dialogue according to that emotional state. For example, if the stress level is high, it selects a topic that will help the care recipient relax. In addition, AI monitors the emotional state of the care recipient and adjusts the content of the dialogue according to that emotional state. For example, if the emotional state is unstable, it selects a topic that will give a sense of security. In addition, AI analyzes the emotional state of the care recipient and adjusts the content of the dialogue according to that emotional state. For example, if the emotional state is positive, it selects a fun topic. This makes it possible to elicit positive emotions from the care recipient.

[0067] The social activity support unit can support communication between the care recipient's family and friends and automate the sending and receiving of video calls and messages. In the social activity support unit, for example, AI automates video calls with the care recipient's family and friends and supports communication. For example, it schedules regular video calls and automatically starts the calls. In addition, AI automates the sending and receiving of messages with the care recipient's family and friends and supports communication. For example, it automatically sends and replies to regular messages. In addition, AI supports communication between the care recipient's family and friends and automates the sending and receiving of video calls and messages. For example, it reminds the care recipient of important events and anniversaries and promotes communication. In this way, it can support communication between the care recipient's family and friends.

[0068] The social activity support unit can suggest online communities and events based on the hobbies and interests of the care recipient. In the social activity support unit, for example, AI analyzes the hobbies and interests of the care recipient and suggests appropriate online communities. For example, it introduces forums and groups related to hobbies. Furthermore, AI suggests online events that the care recipient can participate in based on their interests. For example, it introduces webinars and virtual workshops. Furthermore, AI analyzes the hobbies and interests of the care recipient and suggests online communities and events. For example, it introduces online classes and seminars related to hobbies. In this way, it is possible to suggest appropriate online communities and events based on the hobbies and interests of the care recipient.

[0069] The social activity support unit can use the emotion estimation function to suggest social activities that correspond to the emotional state of the care recipient. In the social activity support unit, for example, AI analyzes the emotional state of the care recipient and suggests social activities that correspond to the emotional state. For example, if the stress level is high, a relaxation activity is suggested. In addition, AI monitors the emotional state of the care recipient and suggests social activities that correspond to the emotional state. For example, if the emotional state is unstable, an activity that provides a sense of security is suggested. In addition, AI analyzes the emotional state of the care recipient and suggests social activities that correspond to the emotional state. For example, if the emotional state is positive, a fun activity is suggested. In this way, social activities can be suggested that correspond to the emotional state of the care recipient.

[0070] The drug management unit can analyze the history of drug allergies and side effects of the care recipient and automatically warn of risky drugs. For example, AI can analyze the drug allergy history of the care recipient and automatically warn of risky drugs. For example, it can identify drugs that may cause allergic reactions. AI can also analyze the side effect history of the care recipient and automatically warn of risky drugs. For example, it can identify drugs that have caused side effects in the past and issue a warning. AI can also analyze the history of drug allergies and side effects of the care recipient and automatically warn of risky drugs. For example, it can assess the risks when prescribing a new drug and issue a warning. This makes it possible to automatically warn care recipients of risky drugs.

[0071] The medication management unit can monitor the effects of medication and notify medical professionals if the medication is ineffective or if side effects occur. For example, the medication management unit uses AI to monitor the effects of medication administered to the care recipient and notify medical professionals if the medication is ineffective. For example, a notification is sent if blood pressure does not decrease. The AI ​​can also monitor the side effects of medication administered to the care recipient and notify medical professionals if side effects occur. For example, a notification is sent if a rash or nausea occurs. The AI ​​can also monitor the effects and side effects of medication and notify medical professionals if an abnormality is detected. For example, a notification is sent if the medication is ineffective or if side effects occur. This allows the medication effects and side effects to be monitored and medical professionals to be notified if an abnormality is detected.

[0072] The medication management unit can use the emotion estimation function to adjust the medication reminder method according to the emotional state of the care recipient. For example, the medication management unit uses AI to analyze the emotional state of the care recipient and adjust the medication reminder method according to the emotional state. For example, if the stress level is high, the medication management unit will use a gentle tone to remind the care recipient. The AI ​​can also monitor the emotional state of the care recipient and adjust the medication reminder method according to the emotional state. For example, if the emotional state is unstable, the medication reminder will provide a sense of security. The AI ​​can also analyze the emotional state of the care recipient and adjust the medication reminder method according to the emotional state. For example, if the emotional state is positive, the medication reminder will be accompanied by encouraging words. This makes it possible to adjust the medication reminder method according to the emotional state of the care recipient.

[0073] The medication management unit can store medication administration histories in the cloud, allowing medical professionals to access them in real time. For example, the medication management unit uses AI to store the medication administration history of the care recipient in the cloud, allowing medical professionals to access it in real time. For example, data on administration times and dosages is uploaded to the cloud. The AI ​​also stores the medication administration history in the cloud, allowing medical professionals to access it in real time. For example, data on drug effects and side effects is uploaded to the cloud. The AI ​​also stores the medication administration history of the care recipient in the cloud, allowing medical professionals to access it in real time. For example, drug inventory status and the need for refills is uploaded to the cloud. This allows medical professionals to access the medication administration history in real time.

[0074] The medication management unit can optimize the timing of medication intake based on the diet and lifestyle habits of the care recipient. In the medication management unit, for example, AI analyzes the dietary data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of meals. In addition, AI analyzes the lifestyle data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of sleep patterns. In addition, AI integrates the dietary and lifestyle data of the care recipient and optimizes the timing of medication intake. For example, it adjusts the time to take medication to match the timing of exercise. In this way, it is possible to optimize the timing of medication intake based on the dietary and lifestyle habits of the care recipient.

[0075] The medication management unit can use the emotion estimation function to propose a medication management plan according to the emotional state of the care recipient. For example, the medication management unit uses AI to analyze the emotional state of the care recipient and propose a medication management plan according to the emotional state. For example, if the stress level is high, it will propose medication for stress reduction. In addition, the AI ​​monitors the emotional state of the care recipient and proposes a medication management plan according to the emotional state. For example, if the emotional state is unstable, it will propose medication for mental health care. In addition, the AI ​​analyzes the emotional state of the care recipient and proposes a medication management plan according to the emotional state. For example, if the emotional state is positive, it will propose supplements for maintaining health. In this way, it is possible to propose a medication management plan according to the emotional state of the care recipient.

[0076] The care record creation unit can automatically analyze the care records and suggest improvements to the care plan. In the care record creation unit, for example, AI automatically analyzes the care records and suggests improvements to the care plan. For example, it analyzes the health condition and activity data of the care recipient and identifies areas for improvement. In addition, AI suggests improvements to the care plan based on the care records. For example, it analyzes the diet and exercise data of the care recipient and identifies areas for improvement. In addition, AI automatically analyzes the care records and suggests improvements to the care plan. For example, it analyzes the emotional state and social activity data of the care recipient and identifies areas for improvement. In this way, improvements to the care plan can be automatically suggested.

[0077] The care record creation unit can summarize the care records using natural language processing technology so that they can be quickly checked by care staff. In the care record creation unit, for example, AI summarizes the care records using natural language processing technology so that they can be quickly checked by care staff. For example, it converts long records into short summaries. AI also summarizes the care records so that they can be quickly checked by care staff. For example, it highlights important information and abnormalities. AI also summarizes the care records using natural language processing technology so that they can be quickly checked by care staff. For example, it converts daily records into weekly or monthly summaries. This allows the care records to be quickly checked.

[0078] The care record creation unit can use the emotion estimation function to record the emotional state of the care recipient and reflect it in the care plan. In the care record creation unit, for example, AI analyzes the emotional state of the care recipient and records the emotional state. For example, it records daily emotion scores and reflects them in the care plan. In addition, AI monitors the emotional state of the care recipient and records the emotional state. For example, it records stress levels and happiness levels and reflects them in the care plan. In addition, AI analyzes the emotional state of the care recipient and records the emotional state and reflects them in the care plan. For example, it records fluctuations in emotional state and reflects them in the care plan. In this way, the emotional state of the care recipient can be recorded and reflected in the care plan.

[0079] The care record creation unit can store the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. In the care record creation unit, for example, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, daily records may be uploaded to the cloud. Also, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, health status and activity data may be uploaded to the cloud. Also, AI stores the care records in the cloud, allowing them to be accessed in real time by care staff and medical professionals in remote locations. For example, emotional status and social activity data may be uploaded to the cloud. This allows care staff and medical professionals in remote locations to access the care records in real time.

[0080] The care record creation unit can convert care records into visual notes or mind maps to make them easier to understand visually. In the care record creation unit, for example, AI can convert care records into visual notes to make them easier to understand visually. For example, important information can be displayed using diagrams and icons. AI can also convert care records into mind maps to make them easier to understand visually. For example, related information can be displayed as nodes and connected by links. AI can also convert care records into visual notes or mind maps to make them easier to understand visually. For example, daily records can be visually organized to make it easier to grasp the overall picture. This makes care records easier to understand visually.

[0081] The care record creation unit can use the emotion estimation function to adjust the method of creating care records according to the emotional state of the care recipient. In the care record creation unit, for example, AI analyzes the emotional state of the care recipient and adjusts the method of creating care records according to the emotional state. For example, detailed records are made when the stress level is high. In addition, AI monitors the emotional state of the care recipient and adjusts the method of creating care records according to the emotional state. For example, specific information is emphasized when the emotional state is unstable. In addition, AI analyzes the emotional state of the care recipient and adjusts the method of creating care records according to the emotional state. For example, concise records are made when the emotional state is positive. In this way, the method of creating care records can be adjusted according to the emotional state of the care recipient.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The care service proxy system can further include a voice recognition unit. The voice recognition unit can analyze the voice of the care recipient and understand everyday conversations and instructions. For example, if the care recipient says, "I want a drink of water," the voice recognition unit understands the instruction and notifies the care staff. The voice recognition unit can also analyze the voice data of the care recipient and detect changes in health status. For example, it can detect signs of stress or fatigue based on changes in tone of voice and speaking style. Furthermore, the voice recognition unit can learn individual conversation styles based on the voice data of the care recipient and realize natural conversations. This facilitates communication with the care recipient and improves the quality of care.

[0084] The caregiving service agent system can further include an environmental monitoring unit. The environmental monitoring unit can monitor the living environment of the care recipient and maintain a comfortable environment. For example, it can monitor room temperature and humidity and control air conditioners and humidifiers to keep them within appropriate ranges. The environmental monitoring unit can also monitor air quality and activate air purifiers as needed. Furthermore, the environmental monitoring unit can monitor lighting brightness and sound levels and adjust them to maintain the comfort of the care recipient. This allows the care recipient to live in a comfortable environment.

[0085] The caregiving service proxy system can further include a nutrition management unit. The nutrition management unit can record the dietary content of the care recipient and evaluate the nutritional balance. For example, it can analyze the calories and nutrients in meals and evaluate the health condition. The nutrition management unit can also provide individual nutritional advice based on the care recipient's dietary history. For example, if a specific nutrient is lacking, it can suggest ingredients and recipes to supplement it. Furthermore, the nutrition management unit can predict future health risks and suggest preventive measures based on the care recipient's dietary data. This helps maintain the care recipient's health and prevent illness.

[0086] The caregiving service agent system can further include an exercise support unit. The exercise support unit can collect exercise data from the care recipient and propose an appropriate exercise plan. For example, it can analyze the number of steps and exercise time and propose exercises to address lack of exercise. The exercise support unit can also provide individual exercise advice based on the care recipient's exercise history. For example, it can propose exercises to strengthen specific muscles. Furthermore, the exercise support unit can predict future health risks and propose preventive measures based on the care recipient's exercise data. This helps maintain the care recipient's health and prevent illness.

[0087] The caregiving task proxy system can further include a sleep management unit. The sleep management unit can collect sleep data of the care recipient and evaluate the quality of sleep. For example, it can analyze sleep duration and sleep depth to evaluate sleep quality. The sleep management unit can also provide individual sleep advice based on the care recipient's sleep history. For example, it can suggest improvements to the sleep environment and relaxation methods. Furthermore, the sleep management unit can predict future health risks and suggest preventive measures based on the care recipient's sleep data. This helps maintain the care recipient's health and prevent illness.

[0088] The caregiving service proxy system can further use the emotion estimation function to provide music and videos that match the emotional state of the care recipient. For example, relaxing music can be played when the stress level is high. The emotion estimation function can also be used to provide videos that match the emotional state of the care recipient. For example, videos that give a sense of security can be played when the emotional state is unstable. The emotion estimation function can also be used to provide entertainment that matches the emotional state of the care recipient. For example, fun movies and TV shows can be suggested when the emotional state is positive. In this way, entertainment that matches the emotional state of the care recipient can be provided.

[0089] The caregiving service agent system can further use the emotion estimation function to suggest relaxation exercises according to the emotional state of the care recipient. For example, deep breathing or meditation can be suggested when the stress level is high. The emotion estimation function can also be used to suggest relaxation exercises according to the emotional state of the care recipient. For example, yoga or stretching can be suggested when the emotional state is unstable. The emotion estimation function can also be used to suggest relaxation exercises according to the emotional state of the care recipient. For example, light exercise or dancing can be suggested when the emotional state is positive. In this way, relaxation exercises can be provided according to the emotional state of the care recipient.

[0090] The caregiving service proxy system can further use the emotion estimation function to provide mental health care according to the emotional state of the care recipient. For example, counseling or therapy can be suggested when the stress level is high. The emotion estimation function can also be used to provide mental health care according to the emotional state of the care recipient. For example, relaxation techniques can be suggested when the emotional state is unstable. The emotion estimation function can also be used to provide mental health care according to the emotional state of the care recipient. For example, activities that increase self-esteem can be suggested when the emotional state is positive. In this way, mental health care according to the emotional state of the care recipient can be provided.

[0091] The caregiving service agent system can further use an emotion estimation function to adjust the communication method according to the emotional state of the care recipient. For example, if the stress level is high, the system can speak in a gentle tone. The emotion estimation function can also be used to adjust the communication method according to the emotional state of the care recipient. For example, if the emotional state is unstable, words that give a sense of security can be selected. The emotion estimation function can also be used to adjust the communication method according to the emotional state of the care recipient. For example, if the emotional state is positive, words of encouragement can be added. This makes it possible to provide a communication method that suits the emotional state of the care recipient.

[0092] The care work proxy system can further use the emotion estimation function to adjust the care plan according to the emotional state of the care recipient. For example, if the stress level is high, relaxation activities can be increased. The emotion estimation function can also be used to adjust the care plan according to the emotional state of the care recipient. For example, if the emotional state is unstable, the time for mental health care can be increased. The emotion estimation function can also be used to adjust the care plan according to the emotional state of the care recipient. For example, if the emotional state is positive, the time for social activities can be increased. This makes it possible to provide a care plan according to the emotional state of the care recipient.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The health monitoring unit monitors the health of the care recipient. For example, a wearable device can be used to collect vital signs such as heart rate, blood pressure, and body temperature, and the AI ​​can analyze this data. If an abnormality is detected, the health monitoring unit can notify care staff or a medical institution. Step 2: The social activity support module supports the care recipient's social activities. For example, the AI ​​can interact with the care recipient and encourage social activities through everyday conversations and games. The social activity support module can also suggest appropriate activities based on the care recipient's hobbies and interests. Step 3: The Medication Management Department manages the care recipient's medication. For example, AI can remind them to take their medication and instruct them to take the right medication at the right time. The Medication Management Department can also manage medication inventory and suggest refills as needed. Step 4: The care record creation unit automatically creates care records. For example, AI can record the health status and daily activities of the care recipient so that care staff can review them later. The care record creation unit can also analyze the care records and suggest improvements to the care plan.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a health monitoring unit that monitors the health status of the care recipient; A social activities support department that supports social activities for care recipients; A drug management department that manages medication for care recipients; A care record creation unit that creates a care record. A system characterized by:

2. The health monitoring unit: Wearable devices will be used to collect vital signs such as heart rate, blood pressure, and temperature, and the AI ​​will analyze this data.

2. The system of claim 1.

3. The health monitoring unit: Generate an individual health prediction model based on vital sign data to predict future health risks 2. The system of claim 1.

4. The health monitoring unit: Dietary and exercise data for care recipients will also be collected to assess their overall health.

2. The system of claim 1.

5. The health monitoring unit: Monitor the care recipient's stress level and offer suggestions for stress reduction 2. The system of claim 1.

6. The health monitoring unit: Based on the health monitoring data, AI generates and provides personalized health advice to the care recipient.

2. The system of claim 1.

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