System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting health information of a person to be cared for; means for organizing and transmitting the inputted health information to a server; means for analyzing the health information stored in the server and generating a AI model; and means for providing a care plan generated by the AI model to a device of the person to be cared for.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] Creating care plans for elderly people and those requiring special care requires a thorough understanding of each individual's health condition and needs, which is time-consuming and labor-intensive. This makes it difficult to provide an efficient and appropriate care plan, increasing the burden on family and caregivers. Furthermore, it is difficult to continuously update such care plans and provide them in real time. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for inputting health data of a care recipient, a means for organizing the input health data and sending it to a server, a means for analyzing the health data stored on the server and generating an AI model, and a means for providing the care plan generated by the AI ​​model to the care recipient's device. This system quickly provides a customized care plan based on the care recipient's health condition, reducing the burden on family members and caregivers. Furthermore, by updating the care plan in real time and providing appropriate instructions to the care recipient, continuous and efficient care is achieved.

[0006] "Care recipients" refers to older adults and individuals with physical or cognitive limitations who require special care and assistance.

[0007] "Health data" refers to information about the care recipient's health condition, such as their daily body temperature, blood pressure, activity level, diet, and symptoms.

[0008] "Means" refers to an apparatus, system, software component, or combination thereof for accomplishing a particular purpose.

[0009] "Server" refers to a computer system for collecting, storing, and analyzing data.

[0010] "Terminal" refers to a device (smartphone, tablet, dedicated equipment, etc.) used by the care recipient or caregiver to enter data and check the care plan.

[0011] An "AI model" refers to an algorithm or program that uses machine learning and data mining techniques to analyze data and perform a specific task (in this case, generating a care plan).

[0012] "Care plan" means a plan that includes instructions and recommendations for daily living and medical care based on the health status and needs of the person receiving care.

[0013] "Means of input" refers to the interface or method for collecting health data of the care recipient and inputting it into the system.

[0014] "Means of sending" refers to the process or mechanism by which data is sent from the device to the server.

[0015] "Means for analysis" refers to methods and devices for analyzing collected data using statistical and machine learning techniques.

[0016] "Means for providing" refers to a method or system for communicating the generated care plan to the care recipient or caregiver. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention is a system for generating and providing an optimal care plan for elderly people and people requiring special care. The system includes the following means:

[0039] 1. Data Collection Module

[0040] The user (care recipient) uses a dedicated application or device to input daily health data, such as body temperature, blood pressure, dietary habits, and symptoms.

[0041] The terminal organizes this input data and sends it to the server in an appropriate format.

[0042] 2. Analysis and Model Generation Module

[0043] The server receives the health data of the care recipient sent from the terminal and stores it in a database.

[0044] The server analyzes the stored data and uses machine learning algorithms to train an AI model that can generate a care plan based on the care recipient's health status and individual needs.

[0045] 3. Care plan provision module

[0046] The server updates the care plan generated by the AI ​​model in real time and provides it to the device.

[0047] The device displays the provided care plan to the user and gives instructions for implementation, such as "We recommend a low-salt meal for lunch today" and "Take 20 minutes of light exercise in the afternoon."

[0048] Users (care recipients, family members, caregivers) carry out their daily lives according to this care plan.

[0049] Specific examples

[0050] Example 1: Morning data entry and care plan provision

[0051] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0052] 2. The terminal organizes the entered data and sends it to the server.

[0053] 3. The server receives the data and analyzes it using an AI model in combination with past data.

[0054] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0055] 5. This care plan is sent to the device and displayed to the user.

[0056] Example 2: Update care plan based on afternoon health change

[0057] 1. In the afternoon, the user (care recipient) feels a change in their physical condition and enters "I feel dizzy" into the app.

[0058] 2. The device organizes this information and sends it to the server.

[0059] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0060] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0061] 5. The updated care plan is sent to the device and displayed to the user.

[0062] This system allows care recipients to receive customized care based on their health condition and needs, reducing the burden on their families and caregivers. The system continuously updates care plans in real time, ensuring optimal care is provided to care recipients every day.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user (care recipient) uses a dedicated app or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0066] Step 2:

[0067] The terminal organizes the health data entered by the user and transmits the data to the server, where it is converted into an appropriate format and sent to the server via the network.

[0068] Step 3:

[0069] The server receives the data sent from the device and stores it in a database, allowing the care recipient's health data to be organized and recorded by time.

[0070] Step 4:

[0071] The server periodically analyzes the stored data, which includes analyzing the care recipient's past health data and trends, and assessing their current health status.

[0072] Step 5:

[0073] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates a care plan that best suits the individual needs of the care recipient.

[0074] Step 6:

[0075] The server then sends the care plan generated by the AI ​​model to the device, which includes specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0076] Step 7:

[0077] The terminal displays the care plan received from the server to the user, and the displayed care plan is presented in a format that is easy for the care recipient to carry out in their daily lives.

[0078] Step 8:

[0079] The user (care recipient, family member, or caregiver) performs daily activities based on the displayed care plan, for example, preparing the prescribed meals and performing the designated exercises.

[0080] Step 9:

[0081] If the user's health condition changes due to the situation, they can enter additional health data, for example, reporting a symptom such as "feeling dizzy" in the afternoon to the dedicated app.

[0082] Step 10:

[0083] The terminal reorganizes the newly entered data and sends it to the server.

[0084] Step 11:

[0085] After receiving the new data, the server reanalyzes it with the existing data and uses the AI ​​model to update the care plan, which may include instructions such as "drink lots of fluids and take 15 minutes of rest."

[0086] Step 12:

[0087] The server sends the updated care plan to the terminal, which displays it to the user.

[0088] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] To provide optimal care plans in real time for elderly people and those requiring special care, it is necessary to efficiently collect and analyze large amounts of health data and appropriately generate and update care plans based on the data to meet individual needs. However, conventional systems often require manual data collection and analysis, making it difficult to respond immediately. In addition, the care plans provided are uniform, making it difficult to provide optimal care plans tailored to the condition of each individual care recipient.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and transmitting it to the computer system, a means for analyzing the health data stored in the computer system and generating a machine learning model, and a means for providing the care plan generated by the machine learning model to the care recipient's device. This allows an optimal care plan based on the care recipient's health condition to be generated and provided in real time, making it possible to provide more effective and personalized care to elderly people and care recipients who require special care.

[0094] "Care recipient" refers to an individual who requires special care based on their health condition or living situation.

[0095] "Health data" refers to numerical values ​​and records that indicate the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and symptoms.

[0096] "Computer system" is a collective term for hardware and software that receives, stores, and analyzes input data and generates and operates machine learning models.

[0097] A "machine learning model" is an algorithm that learns patterns and relationships from large amounts of data and makes predictions and suggestions based on new data.

[0098] A "care plan" is a plan that includes specific instructions and recommendations for daily living based on the health condition and individual needs of the person receiving care.

[0099] "Device" refers to any electronic device or device used by a care recipient, family member, or caregiver to receive or implement a care plan.

[0100] "Means of input" refers to the interfaces and devices that allow care recipients and their caregivers to collect and record health data.

[0101] "Processing means" refers to the processes and software used to convert collected health data into an appropriate format so that it can be transmitted.

[0102] "Transmission means" refers to the network infrastructure and communication protocols used to transmit the organized health data to a computer system or server.

[0103] "Means for analysis" refers to algorithms or software used to analyze stored health data and identify patterns or anomalies.

[0104] "Means of generation" refers to the methods and technologies for generating a machine learning model based on analyzed data and creating an optimal care plan.

[0105] "Means for providing" refers to the infrastructure and software required to send the generated care plan to the care recipient's device and enable it to be displayed and executed.

[0106] MODE FOR CARRYING OUT THE INVENTION

[0107] This invention is a system that generates and provides optimal care plans for elderly people and those requiring special care. The system inputs the care recipient's health data, uses an AI model based on this data, and generates and provides a care plan in real time.

[0108] Data Collection Module

[0109] The user (care recipient) enters daily health data using a dedicated application or device (smartphone, tablet, etc.). The data entered includes body temperature, blood pressure, dietary details, symptoms, etc. For example, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0110] The terminal then processes these inputs and formats them into the appropriate format, including validating and optionally formatting the data, before sending the resulting data in an encrypted form to the server.

[0111] Analysis and Model Generation Module

[0112] The server receives the encrypted data sent from the device and stores it in a database. During the storage process, the data is checked for consistency and integrity.

[0113] The server then uses the stored health data to train an AI model using machine learning algorithms (e.g., TensorFlow or PyTorch). The training process utilizes past data and adds new data as it progresses.

[0114] The server generates a prompt based on the current user's health data and inputs it into the AI ​​model. An example of a prompt might be, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate an optimal morning care plan."

[0115] Care plan delivery module

[0116] Based on the input prompt, the AI ​​model generates an optimal care plan for the care recipient, such as "Eat a light breakfast, followed by a 30-minute walk."

[0117] The server updates the generated care plan in real time, encrypts it, and sends it to the device, which then decrypts the data and displays it to the user. The displayed content includes specific instructions, such as "Recommend a low-salt meal for lunch today" or "Try 20 minutes of light exercise this afternoon."

[0118] Care plan implementation and feedback

[0119] Users (care recipients, family members, and caregivers) carry out their daily activities according to the displayed care plan, and then report the progress of the care plan to the app as feedback after the plan is completed.

[0120] The device sends the feedback data to the server, which receives it and updates the database. New feedback data is also used to train the AI ​​model, which is updated accordingly.

[0121] Specific examples

[0122] Example 1: Morning data entry and care plan provision

[0123] 1. In the morning, the user (care recipient) enters "body temperature 36.8 degrees, blood pressure 130 / 85" into a dedicated app.

[0124] 2. The terminal encrypts the entered data and sends it to the server.

[0125] 3. The server receives and stores the data, and analyzes it using an AI model in combination with past data.

[0126] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0127] 5. This care plan is encrypted, sent to the device, and displayed to the user.

[0128] Example 2: Update care plan based on afternoon health change

[0129] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app.

[0130] 2. The device encrypts this information and sends it to the server.

[0131] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0132] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0133] 5. The updated care plan is encrypted, sent to the device, and displayed to the user.

[0134] This system provides customized care based on the care recipient's health condition and needs, reducing the burden on family and caregivers. The system continuously updates the care plan in real time, allowing the care recipient to receive optimal care every day.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] User health data entry

[0138] The user (care recipient) uses a dedicated application to input daily health data such as body temperature, blood pressure, dietary details, and symptoms. This is done using devices such as smartphones and tablets. Specifically, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0139] Input: Health data entered by the user into the app

[0140] Output: Organized health data (e.g., body temperature 36.8°C, blood pressure 130 / 85)

[0141] Step 2:

[0142] Data organization and transmission by terminal

[0143] The device receives the health data entered by the user, verifies the format, and organizes it. After formatting the data, it encrypts it and securely transmits it to the server. Specifically, this includes the operation of the device encrypting the input data and transmitting it to the server.

[0144] Input: Organized health data

[0145] Data processing: Data encryption processing

[0146] Output: Encrypted health data

[0147] Step 3:

[0148] Data reception and storage by the server

[0149] The server receives the encrypted data sent from the device, decrypts the data, and stores it in a database. This process verifies the integrity and completeness of the data. Specifically, this process involves the server decrypting the encrypted data and storing it in a database.

[0150] Input: Encrypted health data

[0151] Data processing: Data decryption and integrity check

[0152] Output: Health data stored in a database

[0153] Step 4:

[0154] Server-based data analysis and AI model training

[0155] The server analyzes the stored health data and trains an AI model using machine learning algorithms (e.g., TensorFlow, PyTorch). It optimizes the algorithm using past data and adds new data to the learning process. Specifically, the server uses the stored data to train the model.

[0156] Input: Health data stored in a database

[0157] Data Computing: Analyzing data and applying algorithms to train AI models

[0158] Output: A trained AI model

[0159] Step 5:

[0160] Server-generated prompts and care plan creation

[0161] Based on the trained AI model, the server generates a prompt corresponding to the current user's health data and inputs it into the model. An example of a prompt is, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate the optimal morning care plan." Based on this prompt, the AI ​​model generates the optimal care plan according to each individual's health condition.

[0162] Input: Current user's health data, prompt text

[0163] Data Calculation: Care plan generation using an AI model based on prompt statements

[0164] Output: Generated care plan

[0165] Step 6:

[0166] Encrypt and send care plans

[0167] The server encrypts the generated care plan and sends it to the terminal. The terminal decrypts the encrypted data and prepares it for display to the user. Specifically, this includes the operation of the server encrypting the care plan and sending it to the terminal.

[0168] Input: Generated care plan

[0169] Data Processing: Care Plan Encryption

[0170] Output: Encrypted care plan

[0171] Step 7:

[0172] Displaying care plans on devices

[0173] The device decrypts the encrypted care plan it receives and displays it to the user. The displayed content includes specific instructions such as "We recommend a low-salt meal for lunch today" and "Try 20 minutes of light exercise this afternoon." Specifically, the device decodes the care plan and displays it on the screen.

[0174] Input: Encrypted care plan data

[0175] Data processing: Decryption of care plan data

[0176] Output: Decoded care plan display

[0177] Step 8:

[0178] User implementation of care plans and feedback

[0179] Users (care recipients, family members, caregivers) carry out their daily activities according to the displayed care plan. After that, they report the implementation status of the care plan and feedback within the app. Specifically, this includes the user carrying out the care plan and providing feedback on the results to the app.

[0180] Input: The result of the user's executed care plan

[0181] Output: Implementation status data fed back into the app

[0182] Step 9:

[0183] Feedback data transmission by terminal

[0184] The device receives and organizes the feedback data from the user and sends it to the server. Specifically, the device organizes the feedback data and sends it back to the server.

[0185] Input: User-entered feedback data

[0186] Data processing: Feedback data cleansing and encryption

[0187] Output: Feedback data sent to the server

[0188] Step 10:

[0189] Server-based data updates and model retraining

[0190] The server receives the feedback data and updates the database. Based on the new data, the AI ​​model is retrained to generate more accurate care plans. Specifically, the server uses the feedback data to continuously optimize the AI ​​model.

[0191] Input: Feedback of implementation status data

[0192] Data Computing: Retraining AI models using feedback data

[0193] Output: Updated AI model

[0194] These are the programming steps of the system, which, based on this detailed processing, will provide a real-time, personalized care plan for the elderly and those with special needs.

[0195] (Application example 1)

[0196] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0197] Daily health management is important for the elderly and those receiving special care, but it is difficult to manage this individually, so there is a need to provide appropriate care plans.In addition, there is currently a lack of support systems that allow elderly people who visit stores to understand their own health status and select appropriate products and care methods.

[0198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0199] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and sending it to the server, and a means for acquiring the health data from an input device installed in the physical store, thereby enabling elderly people and care recipients who require special care to efficiently manage their health conditions and receive appropriate care plans even in the store.

[0200] "Care recipients" refers to people who require support in daily life, such as elderly people or people with disabilities who require special assistance or care.

[0201] "Health data" refers to information that indicates the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and changes in physical condition.

[0202] "Server" refers to a computer system for collecting, storing, and analyzing data over a network.

[0203] "AI model" refers to a computational model that uses machine learning algorithms to generate appropriate care plans from input data.

[0204] A "care plan" refers to specific care methods and health management instructions created based on the health condition and needs of the person receiving care.

[0205] "Terminal" refers to an electronic device used by the care recipient to input data and check the care plan.

[0206] A "physical store" refers to a physical store, such as a supermarket or drugstore, where consumers can visit and receive goods or services.

[0207] "Input device" refers to a hardware device or interface that allows a care recipient to record or input health data.

[0208] "Display Device" refers to a display or screen used to present generated care plans and health information to a user.

[0209] "Analysis" refers to the process of processing collected data and deriving meaningful results or information.

[0210] This invention aims to realize a system for generating and providing optimal care plans for elderly people and those requiring special care. This system consists of a data collection module, an analysis and model generation module, and a care plan provision module.

[0211] 1. Data Collection Module

[0212] The server collects daily health data from the care recipient. This data includes body temperature, blood pressure, dietary habits, symptoms, etc., and is entered by the care recipient themselves using a smartphone, tablet, or an input device installed in the store. The entered data is organized by the device and sent to the server in an appropriate format.

[0213] 2. Analysis and Model Generation Module

[0214] The server receives the transmitted health data and stores it in a database. It then analyzes the stored data and uses machine learning algorithms to train a generative AI model. This generative AI model generates an optimal care plan based on the care recipient's health condition and individual needs. For example, a deep learning framework such as TensorFlow is used to build the AI ​​model.

[0215] 3. Care plan provision module

[0216] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. The device displays the care plan to the care recipient and gives instructions for its implementation. The care recipient follows these instructions to live their daily life. For example, specific health management instructions such as "We recommend a low-sodium diet today" are displayed in real time on a display device installed in a physical store.

[0217] Specific examples

[0218] Example 1: In-store health data entry and care plan provision

[0219] 1. The care recipient enters their temperature and blood pressure at an interactive kiosk in a physical store. For example, they might enter "Temperature is 37.2°C, Blood pressure is 140 / 90."

[0220] 2. The terminal organizes the entered data and sends it to the server.

[0221] 3. The server receives the data and analyzes it using an AI model in conjunction with past data.

[0222] 4. The AI ​​model generates an appropriate care plan and provides specific advice, such as "We recommend a low-sodium diet today."

[0223] 5. This care plan is sent to a display device in the store and displayed to the care recipient.

[0224] Example 2: Update care plan based on afternoon health change

[0225] 1. In the afternoon, the person being cared for feels a change in their physical condition and types "I feel dizzy" into their smartphone.

[0226] 2. The device organizes this information and sends it to the server.

[0227] 3. The server analyzes the newly received data using an AI model and generates an updated care plan, such as "Drink plenty of fluids and take 15 minutes of rest."

[0228] 4. The updated care plan is sent to the smartphone and displayed to the care recipient.

[0229] Prompt Sentence Examples

[0230] "Generate an appropriate care plan based on the user's health data. For example, if the user's temperature is 37.2 degrees, blood pressure is 140 / 90, and dizziness is present, provide a care plan that recommends a low-sodium diet. Also recommend specific foods and supplements."

[0231] This system allows elderly people and those requiring special care to efficiently manage their health and receive appropriate care while in the store.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] The care recipient inputs health data. Using a smartphone or an interactive kiosk in a physical store as the input device, data such as body temperature, blood pressure, and changes in physical condition are entered. This input data is received and organized by a terminal. The input is specific numerical values ​​and diagnosis details, such as "body temperature is 37.2 degrees, blood pressure is 140 / 90."

[0235] Step 2:

[0236] The device sends the organized health data to a server. This transmission uses an internet connection and a secure protocol (e.g., HTTPS). For example, a smartphone app uploads the input data to a cloud server. The data is then formatted in a standard format such as JSON.

[0237] Step 3:

[0238] The server receives the health data and stores it in a database. This storage process uses an SQL database or NoSQL database. The stored data is also integrated with past data. The database organizes and stores data for each care recipient.

[0239] Step 4:

[0240] The server analyzes the stored data and updates the AI ​​model. Specifically, it uses machine learning frameworks such as TensorFlow to train a generative AI model. The entire stored health data is used as input data, and the model is updated based on trends in past and new data. Appropriate data preprocessing (e.g., normalization) is performed during this process.

[0241] Step 5:

[0242] The server generates a care plan based on the AI ​​model. Here, the generative AI model receives new data as input and outputs a specific care plan (e.g., "recommend a low-sodium diet") as an analysis result. The generated care plan includes detailed instructions based on specific rules and algorithms.

[0243] Step 6:

[0244] The server sends the generated care plan to the device using a protocol that allows for real-time data transmission (e.g., WebSocket). The care plan is displayed on the device's interface, and specific instructions are sent to the care recipient as notifications.

[0245] Step 7:

[0246] The device displays the provided care plan to the care recipient. The care plan is visualized on a display or smartphone screen, and is displayed in the form of, for example, "These ingredients are recommended today." In a physical store, the display will show content such as "A low-sodium diet is recommended today."

[0247] Step 8:

[0248] Care recipients follow a care plan and take specific actions, such as choosing recommended foods from store shelves or taking rest at designated times, to improve their health.

[0249] In this way, through the specific processing flow of each step, elderly people and those requiring special care can receive an appropriate and individualized care plan.

[0250] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0251] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take into consideration the user's mental health. The system includes the following means:

[0252] 1. Data Collection Module

[0253] The user (care recipient) uses a dedicated application or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0254] The terminal organizes this input data and sends it to the server in an appropriate format.

[0255] 2. Emotion Recognition Module

[0256] The emotion engine analyzes the facial expressions and tone of voice of the user (care recipient) through the device's camera and microphone. For example, the system recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone.

[0257] The device organizes the recognized emotion data and sends it to the server.

[0258] 3. Analysis and Model Generation Module

[0259] The server receives the health and emotion data sent from the device and stores them in a database.

[0260] The server analyzes this data comprehensively to assess the overall health of the care recipient, which involves training an AI model using machine learning algorithms, which is capable of generating a care plan based on the care recipient's health and emotional state.

[0261] 4. Care plan provision module

[0262] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" or "The user's emotional state is unstable, so allow time for relaxation."

[0263] The terminal displays the provided care plan to the user, and the displayed care plan is presented in a format that is easy for the care recipient to follow.

[0264] 5. Implementation and Feedback

[0265] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[0266] After execution, emotional and health data can be entered again to provide feedback that helps the system generate the next care plan.

[0267] Specific examples

[0268] Example 1: Morning data entry and emotion recognition

[0269] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0270] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[0271] 3. The device organizes this data and sends it to the server.

[0272] Example 2: Health status changes and emotional feedback

[0273] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[0274] 2. The device organizes this information and sends it to the server.

[0275] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0276] 4. This care plan is sent to the device and displayed to the user.

[0277] This approach reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates the care plan in real time, enabling optimal care for the care recipient on a daily basis.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] The user (care recipient) uses a dedicated application or wearable device to input daily health data, for example, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0281] Step 2:

[0282] The device organizes the health data entered, converts it into an appropriate format, and sends it to the server. The data is organized in the form of user ID, date and time, measurement values, etc.

[0283] Step 3:

[0284] The user (care recipient) records their facial expressions and tone of voice using the device's camera and microphone. This information is analyzed by the emotion engine. For example, the emotion is recognized as "calm" based on facial expression analysis by the camera, and "anxious" based on tone of voice analysis by the microphone.

[0285] Step 4:

[0286] The device organizes the emotional data analyzed by the emotion engine and sends it to the server. This data is also organized in the form of user ID, date, time, emotional state, etc.

[0287] Step 5:

[0288] The server receives the health and emotion data sent from the device and stores it in a database, allowing comprehensive health data of the care recipient to be managed in a unified manner.

[0289] Step 6:

[0290] The server periodically analyzes the stored data, and performs an integrated analysis of health and emotional data to assess the overall health status and emotional trends of the care recipient.

[0291] Step 7:

[0292] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates an optimal care plan based on the care recipient's health and emotional state.

[0293] Step 8:

[0294] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" and "Due to the patient's unstable emotional state, time should be allotted for relaxation."

[0295] Step 9:

[0296] The terminal displays the provided care plan to the user in a format that makes it easy for the user to carry out the plan in their daily lives.

[0297] Step 10:

[0298] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[0299] Step 11:

[0300] If the situation changes the user's health or emotional state, the user can input additional data, for example, a symptom such as "I feel dizzy," and the emotion engine will recognize this as "stress."

[0301] Step 12:

[0302] The device organizes the newly entered health and emotion data and sends it back to the server.

[0303] Step 13:

[0304] After receiving the new data, the server reanalyzes it, integrating it with the existing data, and updates the care plan using the AI ​​model, which may include instructions such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0305] Step 14:

[0306] The server sends the updated care plan to the terminal, which displays it to the user.

[0307] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[0308] Example 2

[0309] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] Existing care systems mainly provide care plans based solely on the health data of the care recipient, but they are unable to generate care plans that take into account emotional changes and mental health conditions. This means that the mental stress and anxiety of the care recipient are not properly addressed, making comprehensive health management difficult.

[0311] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting the health data of the care recipient, a means for organizing the input health data and transmitting it to the server, a means for recognizing, organizing, and transmitting emotional data of the care recipient using devices such as a camera and a microphone, a means for comprehensively analyzing the health data and emotional data stored in the server and generating an AI model, and a means for providing the care plan generated by the AI ​​model to the care recipient's device. This makes it possible to generate and provide an optimal care plan that takes into account not only the physical health state of the care recipient but also the mental health state.

[0312] "Health data" refers to data that indicates the physical condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[0313] "Emotion data" is data that indicates the emotional state of the care recipient analyzed based on facial expressions, tone of voice, and the like.

[0314] A "terminal" is a device used by a care recipient to input health data and emotional data and to check the displayed care plan.

[0315] The "server" is a computer system that stores collected health and emotional data, generates an AI model based on this data, and creates a care plan.

[0316] A "care plan" is a set of specific instructions and suggestions for actions that the care recipient should implement in their daily lives, generated based on an AI model.

[0317] The "AI model" is a statistical and machine learning model that generates care plans based on algorithms trained using the care recipient's health and emotional data.

[0318] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take mental health into consideration. This system includes the following means.

[0319] First, the user (care recipient) enters their daily health data (body temperature, blood pressure, symptoms, etc.) using a dedicated application or wearable device. For example, they might enter, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85." The hardware used includes smartphones, tablets, and various devices that measure health data (thermometers and blood pressure monitors).

[0320] The device organizes this input data and sends it to the server in an appropriate format. Similarly, the emotion engine analyzes the user's facial expressions and tone of voice through the device's camera and microphone. For example, the device recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone. The device also organizes this emotion data and sends it to the server.

[0321] The server receives the health and emotional data sent from the device and stores it in a database. This process requires the use of a database management system (DBMS) to ensure data security and integrity. The server then comprehensively analyzes this data and evaluates the overall health status of the care recipient. This evaluation involves applying machine learning algorithms using Python and R to train an AI model. This AI model is capable of generating a care plan based on the care recipient's health and emotional status.

[0322] The generated care plan is updated in real time by the server and provided to the device. For example, it may contain specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "Due to unstable emotional state, take time to relax." The device displays the provided care plan to the user in a format that is easy for the care recipient to follow.

[0323] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, such as preparing meals as instructed and ensuring time for exercise and relaxation. After carrying out these activities, users can enter their emotional and health data again to provide feedback that the system uses to generate the next care plan.

[0324] Specific examples

[0325] Example 1: Morning data entry and emotion recognition

[0326] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0327] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[0328] 3. The device organizes this data and sends it to the server.

[0329] Example 2: Health status changes and emotional feedback

[0330] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[0331] 2. The device organizes this information and sends it to the server.

[0332] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0333] 4. This care plan is sent to the device and displayed to the user.

[0334] This reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates care plans in real time, enabling optimal care for care recipients on a daily basis.

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] Step 1: Data collection

[0337] The user (care recipient) launches the dedicated app every morning and inputs health data (body temperature, blood pressure, symptoms, etc.). For example, the user might input "body temperature is 36.8 degrees, blood pressure is 130 / 85." The input data (health data) is provided to the device. The input health data undergoes format checks (data validation) within the application. For example, it is checked whether the temperature is a valid number and whether the units of blood pressure are correct. Data that passes the format check is sent to the next step.

[0338] Step 2: Emotion Recognition

[0339] The user (care recipient) takes a picture of their face using the device's camera and records their voice using the microphone. The input is facial image and audio data. The device sends this data to an emotion recognition engine, which analyzes their emotional state. For example, it determines whether they are smiling from the facial image and analyzes their audio to determine whether they are relaxed or tense. This process yields emotion recognition results such as "calm" or "stressed." The analysis results are stored in the device and sent to the next step.

[0340] Step 3: Send data

[0341] The device transmits the collected health data and emotion data to the server. Appropriate security measures (e.g., encryption methods) are applied during this process. The input is the health data and emotion recognition results that have passed format checks, and these are transmitted to the server. The output is the data transmitted to the server.

[0342] Step 4: Data integration and storage

[0343] The server receives the health and emotion data sent from the device. The input is the data sent from the device. The server stores this data in a database management system (DBMS). For example, new data is stored in the appropriate fields, and recorded as "October 10, 2023, Body Temperature: 36.8°C, Blood Pressure: 130 / 85". The output is the integrated data stored in the database.

[0344] Step 5: Analysis by AI model

[0345] The server uses the stored data to assess the overall health of the care recipient. The input is the stored health data and emotional data. This process involves analyzing the data using machine learning algorithms using Python or R. For example, it may identify a tendency for recent blood pressure values ​​to be high or recent stress levels to be high. The output is the analysis results and an updated AI model.

[0346] Step 6: Create a care plan

[0347] The server generates a care plan based on the analysis results of the AI ​​model. The input is the analysis results of the AI ​​model. The generated care plan includes specific instructions for actions, such as "have a light breakfast," "take a 30-minute walk," and "take deep breaths to relax." The output is the generated care plan.

[0348] Step 7: Provide a care plan

[0349] The server sends the generated care plan to the terminal in real time. The input is the generated care plan. The terminal notifies the user of the received care plan and displays it on the screen. For example, it might say, "Today's care plan: light breakfast, 30-minute walk, deep breathing." The output is the care plan displayed to the user.

[0350] Step 8: Implementation and Feedback

[0351] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they perform the prescribed exercises, prepare the designated meals, and ensure time for relaxation. After carrying out these activities, the user again inputs health and emotional data. The input is the newly collected health and emotional data. The device organizes this data and sends it to the server, which receives it as feedback and uses it to generate the next care plan. The output is updated feedback data.

[0352] By linking each step in this way, an optimal care plan based on the physical and mental health status of the care recipient can be generated and provided.

[0353] (Application example 2)

[0354] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0355] The current nursing care system struggles to provide comprehensive care plans that consider not only the physical health of elderly people and those requiring special care, but also their mental health. Furthermore, the lack of real-time data collection and emotion recognition makes it difficult to provide appropriate care plans quickly. Furthermore, the lack of care plans that take into account emotional states leaves a great need for effective support for the mental health of care recipients.

[0356] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the health data and emotional data of the care recipient and generating an AI model, means for recognizing the emotional state of the care recipient using a smart device and transmitting the data to the server, and means for providing the care plan generated by the AI ​​model to the care recipient's terminal. This makes it possible to provide a real-time care plan based on the physical and mental health conditions of the care recipient.

[0357] "Health data of the care recipient" refers to information indicating the physical health condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[0358] A "server" is a central processing unit for receiving, storing, and analyzing data transmitted over a network.

[0359] "Emotional data" is information about the emotional state of the care recipient analyzed from facial expressions and tone of voice.

[0360] The "AI model" is a predictive model constructed using machine learning algorithms based on the health and emotional data of the care recipient.

[0361] A "care plan" is a plan generated using an AI model that includes specific care instructions and advice for the care recipient to carry out in their daily lives.

[0362] A "terminal" is a device such as a smartphone or smart glasses used by the care recipient.

[0363] A "smart device" is a device that is connected to the Internet and has the ability to collect and recognize the health and emotional state of the person being cared for in real time.

[0364] "Data collection means" refers to equipment and software for acquiring health data and emotional data from the care recipient.

[0365] "Emotion recognition means" is a technology for recognizing the emotional state of the care recipient by analyzing their facial expressions and voice.

[0366] The "data transmission means" is a function for transmitting acquired data to a server.

[0367] "Data analysis means" refers to the process of comprehensively analyzing health data and emotional data on a server to generate an AI model.

[0368] The "care plan providing means" is a mechanism for displaying and providing the generated care plan on the care recipient's terminal.

[0369] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it also takes into consideration the user's mental health. The embodiments for implementing this invention are as follows.

[0370] The system uses the following hardware and software:

[0371] Smart devices (e.g., smart glasses)

[0372] Camera and microphone

[0373] server

[0374] Client terminal (smartphone, etc.)

[0375] Emotion Recognizer

[0376] Data transmission software (requests library)

[0377] Display control software (GlassesDisplay)

[0378] Data collection

[0379] The device has a means to input the care recipient's daily health data (body temperature, blood pressure, symptoms, etc.). The input data is organized and sent to the server in an appropriate format. It is also equipped with an emotion engine that uses the smart device's camera and microphone to analyze facial expressions and voice in real time and recognize emotional data. The emotional data is also organized and sent to the server.

[0380] Data analysis

[0381] The server receives the health and emotional data sent from the device and stores it in a database. It then comprehensively analyzes this data and generates an AI model using a machine learning algorithm to assess the overall health and emotional state of the care recipient. This AI model then generates a care plan based on the care recipient's health and emotional state.

[0382] Care plan provided

[0383] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. For example, it may include specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "The user's emotional state is unstable, so allow time for relaxation." The device then displays the provided care plan to the care recipient, using the smart device's display to present the instructions in a clear and easy-to-follow format.

[0384] Execution and Feedback

[0385] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they prepare the prescribed meals and ensure they have time for designated exercise and relaxation. After carrying out these activities, they can input their emotional and health data again to receive feedback that can be used by the system to generate the next care plan.

[0386] Examples of concrete examples and prompts

[0387] Examples:

[0388] In the morning, a user wearing smart glasses enters their body temperature and blood pressure into the app. For example, they might enter "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0389] The smart glasses analyze the user's facial expressions, and the emotion engine recognizes a "calm state."

[0390] The data is sent to a server, and a care plan such as "Eat a light breakfast, then take a 30-minute walk" is displayed on the smart glasses.

[0391] Example prompt sentence:

[0392] text

[0393] Please enter your temperature today: 36.8

[0394] Enter your blood pressure today: 130 / 85

[0395] Capturing frames and analyzing facial expressions...

[0396] Emotional state: Calm

[0397] Sending data to server...

[0398] Care plan: "Light breakfast followed by a 30-minute walk is recommended."

[0399] In this way, the system can provide customized care based on the care recipient's physical and mental health status, reducing the burden on family members and caregivers.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] The device receives health data, such as body temperature and blood pressure, entered by the user (care recipient). Based on this, the device organizes the health data and converts it into an appropriate format. For example, if the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85," the data is converted into JSON format. This is the input. The output is the organized health data.

[0403] Step 2:

[0404] The device uses the camera and microphone of the smart device (e.g., smart glasses) to capture the user's facial expressions and tone of voice. Emotion recognition software (EmotionRecognizer) is used to analyze the user's emotional state from the captured data. For example, emotional data such as "calm" or "unsettled" is output.

[0405] Step 3:

[0406] The device organizes the collected health and emotion data and sends it to the server. This process uses data transmission software (requests library), which receives the organized health and emotion data as input and sends it to the server as output.

[0407] Step 4:

[0408] The server stores the received health and emotional data in a database, thereby maintaining a history of the care recipient's overall health and emotional state. The input is the received data, and the output is the information stored in the database.

[0409] Step 5:

[0410] The server comprehensively analyzes the stored health and emotional data and generates an AI model using a machine learning algorithm. This AI model is capable of generating a care plan based on the care recipient's health and emotional state. The input is the data in the database, and the output is the generated AI model.

[0411] Step 6:

[0412] The server uses the AI ​​model to generate care plans that are updated in real time, such as "Eat a light breakfast followed by a 30-minute walk" or "Allow time for relaxation."

[0413] Step 7:

[0414] The terminal displays the care plan received from the server on the care recipient's smart device. The smart glasses' display control software (GlassesDisplay) is used to display the care plan in a format that is easy for the user to understand. The input is the care plan from the server, and the output is the care plan displayed on the smart device.

[0415] Step 8:

[0416] Users (care recipients, family members, caregivers) carry out their daily lives according to the care plan displayed on the device. For example, they prepare the meals instructed and ensure time for designated exercise and relaxation. This executes the care plan, and the results are fed back to the server via the device. The input is the care content carried out by the user, and the output is the data fed back to the system.

[0417] This specific processing step makes it possible to monitor the health and emotional state of the care recipient in real time and provide an optimal care plan based on that.

[0418] 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.

[0419] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0420] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0421] [Second embodiment]

[0422] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0423] 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.

[0424] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0425] 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.

[0426] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0427] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0428] 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.

[0429] 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.

[0430] 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 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.

[0431] 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.

[0432] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0433] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0434] The present invention is a system for generating and providing an optimal care plan for elderly people and people requiring special care. The system includes the following means:

[0435] 1. Data Collection Module

[0436] The user (care recipient) uses a dedicated application or device to input daily health data, such as body temperature, blood pressure, dietary habits, and symptoms.

[0437] The terminal organizes this input data and sends it to the server in an appropriate format.

[0438] 2. Analysis and Model Generation Module

[0439] The server receives the health data of the care recipient sent from the terminal and stores it in a database.

[0440] The server analyzes the stored data and uses machine learning algorithms to train an AI model that can generate a care plan based on the care recipient's health status and individual needs.

[0441] 3. Care plan provision module

[0442] The server updates the care plan generated by the AI ​​model in real time and provides it to the device.

[0443] The device displays the provided care plan to the user and gives instructions for implementation, such as "We recommend a low-salt meal for lunch today" and "Take 20 minutes of light exercise in the afternoon."

[0444] Users (care recipients, family members, caregivers) carry out their daily lives according to this care plan.

[0445] Specific examples

[0446] Example 1: Morning data entry and care plan provision

[0447] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0448] 2. The terminal organizes the entered data and sends it to the server.

[0449] 3. The server receives the data and analyzes it using an AI model in combination with past data.

[0450] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0451] 5. This care plan is sent to the device and displayed to the user.

[0452] Example 2: Update care plan based on afternoon health change

[0453] 1. In the afternoon, the user (care recipient) feels a change in their physical condition and enters "I feel dizzy" into the app.

[0454] 2. The device organizes this information and sends it to the server.

[0455] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0456] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0457] 5. The updated care plan is sent to the device and displayed to the user.

[0458] This system allows care recipients to receive customized care based on their health condition and needs, reducing the burden on their families and caregivers. The system continuously updates care plans in real time, ensuring optimal care is provided to care recipients every day.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] The user (care recipient) uses a dedicated app or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0462] Step 2:

[0463] The terminal organizes the health data entered by the user and transmits the data to the server, where it is converted into an appropriate format and sent to the server via the network.

[0464] Step 3:

[0465] The server receives the data sent from the device and stores it in a database, allowing the care recipient's health data to be organized and recorded by time.

[0466] Step 4:

[0467] The server periodically analyzes the stored data, which includes analyzing the care recipient's past health data and trends, and assessing their current health status.

[0468] Step 5:

[0469] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates a care plan that best suits the individual needs of the care recipient.

[0470] Step 6:

[0471] The server then sends the care plan generated by the AI ​​model to the device, which includes specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0472] Step 7:

[0473] The terminal displays the care plan received from the server to the user, and the displayed care plan is presented in a format that is easy for the care recipient to carry out in their daily lives.

[0474] Step 8:

[0475] The user (care recipient, family member, or caregiver) performs daily activities based on the displayed care plan, for example, preparing the prescribed meals and performing the designated exercises.

[0476] Step 9:

[0477] If the user's health condition changes due to the situation, they can enter additional health data, for example, reporting a symptom such as "feeling dizzy" in the afternoon to the dedicated app.

[0478] Step 10:

[0479] The terminal reorganizes the newly entered data and sends it to the server.

[0480] Step 11:

[0481] After receiving the new data, the server reanalyzes it with the existing data and uses the AI ​​model to update the care plan, which may include instructions such as "drink lots of fluids and take 15 minutes of rest."

[0482] Step 12:

[0483] The server sends the updated care plan to the terminal, which displays it to the user.

[0484] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[0485] Example 1

[0486] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] To provide optimal care plans in real time for elderly people and those requiring special care, it is necessary to efficiently collect and analyze large amounts of health data and appropriately generate and update care plans based on the data to meet individual needs. However, conventional systems often require manual data collection and analysis, making it difficult to respond immediately. In addition, the care plans provided are uniform, making it difficult to provide optimal care plans tailored to the condition of each individual care recipient.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0489] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and transmitting it to the computer system, a means for analyzing the health data stored in the computer system and generating a machine learning model, and a means for providing the care plan generated by the machine learning model to the care recipient's device. This allows an optimal care plan based on the care recipient's health condition to be generated and provided in real time, making it possible to provide more effective and personalized care to elderly people and care recipients who require special care.

[0490] "Care recipient" refers to an individual who requires special care based on their health condition or living situation.

[0491] "Health data" refers to numerical values ​​and records that indicate the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and symptoms.

[0492] "Computer system" is a collective term for hardware and software that receives, stores, and analyzes input data and generates and operates machine learning models.

[0493] A "machine learning model" is an algorithm that learns patterns and relationships from large amounts of data and makes predictions and suggestions based on new data.

[0494] A "care plan" is a plan that includes specific instructions and recommendations for daily living based on the health condition and individual needs of the person receiving care.

[0495] "Device" refers to any electronic device or device used by a care recipient, family member, or caregiver to receive or implement a care plan.

[0496] "Means of input" refers to the interfaces and devices that allow care recipients and their caregivers to collect and record health data.

[0497] "Processing means" refers to the processes and software used to convert collected health data into an appropriate format so that it can be transmitted.

[0498] "Transmission means" refers to the network infrastructure and communication protocols used to transmit the organized health data to a computer system or server.

[0499] "Means for analysis" refers to algorithms or software used to analyze stored health data and identify patterns or anomalies.

[0500] "Means of generation" refers to the methods and technologies for generating a machine learning model based on analyzed data and creating an optimal care plan.

[0501] "Means for providing" refers to the infrastructure and software required to send the generated care plan to the care recipient's device and enable it to be displayed and executed.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] This invention is a system that generates and provides optimal care plans for elderly people and those requiring special care. The system inputs the care recipient's health data, uses an AI model based on this data, and generates and provides a care plan in real time.

[0504] Data Collection Module

[0505] The user (care recipient) enters daily health data using a dedicated application or device (smartphone, tablet, etc.). The data entered includes body temperature, blood pressure, dietary details, symptoms, etc. For example, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0506] The terminal then processes these inputs and formats them into the appropriate format, including validating and optionally formatting the data, before sending the resulting data in an encrypted form to the server.

[0507] Analysis and Model Generation Module

[0508] The server receives the encrypted data sent from the device and stores it in a database. During the storage process, the data is checked for consistency and integrity.

[0509] The server then uses the stored health data to train an AI model using machine learning algorithms (e.g., TensorFlow or PyTorch). The training process utilizes past data and adds new data as it progresses.

[0510] The server generates a prompt based on the current user's health data and inputs it into the AI ​​model. An example of a prompt might be, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate an optimal morning care plan."

[0511] Care plan delivery module

[0512] Based on the input prompt, the AI ​​model generates an optimal care plan for the care recipient, such as "Eat a light breakfast, followed by a 30-minute walk."

[0513] The server updates the generated care plan in real time, encrypts it, and sends it to the device, which then decrypts the data and displays it to the user. The displayed content includes specific instructions, such as "Recommend a low-salt meal for lunch today" or "Try 20 minutes of light exercise this afternoon."

[0514] Care plan implementation and feedback

[0515] Users (care recipients, family members, and caregivers) carry out their daily activities according to the displayed care plan, and then report the progress of the care plan to the app as feedback after the plan is completed.

[0516] The device sends the feedback data to the server, which receives it and updates the database. New feedback data is also used to train the AI ​​model, which is updated accordingly.

[0517] Specific examples

[0518] Example 1: Morning data entry and care plan provision

[0519] 1. In the morning, the user (care recipient) enters "body temperature 36.8 degrees, blood pressure 130 / 85" into a dedicated app.

[0520] 2. The terminal encrypts the entered data and sends it to the server.

[0521] 3. The server receives and stores the data, and analyzes it using an AI model in combination with past data.

[0522] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0523] 5. This care plan is encrypted, sent to the device, and displayed to the user.

[0524] Example 2: Update care plan based on afternoon health change

[0525] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app.

[0526] 2. The device encrypts this information and sends it to the server.

[0527] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0528] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0529] 5. The updated care plan is encrypted, sent to the device, and displayed to the user.

[0530] This system provides customized care based on the care recipient's health condition and needs, reducing the burden on family and caregivers. The system continuously updates the care plan in real time, allowing the care recipient to receive optimal care every day.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] User health data entry

[0534] The user (care recipient) uses a dedicated application to input daily health data such as body temperature, blood pressure, dietary details, and symptoms. This is done using devices such as smartphones and tablets. Specifically, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0535] Input: Health data entered by the user into the app

[0536] Output: Organized health data (e.g., body temperature 36.8°C, blood pressure 130 / 85)

[0537] Step 2:

[0538] Data organization and transmission by terminal

[0539] The device receives the health data entered by the user, verifies the format, and organizes it. After formatting the data, it encrypts it and securely transmits it to the server. Specifically, this includes the operation of the device encrypting the input data and transmitting it to the server.

[0540] Input: Organized health data

[0541] Data processing: Data encryption processing

[0542] Output: Encrypted health data

[0543] Step 3:

[0544] Data reception and storage by the server

[0545] The server receives the encrypted data sent from the device, decrypts the data, and stores it in a database. This process verifies the integrity and completeness of the data. Specifically, this process involves the server decrypting the encrypted data and storing it in a database.

[0546] Input: Encrypted health data

[0547] Data processing: Data decryption and integrity check

[0548] Output: Health data stored in a database

[0549] Step 4:

[0550] Server-based data analysis and AI model training

[0551] The server analyzes the stored health data and trains an AI model using machine learning algorithms (e.g., TensorFlow, PyTorch). It optimizes the algorithm using past data and adds new data to the learning process. Specifically, the server uses the stored data to train the model.

[0552] Input: Health data stored in a database

[0553] Data Computing: Analyzing data and applying algorithms to train AI models

[0554] Output: A trained AI model

[0555] Step 5:

[0556] Server-generated prompts and care plan creation

[0557] Based on the trained AI model, the server generates a prompt corresponding to the current user's health data and inputs it into the model. An example of a prompt is, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate the optimal morning care plan." Based on this prompt, the AI ​​model generates the optimal care plan according to each individual's health condition.

[0558] Input: Current user's health data, prompt text

[0559] Data Calculation: Care plan generation using an AI model based on prompt statements

[0560] Output: Generated care plan

[0561] Step 6:

[0562] Encrypt and send care plans

[0563] The server encrypts the generated care plan and sends it to the terminal. The terminal decrypts the encrypted data and prepares it for display to the user. Specifically, this includes the operation of the server encrypting the care plan and sending it to the terminal.

[0564] Input: Generated care plan

[0565] Data Processing: Care Plan Encryption

[0566] Output: Encrypted care plan

[0567] Step 7:

[0568] Displaying care plans on devices

[0569] The device decrypts the encrypted care plan it receives and displays it to the user. The displayed content includes specific instructions such as "We recommend a low-salt meal for lunch today" and "Try 20 minutes of light exercise this afternoon." Specifically, the device decodes the care plan and displays it on the screen.

[0570] Input: Encrypted care plan data

[0571] Data processing: Decryption of care plan data

[0572] Output: Decoded care plan display

[0573] Step 8:

[0574] User implementation of care plans and feedback

[0575] Users (care recipients, family members, caregivers) carry out their daily activities according to the displayed care plan. After that, they report the implementation status of the care plan and feedback within the app. Specifically, this includes the user carrying out the care plan and providing feedback on the results to the app.

[0576] Input: The result of the user's executed care plan

[0577] Output: Implementation status data fed back into the app

[0578] Step 9:

[0579] Feedback data transmission by terminal

[0580] The device receives and organizes the feedback data from the user and sends it to the server. Specifically, the device organizes the feedback data and sends it back to the server.

[0581] Input: User-entered feedback data

[0582] Data processing: Feedback data cleansing and encryption

[0583] Output: Feedback data sent to the server

[0584] Step 10:

[0585] Server-based data updates and model retraining

[0586] The server receives the feedback data and updates the database. Based on the new data, the AI ​​model is retrained to generate more accurate care plans. Specifically, the server uses the feedback data to continuously optimize the AI ​​model.

[0587] Input: Feedback of implementation status data

[0588] Data Computing: Retraining AI models using feedback data

[0589] Output: Updated AI model

[0590] These are the programming steps of the system, which, based on this detailed processing, will provide a real-time, personalized care plan for the elderly and those with special needs.

[0591] (Application example 1)

[0592] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] Daily health management is important for the elderly and those receiving special care, but it is difficult to manage this individually, so there is a need to provide appropriate care plans.In addition, there is currently a lack of support systems that allow elderly people who visit stores to understand their own health status and select appropriate products and care methods.

[0594] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0595] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and sending it to the server, and a means for acquiring the health data from an input device installed in the physical store, thereby enabling elderly people and care recipients who require special care to efficiently manage their health conditions and receive appropriate care plans even in the store.

[0596] "Care recipients" refers to people who require support in daily life, such as elderly people or people with disabilities who require special assistance or care.

[0597] "Health data" refers to information that indicates the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and changes in physical condition.

[0598] "Server" refers to a computer system for collecting, storing, and analyzing data over a network.

[0599] "AI model" refers to a computational model that uses machine learning algorithms to generate appropriate care plans from input data.

[0600] A "care plan" refers to specific care methods and health management instructions created based on the health condition and needs of the person receiving care.

[0601] "Terminal" refers to an electronic device used by the care recipient to input data and check the care plan.

[0602] A "physical store" refers to a physical store, such as a supermarket or drugstore, where consumers can visit and receive goods or services.

[0603] "Input device" refers to a hardware device or interface that allows a care recipient to record or input health data.

[0604] "Display Device" refers to a display or screen used to present generated care plans and health information to a user.

[0605] "Analysis" refers to the process of processing collected data and deriving meaningful results or information.

[0606] This invention aims to realize a system for generating and providing optimal care plans for elderly people and those requiring special care. This system consists of a data collection module, an analysis and model generation module, and a care plan provision module.

[0607] 1. Data Collection Module

[0608] The server collects daily health data from the care recipient. This data includes body temperature, blood pressure, dietary habits, symptoms, etc., and is entered by the care recipient themselves using a smartphone, tablet, or an input device installed in the store. The entered data is organized by the device and sent to the server in an appropriate format.

[0609] 2. Analysis and Model Generation Module

[0610] The server receives the transmitted health data and stores it in a database. It then analyzes the stored data and uses machine learning algorithms to train a generative AI model. This generative AI model generates an optimal care plan based on the care recipient's health condition and individual needs. For example, a deep learning framework such as TensorFlow is used to build the AI ​​model.

[0611] 3. Care plan provision module

[0612] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. The device displays the care plan to the care recipient and gives instructions for its implementation. The care recipient follows these instructions to live their daily life. For example, specific health management instructions such as "We recommend a low-sodium diet today" are displayed in real time on a display device installed in a physical store.

[0613] Specific examples

[0614] Example 1: In-store health data entry and care plan provision

[0615] 1. The care recipient enters their temperature and blood pressure at an interactive kiosk in a physical store. For example, they might enter "Temperature is 37.2°C, Blood pressure is 140 / 90."

[0616] 2. The terminal organizes the entered data and sends it to the server.

[0617] 3. The server receives the data and analyzes it using an AI model in conjunction with past data.

[0618] 4. The AI ​​model generates an appropriate care plan and provides specific advice, such as "We recommend a low-sodium diet today."

[0619] 5. This care plan is sent to a display device in the store and displayed to the care recipient.

[0620] Example 2: Update care plan based on afternoon health change

[0621] 1. In the afternoon, the person being cared for feels a change in their physical condition and types "I feel dizzy" into their smartphone.

[0622] 2. The device organizes this information and sends it to the server.

[0623] 3. The server analyzes the newly received data using an AI model and generates an updated care plan, such as "Drink plenty of fluids and take 15 minutes of rest."

[0624] 4. The updated care plan is sent to the smartphone and displayed to the care recipient.

[0625] Prompt Sentence Examples

[0626] "Generate an appropriate care plan based on the user's health data. For example, if the user's temperature is 37.2 degrees, blood pressure is 140 / 90, and dizziness is present, provide a care plan that recommends a low-sodium diet. Also recommend specific foods and supplements."

[0627] This system allows elderly people and those requiring special care to efficiently manage their health and receive appropriate care while in the store.

[0628] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0629] Step 1:

[0630] The care recipient inputs health data. Using a smartphone or an interactive kiosk in a physical store as the input device, data such as body temperature, blood pressure, and changes in physical condition are entered. This input data is received and organized by a terminal. The input is specific numerical values ​​and diagnosis details, such as "body temperature is 37.2 degrees, blood pressure is 140 / 90."

[0631] Step 2:

[0632] The device sends the organized health data to a server. This transmission uses an internet connection and a secure protocol (e.g., HTTPS). For example, a smartphone app uploads the input data to a cloud server. The data is then formatted in a standard format such as JSON.

[0633] Step 3:

[0634] The server receives the health data and stores it in a database. This storage process uses an SQL database or NoSQL database. The stored data is also integrated with past data. The database organizes and stores data for each care recipient.

[0635] Step 4:

[0636] The server analyzes the stored data and updates the AI ​​model. Specifically, it uses machine learning frameworks such as TensorFlow to train a generative AI model. The entire stored health data is used as input data, and the model is updated based on trends in past and new data. Appropriate data preprocessing (e.g., normalization) is performed during this process.

[0637] Step 5:

[0638] The server generates a care plan based on the AI ​​model. Here, the generative AI model receives new data as input and outputs a specific care plan (e.g., "recommend a low-sodium diet") as an analysis result. The generated care plan includes detailed instructions based on specific rules and algorithms.

[0639] Step 6:

[0640] The server sends the generated care plan to the device using a protocol that allows for real-time data transmission (e.g., WebSocket). The care plan is displayed on the device's interface, and specific instructions are sent to the care recipient as notifications.

[0641] Step 7:

[0642] The device displays the provided care plan to the care recipient. The care plan is visualized on a display or smartphone screen, and is displayed in the form of, for example, "These ingredients are recommended today." In a physical store, the display will show content such as "A low-sodium diet is recommended today."

[0643] Step 8:

[0644] Care recipients follow a care plan and take specific actions, such as choosing recommended foods from store shelves or taking rest at designated times, to improve their health.

[0645] In this way, through the specific processing flow of each step, elderly people and those requiring special care can receive an appropriate and individualized care plan.

[0646] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0647] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take into consideration the user's mental health. The system includes the following means:

[0648] 1. Data Collection Module

[0649] The user (care recipient) uses a dedicated application or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0650] The terminal organizes this input data and sends it to the server in an appropriate format.

[0651] 2. Emotion Recognition Module

[0652] The emotion engine analyzes the facial expressions and tone of voice of the user (care recipient) through the device's camera and microphone. For example, the system recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone.

[0653] The device organizes the recognized emotion data and sends it to the server.

[0654] 3. Analysis and Model Generation Module

[0655] The server receives the health and emotion data sent from the device and stores them in a database.

[0656] The server analyzes this data comprehensively to assess the overall health of the care recipient, which involves training an AI model using machine learning algorithms, which is capable of generating a care plan based on the care recipient's health and emotional state.

[0657] 4. Care plan provision module

[0658] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" or "The user's emotional state is unstable, so allow time for relaxation."

[0659] The terminal displays the provided care plan to the user, and the displayed care plan is presented in a format that is easy for the care recipient to follow.

[0660] 5. Implementation and Feedback

[0661] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[0662] After execution, emotional and health data can be entered again to provide feedback that helps the system generate the next care plan.

[0663] Specific examples

[0664] Example 1: Morning data entry and emotion recognition

[0665] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0666] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[0667] 3. The device organizes this data and sends it to the server.

[0668] Example 2: Health status changes and emotional feedback

[0669] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[0670] 2. The device organizes this information and sends it to the server.

[0671] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0672] 4. This care plan is sent to the device and displayed to the user.

[0673] This approach reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates the care plan in real time, enabling optimal care for the care recipient on a daily basis.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] The user (care recipient) uses a dedicated application or wearable device to input daily health data, for example, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0677] Step 2:

[0678] The device organizes the health data entered, converts it into an appropriate format, and sends it to the server. The data is organized in the form of user ID, date and time, measurement values, etc.

[0679] Step 3:

[0680] The user (care recipient) records their facial expressions and tone of voice using the device's camera and microphone. This information is analyzed by the emotion engine. For example, the emotion is recognized as "calm" based on facial expression analysis by the camera, and "anxious" based on tone of voice analysis by the microphone.

[0681] Step 4:

[0682] The device organizes the emotional data analyzed by the emotion engine and sends it to the server. This data is also organized in the form of user ID, date, time, emotional state, etc.

[0683] Step 5:

[0684] The server receives the health and emotion data sent from the device and stores it in a database, allowing comprehensive health data of the care recipient to be managed in a unified manner.

[0685] Step 6:

[0686] The server periodically analyzes the stored data, and performs an integrated analysis of health and emotional data to assess the overall health status and emotional trends of the care recipient.

[0687] Step 7:

[0688] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates an optimal care plan based on the care recipient's health and emotional state.

[0689] Step 8:

[0690] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" and "Due to the patient's unstable emotional state, time should be allotted for relaxation."

[0691] Step 9:

[0692] The terminal displays the provided care plan to the user in a format that makes it easy for the user to carry out the plan in their daily lives.

[0693] Step 10:

[0694] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[0695] Step 11:

[0696] If the situation changes the user's health or emotional state, the user can input additional data, for example, a symptom such as "I feel dizzy," and the emotion engine will recognize this as "stress."

[0697] Step 12:

[0698] The device organizes the newly entered health and emotion data and sends it back to the server.

[0699] Step 13:

[0700] After receiving the new data, the server reanalyzes it, integrating it with the existing data, and updates the care plan using the AI ​​model, which may include instructions such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0701] Step 14:

[0702] The server sends the updated care plan to the terminal, which displays it to the user.

[0703] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[0704] Example 2

[0705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0706] Existing care systems mainly provide care plans based solely on the health data of the care recipient, but they are unable to generate care plans that take into account emotional changes and mental health conditions. This means that the mental stress and anxiety of the care recipient are not properly addressed, making comprehensive health management difficult.

[0707] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting the health data of the care recipient, a means for organizing the input health data and transmitting it to the server, a means for recognizing, organizing, and transmitting emotional data of the care recipient using devices such as a camera and a microphone, a means for comprehensively analyzing the health data and emotional data stored in the server and generating an AI model, and a means for providing the care plan generated by the AI ​​model to the care recipient's device. This makes it possible to generate and provide an optimal care plan that takes into account not only the physical health state of the care recipient but also the mental health state.

[0708] "Health data" refers to data that indicates the physical condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[0709] "Emotion data" is data that indicates the emotional state of the care recipient analyzed based on facial expressions, tone of voice, and the like.

[0710] A "terminal" is a device used by a care recipient to input health data and emotional data and to check the displayed care plan.

[0711] The "server" is a computer system that stores collected health and emotional data, generates an AI model based on this data, and creates a care plan.

[0712] A "care plan" is a set of specific instructions and suggestions for actions that the care recipient should implement in their daily lives, generated based on an AI model.

[0713] The "AI model" is a statistical and machine learning model that generates care plans based on algorithms trained using the care recipient's health and emotional data.

[0714] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take mental health into consideration. This system includes the following means.

[0715] First, the user (care recipient) enters their daily health data (body temperature, blood pressure, symptoms, etc.) using a dedicated application or wearable device. For example, they might enter, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85." The hardware used includes smartphones, tablets, and various devices that measure health data (thermometers and blood pressure monitors).

[0716] The device organizes this input data and sends it to the server in an appropriate format. Similarly, the emotion engine analyzes the user's facial expressions and tone of voice through the device's camera and microphone. For example, the device recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone. The device also organizes this emotion data and sends it to the server.

[0717] The server receives the health and emotional data sent from the device and stores it in a database. This process requires the use of a database management system (DBMS) to ensure data security and integrity. The server then comprehensively analyzes this data and evaluates the overall health status of the care recipient. This evaluation involves applying machine learning algorithms using Python and R to train an AI model. This AI model is capable of generating a care plan based on the care recipient's health and emotional status.

[0718] The generated care plan is updated in real time by the server and provided to the device. For example, it may contain specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "Due to unstable emotional state, take time to relax." The device displays the provided care plan to the user in a format that is easy for the care recipient to follow.

[0719] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, such as preparing meals as instructed and ensuring time for exercise and relaxation. After carrying out these activities, users can enter their emotional and health data again to provide feedback that the system uses to generate the next care plan.

[0720] Specific examples

[0721] Example 1: Morning data entry and emotion recognition

[0722] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0723] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[0724] 3. The device organizes this data and sends it to the server.

[0725] Example 2: Health status changes and emotional feedback

[0726] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[0727] 2. The device organizes this information and sends it to the server.

[0728] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[0729] 4. This care plan is sent to the device and displayed to the user.

[0730] This reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates care plans in real time, enabling optimal care for care recipients on a daily basis.

[0731] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0732] Step 1: Data collection

[0733] The user (care recipient) launches the dedicated app every morning and inputs health data (body temperature, blood pressure, symptoms, etc.). For example, the user might input "body temperature is 36.8 degrees, blood pressure is 130 / 85." The input data (health data) is provided to the device. The input health data undergoes format checks (data validation) within the application. For example, it is checked whether the temperature is a valid number and whether the units of blood pressure are correct. Data that passes the format check is sent to the next step.

[0734] Step 2: Emotion Recognition

[0735] The user (care recipient) takes a picture of their face using the device's camera and records their voice using the microphone. The input is facial image and audio data. The device sends this data to an emotion recognition engine, which analyzes their emotional state. For example, it determines whether they are smiling from the facial image and analyzes their audio to determine whether they are relaxed or tense. This process yields emotion recognition results such as "calm" or "stressed." The analysis results are stored in the device and sent to the next step.

[0736] Step 3: Send data

[0737] The device transmits the collected health data and emotion data to the server. Appropriate security measures (e.g., encryption methods) are applied during this process. The input is the health data and emotion recognition results that have passed format checks, and these are transmitted to the server. The output is the data transmitted to the server.

[0738] Step 4: Data integration and storage

[0739] The server receives the health and emotion data sent from the device. The input is the data sent from the device. The server stores this data in a database management system (DBMS). For example, new data is stored in the appropriate fields, and recorded as "October 10, 2023, Body Temperature: 36.8°C, Blood Pressure: 130 / 85". The output is the integrated data stored in the database.

[0740] Step 5: Analysis by AI model

[0741] The server uses the stored data to assess the overall health of the care recipient. The input is the stored health data and emotional data. This process involves analyzing the data using machine learning algorithms using Python or R. For example, it may identify a tendency for recent blood pressure values ​​to be high or recent stress levels to be high. The output is the analysis results and an updated AI model.

[0742] Step 6: Create a care plan

[0743] The server generates a care plan based on the analysis results of the AI ​​model. The input is the analysis results of the AI ​​model. The generated care plan includes specific instructions for actions, such as "have a light breakfast," "take a 30-minute walk," and "take deep breaths to relax." The output is the generated care plan.

[0744] Step 7: Provide a care plan

[0745] The server sends the generated care plan to the terminal in real time. The input is the generated care plan. The terminal notifies the user of the received care plan and displays it on the screen. For example, it might say, "Today's care plan: light breakfast, 30-minute walk, deep breathing." The output is the care plan displayed to the user.

[0746] Step 8: Implementation and Feedback

[0747] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they perform the prescribed exercises, prepare the designated meals, and ensure time for relaxation. After carrying out these activities, the user again inputs health and emotional data. The input is the newly collected health and emotional data. The device organizes this data and sends it to the server, which receives it as feedback and uses it to generate the next care plan. The output is updated feedback data.

[0748] By linking each step in this way, an optimal care plan based on the physical and mental health status of the care recipient can be generated and provided.

[0749] (Application example 2)

[0750] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0751] The current nursing care system struggles to provide comprehensive care plans that consider not only the physical health of elderly people and those requiring special care, but also their mental health. Furthermore, the lack of real-time data collection and emotion recognition makes it difficult to provide appropriate care plans quickly. Furthermore, the lack of care plans that take into account emotional states leaves a great need for effective support for the mental health of care recipients.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the health data and emotional data of the care recipient and generating an AI model, means for recognizing the emotional state of the care recipient using a smart device and transmitting the data to the server, and means for providing the care plan generated by the AI ​​model to the care recipient's terminal. This makes it possible to provide a real-time care plan based on the physical and mental health conditions of the care recipient.

[0753] "Health data of the care recipient" refers to information indicating the physical health condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[0754] A "server" is a central processing unit for receiving, storing, and analyzing data transmitted over a network.

[0755] "Emotional data" is information about the emotional state of the care recipient analyzed from facial expressions and tone of voice.

[0756] The "AI model" is a predictive model constructed using machine learning algorithms based on the health and emotional data of the care recipient.

[0757] A "care plan" is a plan generated using an AI model that includes specific care instructions and advice for the care recipient to carry out in their daily lives.

[0758] A "terminal" is a device such as a smartphone or smart glasses used by the care recipient.

[0759] A "smart device" is a device that is connected to the Internet and has the ability to collect and recognize the health and emotional state of the person being cared for in real time.

[0760] "Data collection means" refers to equipment and software for acquiring health data and emotional data from the care recipient.

[0761] "Emotion recognition means" is a technology for recognizing the emotional state of the care recipient by analyzing their facial expressions and voice.

[0762] The "data transmission means" is a function for transmitting acquired data to a server.

[0763] "Data analysis means" refers to the process of comprehensively analyzing health data and emotional data on a server to generate an AI model.

[0764] The "care plan providing means" is a mechanism for displaying and providing the generated care plan on the care recipient's terminal.

[0765] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it also takes into consideration the user's mental health. The embodiments for implementing this invention are as follows.

[0766] The system uses the following hardware and software:

[0767] Smart devices (e.g., smart glasses)

[0768] Camera and microphone

[0769] server

[0770] Client terminal (smartphone, etc.)

[0771] Emotion Recognizer

[0772] Data transmission software (requests library)

[0773] Display control software (GlassesDisplay)

[0774] Data collection

[0775] The device has a means to input the care recipient's daily health data (body temperature, blood pressure, symptoms, etc.). The input data is organized and sent to the server in an appropriate format. It is also equipped with an emotion engine that uses the smart device's camera and microphone to analyze facial expressions and voice in real time and recognize emotional data. The emotional data is also organized and sent to the server.

[0776] Data analysis

[0777] The server receives the health and emotional data sent from the device and stores it in a database. It then comprehensively analyzes this data and generates an AI model using a machine learning algorithm to assess the overall health and emotional state of the care recipient. This AI model then generates a care plan based on the care recipient's health and emotional state.

[0778] Care plan provided

[0779] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. For example, it may include specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "The user's emotional state is unstable, so allow time for relaxation." The device then displays the provided care plan to the care recipient, using the smart device's display to present the instructions in a clear and easy-to-follow format.

[0780] Execution and Feedback

[0781] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they prepare the prescribed meals and ensure they have time for designated exercise and relaxation. After carrying out these activities, they can input their emotional and health data again to receive feedback that can be used by the system to generate the next care plan.

[0782] Examples of concrete examples and prompts

[0783] Examples:

[0784] In the morning, a user wearing smart glasses enters their body temperature and blood pressure into the app. For example, they might enter "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0785] The smart glasses analyze the user's facial expressions, and the emotion engine recognizes a "calm state."

[0786] The data is sent to a server, and a care plan such as "Eat a light breakfast, then take a 30-minute walk" is displayed on the smart glasses.

[0787] Example prompt sentence:

[0788] text

[0789] Please enter your temperature today: 36.8

[0790] Enter your blood pressure today: 130 / 85

[0791] Capturing frames and analyzing facial expressions...

[0792] Emotional state: Calm

[0793] Sending data to server...

[0794] Care plan: "Light breakfast followed by a 30-minute walk is recommended."

[0795] In this way, the system can provide customized care based on the care recipient's physical and mental health status, reducing the burden on family members and caregivers.

[0796] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0797] Step 1:

[0798] The device receives health data, such as body temperature and blood pressure, entered by the user (care recipient). Based on this, the device organizes the health data and converts it into an appropriate format. For example, if the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85," the data is converted into JSON format. This is the input. The output is the organized health data.

[0799] Step 2:

[0800] The device uses the camera and microphone of the smart device (e.g., smart glasses) to capture the user's facial expressions and tone of voice. Emotion recognition software (EmotionRecognizer) is used to analyze the user's emotional state from the captured data. For example, emotional data such as "calm" or "unsettled" is output.

[0801] Step 3:

[0802] The device organizes the collected health and emotion data and sends it to the server. This process uses data transmission software (requests library), which receives the organized health and emotion data as input and sends it to the server as output.

[0803] Step 4:

[0804] The server stores the received health and emotional data in a database, thereby maintaining a history of the care recipient's overall health and emotional state. The input is the received data, and the output is the information stored in the database.

[0805] Step 5:

[0806] The server comprehensively analyzes the stored health and emotional data and generates an AI model using a machine learning algorithm. This AI model is capable of generating a care plan based on the care recipient's health and emotional state. The input is the data in the database, and the output is the generated AI model.

[0807] Step 6:

[0808] The server uses the AI ​​model to generate care plans that are updated in real time, such as "Eat a light breakfast followed by a 30-minute walk" or "Allow time for relaxation."

[0809] Step 7:

[0810] The terminal displays the care plan received from the server on the care recipient's smart device. The smart glasses' display control software (GlassesDisplay) is used to display the care plan in a format that is easy for the user to understand. The input is the care plan from the server, and the output is the care plan displayed on the smart device.

[0811] Step 8:

[0812] Users (care recipients, family members, caregivers) carry out their daily lives according to the care plan displayed on the device. For example, they prepare the meals instructed and ensure time for designated exercise and relaxation. This executes the care plan, and the results are fed back to the server via the device. The input is the care content carried out by the user, and the output is the data fed back to the system.

[0813] This specific processing step makes it possible to monitor the health and emotional state of the care recipient in real time and provide an optimal care plan based on that.

[0814] 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.

[0815] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0816] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0817] [Third embodiment]

[0818] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0819] 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.

[0820] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0821] 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.

[0822] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0823] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0824] 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.

[0825] 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.

[0826] 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 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.

[0827] 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.

[0828] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0829] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0830] The present invention is a system for generating and providing an optimal care plan for elderly people and people requiring special care. The system includes the following means:

[0831] 1. Data Collection Module

[0832] The user (care recipient) uses a dedicated application or device to input daily health data, such as body temperature, blood pressure, dietary habits, and symptoms.

[0833] The terminal organizes this input data and sends it to the server in an appropriate format.

[0834] 2. Analysis and Model Generation Module

[0835] The server receives the health data of the care recipient sent from the terminal and stores it in a database.

[0836] The server analyzes the stored data and uses machine learning algorithms to train an AI model that can generate a care plan based on the care recipient's health status and individual needs.

[0837] 3. Care plan provision module

[0838] The server updates the care plan generated by the AI ​​model in real time and provides it to the device.

[0839] The device displays the provided care plan to the user and gives instructions for implementation, such as "We recommend a low-salt meal for lunch today" and "Take 20 minutes of light exercise in the afternoon."

[0840] Users (care recipients, family members, caregivers) carry out their daily lives according to this care plan.

[0841] Specific examples

[0842] Example 1: Morning data entry and care plan provision

[0843] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[0844] 2. The terminal organizes the entered data and sends it to the server.

[0845] 3. The server receives the data and analyzes it using an AI model in combination with past data.

[0846] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0847] 5. This care plan is sent to the device and displayed to the user.

[0848] Example 2: Update care plan based on afternoon health change

[0849] 1. In the afternoon, the user (care recipient) feels a change in their physical condition and enters "I feel dizzy" into the app.

[0850] 2. The device organizes this information and sends it to the server.

[0851] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0852] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0853] 5. The updated care plan is sent to the device and displayed to the user.

[0854] This system allows care recipients to receive customized care based on their health condition and needs, reducing the burden on their families and caregivers. The system continuously updates care plans in real time, ensuring optimal care is provided to care recipients every day.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] The user (care recipient) uses a dedicated app or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[0858] Step 2:

[0859] The terminal organizes the health data entered by the user and transmits the data to the server, where it is converted into an appropriate format and sent to the server via the network.

[0860] Step 3:

[0861] The server receives the data sent from the device and stores it in a database, allowing the care recipient's health data to be organized and recorded by time.

[0862] Step 4:

[0863] The server periodically analyzes the stored data, which includes analyzing the care recipient's past health data and trends, and assessing their current health status.

[0864] Step 5:

[0865] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates a care plan that best suits the individual needs of the care recipient.

[0866] Step 6:

[0867] The server then sends the care plan generated by the AI ​​model to the device, which includes specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0868] Step 7:

[0869] The terminal displays the care plan received from the server to the user, and the displayed care plan is presented in a format that is easy for the care recipient to carry out in their daily lives.

[0870] Step 8:

[0871] The user (care recipient, family member, or caregiver) performs daily activities based on the displayed care plan, for example, preparing the prescribed meals and performing the designated exercises.

[0872] Step 9:

[0873] If the user's health condition changes due to the situation, they can enter additional health data, for example, reporting a symptom such as "feeling dizzy" in the afternoon to the dedicated app.

[0874] Step 10:

[0875] The terminal reorganizes the newly entered data and sends it to the server.

[0876] Step 11:

[0877] After receiving the new data, the server reanalyzes it with the existing data and uses the AI ​​model to update the care plan, which may include instructions such as "drink lots of fluids and take 15 minutes of rest."

[0878] Step 12:

[0879] The server sends the updated care plan to the terminal, which displays it to the user.

[0880] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[0881] Example 1

[0882] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] To provide optimal care plans in real time for elderly people and those requiring special care, it is necessary to efficiently collect and analyze large amounts of health data and appropriately generate and update care plans based on the data to meet individual needs. However, conventional systems often require manual data collection and analysis, making it difficult to respond immediately. In addition, the care plans provided are uniform, making it difficult to provide optimal care plans tailored to the condition of each individual care recipient.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0885] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and transmitting it to the computer system, a means for analyzing the health data stored in the computer system and generating a machine learning model, and a means for providing the care plan generated by the machine learning model to the care recipient's device. This allows an optimal care plan based on the care recipient's health condition to be generated and provided in real time, making it possible to provide more effective and personalized care to elderly people and care recipients who require special care.

[0886] "Care recipient" refers to an individual who requires special care based on their health condition or living situation.

[0887] "Health data" refers to numerical values ​​and records that indicate the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and symptoms.

[0888] "Computer system" is a collective term for hardware and software that receives, stores, and analyzes input data and generates and operates machine learning models.

[0889] A "machine learning model" is an algorithm that learns patterns and relationships from large amounts of data and makes predictions and suggestions based on new data.

[0890] A "care plan" is a plan that includes specific instructions and recommendations for daily living based on the health condition and individual needs of the person receiving care.

[0891] "Device" refers to any electronic device or device used by a care recipient, family member, or caregiver to receive or implement a care plan.

[0892] "Means of input" refers to the interfaces and devices that allow care recipients and their caregivers to collect and record health data.

[0893] "Processing means" refers to the processes and software used to convert collected health data into an appropriate format so that it can be transmitted.

[0894] "Transmission means" refers to the network infrastructure and communication protocols used to transmit the organized health data to a computer system or server.

[0895] "Means for analysis" refers to algorithms or software used to analyze stored health data and identify patterns or anomalies.

[0896] "Means of generation" refers to the methods and technologies for generating a machine learning model based on analyzed data and creating an optimal care plan.

[0897] "Means for providing" refers to the infrastructure and software required to send the generated care plan to the care recipient's device and enable it to be displayed and executed.

[0898] MODE FOR CARRYING OUT THE INVENTION

[0899] This invention is a system that generates and provides optimal care plans for elderly people and those requiring special care. The system inputs the care recipient's health data, uses an AI model based on this data, and generates and provides a care plan in real time.

[0900] Data Collection Module

[0901] The user (care recipient) enters daily health data using a dedicated application or device (smartphone, tablet, etc.). The data entered includes body temperature, blood pressure, dietary details, symptoms, etc. For example, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0902] The terminal then processes these inputs and formats them into the appropriate format, including validating and optionally formatting the data, before sending the resulting data in an encrypted form to the server.

[0903] Analysis and Model Generation Module

[0904] The server receives the encrypted data sent from the device and stores it in a database. During the storage process, the data is checked for consistency and integrity.

[0905] The server then uses the stored health data to train an AI model using machine learning algorithms (e.g., TensorFlow or PyTorch). The training process utilizes past data and adds new data as it progresses.

[0906] The server generates a prompt based on the current user's health data and inputs it into the AI ​​model. An example of a prompt might be, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate an optimal morning care plan."

[0907] Care plan delivery module

[0908] Based on the input prompt, the AI ​​model generates an optimal care plan for the care recipient, such as "Eat a light breakfast, followed by a 30-minute walk."

[0909] The server updates the generated care plan in real time, encrypts it, and sends it to the device, which then decrypts the data and displays it to the user. The displayed content includes specific instructions, such as "Recommend a low-salt meal for lunch today" or "Try 20 minutes of light exercise this afternoon."

[0910] Care plan implementation and feedback

[0911] Users (care recipients, family members, and caregivers) carry out their daily activities according to the displayed care plan, and then report the progress of the care plan to the app as feedback after the plan is completed.

[0912] The device sends the feedback data to the server, which receives it and updates the database. New feedback data is also used to train the AI ​​model, which is updated accordingly.

[0913] Specific examples

[0914] Example 1: Morning data entry and care plan provision

[0915] 1. In the morning, the user (care recipient) enters "body temperature 36.8 degrees, blood pressure 130 / 85" into a dedicated app.

[0916] 2. The terminal encrypts the entered data and sends it to the server.

[0917] 3. The server receives and stores the data, and analyzes it using an AI model in combination with past data.

[0918] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[0919] 5. This care plan is encrypted, sent to the device, and displayed to the user.

[0920] Example 2: Update care plan based on afternoon health change

[0921] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app.

[0922] 2. The device encrypts this information and sends it to the server.

[0923] 3. The server receives the new data and analyzes it based on the AI ​​model.

[0924] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[0925] 5. The updated care plan is encrypted, sent to the device, and displayed to the user.

[0926] This system provides customized care based on the care recipient's health condition and needs, reducing the burden on family and caregivers. The system continuously updates the care plan in real time, allowing the care recipient to receive optimal care every day.

[0927] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0928] Step 1:

[0929] User health data entry

[0930] The user (care recipient) uses a dedicated application to input daily health data such as body temperature, blood pressure, dietary details, and symptoms. This is done using devices such as smartphones and tablets. Specifically, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[0931] Input: Health data entered by the user into the app

[0932] Output: Organized health data (e.g., body temperature 36.8°C, blood pressure 130 / 85)

[0933] Step 2:

[0934] Data organization and transmission by terminal

[0935] The device receives the health data entered by the user, verifies the format, and organizes it. After formatting the data, it encrypts it and securely transmits it to the server. Specifically, this includes the operation of the device encrypting the input data and transmitting it to the server.

[0936] Input: Organized health data

[0937] Data processing: Data encryption processing

[0938] Output: Encrypted health data

[0939] Step 3:

[0940] Data reception and storage by the server

[0941] The server receives the encrypted data sent from the device, decrypts the data, and stores it in a database. This process verifies the integrity and completeness of the data. Specifically, this process involves the server decrypting the encrypted data and storing it in a database.

[0942] Input: Encrypted health data

[0943] Data processing: Data decryption and integrity check

[0944] Output: Health data stored in a database

[0945] Step 4:

[0946] Server-based data analysis and AI model training

[0947] The server analyzes the stored health data and trains an AI model using machine learning algorithms (e.g., TensorFlow, PyTorch). It optimizes the algorithm using past data and adds new data to the learning process. Specifically, the server uses the stored data to train the model.

[0948] Input: Health data stored in a database

[0949] Data Computing: Analyzing data and applying algorithms to train AI models

[0950] Output: A trained AI model

[0951] Step 5:

[0952] Server-generated prompts and care plan creation

[0953] Based on the trained AI model, the server generates a prompt corresponding to the current user's health data and inputs it into the model. An example of a prompt is, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate the optimal morning care plan." Based on this prompt, the AI ​​model generates the optimal care plan according to each individual's health condition.

[0954] Input: Current user's health data, prompt text

[0955] Data Calculation: Care plan generation using an AI model based on prompt statements

[0956] Output: Generated care plan

[0957] Step 6:

[0958] Encrypt and send care plans

[0959] The server encrypts the generated care plan and sends it to the terminal. The terminal decrypts the encrypted data and prepares it for display to the user. Specifically, this includes the operation of the server encrypting the care plan and sending it to the terminal.

[0960] Input: Generated care plan

[0961] Data Processing: Care Plan Encryption

[0962] Output: Encrypted care plan

[0963] Step 7:

[0964] Displaying care plans on devices

[0965] The device decrypts the encrypted care plan it receives and displays it to the user. The displayed content includes specific instructions such as "We recommend a low-salt meal for lunch today" and "Try 20 minutes of light exercise this afternoon." Specifically, the device decodes the care plan and displays it on the screen.

[0966] Input: Encrypted care plan data

[0967] Data processing: Decryption of care plan data

[0968] Output: Decoded care plan display

[0969] Step 8:

[0970] User implementation of care plans and feedback

[0971] Users (care recipients, family members, caregivers) carry out their daily activities according to the displayed care plan. After that, they report the implementation status of the care plan and feedback within the app. Specifically, this includes the user carrying out the care plan and providing feedback on the results to the app.

[0972] Input: The result of the user's executed care plan

[0973] Output: Implementation status data fed back into the app

[0974] Step 9:

[0975] Feedback data transmission by terminal

[0976] The device receives and organizes the feedback data from the user and sends it to the server. Specifically, the device organizes the feedback data and sends it back to the server.

[0977] Input: User-entered feedback data

[0978] Data processing: Feedback data cleansing and encryption

[0979] Output: Feedback data sent to the server

[0980] Step 10:

[0981] Server-based data updates and model retraining

[0982] The server receives the feedback data and updates the database. Based on the new data, the AI ​​model is retrained to generate more accurate care plans. Specifically, the server uses the feedback data to continuously optimize the AI ​​model.

[0983] Input: Feedback of implementation status data

[0984] Data Computing: Retraining AI models using feedback data

[0985] Output: Updated AI model

[0986] These are the programming steps of the system, which, based on this detailed processing, will provide a real-time, personalized care plan for the elderly and those with special needs.

[0987] (Application example 1)

[0988] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0989] Daily health management is important for the elderly and those receiving special care, but it is difficult to manage this individually, so there is a need to provide appropriate care plans.In addition, there is currently a lack of support systems that allow elderly people who visit stores to understand their own health status and select appropriate products and care methods.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0991] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and sending it to the server, and a means for acquiring the health data from an input device installed in the physical store, thereby enabling elderly people and care recipients who require special care to efficiently manage their health conditions and receive appropriate care plans even in the store.

[0992] "Care recipients" refers to people who require support in daily life, such as elderly people or people with disabilities who require special assistance or care.

[0993] "Health data" refers to information that indicates the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and changes in physical condition.

[0994] "Server" refers to a computer system for collecting, storing, and analyzing data over a network.

[0995] "AI model" refers to a computational model that uses machine learning algorithms to generate appropriate care plans from input data.

[0996] A "care plan" refers to specific care methods and health management instructions created based on the health condition and needs of the person receiving care.

[0997] "Terminal" refers to an electronic device used by the care recipient to input data and check the care plan.

[0998] A "physical store" refers to a physical store, such as a supermarket or drugstore, where consumers can visit and receive goods or services.

[0999] "Input device" refers to a hardware device or interface that allows a care recipient to record or input health data.

[1000] "Display Device" refers to a display or screen used to present generated care plans and health information to a user.

[1001] "Analysis" refers to the process of processing collected data and deriving meaningful results or information.

[1002] This invention aims to realize a system for generating and providing optimal care plans for elderly people and those requiring special care. This system consists of a data collection module, an analysis and model generation module, and a care plan provision module.

[1003] 1. Data Collection Module

[1004] The server collects daily health data from the care recipient. This data includes body temperature, blood pressure, dietary habits, symptoms, etc., and is entered by the care recipient themselves using a smartphone, tablet, or an input device installed in the store. The entered data is organized by the device and sent to the server in an appropriate format.

[1005] 2. Analysis and Model Generation Module

[1006] The server receives the transmitted health data and stores it in a database. It then analyzes the stored data and uses machine learning algorithms to train a generative AI model. This generative AI model generates an optimal care plan based on the care recipient's health condition and individual needs. For example, a deep learning framework such as TensorFlow is used to build the AI ​​model.

[1007] 3. Care plan provision module

[1008] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. The device displays the care plan to the care recipient and gives instructions for its implementation. The care recipient follows these instructions to live their daily life. For example, specific health management instructions such as "We recommend a low-sodium diet today" are displayed in real time on a display device installed in a physical store.

[1009] Specific examples

[1010] Example 1: In-store health data entry and care plan provision

[1011] 1. The care recipient enters their temperature and blood pressure at an interactive kiosk in a physical store. For example, they might enter "Temperature is 37.2°C, Blood pressure is 140 / 90."

[1012] 2. The terminal organizes the entered data and sends it to the server.

[1013] 3. The server receives the data and analyzes it using an AI model in conjunction with past data.

[1014] 4. The AI ​​model generates an appropriate care plan and provides specific advice, such as "We recommend a low-sodium diet today."

[1015] 5. This care plan is sent to a display device in the store and displayed to the care recipient.

[1016] Example 2: Update care plan based on afternoon health change

[1017] 1. In the afternoon, the person being cared for feels a change in their physical condition and types "I feel dizzy" into their smartphone.

[1018] 2. The device organizes this information and sends it to the server.

[1019] 3. The server analyzes the newly received data using an AI model and generates an updated care plan, such as "Drink plenty of fluids and take 15 minutes of rest."

[1020] 4. The updated care plan is sent to the smartphone and displayed to the care recipient.

[1021] Prompt Sentence Examples

[1022] "Generate an appropriate care plan based on the user's health data. For example, if the user's temperature is 37.2 degrees, blood pressure is 140 / 90, and dizziness is present, provide a care plan that recommends a low-sodium diet. Also recommend specific foods and supplements."

[1023] This system allows elderly people and those requiring special care to efficiently manage their health and receive appropriate care while in the store.

[1024] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1025] Step 1:

[1026] The care recipient inputs health data. Using a smartphone or an interactive kiosk in a physical store as the input device, data such as body temperature, blood pressure, and changes in physical condition are entered. This input data is received and organized by a terminal. The input is specific numerical values ​​and diagnosis details, such as "body temperature is 37.2 degrees, blood pressure is 140 / 90."

[1027] Step 2:

[1028] The device sends the organized health data to a server. This transmission uses an internet connection and a secure protocol (e.g., HTTPS). For example, a smartphone app uploads the input data to a cloud server. The data is then formatted in a standard format such as JSON.

[1029] Step 3:

[1030] The server receives the health data and stores it in a database. This storage process uses an SQL database or NoSQL database. The stored data is also integrated with past data. The database organizes and stores data for each care recipient.

[1031] Step 4:

[1032] The server analyzes the stored data and updates the AI ​​model. Specifically, it uses machine learning frameworks such as TensorFlow to train a generative AI model. The entire stored health data is used as input data, and the model is updated based on trends in past and new data. Appropriate data preprocessing (e.g., normalization) is performed during this process.

[1033] Step 5:

[1034] The server generates a care plan based on the AI ​​model. Here, the generative AI model receives new data as input and outputs a specific care plan (e.g., "recommend a low-sodium diet") as an analysis result. The generated care plan includes detailed instructions based on specific rules and algorithms.

[1035] Step 6:

[1036] The server sends the generated care plan to the device using a protocol that allows for real-time data transmission (e.g., WebSocket). The care plan is displayed on the device's interface, and specific instructions are sent to the care recipient as notifications.

[1037] Step 7:

[1038] The device displays the provided care plan to the care recipient. The care plan is visualized on a display or smartphone screen, and is displayed in the form of, for example, "These ingredients are recommended today." In a physical store, the display will show content such as "A low-sodium diet is recommended today."

[1039] Step 8:

[1040] Care recipients follow a care plan and take specific actions, such as choosing recommended foods from store shelves or taking rest at designated times, to improve their health.

[1041] In this way, through the specific processing flow of each step, elderly people and those requiring special care can receive an appropriate and individualized care plan.

[1042] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1043] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take into consideration the user's mental health. The system includes the following means:

[1044] 1. Data Collection Module

[1045] The user (care recipient) uses a dedicated application or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[1046] The terminal organizes this input data and sends it to the server in an appropriate format.

[1047] 2. Emotion Recognition Module

[1048] The emotion engine analyzes the facial expressions and tone of voice of the user (care recipient) through the device's camera and microphone. For example, the system recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone.

[1049] The device organizes the recognized emotion data and sends it to the server.

[1050] 3. Analysis and Model Generation Module

[1051] The server receives the health and emotion data sent from the device and stores them in a database.

[1052] The server analyzes this data comprehensively to assess the overall health of the care recipient, which involves training an AI model using machine learning algorithms, which is capable of generating a care plan based on the care recipient's health and emotional state.

[1053] 4. Care plan provision module

[1054] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" or "The user's emotional state is unstable, so allow time for relaxation."

[1055] The terminal displays the provided care plan to the user, and the displayed care plan is presented in a format that is easy for the care recipient to follow.

[1056] 5. Implementation and Feedback

[1057] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[1058] After execution, emotional and health data can be entered again to provide feedback that helps the system generate the next care plan.

[1059] Specific examples

[1060] Example 1: Morning data entry and emotion recognition

[1061] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[1062] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[1063] 3. The device organizes this data and sends it to the server.

[1064] Example 2: Health status changes and emotional feedback

[1065] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[1066] 2. The device organizes this information and sends it to the server.

[1067] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1068] 4. This care plan is sent to the device and displayed to the user.

[1069] This approach reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates the care plan in real time, enabling optimal care for the care recipient on a daily basis.

[1070] The processing flow will be explained below.

[1071] Step 1:

[1072] The user (care recipient) uses a dedicated application or wearable device to input daily health data, for example, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[1073] Step 2:

[1074] The device organizes the health data entered, converts it into an appropriate format, and sends it to the server. The data is organized in the form of user ID, date and time, measurement values, etc.

[1075] Step 3:

[1076] The user (care recipient) records their facial expressions and tone of voice using the device's camera and microphone. This information is analyzed by the emotion engine. For example, the emotion is recognized as "calm" based on facial expression analysis by the camera, and "anxious" based on tone of voice analysis by the microphone.

[1077] Step 4:

[1078] The device organizes the emotional data analyzed by the emotion engine and sends it to the server. This data is also organized in the form of user ID, date, time, emotional state, etc.

[1079] Step 5:

[1080] The server receives the health and emotion data sent from the device and stores it in a database, allowing comprehensive health data of the care recipient to be managed in a unified manner.

[1081] Step 6:

[1082] The server periodically analyzes the stored data, and performs an integrated analysis of health and emotional data to assess the overall health status and emotional trends of the care recipient.

[1083] Step 7:

[1084] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates an optimal care plan based on the care recipient's health and emotional state.

[1085] Step 8:

[1086] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" and "Due to the patient's unstable emotional state, time should be allotted for relaxation."

[1087] Step 9:

[1088] The terminal displays the provided care plan to the user in a format that makes it easy for the user to carry out the plan in their daily lives.

[1089] Step 10:

[1090] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[1091] Step 11:

[1092] If the situation changes the user's health or emotional state, the user can input additional data, for example, a symptom such as "I feel dizzy," and the emotion engine will recognize this as "stress."

[1093] Step 12:

[1094] The device organizes the newly entered health and emotion data and sends it back to the server.

[1095] Step 13:

[1096] After receiving the new data, the server reanalyzes it, integrating it with the existing data, and updates the care plan using the AI ​​model, which may include instructions such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1097] Step 14:

[1098] The server sends the updated care plan to the terminal, which displays it to the user.

[1099] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[1100] Example 2

[1101] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1102] Existing care systems mainly provide care plans based solely on the health data of the care recipient, but they are unable to generate care plans that take into account emotional changes and mental health conditions. This means that the mental stress and anxiety of the care recipient are not properly addressed, making comprehensive health management difficult.

[1103] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting the health data of the care recipient, a means for organizing the input health data and transmitting it to the server, a means for recognizing, organizing, and transmitting emotional data of the care recipient using devices such as a camera and a microphone, a means for comprehensively analyzing the health data and emotional data stored in the server and generating an AI model, and a means for providing the care plan generated by the AI ​​model to the care recipient's device. This makes it possible to generate and provide an optimal care plan that takes into account not only the physical health state of the care recipient but also the mental health state.

[1104] "Health data" refers to data that indicates the physical condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[1105] "Emotion data" is data that indicates the emotional state of the care recipient analyzed based on facial expressions, tone of voice, and the like.

[1106] A "terminal" is a device used by a care recipient to input health data and emotional data and to check the displayed care plan.

[1107] The "server" is a computer system that stores collected health and emotional data, generates an AI model based on this data, and creates a care plan.

[1108] A "care plan" is a set of specific instructions and suggestions for actions that the care recipient should implement in their daily lives, generated based on an AI model.

[1109] The "AI model" is a statistical and machine learning model that generates care plans based on algorithms trained using the care recipient's health and emotional data.

[1110] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take mental health into consideration. This system includes the following means.

[1111] First, the user (care recipient) enters their daily health data (body temperature, blood pressure, symptoms, etc.) using a dedicated application or wearable device. For example, they might enter, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85." The hardware used includes smartphones, tablets, and various devices that measure health data (thermometers and blood pressure monitors).

[1112] The device organizes this input data and sends it to the server in an appropriate format. Similarly, the emotion engine analyzes the user's facial expressions and tone of voice through the device's camera and microphone. For example, the device recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone. The device also organizes this emotion data and sends it to the server.

[1113] The server receives the health and emotional data sent from the device and stores it in a database. This process requires the use of a database management system (DBMS) to ensure data security and integrity. The server then comprehensively analyzes this data and evaluates the overall health status of the care recipient. This evaluation involves applying machine learning algorithms using Python and R to train an AI model. This AI model is capable of generating a care plan based on the care recipient's health and emotional status.

[1114] The generated care plan is updated in real time by the server and provided to the device. For example, it may contain specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "Due to unstable emotional state, take time to relax." The device displays the provided care plan to the user in a format that is easy for the care recipient to follow.

[1115] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, such as preparing meals as instructed and ensuring time for exercise and relaxation. After carrying out these activities, users can enter their emotional and health data again to provide feedback that the system uses to generate the next care plan.

[1116] Specific examples

[1117] Example 1: Morning data entry and emotion recognition

[1118] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[1119] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[1120] 3. The device organizes this data and sends it to the server.

[1121] Example 2: Health status changes and emotional feedback

[1122] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[1123] 2. The device organizes this information and sends it to the server.

[1124] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1125] 4. This care plan is sent to the device and displayed to the user.

[1126] This reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates care plans in real time, enabling optimal care for care recipients on a daily basis.

[1127] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1128] Step 1: Data collection

[1129] The user (care recipient) launches the dedicated app every morning and inputs health data (body temperature, blood pressure, symptoms, etc.). For example, the user might input "body temperature is 36.8 degrees, blood pressure is 130 / 85." The input data (health data) is provided to the device. The input health data undergoes format checks (data validation) within the application. For example, it is checked whether the temperature is a valid number and whether the units of blood pressure are correct. Data that passes the format check is sent to the next step.

[1130] Step 2: Emotion Recognition

[1131] The user (care recipient) takes a picture of their face using the device's camera and records their voice using the microphone. The input is facial image and audio data. The device sends this data to an emotion recognition engine, which analyzes their emotional state. For example, it determines whether they are smiling from the facial image and analyzes their audio to determine whether they are relaxed or tense. This process yields emotion recognition results such as "calm" or "stressed." The analysis results are stored in the device and sent to the next step.

[1132] Step 3: Send data

[1133] The device transmits the collected health data and emotion data to the server. Appropriate security measures (e.g., encryption methods) are applied during this process. The input is the health data and emotion recognition results that have passed format checks, and these are transmitted to the server. The output is the data transmitted to the server.

[1134] Step 4: Data integration and storage

[1135] The server receives the health and emotion data sent from the device. The input is the data sent from the device. The server stores this data in a database management system (DBMS). For example, new data is stored in the appropriate fields, and recorded as "October 10, 2023, Body Temperature: 36.8°C, Blood Pressure: 130 / 85". The output is the integrated data stored in the database.

[1136] Step 5: Analysis by AI model

[1137] The server uses the stored data to assess the overall health of the care recipient. The input is the stored health data and emotional data. This process involves analyzing the data using machine learning algorithms using Python or R. For example, it may identify a tendency for recent blood pressure values ​​to be high or recent stress levels to be high. The output is the analysis results and an updated AI model.

[1138] Step 6: Create a care plan

[1139] The server generates a care plan based on the analysis results of the AI ​​model. The input is the analysis results of the AI ​​model. The generated care plan includes specific instructions for actions, such as "have a light breakfast," "take a 30-minute walk," and "take deep breaths to relax." The output is the generated care plan.

[1140] Step 7: Provide a care plan

[1141] The server sends the generated care plan to the terminal in real time. The input is the generated care plan. The terminal notifies the user of the received care plan and displays it on the screen. For example, it might say, "Today's care plan: light breakfast, 30-minute walk, deep breathing." The output is the care plan displayed to the user.

[1142] Step 8: Implementation and Feedback

[1143] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they perform the prescribed exercises, prepare the designated meals, and ensure time for relaxation. After carrying out these activities, the user again inputs health and emotional data. The input is the newly collected health and emotional data. The device organizes this data and sends it to the server, which receives it as feedback and uses it to generate the next care plan. The output is updated feedback data.

[1144] By linking each step in this way, an optimal care plan based on the physical and mental health status of the care recipient can be generated and provided.

[1145] (Application example 2)

[1146] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1147] The current nursing care system struggles to provide comprehensive care plans that consider not only the physical health of elderly people and those requiring special care, but also their mental health. Furthermore, the lack of real-time data collection and emotion recognition makes it difficult to provide appropriate care plans quickly. Furthermore, the lack of care plans that take into account emotional states leaves a great need for effective support for the mental health of care recipients.

[1148] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the health data and emotional data of the care recipient and generating an AI model, means for recognizing the emotional state of the care recipient using a smart device and transmitting the data to the server, and means for providing the care plan generated by the AI ​​model to the care recipient's terminal. This makes it possible to provide a real-time care plan based on the physical and mental health conditions of the care recipient.

[1149] "Health data of the care recipient" refers to information indicating the physical health condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[1150] A "server" is a central processing unit for receiving, storing, and analyzing data transmitted over a network.

[1151] "Emotional data" is information about the emotional state of the care recipient analyzed from facial expressions and tone of voice.

[1152] The "AI model" is a predictive model constructed using machine learning algorithms based on the health and emotional data of the care recipient.

[1153] A "care plan" is a plan generated using an AI model that includes specific care instructions and advice for the care recipient to carry out in their daily lives.

[1154] A "terminal" is a device such as a smartphone or smart glasses used by the care recipient.

[1155] A "smart device" is a device that is connected to the Internet and has the ability to collect and recognize the health and emotional state of the person being cared for in real time.

[1156] "Data collection means" refers to equipment and software for acquiring health data and emotional data from the care recipient.

[1157] "Emotion recognition means" is a technology for recognizing the emotional state of the care recipient by analyzing their facial expressions and voice.

[1158] The "data transmission means" is a function for transmitting acquired data to a server.

[1159] "Data analysis means" refers to the process of comprehensively analyzing health data and emotional data on a server to generate an AI model.

[1160] The "care plan providing means" is a mechanism for displaying and providing the generated care plan on the care recipient's terminal.

[1161] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it also takes into consideration the user's mental health. The embodiments for implementing this invention are as follows.

[1162] The system uses the following hardware and software:

[1163] Smart devices (e.g., smart glasses)

[1164] Camera and microphone

[1165] server

[1166] Client terminal (smartphone, etc.)

[1167] Emotion Recognizer

[1168] Data transmission software (requests library)

[1169] Display control software (GlassesDisplay)

[1170] Data collection

[1171] The device has a means to input the care recipient's daily health data (body temperature, blood pressure, symptoms, etc.). The input data is organized and sent to the server in an appropriate format. It is also equipped with an emotion engine that uses the smart device's camera and microphone to analyze facial expressions and voice in real time and recognize emotional data. The emotional data is also organized and sent to the server.

[1172] Data analysis

[1173] The server receives the health and emotional data sent from the device and stores it in a database. It then comprehensively analyzes this data and generates an AI model using a machine learning algorithm to assess the overall health and emotional state of the care recipient. This AI model then generates a care plan based on the care recipient's health and emotional state.

[1174] Care plan provided

[1175] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. For example, it may include specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "The user's emotional state is unstable, so allow time for relaxation." The device then displays the provided care plan to the care recipient, using the smart device's display to present the instructions in a clear and easy-to-follow format.

[1176] Execution and Feedback

[1177] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they prepare the prescribed meals and ensure they have time for designated exercise and relaxation. After carrying out these activities, they can input their emotional and health data again to receive feedback that can be used by the system to generate the next care plan.

[1178] Examples of concrete examples and prompts

[1179] Examples:

[1180] In the morning, a user wearing smart glasses enters their body temperature and blood pressure into the app. For example, they might enter "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[1181] The smart glasses analyze the user's facial expressions, and the emotion engine recognizes a "calm state."

[1182] The data is sent to a server, and a care plan such as "Eat a light breakfast, then take a 30-minute walk" is displayed on the smart glasses.

[1183] Example prompt sentence:

[1184] text

[1185] Please enter your temperature today: 36.8

[1186] Enter your blood pressure today: 130 / 85

[1187] Capturing frames and analyzing facial expressions...

[1188] Emotional state: Calm

[1189] Sending data to server...

[1190] Care plan: "Light breakfast followed by a 30-minute walk is recommended."

[1191] In this way, the system can provide customized care based on the care recipient's physical and mental health status, reducing the burden on family members and caregivers.

[1192] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1193] Step 1:

[1194] The device receives health data, such as body temperature and blood pressure, entered by the user (care recipient). Based on this, the device organizes the health data and converts it into an appropriate format. For example, if the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85," the data is converted into JSON format. This is the input. The output is the organized health data.

[1195] Step 2:

[1196] The device uses the camera and microphone of the smart device (e.g., smart glasses) to capture the user's facial expressions and tone of voice. Emotion recognition software (EmotionRecognizer) is used to analyze the user's emotional state from the captured data. For example, emotional data such as "calm" or "unsettled" is output.

[1197] Step 3:

[1198] The device organizes the collected health and emotion data and sends it to the server. This process uses data transmission software (requests library), which receives the organized health and emotion data as input and sends it to the server as output.

[1199] Step 4:

[1200] The server stores the received health and emotional data in a database, thereby maintaining a history of the care recipient's overall health and emotional state. The input is the received data, and the output is the information stored in the database.

[1201] Step 5:

[1202] The server comprehensively analyzes the stored health and emotional data and generates an AI model using a machine learning algorithm. This AI model is capable of generating a care plan based on the care recipient's health and emotional state. The input is the data in the database, and the output is the generated AI model.

[1203] Step 6:

[1204] The server uses the AI ​​model to generate care plans that are updated in real time, such as "Eat a light breakfast followed by a 30-minute walk" or "Allow time for relaxation."

[1205] Step 7:

[1206] The terminal displays the care plan received from the server on the care recipient's smart device. The smart glasses' display control software (GlassesDisplay) is used to display the care plan in a format that is easy for the user to understand. The input is the care plan from the server, and the output is the care plan displayed on the smart device.

[1207] Step 8:

[1208] Users (care recipients, family members, caregivers) carry out their daily lives according to the care plan displayed on the device. For example, they prepare the meals instructed and ensure time for designated exercise and relaxation. This executes the care plan, and the results are fed back to the server via the device. The input is the care content carried out by the user, and the output is the data fed back to the system.

[1209] This specific processing step makes it possible to monitor the health and emotional state of the care recipient in real time and provide an optimal care plan based on that.

[1210] 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.

[1211] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1212] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1213] [Fourth embodiment]

[1214] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1215] 7, a 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.

[1216] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1217] 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.

[1218] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1219] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1220] 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.

[1221] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1222] 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.

[1223] 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 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.

[1224] 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.

[1225] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1226] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1227] The present invention is a system for generating and providing an optimal care plan for elderly people and people requiring special care. The system includes the following means:

[1228] 1. Data Collection Module

[1229] The user (care recipient) uses a dedicated application or device to input daily health data, such as body temperature, blood pressure, dietary habits, and symptoms.

[1230] The terminal organizes this input data and sends it to the server in an appropriate format.

[1231] 2. Analysis and Model Generation Module

[1232] The server receives the health data of the care recipient sent from the terminal and stores it in a database.

[1233] The server analyzes the stored data and uses machine learning algorithms to train an AI model that can generate a care plan based on the care recipient's health status and individual needs.

[1234] 3. Care plan provision module

[1235] The server updates the care plan generated by the AI ​​model in real time and provides it to the device.

[1236] The device displays the provided care plan to the user and gives instructions for implementation, such as "We recommend a low-salt meal for lunch today" and "Take 20 minutes of light exercise in the afternoon."

[1237] Users (care recipients, family members, caregivers) carry out their daily lives according to this care plan.

[1238] Specific examples

[1239] Example 1: Morning data entry and care plan provision

[1240] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[1241] 2. The terminal organizes the entered data and sends it to the server.

[1242] 3. The server receives the data and analyzes it using an AI model in combination with past data.

[1243] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[1244] 5. This care plan is sent to the device and displayed to the user.

[1245] Example 2: Update care plan based on afternoon health change

[1246] 1. In the afternoon, the user (care recipient) feels a change in their physical condition and enters "I feel dizzy" into the app.

[1247] 2. The device organizes this information and sends it to the server.

[1248] 3. The server receives the new data and analyzes it based on the AI ​​model.

[1249] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[1250] 5. The updated care plan is sent to the device and displayed to the user.

[1251] This system allows care recipients to receive customized care based on their health condition and needs, reducing the burden on their families and caregivers. The system continuously updates care plans in real time, ensuring optimal care is provided to care recipients every day.

[1252] The processing flow will be explained below.

[1253] Step 1:

[1254] The user (care recipient) uses a dedicated app or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[1255] Step 2:

[1256] The terminal organizes the health data entered by the user and transmits the data to the server, where it is converted into an appropriate format and sent to the server via the network.

[1257] Step 3:

[1258] The server receives the data sent from the device and stores it in a database, allowing the care recipient's health data to be organized and recorded by time.

[1259] Step 4:

[1260] The server periodically analyzes the stored data, which includes analyzing the care recipient's past health data and trends, and assessing their current health status.

[1261] Step 5:

[1262] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates a care plan that best suits the individual needs of the care recipient.

[1263] Step 6:

[1264] The server then sends the care plan generated by the AI ​​model to the device, which includes specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[1265] Step 7:

[1266] The terminal displays the care plan received from the server to the user, and the displayed care plan is presented in a format that is easy for the care recipient to carry out in their daily lives.

[1267] Step 8:

[1268] The user (care recipient, family member, or caregiver) performs daily activities based on the displayed care plan, for example, preparing the prescribed meals and performing the designated exercises.

[1269] Step 9:

[1270] If the user's health condition changes due to the situation, they can enter additional health data, for example, reporting a symptom such as "feeling dizzy" in the afternoon to the dedicated app.

[1271] Step 10:

[1272] The terminal reorganizes the newly entered data and sends it to the server.

[1273] Step 11:

[1274] After receiving the new data, the server reanalyzes it with the existing data and uses the AI ​​model to update the care plan, which may include instructions such as "drink lots of fluids and take 15 minutes of rest."

[1275] Step 12:

[1276] The server sends the updated care plan to the terminal, which displays it to the user.

[1277] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[1278] Example 1

[1279] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1280] To provide optimal care plans in real time for elderly people and those requiring special care, it is necessary to efficiently collect and analyze large amounts of health data and appropriately generate and update care plans based on the data to meet individual needs. However, conventional systems often require manual data collection and analysis, making it difficult to respond immediately. In addition, the care plans provided are uniform, making it difficult to provide optimal care plans tailored to the condition of each individual care recipient.

[1281] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1282] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and transmitting it to the computer system, a means for analyzing the health data stored in the computer system and generating a machine learning model, and a means for providing the care plan generated by the machine learning model to the care recipient's device. This allows an optimal care plan based on the care recipient's health condition to be generated and provided in real time, making it possible to provide more effective and personalized care to elderly people and care recipients who require special care.

[1283] "Care recipient" refers to an individual who requires special care based on their health condition or living situation.

[1284] "Health data" refers to numerical values ​​and records that indicate the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and symptoms.

[1285] "Computer system" is a collective term for hardware and software that receives, stores, and analyzes input data and generates and operates machine learning models.

[1286] A "machine learning model" is an algorithm that learns patterns and relationships from large amounts of data and makes predictions and suggestions based on new data.

[1287] A "care plan" is a plan that includes specific instructions and recommendations for daily living based on the health condition and individual needs of the person receiving care.

[1288] "Device" refers to any electronic device or device used by a care recipient, family member, or caregiver to receive or implement a care plan.

[1289] "Means of input" refers to the interfaces and devices that allow care recipients and their caregivers to collect and record health data.

[1290] "Processing means" refers to the processes and software used to convert collected health data into an appropriate format so that it can be transmitted.

[1291] "Transmission means" refers to the network infrastructure and communication protocols used to transmit the organized health data to a computer system or server.

[1292] "Means for analysis" refers to algorithms or software used to analyze stored health data and identify patterns or anomalies.

[1293] "Means of generation" refers to the methods and technologies for generating a machine learning model based on analyzed data and creating an optimal care plan.

[1294] "Means for providing" refers to the infrastructure and software required to send the generated care plan to the care recipient's device and enable it to be displayed and executed.

[1295] MODE FOR CARRYING OUT THE INVENTION

[1296] This invention is a system that generates and provides optimal care plans for elderly people and those requiring special care. The system inputs the care recipient's health data, uses an AI model based on this data, and generates and provides a care plan in real time.

[1297] Data Collection Module

[1298] The user (care recipient) enters daily health data using a dedicated application or device (smartphone, tablet, etc.). The data entered includes body temperature, blood pressure, dietary details, symptoms, etc. For example, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[1299] The terminal then processes these inputs and formats them into the appropriate format, including validating and optionally formatting the data, before sending the resulting data in an encrypted form to the server.

[1300] Analysis and Model Generation Module

[1301] The server receives the encrypted data sent from the device and stores it in a database. During the storage process, the data is checked for consistency and integrity.

[1302] The server then uses the stored health data to train an AI model using machine learning algorithms (e.g., TensorFlow or PyTorch). The training process utilizes past data and adds new data as it progresses.

[1303] The server generates a prompt based on the current user's health data and inputs it into the AI ​​model. An example of a prompt might be, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate an optimal morning care plan."

[1304] Care plan delivery module

[1305] Based on the input prompt, the AI ​​model generates an optimal care plan for the care recipient, such as "Eat a light breakfast, followed by a 30-minute walk."

[1306] The server updates the generated care plan in real time, encrypts it, and sends it to the device, which then decrypts the data and displays it to the user. The displayed content includes specific instructions, such as "Recommend a low-salt meal for lunch today" or "Try 20 minutes of light exercise this afternoon."

[1307] Care plan implementation and feedback

[1308] Users (care recipients, family members, and caregivers) carry out their daily activities according to the displayed care plan, and then report the progress of the care plan to the app as feedback after the plan is completed.

[1309] The device sends the feedback data to the server, which receives it and updates the database. New feedback data is also used to train the AI ​​model, which is updated accordingly.

[1310] Specific examples

[1311] Example 1: Morning data entry and care plan provision

[1312] 1. In the morning, the user (care recipient) enters "body temperature 36.8 degrees, blood pressure 130 / 85" into a dedicated app.

[1313] 2. The terminal encrypts the entered data and sends it to the server.

[1314] 3. The server receives and stores the data, and analyzes it using an AI model in combination with past data.

[1315] 4. The AI ​​model generates a care plan such as "Eat a light breakfast followed by a 30-minute walk is recommended."

[1316] 5. This care plan is encrypted, sent to the device, and displayed to the user.

[1317] Example 2: Update care plan based on afternoon health change

[1318] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app.

[1319] 2. The device encrypts this information and sends it to the server.

[1320] 3. The server receives the new data and analyzes it based on the AI ​​model.

[1321] 4. The AI ​​model generates an updated care plan, such as "Drink lots of fluids and get 15 minutes of rest."

[1322] 5. The updated care plan is encrypted, sent to the device, and displayed to the user.

[1323] This system provides customized care based on the care recipient's health condition and needs, reducing the burden on family and caregivers. The system continuously updates the care plan in real time, allowing the care recipient to receive optimal care every day.

[1324] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1325] Step 1:

[1326] User health data entry

[1327] The user (care recipient) uses a dedicated application to input daily health data such as body temperature, blood pressure, dietary details, and symptoms. This is done using devices such as smartphones and tablets. Specifically, the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[1328] Input: Health data entered by the user into the app

[1329] Output: Organized health data (e.g., body temperature 36.8°C, blood pressure 130 / 85)

[1330] Step 2:

[1331] Data organization and transmission by terminal

[1332] The device receives the health data entered by the user, verifies the format, and organizes it. After formatting the data, it encrypts it and securely transmits it to the server. Specifically, this includes the operation of the device encrypting the input data and transmitting it to the server.

[1333] Input: Organized health data

[1334] Data processing: Data encryption processing

[1335] Output: Encrypted health data

[1336] Step 3:

[1337] Data reception and storage by the server

[1338] The server receives the encrypted data sent from the device, decrypts the data, and stores it in a database. This process verifies the integrity and completeness of the data. Specifically, this process involves the server decrypting the encrypted data and storing it in a database.

[1339] Input: Encrypted health data

[1340] Data processing: Data decryption and integrity check

[1341] Output: Health data stored in a database

[1342] Step 4:

[1343] Server-based data analysis and AI model training

[1344] The server analyzes the stored health data and trains an AI model using machine learning algorithms (e.g., TensorFlow, PyTorch). It optimizes the algorithm using past data and adds new data to the learning process. Specifically, the server uses the stored data to train the model.

[1345] Input: Health data stored in a database

[1346] Data Computing: Analyzing data and applying algorithms to train AI models

[1347] Output: A trained AI model

[1348] Step 5:

[1349] Server-generated prompts and care plan creation

[1350] Based on the trained AI model, the server generates a prompt corresponding to the current user's health data and inputs it into the model. An example of a prompt is, "Please analyze the health data of an elderly person (body temperature 36.8°C, blood pressure 130 / 85) and generate the optimal morning care plan." Based on this prompt, the AI ​​model generates the optimal care plan according to each individual's health condition.

[1351] Input: Current user's health data, prompt text

[1352] Data Calculation: Care plan generation using an AI model based on prompt statements

[1353] Output: Generated care plan

[1354] Step 6:

[1355] Encrypt and send care plans

[1356] The server encrypts the generated care plan and sends it to the terminal. The terminal decrypts the encrypted data and prepares it for display to the user. Specifically, this includes the operation of the server encrypting the care plan and sending it to the terminal.

[1357] Input: Generated care plan

[1358] Data Processing: Care Plan Encryption

[1359] Output: Encrypted care plan

[1360] Step 7:

[1361] Displaying care plans on devices

[1362] The device decrypts the encrypted care plan it receives and displays it to the user. The displayed content includes specific instructions such as "We recommend a low-salt meal for lunch today" and "Try 20 minutes of light exercise this afternoon." Specifically, the device decodes the care plan and displays it on the screen.

[1363] Input: Encrypted care plan data

[1364] Data processing: Decryption of care plan data

[1365] Output: Decoded care plan display

[1366] Step 8:

[1367] User implementation of care plans and feedback

[1368] Users (care recipients, family members, caregivers) carry out their daily activities according to the displayed care plan. After that, they report the implementation status of the care plan and feedback within the app. Specifically, this includes the user carrying out the care plan and providing feedback on the results to the app.

[1369] Input: The result of the user's executed care plan

[1370] Output: Implementation status data fed back into the app

[1371] Step 9:

[1372] Feedback data transmission by terminal

[1373] The device receives and organizes the feedback data from the user and sends it to the server. Specifically, the device organizes the feedback data and sends it back to the server.

[1374] Input: User-entered feedback data

[1375] Data processing: Feedback data cleansing and encryption

[1376] Output: Feedback data sent to the server

[1377] Step 10:

[1378] Server-based data updates and model retraining

[1379] The server receives the feedback data and updates the database. Based on the new data, the AI ​​model is retrained to generate more accurate care plans. Specifically, the server uses the feedback data to continuously optimize the AI ​​model.

[1380] Input: Feedback of implementation status data

[1381] Data Computing: Retraining AI models using feedback data

[1382] Output: Updated AI model

[1383] These are the programming steps of the system, which, based on this detailed processing, will provide a real-time, personalized care plan for the elderly and those with special needs.

[1384] (Application example 1)

[1385] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1386] Daily health management is important for the elderly and those receiving special care, but it is difficult to manage this individually, so there is a need to provide appropriate care plans.In addition, there is currently a lack of support systems that allow elderly people who visit stores to understand their own health status and select appropriate products and care methods.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1388] In this invention, the server includes a means for inputting health data of the care recipient, a means for organizing the input health data and sending it to the server, and a means for acquiring the health data from an input device installed in the physical store, thereby enabling elderly people and care recipients who require special care to efficiently manage their health conditions and receive appropriate care plans even in the store.

[1389] "Care recipients" refers to people who require support in daily life, such as elderly people or people with disabilities who require special assistance or care.

[1390] "Health data" refers to information that indicates the health status of the person receiving care, such as body temperature, blood pressure, dietary content, and changes in physical condition.

[1391] "Server" refers to a computer system for collecting, storing, and analyzing data over a network.

[1392] "AI model" refers to a computational model that uses machine learning algorithms to generate appropriate care plans from input data.

[1393] A "care plan" refers to specific care methods and health management instructions created based on the health condition and needs of the person receiving care.

[1394] "Terminal" refers to an electronic device used by the care recipient to input data and check the care plan.

[1395] A "physical store" refers to a physical store, such as a supermarket or drugstore, where consumers can visit and receive goods or services.

[1396] "Input device" refers to a hardware device or interface that allows a care recipient to record or input health data.

[1397] "Display Device" refers to a display or screen used to present generated care plans and health information to a user.

[1398] "Analysis" refers to the process of processing collected data and deriving meaningful results or information.

[1399] This invention aims to realize a system for generating and providing optimal care plans for elderly people and those requiring special care. This system consists of a data collection module, an analysis and model generation module, and a care plan provision module.

[1400] 1. Data Collection Module

[1401] The server collects daily health data from the care recipient. This data includes body temperature, blood pressure, dietary habits, symptoms, etc., and is entered by the care recipient themselves using a smartphone, tablet, or an input device installed in the store. The entered data is organized by the device and sent to the server in an appropriate format.

[1402] 2. Analysis and Model Generation Module

[1403] The server receives the transmitted health data and stores it in a database. It then analyzes the stored data and uses machine learning algorithms to train a generative AI model. This generative AI model generates an optimal care plan based on the care recipient's health condition and individual needs. For example, a deep learning framework such as TensorFlow is used to build the AI ​​model.

[1404] 3. Care plan provision module

[1405] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. The device displays the care plan to the care recipient and gives instructions for its implementation. The care recipient follows these instructions to live their daily life. For example, specific health management instructions such as "We recommend a low-sodium diet today" are displayed in real time on a display device installed in a physical store.

[1406] Specific examples

[1407] Example 1: In-store health data entry and care plan provision

[1408] 1. The care recipient enters their temperature and blood pressure at an interactive kiosk in a physical store. For example, they might enter "Temperature is 37.2°C, Blood pressure is 140 / 90."

[1409] 2. The terminal organizes the entered data and sends it to the server.

[1410] 3. The server receives the data and analyzes it using an AI model in conjunction with past data.

[1411] 4. The AI ​​model generates an appropriate care plan and provides specific advice, such as "We recommend a low-sodium diet today."

[1412] 5. This care plan is sent to a display device in the store and displayed to the care recipient.

[1413] Example 2: Update care plan based on afternoon health change

[1414] 1. In the afternoon, the person being cared for feels a change in their physical condition and types "I feel dizzy" into their smartphone.

[1415] 2. The device organizes this information and sends it to the server.

[1416] 3. The server analyzes the newly received data using an AI model and generates an updated care plan, such as "Drink plenty of fluids and take 15 minutes of rest."

[1417] 4. The updated care plan is sent to the smartphone and displayed to the care recipient.

[1418] Prompt Sentence Examples

[1419] "Generate an appropriate care plan based on the user's health data. For example, if the user's temperature is 37.2 degrees, blood pressure is 140 / 90, and dizziness is present, provide a care plan that recommends a low-sodium diet. Also recommend specific foods and supplements."

[1420] This system allows elderly people and those requiring special care to efficiently manage their health and receive appropriate care while in the store.

[1421] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1422] Step 1:

[1423] The care recipient inputs health data. Using a smartphone or an interactive kiosk in a physical store as the input device, data such as body temperature, blood pressure, and changes in physical condition are entered. This input data is received and organized by a terminal. The input is specific numerical values ​​and diagnosis details, such as "body temperature is 37.2 degrees, blood pressure is 140 / 90."

[1424] Step 2:

[1425] The device sends the organized health data to a server. This transmission uses an internet connection and a secure protocol (e.g., HTTPS). For example, a smartphone app uploads the input data to a cloud server. The data is then formatted in a standard format such as JSON.

[1426] Step 3:

[1427] The server receives the health data and stores it in a database. This storage process uses an SQL database or NoSQL database. The stored data is also integrated with past data. The database organizes and stores data for each care recipient.

[1428] Step 4:

[1429] The server analyzes the stored data and updates the AI ​​model. Specifically, it uses machine learning frameworks such as TensorFlow to train a generative AI model. The entire stored health data is used as input data, and the model is updated based on trends in past and new data. Appropriate data preprocessing (e.g., normalization) is performed during this process.

[1430] Step 5:

[1431] The server generates a care plan based on the AI ​​model. Here, the generative AI model receives new data as input and outputs a specific care plan (e.g., "recommend a low-sodium diet") as an analysis result. The generated care plan includes detailed instructions based on specific rules and algorithms.

[1432] Step 6:

[1433] The server sends the generated care plan to the device using a protocol that allows for real-time data transmission (e.g., WebSocket). The care plan is displayed on the device's interface, and specific instructions are sent to the care recipient as notifications.

[1434] Step 7:

[1435] The device displays the provided care plan to the care recipient. The care plan is visualized on a display or smartphone screen, and is displayed in the form of, for example, "These ingredients are recommended today." In a physical store, the display will show content such as "A low-sodium diet is recommended today."

[1436] Step 8:

[1437] Care recipients follow a care plan and take specific actions, such as choosing recommended foods from store shelves or taking rest at designated times, to improve their health.

[1438] In this way, through the specific processing flow of each step, elderly people and those requiring special care can receive an appropriate and individualized care plan.

[1439] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1440] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take into consideration the user's mental health. The system includes the following means:

[1441] 1. Data Collection Module

[1442] The user (care recipient) uses a dedicated application or wearable device to input daily health data (body temperature, blood pressure, symptoms, etc.). For example, they might input "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[1443] The terminal organizes this input data and sends it to the server in an appropriate format.

[1444] 2. Emotion Recognition Module

[1445] The emotion engine analyzes the facial expressions and tone of voice of the user (care recipient) through the device's camera and microphone. For example, the system recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone.

[1446] The device organizes the recognized emotion data and sends it to the server.

[1447] 3. Analysis and Model Generation Module

[1448] The server receives the health and emotion data sent from the device and stores them in a database.

[1449] The server analyzes this data comprehensively to assess the overall health of the care recipient, which involves training an AI model using machine learning algorithms, which is capable of generating a care plan based on the care recipient's health and emotional state.

[1450] 4. Care plan provision module

[1451] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" or "The user's emotional state is unstable, so allow time for relaxation."

[1452] The terminal displays the provided care plan to the user, and the displayed care plan is presented in a format that is easy for the care recipient to follow.

[1453] 5. Implementation and Feedback

[1454] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[1455] After execution, emotional and health data can be entered again to provide feedback that helps the system generate the next care plan.

[1456] Specific examples

[1457] Example 1: Morning data entry and emotion recognition

[1458] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[1459] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[1460] 3. The device organizes this data and sends it to the server.

[1461] Example 2: Health status changes and emotional feedback

[1462] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[1463] 2. The device organizes this information and sends it to the server.

[1464] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1465] 4. This care plan is sent to the device and displayed to the user.

[1466] This approach reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates the care plan in real time, enabling optimal care for the care recipient on a daily basis.

[1467] The processing flow will be explained below.

[1468] Step 1:

[1469] The user (care recipient) uses a dedicated application or wearable device to input daily health data, for example, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85."

[1470] Step 2:

[1471] The device organizes the health data entered, converts it into an appropriate format, and sends it to the server. The data is organized in the form of user ID, date and time, measurement values, etc.

[1472] Step 3:

[1473] The user (care recipient) records their facial expressions and tone of voice using the device's camera and microphone. This information is analyzed by the emotion engine. For example, the emotion is recognized as "calm" based on facial expression analysis by the camera, and "anxious" based on tone of voice analysis by the microphone.

[1474] Step 4:

[1475] The device organizes the emotional data analyzed by the emotion engine and sends it to the server. This data is also organized in the form of user ID, date, time, emotional state, etc.

[1476] Step 5:

[1477] The server receives the health and emotion data sent from the device and stores it in a database, allowing comprehensive health data of the care recipient to be managed in a unified manner.

[1478] Step 6:

[1479] The server periodically analyzes the stored data, and performs an integrated analysis of health and emotional data to assess the overall health status and emotional trends of the care recipient.

[1480] Step 7:

[1481] The server uses machine learning algorithms to train an AI model based on the analysis results, which then generates an optimal care plan based on the care recipient's health and emotional state.

[1482] Step 8:

[1483] The server updates the care plan generated by the AI ​​model in real time and provides it to the device, including specific instructions such as "Eat a light breakfast followed by a 30-minute walk is recommended" and "Due to the patient's unstable emotional state, time should be allotted for relaxation."

[1484] Step 9:

[1485] The terminal displays the provided care plan to the user in a format that makes it easy for the user to carry out the plan in their daily lives.

[1486] Step 10:

[1487] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, for example, preparing instructed meals and ensuring designated exercise and relaxation times.

[1488] Step 11:

[1489] If the situation changes the user's health or emotional state, the user can input additional data, for example, a symptom such as "I feel dizzy," and the emotion engine will recognize this as "stress."

[1490] Step 12:

[1491] The device organizes the newly entered health and emotion data and sends it back to the server.

[1492] Step 13:

[1493] After receiving the new data, the server reanalyzes it, integrating it with the existing data, and updates the care plan using the AI ​​model, which may include instructions such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1494] Step 14:

[1495] The server sends the updated care plan to the terminal, which displays it to the user.

[1496] This allows the entire system to work together, making it possible to continue providing optimal care plans to care recipients in real time.

[1497] Example 2

[1498] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1499] Existing care systems mainly provide care plans based solely on the health data of the care recipient, but they are unable to generate care plans that take into account emotional changes and mental health conditions. This means that the mental stress and anxiety of the care recipient are not properly addressed, making comprehensive health management difficult.

[1500] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for inputting the health data of the care recipient, a means for organizing the input health data and transmitting it to the server, a means for recognizing, organizing, and transmitting emotional data of the care recipient using devices such as a camera and a microphone, a means for comprehensively analyzing the health data and emotional data stored in the server and generating an AI model, and a means for providing the care plan generated by the AI ​​model to the care recipient's device. This makes it possible to generate and provide an optimal care plan that takes into account not only the physical health state of the care recipient but also the mental health state.

[1501] "Health data" refers to data that indicates the physical condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[1502] "Emotion data" is data that indicates the emotional state of the care recipient analyzed based on facial expressions, tone of voice, and the like.

[1503] A "terminal" is a device used by a care recipient to input health data and emotional data and to check the displayed care plan.

[1504] The "server" is a computer system that stores collected health and emotional data, generates an AI model based on this data, and creates a care plan.

[1505] A "care plan" is a set of specific instructions and suggestions for actions that the care recipient should implement in their daily lives, generated based on an AI model.

[1506] The "AI model" is a statistical and machine learning model that generates care plans based on algorithms trained using the care recipient's health and emotional data.

[1507] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it provides care plans that also take mental health into consideration. This system includes the following means.

[1508] First, the user (care recipient) enters their daily health data (body temperature, blood pressure, symptoms, etc.) using a dedicated application or wearable device. For example, they might enter, "This morning's body temperature was 36.8 degrees, and blood pressure was 130 / 85." The hardware used includes smartphones, tablets, and various devices that measure health data (thermometers and blood pressure monitors).

[1509] The device organizes this input data and sends it to the server in an appropriate format. Similarly, the emotion engine analyzes the user's facial expressions and tone of voice through the device's camera and microphone. For example, the device recognizes the user's emotional state from facial expression analysis on the camera and tone of voice recorded by the microphone. The device also organizes this emotion data and sends it to the server.

[1510] The server receives the health and emotional data sent from the device and stores it in a database. This process requires the use of a database management system (DBMS) to ensure data security and integrity. The server then comprehensively analyzes this data and evaluates the overall health status of the care recipient. This evaluation involves applying machine learning algorithms using Python and R to train an AI model. This AI model is capable of generating a care plan based on the care recipient's health and emotional status.

[1511] The generated care plan is updated in real time by the server and provided to the device. For example, it may contain specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "Due to unstable emotional state, take time to relax." The device displays the provided care plan to the user in a format that is easy for the care recipient to follow.

[1512] Users (care recipients, family members, and caregivers) carry out daily activities according to the displayed care plan, such as preparing meals as instructed and ensuring time for exercise and relaxation. After carrying out these activities, users can enter their emotional and health data again to provide feedback that the system uses to generate the next care plan.

[1513] Specific examples

[1514] Example 1: Morning data entry and emotion recognition

[1515] 1. The user (care recipient) enters their body temperature and blood pressure into the dedicated app in the morning. For example, they might enter "body temperature 36.8 degrees, blood pressure 130 / 85."

[1516] 2. In addition, facial expressions are captured by the camera and the emotion engine analyzes the emotion as "calm" or "unsettled."

[1517] 3. The device organizes this data and sends it to the server.

[1518] Example 2: Health status changes and emotional feedback

[1519] 1. In the afternoon, the user (care recipient) notices a change in their physical condition and enters "I feel dizzy" into the app. At the same time, the emotion engine recognizes the "stressed state" through the camera.

[1520] 2. The device organizes this information and sends it to the server.

[1521] 3. The server receives the new data and analyzes it with the AI ​​model, which generates a care plan that includes emotional care such as "drink lots of fluids and take 15 minutes of rest" and "try relaxation music."

[1522] 4. This care plan is sent to the device and displayed to the user.

[1523] This reduces the burden on families and caregivers by providing customized care based on the care recipient's physical and mental health. The system continuously updates care plans in real time, enabling optimal care for care recipients on a daily basis.

[1524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1525] Step 1: Data collection

[1526] The user (care recipient) launches the dedicated app every morning and inputs health data (body temperature, blood pressure, symptoms, etc.). For example, the user might input "body temperature is 36.8 degrees, blood pressure is 130 / 85." The input data (health data) is provided to the device. The input health data undergoes format checks (data validation) within the application. For example, it is checked whether the temperature is a valid number and whether the units of blood pressure are correct. Data that passes the format check is sent to the next step.

[1527] Step 2: Emotion Recognition

[1528] The user (care recipient) takes a picture of their face using the device's camera and records their voice using the microphone. The input is facial image and audio data. The device sends this data to an emotion recognition engine, which analyzes their emotional state. For example, it determines whether they are smiling from the facial image and analyzes their audio to determine whether they are relaxed or tense. This process yields emotion recognition results such as "calm" or "stressed." The analysis results are stored in the device and sent to the next step.

[1529] Step 3: Send data

[1530] The device transmits the collected health data and emotion data to the server. Appropriate security measures (e.g., encryption methods) are applied during this process. The input is the health data and emotion recognition results that have passed format checks, and these are transmitted to the server. The output is the data transmitted to the server.

[1531] Step 4: Data integration and storage

[1532] The server receives the health and emotion data sent from the device. The input is the data sent from the device. The server stores this data in a database management system (DBMS). For example, new data is stored in the appropriate fields, and recorded as "October 10, 2023, Body Temperature: 36.8°C, Blood Pressure: 130 / 85". The output is the integrated data stored in the database.

[1533] Step 5: Analysis by AI model

[1534] The server uses the stored data to assess the overall health of the care recipient. The input is the stored health data and emotional data. This process involves analyzing the data using machine learning algorithms using Python or R. For example, it may identify a tendency for recent blood pressure values ​​to be high or recent stress levels to be high. The output is the analysis results and an updated AI model.

[1535] Step 6: Create a care plan

[1536] The server generates a care plan based on the analysis results of the AI ​​model. The input is the analysis results of the AI ​​model. The generated care plan includes specific instructions for actions, such as "have a light breakfast," "take a 30-minute walk," and "take deep breaths to relax." The output is the generated care plan.

[1537] Step 7: Provide a care plan

[1538] The server sends the generated care plan to the terminal in real time. The input is the generated care plan. The terminal notifies the user of the received care plan and displays it on the screen. For example, it might say, "Today's care plan: light breakfast, 30-minute walk, deep breathing." The output is the care plan displayed to the user.

[1539] Step 8: Implementation and Feedback

[1540] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they perform the prescribed exercises, prepare the designated meals, and ensure time for relaxation. After carrying out these activities, the user again inputs health and emotional data. The input is the newly collected health and emotional data. The device organizes this data and sends it to the server, which receives it as feedback and uses it to generate the next care plan. The output is updated feedback data.

[1541] By linking each step in this way, an optimal care plan based on the physical and mental health status of the care recipient can be generated and provided.

[1542] (Application example 2)

[1543] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1544] The current nursing care system struggles to provide comprehensive care plans that consider not only the physical health of elderly people and those requiring special care, but also their mental health. Furthermore, the lack of real-time data collection and emotion recognition makes it difficult to provide appropriate care plans quickly. Furthermore, the lack of care plans that take into account emotional states leaves a great need for effective support for the mental health of care recipients.

[1545] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the health data and emotional data of the care recipient and generating an AI model, means for recognizing the emotional state of the care recipient using a smart device and transmitting the data to the server, and means for providing the care plan generated by the AI ​​model to the care recipient's terminal. This makes it possible to provide a real-time care plan based on the physical and mental health conditions of the care recipient.

[1546] "Health data of the care recipient" refers to information indicating the physical health condition of the care recipient, such as body temperature, blood pressure, and symptoms.

[1547] A "server" is a central processing unit for receiving, storing, and analyzing data transmitted over a network.

[1548] "Emotional data" is information about the emotional state of the care recipient analyzed from facial expressions and tone of voice.

[1549] The "AI model" is a predictive model constructed using machine learning algorithms based on the health and emotional data of the care recipient.

[1550] A "care plan" is a plan generated using an AI model that includes specific care instructions and advice for the care recipient to carry out in their daily lives.

[1551] A "terminal" is a device such as a smartphone or smart glasses used by the care recipient.

[1552] A "smart device" is a device that is connected to the Internet and has the ability to collect and recognize the health and emotional state of the person being cared for in real time.

[1553] "Data collection means" refers to equipment and software for acquiring health data and emotional data from the care recipient.

[1554] "Emotion recognition means" is a technology for recognizing the emotional state of the care recipient by analyzing their facial expressions and voice.

[1555] The "data transmission means" is a function for transmitting acquired data to a server.

[1556] "Data analysis means" refers to the process of comprehensively analyzing health data and emotional data on a server to generate an AI model.

[1557] The "care plan providing means" is a mechanism for displaying and providing the generated care plan on the care recipient's terminal.

[1558] This invention is a system for generating and providing optimal care plans for elderly people and those requiring special care, and by combining it with an emotion engine that recognizes the user's emotions, it also takes into consideration the user's mental health. The embodiments for implementing this invention are as follows.

[1559] The system uses the following hardware and software:

[1560] Smart devices (e.g., smart glasses)

[1561] Camera and microphone

[1562] server

[1563] Client terminal (smartphone, etc.)

[1564] Emotion Recognizer

[1565] Data transmission software (requests library)

[1566] Display control software (GlassesDisplay)

[1567] Data collection

[1568] The device has a means to input the care recipient's daily health data (body temperature, blood pressure, symptoms, etc.). The input data is organized and sent to the server in an appropriate format. It is also equipped with an emotion engine that uses the smart device's camera and microphone to analyze facial expressions and voice in real time and recognize emotional data. The emotional data is also organized and sent to the server.

[1569] Data analysis

[1570] The server receives the health and emotional data sent from the device and stores it in a database. It then comprehensively analyzes this data and generates an AI model using a machine learning algorithm to assess the overall health and emotional state of the care recipient. This AI model then generates a care plan based on the care recipient's health and emotional state.

[1571] Care plan provided

[1572] The server updates the care plan generated by the AI ​​model in real time and provides it to the device. For example, it may include specific instructions such as "Eat a light breakfast, then take a 30-minute walk" or "The user's emotional state is unstable, so allow time for relaxation." The device then displays the provided care plan to the care recipient, using the smart device's display to present the instructions in a clear and easy-to-follow format.

[1573] Execution and Feedback

[1574] The user (care recipient, family member, or caregiver) carries out daily activities according to the displayed care plan. For example, they prepare the prescribed meals and ensure they have time for designated exercise and relaxation. After carrying out these activities, they can input their emotional and health data again to receive feedback that can be used by the system to generate the next care plan.

[1575] Examples of concrete examples and prompts

[1576] Examples:

[1577] In the morning, a user wearing smart glasses enters their body temperature and blood pressure into the app. For example, they might enter "body temperature is 36.8 degrees, blood pressure is 130 / 85."

[1578] The smart glasses analyze the user's facial expressions, and the emotion engine recognizes a "calm state."

[1579] The data is sent to a server, and a care plan such as "Eat a light breakfast, then take a 30-minute walk" is displayed on the smart glasses.

[1580] Example prompt sentence:

[1581] text

[1582] Please enter your temperature today: 36.8

[1583] Enter your blood pressure today: 130 / 85

[1584] Capturing frames and analyzing facial expressions...

[1585] Emotional state: Calm

[1586] Sending data to server...

[1587] Care plan: "Light breakfast followed by a 30-minute walk is recommended."

[1588] In this way, the system can provide customized care based on the care recipient's physical and mental health status, reducing the burden on family members and caregivers.

[1589] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1590] Step 1:

[1591] The device receives health data, such as body temperature and blood pressure, entered by the user (care recipient). Based on this, the device organizes the health data and converts it into an appropriate format. For example, if the user enters "body temperature is 36.8 degrees, blood pressure is 130 / 85," the data is converted into JSON format. This is the input. The output is the organized health data.

[1592] Step 2:

[1593] The device uses the camera and microphone of the smart device (e.g., smart glasses) to capture the user's facial expressions and tone of voice. Emotion recognition software (EmotionRecognizer) is used to analyze the user's emotional state from the captured data. For example, emotional data such as "calm" or "unsettled" is output.

[1594] Step 3:

[1595] The device organizes the collected health and emotion data and sends it to the server. This process uses data transmission software (requests library), which receives the organized health and emotion data as input and sends it to the server as output.

[1596] Step 4:

[1597] The server stores the received health and emotional data in a database, thereby maintaining a history of the care recipient's overall health and emotional state. The input is the received data, and the output is the information stored in the database.

[1598] Step 5:

[1599] The server comprehensively analyzes the stored health and emotional data and generates an AI model using a machine learning algorithm. This AI model is capable of generating a care plan based on the care recipient's health and emotional state. The input is the data in the database, and the output is the generated AI model.

[1600] Step 6:

[1601] The server uses the AI ​​model to generate care plans that are updated in real time, such as "Eat a light breakfast followed by a 30-minute walk" or "Allow time for relaxation."

[1602] Step 7:

[1603] The terminal displays the care plan received from the server on the care recipient's smart device. The smart glasses' display control software (GlassesDisplay) is used to display the care plan in a format that is easy for the user to understand. The input is the care plan from the server, and the output is the care plan displayed on the smart device.

[1604] Step 8:

[1605] Users (care recipients, family members, caregivers) carry out their daily lives according to the care plan displayed on the device. For example, they prepare the meals instructed and ensure time for designated exercise and relaxation. This executes the care plan, and the results are fed back to the server via the device. The input is the care content carried out by the user, and the output is the data fed back to the system.

[1606] This specific processing step makes it possible to monitor the health and emotional state of the care recipient in real time and provide an optimal care plan based on that.

[1607] 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.

[1608] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1609] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1610] 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.

[1611] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1612] 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.

[1613] 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).

[1614] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1615] 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."

[1616] 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.

[1617] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1618] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1623] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] The following is further disclosed regarding the above embodiment.

[1629] (Claim 1)

[1630] a means for inputting health data of the care recipient;

[1631] A means for organizing the input health data and transmitting it to a server;

[1632] A means for analyzing health data stored on a server and generating an AI model;

[1633] A means for providing the care plan generated by the AI ​​model to a device of the care recipient;

[1634] A system including:

[1635] (Claim 2)

[1636] 10. The system of claim 1, further comprising means for updating the care plan in real time based on the health data of the care recipient.

[1637] (Claim 3)

[1638] 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out.

[1639] "Example 1"

[1640] (Claim 1)

[1641] a means for inputting health data of the care recipient;

[1642] means for organizing and transmitting the entered health data to a computer system;

[1643] a means for analyzing health data stored in a computer system and generating a machine learning model;

[1644] a means for providing a care plan generated by the machine learning model to a device of the care recipient;

[1645] A system including:

[1646] (Claim 2)

[1647] 10. The system of claim 1, further comprising means for updating the care plan in real time based on the health data of the care recipient.

[1648] (Claim 3)

[1649] 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out.

[1650] "Application Example 1"

[1651] (Claim 1)

[1652] a means for inputting health data of the care recipient;

[1653] A means for organizing the input health data and transmitting it to a server;

[1654] A means for analyzing health data stored on a server and generating an AI model;

[1655] A means for providing the care plan generated by the AI ​​model to a device of the care recipient;

[1656] A means for acquiring health data from an input device installed in a physical store;

[1657] A means for providing a care plan on a display device installed in a physical store based on the acquired health data;

[1658] A system including:

[1659] (Claim 2)

[1660] 10. The system of claim 1, further comprising means for updating the care plan in real time based on the health data of the care recipient.

[1661] (Claim 3)

[1662] 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out.

[1663] "Example 2: Combining Emotion Engines"

[1664] (Claim 1)

[1665] a means for inputting health data of the care recipient;

[1666] A means for organizing the input health data and transmitting it to a server;

[1667] A means for recognizing, organizing, and transmitting emotional data of the care recipient to a server using devices such as a camera and a microphone;

[1668] A means for comprehensively analyzing health data and emotion data stored on a server and generating an AI model;

[1669] A means for providing the care plan generated by the AI ​​model to a device of the care recipient;

[1670] A system including:

[1671] (Claim 2)

[1672] 10. The system of claim 1, further comprising means for updating the care plan in real time based on the health and emotional data of the care recipient.

[1673] (Claim 3)

[1674] 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out.

[1675] "Application example 2 when combining emotion engines"

[1676] (Claim 1)

[1677] a means for inputting health data of the care recipient;

[1678] A means for organizing the input health data and transmitting it to a server;

[1679] A means for analyzing the health data and emotion data stored on the server and generating an AI model;

[1680] A means for providing the care plan generated by the AI ​​model to a device of the care recipient;

[1681] a means for recognizing the emotional state of the care recipient using a smart device and transmitting the state to a server;

[1682] A system including:

[1683] (Claim 2)

[1684] 10. The system of claim 1, further comprising means for updating the care plan in real time based on the health data and emotional state of the care recipient.

[1685] (Claim 3)

[1686] 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out. [Explanation of symbols]

[1687] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting health data of the care recipient; A means for organizing the input health data and transmitting it to a server; A means for analyzing health data stored on a server and generating an AI model; A means for providing the care plan generated by the AI ​​model to a device of the care recipient; A system including:

2. The system of claim 1 , further comprising means for updating the care plan in real time based on the care recipient's health data.

3. 10. The system of claim 1, further comprising means for displaying the provided care plan and providing instructions for the care recipient to carry out.

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