System

Abstract

To provide a system capable of drastically reducing the job burdens of a care manager and improving the efficiency of care plan preparation.SOLUTION: Means for displaying a user entry form for entry by the person to be cared for or the family member, means for entering basic information of the person to be cared for and a request of the family member, means for transmitting the entered information to the server, means for receiving and storing the transmitted information in the database, means for activating the generative AI model to analyze the received information and generate a care plan, and means for transmitting the generated care plan to the terminals, A system comprising: means for displaying; means for receiving user modification requests and again inputting to a generative AI model to generate a modified care plan; means for determining a final care plan and storing in a database; and means for periodically collecting care records upon which the generative AI model automatically optimizes the care plan.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] Among care workers, care managers have a particularly heavy workload, with creating care plans requiring a significant amount of time and effort. This workload is one of the reasons for the shortage of care workers and their turnover. Furthermore, the nursing care industry rarely handles quantitative information when creating documents, which has hindered the advancement of IT. To solve this problem, it is necessary to streamline document creation work and reduce the burden on care managers. [Means for solving the problem]

[0005] The present invention is a system including: means for displaying a user input form for input by the care recipient or relatives; means for inputting basic information about the care recipient and requests from relatives; means for transmitting the input information to a server; means for receiving the transmitted information and saving it in a database; means for activating a generative AI model to analyze the received information and generate a care plan; means for transmitting the generated care plan to a terminal and displaying it; means for receiving user requests for revisions and re-inputting the requests into the generative AI model to generate a revised care plan; means for finalizing the care plan and saving it in a database; and means for periodically collecting care records and having the generative AI model automatically optimize the care plan based on them. This significantly reduces the workload of care managers and improves the efficiency of care plan creation.

[0006] A "user input form" is a screen or interface that allows the care recipient or relative to enter the necessary information.

[0007] "Care recipients" refers to elderly or disabled individuals who require care.

[0008] "Relatives" refers to family members or relatives of the care recipient who have needs related to the care recipient's care.

[0009] "Basic information" refers to necessary data about the care recipient, such as name, age, medical history, etc.

[0010] "Requests" refers to the care desired by relatives or special requests.

[0011] "Server" refers to a computer system that receives input information, stores it in a database, and then activates a generative AI model to generate a care plan.

[0012] "Generative AI model" refers to an artificial intelligence program that uses machine learning algorithms to generate a care plan based on the care recipient's information and the wishes of their relatives.

[0013] A "care plan" refers to a written plan that lists the specific care content and schedule to be provided to the person receiving care.

[0014] "Database" refers to a structured data management system for storing collected information and generated care plans.

[0015] "Devices" refer to electronic devices such as computers and smartphones used by care managers and relatives.

[0016] "Modification request" refers to a request for a change or addition submitted by a user to a care plan.

[0017] "Care records" refers to data that records the history and condition of care provided to a care recipient.

[0018] "Optimization" refers to the process of updating and improving the care plan based on regularly collected care records to best meet the needs of the care recipient. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a system that automates the process of creating a care plan by providing a means for displaying a user input form for input by the care recipient or a relative. The system aims to reduce the workload of care managers and create care plans efficiently.

[0041] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0042] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0043] The generated care plan is sent from the server to the device and displayed to the user. The user can review the contents of the care plan through the device and request revisions as necessary. If revisions are requested, the server receives the request and again uses the generative AI model to generate a revised care plan. This process is repeated until a care plan that the user is finally satisfied with is created.

[0044] Once the final care plan is finalized, the server saves it in a database and the final version is displayed on the device. In addition, the server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This allows the system to flexibly respond to changes in the care recipient's situation and needs.

[0045] As a concrete example, consider the following scenario.

[0046] Enter the basic information of a care recipient, "Mr. Tanaka," as follows:

[0047] Name: Tanaka Jiro

[0048] Age: 85

[0049] Medical history: heart disease, diabetes

[0050] Family's wishes: Regular heart checkups, rehabilitation three times a week

[0051] 1. The terminal starts up and a user input form is displayed.

[0052] 2. The user enters Mr. Tanaka's basic information.

[0053] 3. The device sends the entered information to the server.

[0054] 4. The server receives the information and stores it in a database.

[0055] 5. The server launches the generative AI model and generates a care plan, including daily vital signs checks and three rehabilitation sessions per week.

[0056] 6. The care plan is sent to the device and the user reviews it.

[0057] 7. The user requests corrections as needed.

[0058] 8. The server processes the modification request and provides a regenerated care plan.

[0059] 9. The final care plan is finalized and saved in the database.

[0060] 10. The final plan will be displayed on your device.

[0061] 11. The server periodically collects care records and uses AI models to optimize the plan.

[0062] In this way, a care plan that accurately reflects the condition of the care recipient and the wishes of the relatives can be efficiently created.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The device starts up and displays a user input form, allowing the care manager or relative to enter information.

[0066] Step 2:

[0067] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[0068] Step 3:

[0069] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[0070] Step 4:

[0071] The server receives the transmitted information and first stores it in a database for later processing.

[0072] Step 5:

[0073] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[0074] Step 6:

[0075] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[0076] Step 7:

[0077] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[0078] Step 8:

[0079] Users can check the contents of the care plan through their terminal and can request revisions as needed.

[0080] Step 9:

[0081] When a user inputs a correction request, the device sends the information to the server, which receives the correction request and inputs it back into the generative AI model to correct the care plan.

[0082] Step 10:

[0083] The server generates a revised care plan and sends it back to the device, and this process is repeated until the user is satisfied.

[0084] Step 11:

[0085] Once the final care plan is finalized, the server stores it in a database and displays the final version of the care plan on the terminal.

[0086] Step 12:

[0087] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This process ensures that the most appropriate care plan is always provided based on the needs of the care recipient.

[0088] Example 1

[0089] 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."

[0090] In an aging society, there is a growing need to reduce the burden on care managers and relatives and to efficiently create care plans that are appropriate for the care recipients. However, with conventional methods, the manual input of information and creation of plans requires a great deal of time and effort, and the quality of the care plans varies.

[0091] 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.

[0092] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate prompts for generating a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's request for modification and re-inputting the modified care plan into the generative AI model, means for finalizing the care plan and storing it in a database, and means for periodically collecting care records and having the generative AI model automatically optimize the care plan based on them. This reduces the workload of care managers and relatives and enables the creation of an optimal care plan for the care recipient quickly and efficiently.

[0093] A "user input form" is an on-screen interface through which the care recipient or relative can input information.

[0094] "Basic information" refers to basic data about an individual, such as the care recipient's name, age, and medical history.

[0095] "Requests from relatives" refers to the hopes and demands of relatives regarding the care and support provided to the care recipient.

[0096] A "server" is a computer system that manages and processes data over a network.

[0097] "Database" means a digital storage device and system that can systematically store, search, and reference large amounts of information.

[0098] A "generative AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task (here, generating a care plan).

[0099] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[0100] A "care plan" is a written plan that outlines the specific care content and schedule, based on the health condition and needs of the person receiving care.

[0101] "Modification request" refers to a request made by a user to modify or change a generated care plan.

[0102] The present invention is a system that displays a user input form for input by a care recipient or their relatives and automates the process of creating a care plan. The system aims to reduce the workload of care managers and efficiently generate care plans. Detailed embodiments of this system are described below.

[0103] Hardware and software used

[0104] The system uses the following hardware and software:

[0105] Hardware: Devices (PCs, tablets, smartphones, etc.), servers

[0106] Software: Web browser, database (e.g., MySQL (registered trademark), PostgreSQL), generative AI model (e.g., machine learning algorithm such as GPT-4 (registered trademark))

[0107] Initial Settings and Form Display

[0108] When the device is started, a user input form is displayed on the web browser. This form is an interface for the care recipient and their relatives to enter basic information.

[0109] Entering information

[0110] The user (care manager or relative) enters the following information into the form:

[0111] Name of the care recipient (e.g., Tanaka Jiro)

[0112] Age (e.g., 85 years old)

[0113] Medical history (e.g., heart disease, diabetes)

[0114] Family members' wishes (e.g., regular heart checkups, rehabilitation three times a week)

[0115] Once the input is complete, the user clicks the "Submit" button to send the data to the server.

[0116] Data transmission and storage

[0117] The device sends the input information as an HTTP request to the server, which then analyzes the received data and stores it in a database.

[0118] Generate a care plan

[0119] The server retrieves the necessary information from the database and launches the generative AI model. Using a machine learning algorithm (e.g., GPT-4), it analyzes the received data and generates a prompt to generate a care plan. For example, the prompt might be "Generate a care plan appropriate for a patient aged 85, with heart disease and diabetes."

[0120] Viewing and Modifying Care Plans

[0121] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan and make requests for revisions as necessary. Specifically, a request for revision such as "I want to reduce the frequency of rehabilitation from three times a week to two times a week" can be sent from the device to the server.

[0122] Regeneration and finalization

[0123] The server receives the modification request and generates a modified care plan using the generative AI model again. This process is repeated until a care plan that satisfies the user is generated. Once the final care plan is finalized, it is saved in the database. The finalized care plan is also displayed on the device.

[0124] Automatic optimization

[0125] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records, allowing for flexible responses to changes in the care recipient's situation and needs.

[0126] Specific operation example

[0127] As a concrete example, consider the case of a care recipient named "Jiro Tanaka." Below is an example of a series of specific actions.

[0128] 1. The terminal starts up and a user input form is displayed.

[0129] 2. The user enters Mr. Tanaka's basic information (name, age, medical history, and relatives' wishes).

[0130] 3. The device sends the entered information to the server.

[0131] 4. The server receives the information and stores it in a database.

[0132] 5. The server launches the generative AI model and inputs a prompt statement to generate a care plan.

[0133] 6. The generated care plan (e.g., daily vital signs checks, three rehabilitation sessions per week) is sent to the device and reviewed by the user.

[0134] 7. The user makes a request to modify the care plan and checks the regenerated plan.

[0135] 8. The final care plan is finalized and saved in the database.

[0136] 9. The confirmed care plan will be displayed on the device.

[0137] 10. The server periodically collects care records and optimizes the care plan using a generative AI model.

[0138] This system reduces the burden on care managers and relatives and makes it possible to efficiently generate care plans that are best suited to the situation of the person receiving care.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1: Launching a terminal and displaying a user input form

[0141] When the device starts up, it automatically opens a web browser and displays a user input form for the care recipient or relative to enter basic information, including fields for name, age, medical history, and relatives' preferences.

[0142] Input: The user turns on the device.

[0143] Output: A form for user input is displayed in the web browser.

[0144] Step 2: User Enters Basic Information

[0145] The user enters the basic information of the care recipient (name, age, medical history, and relatives' wishes) into the form. Once the information is complete, the user clicks the submit button.

[0146] Input: The user enters data into the form.

[0147] Output: By pressing the submit button, the entered data is saved in the form on the browser.

[0148] Step 3: Send data from the device to the server

[0149] The terminal sends the entered information to the server as an HTTP request. Here, the POST method is used.

[0150] Input: When the submit button is clicked, the form data is packaged into the HTTP request.

[0151] Output: The form data is sent to the server.

[0152] Step 4: Receiving information and storing it in a database

[0153] The server analyzes the received data and stores it in a database by parsing the data to extract the respective fields and inserting them into the database using SQL queries.

[0154] Input: Care recipient information extracted from the HTTP request.

[0155] Output: The information is stored in a database.

[0156] Step 5: Generate a care plan using a generative AI model

[0157] The server retrieves the care recipient's information from the database and launches the generative AI model. A prompt statement is generated and input into the generative AI model to generate a care plan. For example, "Generate a care plan appropriate for an 85-year-old patient with heart disease and diabetes."

[0158] Input: Information of the care recipient obtained from the database.

[0159] Output: A care plan from the generative AI model.

[0160] Step 6: Send the care plan to your device

[0161] The server generates a care plan and sends it to the terminal as an HTTP response, which the user can view in a web browser.

[0162] Input: Generated care plan.

[0163] Output: The care plan is displayed on the terminal.

[0164] Step 7: User reviews care plan and requests modifications

[0165] The user checks the care plan and, if any changes are necessary, sends a request for changes from the terminal to the server. For example, the user might input, "I want to change the frequency of rehabilitation from three times a week to two times a week."

[0166] Input: Request for amendment to care plan.

[0167] Output: A modification request is sent to the server.

[0168] Step 8: Process and regenerate correction requests

[0169] The server receives the modification request and generates a modified care plan by again using the generative AI model. The prompt text is also updated based on the modification request.

[0170] Input: A prompt containing a correction request and the care recipient's information, which is again input into the generative AI model.

[0171] Output: Revised care plan.

[0172] Step 9: Finalize and save the final care plan

[0173] The user checks and confirms the regenerated care plan. When the user presses the confirm button, the confirmed care plan is sent to the server.

[0174] The server stores the finalized care plan in a database.

[0175] Input: Final care plan submitted with confirmation button pressed.

[0176] Output: Final care plan saved in database.

[0177] Step 10: Display the final plan on your device

[0178] The device displays the finalized care plan, which the user can review and use in their daily care.

[0179] Input: Final Care Plan.

[0180] Output: The final care plan is displayed on the terminal.

[0181] Step 11: Server collects care records and optimizes plans

[0182] The server periodically collects care records and uses a generative AI model to automatically optimize the care plan. Through regular data collection, the plan is adjusted to reflect the latest care recipient conditions.

[0183] Input: Routinely collected care records.

[0184] Output: Optimized care plan.

[0185] This flow allows for the efficient automatic generation and optimization of care plans, reducing the workload of care managers and relatives.

[0186] (Application example 1)

[0187] 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."

[0188] Conventional care plan creation systems have the problem of placing a heavy burden on care managers, making the creation process cumbersome and time-consuming. Furthermore, the actual selection and purchase of care products must be done separately, which adds to the time and effort. There is a need for a system that can solve these problems and create care plans and select appropriate care products efficiently and effectively.

[0189] 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.

[0190] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a database, means for periodically collecting care records and automatically optimizing the care plan based on the collected care records, means for generating a care product list at a care product store based on the information input by the care recipient or relative, means for displaying the generated care product list on the smart devices of store staff in real time, and means for linking the suggested care product list with in-store inventory information. This enables care recipients and relatives to efficiently create care plans and easily select and purchase appropriate care products.

[0191] "Care recipients" refers to people such as elderly people and people with disabilities who require care.

[0192] "Relatives" refers to people who are related by blood or marriage to the person receiving care, or people who support the person receiving care in a similar relationship.

[0193] "User input form" refers to the screen or interface provided for the care recipient or relative to enter the required information.

[0194] "Basic information" refers to personal data such as the care recipient's name, age, medical history, etc.

[0195] "Requests" refer to any particular wishes that the care recipient or their relatives have regarding the care plan.

[0196] A "server" refers to a computer system for processing and storing data over a network.

[0197] A "database" refers to a system that efficiently stores, manages, and searches large amounts of data.

[0198] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate results based on specific data.

[0199] A "care plan" refers to a plan that outlines the specific care content and schedule that should be provided to the person receiving care.

[0200] A "terminal" is a device used by a user to enter data and check results, such as a smartphone or tablet.

[0201] A "request for modification" refers to a user's request for desired changes to the generated care plan.

[0202] A "list of nursing care supplies" refers to a list of nursing care supplies required based on the care plan of the person receiving care.

[0203] "Store staff" refers to employees who select and sell nursing care products in physical stores.

[0204] "Smart devices" are electronic devices that can connect to the Internet, such as smartphones, tablets, and smart glasses.

[0205] "Inventory information" refers to the quantity and item information of products currently held in the store.

[0206] The present invention is a system for efficiently creating a care plan and selecting care products. The following describes the program and processing of the system that realizes the present invention.

[0207] Hardware and Software Used

[0208] Server: AWS (registered trademark) EC2

[0209] Database: AWS RDS (MySQL)

[0210] Generative AI model: OpenAI® GPT-4

[0211] Web frontend: React.js

[0212] Backend: Django

[0213] Communication protocol: REST API

[0214] User devices: smartphones, smart glasses

[0215] System program and processing explanation

[0216] 1. Displaying a user input form

[0217] The server uses React.js to display a user input form, in which the care recipient and their relatives enter basic information and requests.

[0218] 2. Data Transmission

[0219] The information sent from the device is received by the Django backend and stored in an AWS RDS database. The server receives this data and makes it available for analysis.

[0220] 3. Care plan generation

[0221] The server runs a generative AI model using OpenAI GPT-4 to analyze the information sent, and then uses machine learning algorithms to generate a care plan based on the care recipient's condition and the relatives' requests. This generative AI model runs in real time via a REST API.

[0222] 4. View care plans

[0223] The generated care plan is returned to the React.js front end and displayed on the device. The user can review the care plan and make corrections if necessary.

[0224] 5. Processing Amendment Requests

[0225] When the user issues a modification request, the server again uses the generative AI model to generate and display a modified care plan. This cycle is repeated until a care plan that satisfies the user is generated.

[0226] 6. Creating a list of care items

[0227] Once the care plan is finalized, the server generates a list of care products, and at this stage, the AI ​​model is used to analyze the information and select the necessary care products.

[0228] 7. Real-time display on smart devices

[0229] The generated list of care products is displayed in real time on the store staff's smart devices (smart glasses or smartphones), allowing them to quickly guide users to the most suitable products.

[0230] 8. Linking with inventory information

[0231] The suggestions on the nursing care product list are linked to the store's inventory information managed on the server, so it is possible to check in real time whether an item is in stock or if it can be reserved.

[0232] Specific examples

[0233] For example, the basic information and requests of the care recipient "Mr. Sato" are as follows:

[0234] Name: Hanako Sato

[0235] Age: 78

[0236] Medical history: high blood pressure, arthritis

[0237] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[0238] Based on this information, the following prompt sentence is input into the generative AI model:

[0239] Prompt Sentence Examples

[0240] "Based on the information below, please suggest the best care plan and necessary nursing care products.

[0241] The care recipient's information is as follows:

[0242] Name: Hanako Sato

[0243] Age: 78

[0244] Medical history: high blood pressure, arthritis

[0245] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[0246] thank you."

[0247] Based on these prompts, the generative AI model generates an optimal care plan and list of care supplies, which are then provided to the user and store staff.

[0248] In this way, the system of the present invention reduces the burden on the care recipient and their relatives, and allows for the creation of a care plan and the selection of care products to be carried out quickly and efficiently.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] The server uses React.js to display a user input form. The user enters basic information about the care recipient and the requests of their relatives. This input form includes information such as name, age, medical history, and care preferences. The entered data is temporarily stored in the device's memory.

[0252] Step 2:

[0253] The device sends the entered information to the Django backend for storage in the AWS RDS database. This data includes the care recipient's information and the relatives' requests entered by the user. The server receives this and stores it in the database.

[0254] Step 3:

[0255] The server uses the received information to launch a generative AI model using OpenAI GPT-4. Here, a prompt sentence is generated using a machine learning algorithm. Using this prompt sentence as input, the AI ​​model generates a care plan. In this generation process, the AI ​​calculates the optimal care plan based on the care recipient's condition and the relatives' requests.

[0256] Step 4:

[0257] The server returns the generated care plan to the React.js front end and displays it on the device. The user can then view the details of the care plan through the device. At this point, the care plan includes daily care details, regular checkups, and rehabilitation schedules.

[0258] Step 5:

[0259] If the user requests an amendment to the care plan, the device sends the amendment to the server, which then reactivates the generative AI model and regenerates the care plan based on the amendment request. This regeneration process is also performed using a machine learning algorithm.

[0260] Step 6:

[0261] The server returns the finalized care plan to the React.js front end and displays it on the terminal. At the same time, the care plan is saved in the AWS RDS database. The user can review the final care plan and confirm it if they are satisfied with its contents.

[0262] Step 7:

[0263] The server generates a list of care items based on the finalized care plan. This list is output by a generative AI model based on the prompt text. Here, the care items required for the care plan are calculated.

[0264] Step 8:

[0265] The server displays the generated list of care products in real time on the store staff's smart devices (smart glasses or smartphones), and the staff guides the user to the appropriate care products based on this information.

[0266] Step 9:

[0267] The server then links the nursing care product list suggestions to the store's inventory information. This communicates with the store's management system via API, updating inventory data in real time. This makes it possible to handle inventory availability and reserve items.

[0268] 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.

[0269] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[0270] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0271] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0272] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[0273] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[0274] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[0275] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[0276] As a concrete example, consider the following scenario.

[0277] Enter the basic information of a care recipient, "Mr. Sato," as follows:

[0278] Name: Sato Jiro

[0279] Age: 78

[0280] Medical history: high blood pressure, arthritis

[0281] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[0282] 1. The terminal starts up and a user input form is displayed.

[0283] 2. The user enters Mr. Sato's basic information.

[0284] 3. The device sends the entered information to the server.

[0285] 4. The server receives the information and stores it in a database.

[0286] 5. The server launches the generative AI model and generates a care plan, which may include, for example, daily morning blood pressure monitoring and twice-weekly physical therapy.

[0287] 6. The care plan is sent to the device and the user reviews it.

[0288] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[0289] 8. The user requests corrections as needed.

[0290] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[0291] 10. The final care plan is finalized and saved in the database.

[0292] 11. The final plan will be displayed on your device.

[0293] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[0294] In this way, this system reduces the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The terminal starts up and displays a user input form, allowing the user (care manager or relative) to enter information.

[0298] Step 2:

[0299] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[0300] Step 3:

[0301] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[0302] Step 4:

[0303] The server receives the transmitted information and stores it in a database for further processing.

[0304] Step 5:

[0305] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[0306] Step 6:

[0307] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[0308] Step 7:

[0309] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[0310] Step 8:

[0311] The user checks the contents of the care plan through the device and can make corrections as needed. At this point, the device collects the user's emotions using voice and text.

[0312] Step 9:

[0313] The emotion engine recognizes the user's emotions through voice and text analysis, for example, analyzing the user's text comments and voice tone to digitize their emotions.

[0314] Step 10:

[0315] The device sends the recognized emotion information to the server, which receives the correction request and the emotion information and re-evaluates it.

[0316] Step 11:

[0317] The server then reactivates the generative AI model and modifies the care plan based on the modification request and emotional information, generating a more personalized care plan taking into account the information from the emotional engine.

[0318] Step 12:

[0319] The server sends the modified care plan back to the device, which displays the modified care plan to the user, and this process is repeated until the user is satisfied.

[0320] Step 13:

[0321] Once the final care plan is finalized, the server stores it in the database and displays the final version of the care plan on the terminal.

[0322] Step 14:

[0323] The server periodically collects care records, and the generative AI model and emotion engine automatically optimize the care plan based on the records. This process provides an optimal care plan that continuously reflects the needs of the care recipient and the user's emotions.

[0324] Example 2

[0325] 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."

[0326] The current care plan creation process is often manual, requiring a great deal of effort and time from care managers and relatives. It is also difficult to properly reflect the feelings and wishes of the care recipient and relatives, making it difficult to provide personalized care. Conventional systems lack a mechanism to consider the user's feelings, and therefore care plans are not always satisfactory.

[0327] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0328] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to a central processing unit, means for receiving the transmitted information and storing it in a data storage device, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to an information display device and displaying it, means for an emotion engine to recognize the user's emotions through voice and text analysis, means for receiving a user's request for modification and inputting it back into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a data storage device, and means for periodically collecting care records and automatically optimizing the care plan based on the collected care records by the generative AI model. This automates the creation and modification of care plans, enabling the provision of personalized care plans that reflect the user's emotions and requests.

[0329] A "user input form" is a screen that is displayed on a terminal for the care recipient or relative to input information about care.

[0330] "Basic information of the care recipient" refers to personal data such as the care recipient's name, age, medical history, etc.

[0331] "Requests from relatives" refers to the specific wishes and demands regarding care provided to the person receiving care.

[0332] "Central Processing Unit" refers to a computer system that receives, analyzes, and processes information sent from a terminal.

[0333] "Data storage" refers to a database system for storing received information.

[0334] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate a care plan based on information about the care recipient and the requests of their relatives.

[0335] "Care plan" refers to a plan that describes the specific care to be provided to the care recipient.

[0336] An "information display device" is a terminal for displaying the generated care plan to the user.

[0337] An "emotion engine" is a system that recognizes a user's emotions by analyzing voice and text, and outputs them as analytical data.

[0338] A "modification request" refers to a user's request for a change or adjustment to a care plan.

[0339] "Care records" are data that record the status and results of care provided to care recipients.

[0340] "Optimization" refers to the process of improving care plans to make them more appropriate and effective based on collected data.

[0341] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[0342] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0343] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0344] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[0345] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[0346] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[0347] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[0348] To illustrate, consider the following scenario:

[0349] Enter the basic information of the care recipient "Sato-san" as follows:

[0350] Name: Sato Jiro

[0351] Age: 78

[0352] Medical history: high blood pressure, arthritis

[0353] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[0354] 1. The terminal starts up and a user input form is displayed.

[0355] 2. The user enters Mr. Sato's basic information.

[0356] 3. The device sends the entered information to the server.

[0357] 4. The server receives the information and stores it in a database.

[0358] 5. The server runs the generative AI model to generate a care plan, for example, one that includes blood pressure monitoring every morning and physical therapy twice a week.

[0359] 6. The care plan is sent to the device and the user reviews it.

[0360] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[0361] 8. The user requests corrections as needed.

[0362] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[0363] 10. The final care plan is finalized and saved in the database.

[0364] 11. The final plan will be displayed on your device.

[0365] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[0366] In this way, this system can reduce the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1:

[0369] Starts the terminal and displays a form for user input

[0370] The device starts up and launches a dedicated application. Next, a user input form is displayed on the screen, allowing the care recipient or relative to enter information. The form contains input fields for name, age, medical history, required care, and relatives' requests. The device provides the user with the initial data for input, and the data entered by the user is output.

[0371] Step 2:

[0372] Enter basic user information

[0373] The user (care manager or relative) enters basic information about the care recipient into the displayed user input form. Specifically, the user enters details such as the care recipient's name, age, medical history, required care, and the relative's requests. The input data is output and sent to the next processing step.

[0374] Step 3:

[0375] Sending information from the device to the server

[0376] The terminal sends the information entered by the user to the server. The terminal executes the sending process, packages the input data from the user into packets, and sends them to the server. The input is the user's input data, and the output is the data received by the server.

[0377] Step 4:

[0378] Receiving information from the server and storing it in the database

[0379] The server receives the information sent from the device. It then stores the received information in a database. Specifically, it issues a query to the database to store information such as name, age, medical history, and requests from relatives. The input is the data sent from the device, and the output is the data stored in the database.

[0380] Step 5:

[0381] Server generation: Launching the AI ​​model and generating care plans

[0382] The server launches the generative AI model, which then analyzes the basic information of the care recipient stored on the server and the wishes of their relatives to generate an appropriate care plan. The model uses machine learning algorithms to make predictions and optimizations based on the input data. The input is information from the database, and the output is the generated care plan.

[0383] Step 6:

[0384] Sending and displaying care plans to devices

[0385] The server sends the care plan to the terminal. The terminal displays the care plan it receives to the user. Specifically, the contents of the care plan are displayed on the screen so that the user can check them. The input is care plan data from the server, and the output is a care plan that is displayed to the user.

[0386] Step 7:

[0387] Emotional analysis of users using an emotion engine

[0388] The emotion engine analyzes the voice and text inputs made by the user when confirming the care plan. Using an emotion recognition algorithm, it analyzes the user's emotions (dissatisfaction, relief, etc.) and sends that information to the server. The input is the user's voice and text data, and the analyzed emotional information is generated as the output.

[0389] Step 8:

[0390] User correction request submission

[0391] The user submits a request to modify the care plan. The terminal transmits the modification request input by the user to the server. The input is the user's modification request data, and the modification data to be transmitted to the server is generated as the output.

[0392] Step 9:

[0393] Server-based correction requests, processing of emotional information, and reevaluation of care plans

[0394] The server receives the correction request and the user's emotional information, then launches the generative AI model again to reevaluate the care plan. A new care plan that takes the emotional information into account is generated. The inputs are the correction request and emotional information, and the output is a corrected care plan.

[0395] Step 10:

[0396] Final care plan database storage

[0397] The server saves the final care plan to a database. The database is queried and the final care plan is stored. The input is the modified care plan and the output is the care plan stored in the database.

[0398] Step 11:

[0399] Displaying the final plan on the device

[0400] The server sends the final care plan to the terminal, which then displays it to the user. The input is the final care plan, and the output is the care plan that is displayed to the user.

[0401] Step 12:

[0402] Periodic server-based care record collection and plan optimization

[0403] The server periodically collects care records and optimizes the care plan using a generative AI model. The data is analyzed together with an emotion engine to improve the care plan. The input is care record data, and the output is an optimized care plan.

[0404] (Application example 2)

[0405] 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."

[0406] In current manufacturing lines, employee fatigue and stress are not properly monitored, resulting in a decline in work efficiency and safety. Furthermore, uniform work plans are provided without considering employees' emotions or feedback, making it difficult to respond flexibly to individual situations. This leads to a decline in work quality and productivity.

[0407] 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 displaying a user input form for input by the care recipient or relative; means for inputting basic information about the care recipient and requests from the relative; means for transmitting the input information to the server; means for receiving the transmitted information and storing it in a database; means for activating a generative AI model to analyze the received information and generate a care plan; means for transmitting the generated care plan to a terminal and displaying it; means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan; means for finalizing the care plan and storing it in a database; means for analyzing the user's emotions using an emotion engine and adjusting the care plan based on the user's emotions; means for presenting the generated care plan to an employee's screen and collecting employee feedback; and means for periodically collecting care records and emotion data and having the generative AI model automatically optimize the care plan based on the collected data. This enables the provision of personalized work plans that take employee emotions and feedback into account, thereby improving work efficiency and safety.

[0408] "User Input Form" means a screen or interface through which an employee or associated person can enter basic information or requests.

[0409] "Basic information" refers to basic data about an individual, such as the employee's name, age, and shift times.

[0410] "Requests" are the desired conditions or requirements of employees or related persons.

[0411] A "server" is a computer system on a network that stores data and performs calculations and analysis.

[0412] A "database" is a storage system that systematically stores information and allows it to be searched and edited.

[0413] A "generative AI model" is an artificial intelligence algorithm that analyzes the information it receives and generates data and plans based on a specified task.

[0414] An "emotion engine" is software that recognizes a user's emotions through voice and text analysis and analyzes the results.

[0415] A "care plan" is a specific action plan created based on the user's basic information, requests, and feelings.

[0416] A "terminal" is a device or apparatus with which a user can interact, and which is used to input and display information.

[0417] "Feedback" refers to the responses and opinions received from employees and related parties, which are used to adjust and optimize the plan.

[0418] MODE FOR CARRYING OUT THE INVENTION

[0419] To implement this invention, the following system program is constructed: This system takes into account the emotions and feedback of employees and automatically adjusts the work plan to improve work efficiency and safety.

[0420] System Overview

[0421] 1. Display and fill in the user input form

[0422] The terminal displays a user input form where employees can enter basic information and requests, such as their name, age, and shift hours.

[0423] 2. Data transmission and storage

[0424] The terminal sends the entered information to the server, which receives it and stores it in a database.

[0425] 3. Analysis and plan generation using generative AI models

[0426] The server launches a generative AI model to analyze the received information and generate an initial work plan.

[0427] 4. Emotion analysis and plan adjustment using an emotion engine

[0428] The device uses a camera and voice recognition system to collect employee emotions in real time, and the server analyzes this data using an emotion engine. Based on the user's emotions, a generative AI model adjusts the work plan.

[0429] 5. Feedback and Recalibration

[0430] The adjusted work plan is displayed on the device and receives feedback from the employee. If necessary, the server adjusts the plan again using the generative AI model and emotion engine.

[0431] 6. Finalize and optimize the plan

[0432] The final work plan is finalized and saved in the database. The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the plan based on them.

[0433] Specific examples of hardware and software used

[0434] Camera: General surveillance camera

[0435] Speech recognition software: Google® Cloud Speech-to-Text API

[0436] Sentiment analysis software: IBM Watson (registered trademark)

[0437] Database: MySQL

[0438] Generative AI model: OpenAI GPT-4

[0439] Front-end framework: React

[0440] Backend framework: Django

[0441] Specific examples

[0442] For example, an employee named "A" is working an eight-hour shift. Before the shift begins, he or she enters basic information using a user input form on a device. During the shift, a camera analyzes A's facial expressions and voice data in real time using the Google Cloud Speech-to-Text API, and IBM Watson emotion analysis software detects "fatigue." This information is sent to a server, and a generative AI model (GPT-4) uses this information to readjust the work plan and insert breaks if necessary. The readjusted work plan is presented on A's device, and A's feedback is collected.

[0443] Prompt Sentence Examples

[0444] plain

[0445] def create_schedule_prompt(employee_info, current_emotion):

[0446] return f"""

[0447] Employee A, age, is on shift. A is currently in {current_emotion} state.

[0448] Use this information to come up with a work plan, including breaks where appropriate.

[0449] """

[0450] current_emotion = "fatigue"

[0451] employee_info = {"Name": "Mr. A", "Age": "50", "Shift": "8 hours"}

[0452] prompt = create_schedule_prompt(employee_info, current_emotion)

[0453] This system provides personalized work plans that take into account employee emotions and feedback, improving work efficiency and safety. The server also periodically collects data, and the generative AI model automatically optimizes the plans based on that data, enabling continuous improvement.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] Displaying a user input form and entering basic information

[0457] Specific operation: The terminal displays a user input form on the screen, and the employee enters basic information (name, age, shift time) and requests. In this way, the terminal collects basic information from the employee.

[0458] Input: User's basic information and requests

[0459] Output: Basic information and requests entered into the terminal

[0460] Step 2:

[0461] Sending and saving input data

[0462] Specific operation: The device sends the collected basic information and requests to the server, which receives this information and stores it in a database.

[0463] Input: Basic information and requests sent from the device

[0464] Output: Basic information and requests stored in the database

[0465] Step 3:

[0466] Generative AI model generates initial plans

[0467] Specific operation: The server launches the generative AI model, analyzes the stored basic information and requests, and generates an initial work plan. The generative AI model uses the prompt sentences to generate a work plan suitable for the employee.

[0468] Input: Basic information and requests stored in the database

[0469] Output: Generated initial work plan

[0470] Step 4:

[0471] Emotion data collection and analysis

[0472] How it works: The device uses a camera and voice recognition system to collect facial and voice data in real time, then sends it to a server, which then uses an emotion engine to analyze the data.

[0473] Input: Facial expression data and voice data collected in real time

[0474] Output: Parsed emotion data

[0475] Step 5:

[0476] Adjusting plans based on sentiment data

[0477] Specific operation: The server inputs the emotion data obtained by the emotion engine into the generative AI model and regenerates a work plan that takes into account the employee's emotions.

[0478] Input: Analyzed emotion data, generative AI model

[0479] Output: Adjusted work plan

[0480] Step 6:

[0481] View the adjusted plan and gather feedback

[0482] Specific operation: The terminal displays the adjusted work plan on the screen and collects feedback from the employee, which is then sent back to the server.

[0483] Input: Adjusted Work Plan

[0484] Output: Employee feedback

[0485] Step 7:

[0486] Readjusting plans based on feedback

[0487] How it works: The server receives feedback from employees and uses the generative AI model to readjust the work plan, repeating this process until employees are satisfied.

[0488] Input: Employee feedback

[0489] Output: Reworked Work Plan

[0490] Step 8:

[0491] Finalize and save the plan

[0492] What it does: The server saves the finalized work plan to a database, allowing for future reference and optimization.

[0493] Input: Reworked Work Plan

[0494] Output: Final work plan stored in the database

[0495] Step 9:

[0496] Regular data collection and optimization

[0497] How it works: The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the work plan based on that data.

[0498] Input: Periodically collected work records and emotional data

[0499] Output: Optimized work plan

[0500] 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.

[0501] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0502] 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.

[0503] [Second embodiment]

[0504] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0505] 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.

[0506] 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).

[0507] 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.

[0508] 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.

[0509] 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).

[0510] 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. 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.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] In the smart glasses 214, the 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.

[0515] 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."

[0516] The present invention is a system that automates the process of creating a care plan by providing a means for displaying a user input form for input by the care recipient or a relative. The system aims to reduce the workload of care managers and create care plans efficiently.

[0517] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0518] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0519] The generated care plan is sent from the server to the device and displayed to the user. The user can review the contents of the care plan through the device and request revisions as necessary. If revisions are requested, the server receives the request and again uses the generative AI model to generate a revised care plan. This process is repeated until a care plan that the user is finally satisfied with is created.

[0520] Once the final care plan is finalized, the server saves it in a database and the final version is displayed on the device. In addition, the server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This allows the system to flexibly respond to changes in the care recipient's situation and needs.

[0521] As a concrete example, consider the following scenario.

[0522] Enter the basic information of a care recipient, "Mr. Tanaka," as follows:

[0523] Name: Tanaka Jiro

[0524] Age: 85

[0525] Medical history: heart disease, diabetes

[0526] Family's wishes: Regular heart checkups, rehabilitation three times a week

[0527] 1. The terminal starts up and a user input form is displayed.

[0528] 2. The user enters Mr. Tanaka's basic information.

[0529] 3. The device sends the entered information to the server.

[0530] 4. The server receives the information and stores it in a database.

[0531] 5. The server launches the generative AI model and generates a care plan, including daily vital signs checks and three rehabilitation sessions per week.

[0532] 6. The care plan is sent to the device and the user reviews it.

[0533] 7. The user requests corrections as needed.

[0534] 8. The server processes the modification request and provides a regenerated care plan.

[0535] 9. The final care plan is finalized and saved in the database.

[0536] 10. The final plan will be displayed on your device.

[0537] 11. The server periodically collects care records and uses AI models to optimize the plan.

[0538] In this way, a care plan that accurately reflects the condition of the care recipient and the wishes of the relatives can be efficiently created.

[0539] The processing flow will be explained below.

[0540] Step 1:

[0541] The device starts up and displays a user input form, allowing the care manager or relative to enter information.

[0542] Step 2:

[0543] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[0544] Step 3:

[0545] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[0546] Step 4:

[0547] The server receives the transmitted information and first stores it in a database for later processing.

[0548] Step 5:

[0549] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[0550] Step 6:

[0551] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[0552] Step 7:

[0553] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[0554] Step 8:

[0555] Users can check the contents of the care plan through their terminal and can request revisions as needed.

[0556] Step 9:

[0557] When a user inputs a correction request, the device sends the information to the server, which receives the correction request and inputs it back into the generative AI model to correct the care plan.

[0558] Step 10:

[0559] The server generates a revised care plan and sends it back to the device, and this process is repeated until the user is satisfied.

[0560] Step 11:

[0561] Once the final care plan is finalized, the server stores it in a database and displays the final version of the care plan on the terminal.

[0562] Step 12:

[0563] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This process ensures that the most appropriate care plan is always provided based on the needs of the care recipient.

[0564] Example 1

[0565] 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."

[0566] In an aging society, there is a growing need to reduce the burden on care managers and relatives and to efficiently create care plans that are appropriate for the care recipients. However, with conventional methods, the manual input of information and creation of plans requires a great deal of time and effort, and the quality of the care plans varies.

[0567] 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.

[0568] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate prompts for generating a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's request for modification and re-inputting the modified care plan into the generative AI model, means for finalizing the care plan and storing it in a database, and means for periodically collecting care records and having the generative AI model automatically optimize the care plan based on them. This reduces the workload of care managers and relatives and enables the creation of an optimal care plan for the care recipient quickly and efficiently.

[0569] A "user input form" is an on-screen interface through which the care recipient or relative can input information.

[0570] "Basic information" refers to basic data about an individual, such as the care recipient's name, age, and medical history.

[0571] "Requests from relatives" refers to the hopes and demands of relatives regarding the care and support provided to the care recipient.

[0572] A "server" is a computer system that manages and processes data over a network.

[0573] "Database" means a digital storage device and system that can systematically store, search, and reference large amounts of information.

[0574] A "generative AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task (here, generating a care plan).

[0575] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[0576] A "care plan" is a written plan that outlines the specific care content and schedule, based on the health condition and needs of the person receiving care.

[0577] "Modification request" refers to a request made by a user to modify or change a generated care plan.

[0578] The present invention is a system that displays a user input form for input by a care recipient or their relatives and automates the process of creating a care plan. The system aims to reduce the workload of care managers and efficiently generate care plans. Detailed embodiments of this system are described below.

[0579] Hardware and software used

[0580] The system uses the following hardware and software:

[0581] Hardware: Devices (PCs, tablets, smartphones, etc.), servers

[0582] Software: Web browser, database (e.g., MySQL, PostgreSQL), generative AI model (e.g., machine learning algorithm such as GPT-4)

[0583] Initial Settings and Form Display

[0584] When the device is started, a user input form is displayed on the web browser. This form is an interface for the care recipient and their relatives to enter basic information.

[0585] Entering information

[0586] The user (care manager or relative) enters the following information into the form:

[0587] Name of the care recipient (e.g., Tanaka Jiro)

[0588] Age (e.g., 85 years old)

[0589] Medical history (e.g., heart disease, diabetes)

[0590] Family members' wishes (e.g., regular heart checkups, rehabilitation three times a week)

[0591] Once the input is complete, the user clicks the "Submit" button to send the data to the server.

[0592] Data transmission and storage

[0593] The device sends the input information as an HTTP request to the server, which then analyzes the received data and stores it in a database.

[0594] Generate a care plan

[0595] The server retrieves the necessary information from the database and launches the generative AI model. Using a machine learning algorithm (e.g., GPT-4), it analyzes the received data and generates a prompt to generate a care plan. For example, the prompt might be "Generate a care plan appropriate for a patient aged 85, with heart disease and diabetes."

[0596] Viewing and Modifying Care Plans

[0597] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan and make requests for revisions as necessary. Specifically, a request for revision such as "I want to reduce the frequency of rehabilitation from three times a week to two times a week" can be sent from the device to the server.

[0598] Regeneration and finalization

[0599] The server receives the modification request and generates a modified care plan using the generative AI model again. This process is repeated until a care plan that satisfies the user is generated. Once the final care plan is finalized, it is saved in the database. The finalized care plan is also displayed on the device.

[0600] Automatic optimization

[0601] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records, allowing for flexible responses to changes in the care recipient's situation and needs.

[0602] Specific operation example

[0603] As a concrete example, consider the case of a care recipient named "Jiro Tanaka." Below is an example of a series of specific actions.

[0604] 1. The terminal starts up and a user input form is displayed.

[0605] 2. The user enters Mr. Tanaka's basic information (name, age, medical history, and relatives' wishes).

[0606] 3. The device sends the entered information to the server.

[0607] 4. The server receives the information and stores it in a database.

[0608] 5. The server launches the generative AI model and inputs a prompt statement to generate a care plan.

[0609] 6. The generated care plan (e.g., daily vital signs checks, three rehabilitation sessions per week) is sent to the device and reviewed by the user.

[0610] 7. The user makes a request to modify the care plan and checks the regenerated plan.

[0611] 8. The final care plan is finalized and saved in the database.

[0612] 9. The confirmed care plan will be displayed on the device.

[0613] 10. The server periodically collects care records and optimizes the care plan using a generative AI model.

[0614] This system reduces the burden on care managers and relatives and makes it possible to efficiently generate care plans that are best suited to the situation of the person receiving care.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Step 1: Launching a terminal and displaying a user input form

[0617] When the device starts up, it automatically opens a web browser and displays a user input form for the care recipient or relative to enter basic information, including fields for name, age, medical history, and relatives' preferences.

[0618] Input: The user turns on the device.

[0619] Output: A form for user input is displayed in the web browser.

[0620] Step 2: User Enters Basic Information

[0621] The user enters the basic information of the care recipient (name, age, medical history, and relatives' wishes) into the form. Once the information is complete, the user clicks the submit button.

[0622] Input: The user enters data into the form.

[0623] Output: By pressing the submit button, the entered data is saved in the form on the browser.

[0624] Step 3: Send data from the device to the server

[0625] The terminal sends the entered information to the server as an HTTP request. Here, the POST method is used.

[0626] Input: When the submit button is clicked, the form data is packaged into the HTTP request.

[0627] Output: The form data is sent to the server.

[0628] Step 4: Receiving information and storing it in a database

[0629] The server analyzes the received data and stores it in a database by parsing the data to extract the respective fields and inserting them into the database using SQL queries.

[0630] Input: Care recipient information extracted from the HTTP request.

[0631] Output: The information is stored in a database.

[0632] Step 5: Generate a care plan using a generative AI model

[0633] The server retrieves the care recipient's information from the database and launches the generative AI model. A prompt statement is generated and input into the generative AI model to generate a care plan. For example, "Generate a care plan appropriate for an 85-year-old patient with heart disease and diabetes."

[0634] Input: Information of the care recipient obtained from the database.

[0635] Output: A care plan from the generative AI model.

[0636] Step 6: Send the care plan to your device

[0637] The server generates a care plan and sends it to the terminal as an HTTP response, which the user can view in a web browser.

[0638] Input: Generated care plan.

[0639] Output: The care plan is displayed on the terminal.

[0640] Step 7: User reviews care plan and requests modifications

[0641] The user checks the care plan and, if any changes are necessary, sends a request for changes from the terminal to the server. For example, the user might input, "I want to change the frequency of rehabilitation from three times a week to two times a week."

[0642] Input: Request for amendment to care plan.

[0643] Output: A modification request is sent to the server.

[0644] Step 8: Process and regenerate correction requests

[0645] The server receives the modification request and generates a modified care plan by again using the generative AI model. The prompt text is also updated based on the modification request.

[0646] Input: A prompt containing a correction request and the care recipient's information, which is again input into the generative AI model.

[0647] Output: Revised care plan.

[0648] Step 9: Finalize and save the final care plan

[0649] The user checks and confirms the regenerated care plan. When the user presses the Confirm button, the confirmed care plan is sent to the server.

[0650] The server stores the finalized care plan in a database.

[0651] Input: Final care plan submitted with confirmation button pressed.

[0652] Output: Final care plan saved in database.

[0653] Step 10: Display the final plan on your device

[0654] The device displays the finalized care plan, which the user can review and use in their daily care.

[0655] Input: Final Care Plan.

[0656] Output: The final care plan is displayed on the terminal.

[0657] Step 11: Server collects care records and optimizes plans

[0658] The server periodically collects care records and uses a generative AI model to automatically optimize the care plan. Through regular data collection, the plan is adjusted to the latest condition of the care recipient.

[0659] Input: Routinely collected care records.

[0660] Output: Optimized care plan.

[0661] This flow allows for the efficient automatic generation and optimization of care plans, reducing the workload of care managers and relatives.

[0662] (Application example 1)

[0663] 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."

[0664] Conventional care plan creation systems have the problem of placing a heavy burden on care managers, making the creation process cumbersome and time-consuming. Furthermore, the actual selection and purchase of care products must be done separately, which adds to the time and effort. There is a need for a system that can solve these problems and create care plans and select appropriate care products efficiently and effectively.

[0665] 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.

[0666] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a database, means for periodically collecting care records and automatically optimizing the care plan based on the collected care records, means for generating a care product list at a care product store based on the information input by the care recipient or relative, means for displaying the generated care product list on the smart devices of store staff in real time, and means for linking the suggested care product list with in-store inventory information. This enables care recipients and relatives to efficiently create care plans and easily select and purchase appropriate care products.

[0667] "Care recipients" refers to people such as elderly people and people with disabilities who require care.

[0668] "Relatives" refers to people who are related by blood or marriage to the person receiving care, or people who support the person receiving care in a similar relationship.

[0669] "User input form" refers to the screen or interface provided for the care recipient or relative to enter the required information.

[0670] "Basic information" refers to personal data such as the care recipient's name, age, medical history, etc.

[0671] "Requests" refer to any particular wishes that the care recipient or their relatives have regarding the care plan.

[0672] A "server" refers to a computer system for processing and storing data over a network.

[0673] A "database" refers to a system that efficiently stores, manages, and searches large amounts of data.

[0674] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate results based on specific data.

[0675] A "care plan" refers to a plan that outlines the specific care content and schedule that should be provided to the person receiving care.

[0676] A "terminal" is a device used by a user to enter data and check results, such as a smartphone or tablet.

[0677] A "request for modification" refers to a user's request for desired changes to the generated care plan.

[0678] A "list of nursing care supplies" refers to a list of nursing care supplies required based on the care plan of the person receiving care.

[0679] "Store staff" refers to employees who select and sell nursing care products in physical stores.

[0680] "Smart devices" are electronic devices that can connect to the Internet, such as smartphones, tablets, and smart glasses.

[0681] "Inventory information" refers to the quantity and item information of products currently held in the store.

[0682] The present invention is a system for efficiently creating a care plan and selecting care products. The following describes the program and processing of the system that realizes the present invention.

[0683] Hardware and Software Used

[0684] Server: AWS EC2

[0685] Database: AWS RDS (MySQL)

[0686] Generative AI model: OpenAI GPT-4

[0687] Web frontend: React.js

[0688] Backend: Django

[0689] Communication protocol: REST API

[0690] User devices: smartphones, smart glasses

[0691] System program and processing explanation

[0692] 1. Displaying a user input form

[0693] The server uses React.js to display a user input form, in which the care recipient and their relatives enter basic information and requests.

[0694] 2. Data Transmission

[0695] The information sent from the device is received by the Django backend and stored in an AWS RDS database. The server receives this data and makes it available for analysis.

[0696] 3. Care plan generation

[0697] The server runs a generative AI model using OpenAI GPT-4 to analyze the information sent, and then uses machine learning algorithms to generate a care plan based on the care recipient's condition and the relatives' requests. This generative AI model runs in real time via a REST API.

[0698] 4. View care plans

[0699] The generated care plan is returned to the React.js front end and displayed on the device. The user can review the care plan and make corrections if necessary.

[0700] 5. Processing Amendment Requests

[0701] When the user issues a modification request, the server again uses the generative AI model to generate and display a modified care plan. This cycle is repeated until a care plan that satisfies the user is generated.

[0702] 6. Creating a list of care items

[0703] Once the care plan is finalized, the server generates a list of care products, and at this stage, the AI ​​model is used to analyze the information and select the necessary care products.

[0704] 7. Real-time display on smart devices

[0705] The generated list of care products is displayed in real time on the store staff's smart devices (smart glasses or smartphones), allowing them to quickly guide users to the most suitable products.

[0706] 8. Linking with inventory information

[0707] The suggestions on the nursing care product list are linked to the store's inventory information managed on the server, so it is possible to check in real time whether an item is in stock or if it can be reserved.

[0708] Specific examples

[0709] For example, the basic information and requests of the care recipient "Mr. Sato" are as follows:

[0710] Name: Hanako Sato

[0711] Age: 78

[0712] Medical history: high blood pressure, arthritis

[0713] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[0714] Based on this information, the following prompt sentence is input into the generative AI model:

[0715] Prompt Sentence Examples

[0716] "Based on the information below, please suggest the best care plan and necessary nursing care products.

[0717] The care recipient's information is as follows:

[0718] Name: Hanako Sato

[0719] Age: 78

[0720] Medical history: high blood pressure, arthritis

[0721] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[0722] thank you."

[0723] Based on these prompts, the generative AI model generates an optimal care plan and list of care supplies, which are then provided to the user and store staff.

[0724] In this way, the system of the present invention reduces the burden on the care recipient and their relatives, and allows for the creation of a care plan and the selection of care products to be carried out quickly and efficiently.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] The server uses React.js to display a user input form. The user enters basic information about the care recipient and the requests of their relatives. This input form includes information such as name, age, medical history, and care preferences. The entered data is temporarily stored in the device's memory.

[0728] Step 2:

[0729] The device sends the entered information to the Django backend for storage in the AWS RDS database. This data includes the care recipient's information and the relatives' requests entered by the user. The server receives this and stores it in the database.

[0730] Step 3:

[0731] The server uses the received information to launch a generative AI model using OpenAI GPT-4. Here, a prompt sentence is generated using a machine learning algorithm. Using this prompt sentence as input, the AI ​​model generates a care plan. In this generation process, the AI ​​calculates the optimal care plan based on the care recipient's condition and the relatives' requests.

[0732] Step 4:

[0733] The server returns the generated care plan to the React.js front end and displays it on the device. The user can then view the details of the care plan through the device. At this point, the care plan includes daily care details, regular checkups, and rehabilitation schedules.

[0734] Step 5:

[0735] If the user requests an amendment to the care plan, the device sends the amendment to the server, which then reactivates the generative AI model and regenerates the care plan based on the amendment request. This regeneration process is also performed using a machine learning algorithm.

[0736] Step 6:

[0737] The server returns the finalized care plan to the React.js front end and displays it on the terminal. At the same time, the care plan is saved in the AWS RDS database. The user can review the final care plan and confirm it if they are satisfied with its contents.

[0738] Step 7:

[0739] The server generates a list of care items based on the finalized care plan. This list is output by a generative AI model based on the prompt text. Here, the care items required for the care plan are calculated.

[0740] Step 8:

[0741] The server displays the generated list of care products in real time on the store staff's smart devices (smart glasses or smartphones), and the staff guides the user to the appropriate care products based on this information.

[0742] Step 9:

[0743] The server then links the nursing care product list suggestions to the store's inventory information. This communicates with the store's management system via API, updating inventory data in real time. This makes it possible to handle inventory availability and reserve items.

[0744] 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.

[0745] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[0746] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0747] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0748] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[0749] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[0750] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[0751] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[0752] As a concrete example, consider the following scenario.

[0753] Enter the basic information of a care recipient, "Mr. Sato," as follows:

[0754] Name: Sato Jiro

[0755] Age: 78

[0756] Medical history: high blood pressure, arthritis

[0757] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[0758] 1. The terminal starts up and a user input form is displayed.

[0759] 2. The user enters Mr. Sato's basic information.

[0760] 3. The device sends the entered information to the server.

[0761] 4. The server receives the information and stores it in a database.

[0762] 5. The server launches the generative AI model and generates a care plan, which may include, for example, daily morning blood pressure monitoring and twice-weekly physical therapy.

[0763] 6. The care plan is sent to the device and the user reviews it.

[0764] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[0765] 8. The user requests corrections as needed.

[0766] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[0767] 10. The final care plan is finalized and saved in the database.

[0768] 11. The final plan will be displayed on your device.

[0769] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[0770] In this way, this system reduces the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The terminal starts up and displays a user input form, allowing the user (care manager or relative) to enter information.

[0774] Step 2:

[0775] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[0776] Step 3:

[0777] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[0778] Step 4:

[0779] The server receives the transmitted information and stores it in a database for further processing.

[0780] Step 5:

[0781] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[0782] Step 6:

[0783] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[0784] Step 7:

[0785] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[0786] Step 8:

[0787] The user checks the contents of the care plan through the device and can make corrections as needed. At this point, the device collects the user's emotions using voice and text.

[0788] Step 9:

[0789] The emotion engine recognizes the user's emotions through voice and text analysis, for example, analyzing the user's text comments and voice tone to digitize their emotions.

[0790] Step 10:

[0791] The device sends the recognized emotion information to the server, which receives the correction request and the emotion information and re-evaluates it.

[0792] Step 11:

[0793] The server then reactivates the generative AI model and modifies the care plan based on the modification request and emotional information, generating a more personalized care plan taking into account the information from the emotional engine.

[0794] Step 12:

[0795] The server sends the modified care plan back to the device, which displays the modified care plan to the user, and this process is repeated until the user is satisfied.

[0796] Step 13:

[0797] Once the final care plan is finalized, the server stores it in the database and displays the final version of the care plan on the terminal.

[0798] Step 14:

[0799] The server periodically collects care records, and the generative AI model and emotion engine automatically optimize the care plan based on the records. This process provides an optimal care plan that continuously reflects the needs of the care recipient and the user's emotions.

[0800] Example 2

[0801] 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."

[0802] The current care plan creation process is often manual, requiring a great deal of effort and time from care managers and relatives. It is also difficult to properly reflect the feelings and wishes of the care recipient and relatives, making it difficult to provide personalized care. Conventional systems lack a mechanism to consider the user's feelings, and therefore care plans are not always satisfactory.

[0803] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0804] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to a central processing unit, means for receiving the transmitted information and storing it in a data storage device, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to an information display device and displaying it, means for an emotion engine to recognize the user's emotions through voice and text analysis, means for receiving a user's request for modification and inputting it back into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a data storage device, and means for periodically collecting care records and automatically optimizing the care plan based on the collected care records by the generative AI model. This automates the creation and modification of care plans, enabling the provision of personalized care plans that reflect the user's emotions and requests.

[0805] A "user input form" is a screen that is displayed on a terminal for the care recipient or relative to input information about care.

[0806] "Basic information of the care recipient" refers to personal data such as the care recipient's name, age, medical history, etc.

[0807] "Requests from relatives" refers to the specific wishes and demands regarding care provided to the person receiving care.

[0808] "Central Processing Unit" refers to a computer system that receives, analyzes, and processes information sent from a terminal.

[0809] "Data storage" refers to a database system for storing received information.

[0810] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate a care plan based on information about the care recipient and the requests of their relatives.

[0811] "Care plan" refers to a plan that describes the specific care to be provided to the care recipient.

[0812] An "information display device" is a terminal for displaying the generated care plan to the user.

[0813] An "emotion engine" is a system that recognizes a user's emotions by analyzing voice and text, and outputs them as analytical data.

[0814] A "modification request" refers to a user's request for a change or adjustment to a care plan.

[0815] "Care records" are data that record the status and results of care provided to care recipients.

[0816] "Optimization" refers to the process of improving care plans to make them more appropriate and effective based on collected data.

[0817] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[0818] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0819] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0820] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[0821] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[0822] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[0823] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[0824] To illustrate, consider the following scenario:

[0825] Enter the basic information of the care recipient "Sato-san" as follows:

[0826] Name: Sato Jiro

[0827] Age: 78

[0828] Medical history: high blood pressure, arthritis

[0829] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[0830] 1. The terminal starts up and a user input form is displayed.

[0831] 2. The user enters Mr. Sato's basic information.

[0832] 3. The device sends the entered information to the server.

[0833] 4. The server receives the information and stores it in a database.

[0834] 5. The server runs the generative AI model to generate a care plan, for example, one that includes blood pressure monitoring every morning and physical therapy twice a week.

[0835] 6. The care plan is sent to the device and the user reviews it.

[0836] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[0837] 8. The user requests corrections as needed.

[0838] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[0839] 10. The final care plan is finalized and saved in the database.

[0840] 11. The final plan will be displayed on your device.

[0841] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[0842] In this way, this system can reduce the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[0843] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0844] Step 1:

[0845] Starts the terminal and displays a form for user input

[0846] The device starts up and starts a dedicated application. Next, a user input form is displayed on the screen for the care recipient or relative to enter information. The form contains input fields for name, age, medical history, required care, and relatives' requests. The initial data for input is provided to the user from the device, and the data entered by the user is output.

[0847] Step 2:

[0848] Enter basic user information

[0849] The user (care manager or relative) enters basic information about the care recipient into the displayed user input form. Specifically, the user enters details such as the care recipient's name, age, medical history, required care, and the relative's requests. The input data is output and sent to the next processing step.

[0850] Step 3:

[0851] Sending information from the device to the server

[0852] The terminal sends the information entered by the user to the server. The terminal executes the sending process, packages the input data from the user into packets, and sends them to the server. The input is the user's input data, and the output is the data received by the server.

[0853] Step 4:

[0854] Receiving information from the server and storing it in the database

[0855] The server receives the information sent from the device. It then stores the received information in a database. Specifically, it issues a query to the database to store information such as name, age, medical history, and requests from relatives. The input is the data sent from the device, and the output is the data stored in the database.

[0856] Step 5:

[0857] Server generation: Launching the AI ​​model and generating care plans

[0858] The server launches the generative AI model, which then analyzes the basic information of the care recipient stored on the server and the wishes of their relatives to generate an appropriate care plan. The model uses machine learning algorithms to make predictions and optimizations based on the input data. The input is information from the database, and the output is the generated care plan.

[0859] Step 6:

[0860] Sending and displaying care plans to devices

[0861] The server sends the care plan to the terminal. The terminal displays the care plan it receives to the user. Specifically, the contents of the care plan are displayed on the screen so that the user can check them. The input is care plan data from the server, and the output is a care plan that is displayed to the user.

[0862] Step 7:

[0863] Emotional analysis of users using an emotion engine

[0864] The emotion engine analyzes the voice and text inputs made by the user when confirming the care plan. Using an emotion recognition algorithm, it analyzes the user's emotions (dissatisfaction, relief, etc.) and sends that information to the server. The input is the user's voice and text data, and the analyzed emotional information is generated as the output.

[0865] Step 8:

[0866] User correction request submission

[0867] The user submits a request to modify the care plan. The terminal transmits the modification request input by the user to the server. The input is the user's modification request data, and the modification data to be transmitted to the server is generated as the output.

[0868] Step 9:

[0869] Server-based correction requests, processing of emotional information, and reevaluation of care plans

[0870] The server receives the correction request and the user's emotional information, then launches the generative AI model again to reevaluate the care plan. A new care plan that takes the emotional information into account is generated. The inputs are the correction request and emotional information, and the output is a corrected care plan.

[0871] Step 10:

[0872] Final care plan database storage

[0873] The server saves the final care plan to a database. The database is queried and the final care plan is stored. The input is the modified care plan and the output is the care plan stored in the database.

[0874] Step 11:

[0875] Displaying the final plan on the device

[0876] The server sends the final care plan to the terminal, which then displays it to the user. The input is the final care plan, and the output is the care plan that is displayed to the user.

[0877] Step 12:

[0878] Periodic server-based care record collection and plan optimization

[0879] The server periodically collects care records and optimizes the care plan using a generative AI model. The data is analyzed together with an emotion engine to improve the care plan. The input is care record data, and the output is an optimized care plan.

[0880] (Application example 2)

[0881] 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."

[0882] In current manufacturing lines, employee fatigue and stress are not properly monitored, resulting in a decline in work efficiency and safety. Furthermore, uniform work plans are provided without considering employees' emotions or feedback, making it difficult to respond flexibly to individual situations. This leads to a decline in work quality and productivity.

[0883] 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 displaying a user input form for input by the care recipient or relative; means for inputting basic information about the care recipient and requests from the relative; means for transmitting the input information to the server; means for receiving the transmitted information and storing it in a database; means for activating a generative AI model to analyze the received information and generate a care plan; means for transmitting the generated care plan to a terminal and displaying it; means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan; means for finalizing the care plan and storing it in a database; means for analyzing the user's emotions using an emotion engine and adjusting the care plan based on the user's emotions; means for presenting the generated care plan to an employee's screen and collecting employee feedback; and means for periodically collecting care records and emotion data and having the generative AI model automatically optimize the care plan based on the collected data. This enables the provision of personalized work plans that take employee emotions and feedback into account, thereby improving work efficiency and safety.

[0884] "User Input Form" means a screen or interface through which an employee or associated person can enter basic information or requests.

[0885] "Basic information" refers to basic data about an individual, such as the employee's name, age, and shift times.

[0886] "Requests" are the desired conditions or requirements of employees or related persons.

[0887] A "server" is a computer system on a network that stores data and performs calculations and analysis.

[0888] A "database" is a storage system that systematically stores information and allows it to be searched and edited.

[0889] A "generative AI model" is an artificial intelligence algorithm that analyzes the information it receives and generates data and plans based on a specified task.

[0890] An "emotion engine" is software that recognizes a user's emotions through voice and text analysis and analyzes the results.

[0891] A "care plan" is a specific action plan created based on the user's basic information, requests, and feelings.

[0892] A "terminal" is a device or apparatus with which a user can interact, and which is used to input and display information.

[0893] "Feedback" refers to the responses and opinions received from employees and related parties, which are used to adjust and optimize the plan.

[0894] MODE FOR CARRYING OUT THE INVENTION

[0895] To implement this invention, the following system program is constructed: This system takes into account the emotions and feedback of employees and automatically adjusts the work plan to improve work efficiency and safety.

[0896] System Overview

[0897] 1. Display and fill in the user input form

[0898] The terminal displays a user input form where employees can enter basic information and requests, such as their name, age, and shift hours.

[0899] 2. Data transmission and storage

[0900] The terminal sends the entered information to the server, which receives it and stores it in a database.

[0901] 3. Analysis and plan generation using generative AI models

[0902] The server launches a generative AI model to analyze the received information and generate an initial work plan.

[0903] 4. Emotion analysis and plan adjustment using an emotion engine

[0904] The device uses a camera and voice recognition system to collect employee emotions in real time, and the server analyzes this data using an emotion engine. Based on the user's emotions, a generative AI model adjusts the work plan.

[0905] 5. Feedback and Recalibration

[0906] The adjusted work plan is displayed on the device and receives feedback from the employee. If necessary, the server adjusts the plan again using the generative AI model and emotion engine.

[0907] 6. Finalize and optimize the plan

[0908] The final work plan is finalized and saved in the database. The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the plan based on them.

[0909] Specific examples of hardware and software used

[0910] Camera: General surveillance camera

[0911] Speech recognition software: Google Cloud Speech-to-Text API

[0912] Sentiment analysis software: IBM Watson

[0913] Database: MySQL

[0914] Generative AI model: OpenAI GPT-4

[0915] Front-end framework: React

[0916] Backend framework: Django

[0917] Specific examples

[0918] For example, an employee named "A" is working an eight-hour shift. Before the shift begins, he or she enters basic information using a user input form on a device. During the shift, a camera analyzes A's facial expressions and voice data in real time using the Google Cloud Speech-to-Text API, and IBM Watson emotion analysis software detects "fatigue." This information is sent to a server, and a generative AI model (GPT-4) uses this information to readjust the work plan and insert breaks if necessary. The readjusted work plan is presented on A's device, and A's feedback is collected.

[0919] Prompt Sentence Examples

[0920] plain

[0921] def create_schedule_prompt(employee_info, current_emotion):

[0922] return f"""

[0923] Employee A, age, is on shift. A is currently in {current_emotion} state.

[0924] Use this information to come up with a work plan, including breaks where appropriate.

[0925] """

[0926] current_emotion = "fatigue"

[0927] employee_info = {"Name": "Mr. A", "Age": "50", "Shift": "8 hours"}

[0928] prompt = create_schedule_prompt(employee_info, current_emotion)

[0929] This system provides personalized work plans that take into account employee emotions and feedback, improving work efficiency and safety. The server also periodically collects data, and the generative AI model automatically optimizes the plans based on that data, enabling continuous improvement.

[0930] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0931] Step 1:

[0932] Displaying a user input form and entering basic information

[0933] Specific operation: The terminal displays a user input form on the screen, and the employee enters basic information (name, age, shift time) and requests. In this way, the terminal collects basic information from the employee.

[0934] Input: User's basic information and requests

[0935] Output: Basic information and requests entered into the terminal

[0936] Step 2:

[0937] Sending and saving input data

[0938] Specific operation: The device sends the collected basic information and requests to the server, which receives this information and stores it in a database.

[0939] Input: Basic information and requests sent from the device

[0940] Output: Basic information and requests stored in the database

[0941] Step 3:

[0942] Generative AI model generates initial plans

[0943] Specific operation: The server launches the generative AI model, analyzes the stored basic information and requests, and generates an initial work plan. The generative AI model uses the prompt sentences to generate a work plan suitable for the employee.

[0944] Input: Basic information and requests stored in the database

[0945] Output: Generated initial work plan

[0946] Step 4:

[0947] Emotional data collection and analysis

[0948] How it works: The device uses a camera and voice recognition system to collect facial and voice data in real time, then sends it to a server, which then uses an emotion engine to analyze the data.

[0949] Input: Facial expression data and voice data collected in real time

[0950] Output: Parsed emotion data

[0951] Step 5:

[0952] Adjusting plans based on sentiment data

[0953] Specific operation: The server inputs the emotion data obtained by the emotion engine into the generative AI model and regenerates a work plan that takes into account the employee's emotions.

[0954] Input: Analyzed emotion data, generative AI model

[0955] Output: Adjusted work plan

[0956] Step 6:

[0957] View the adjusted plan and gather feedback

[0958] Specific operation: The terminal displays the adjusted work plan on the screen and collects feedback from the employee, which is then sent back to the server.

[0959] Input: Adjusted Work Plan

[0960] Output: Employee feedback

[0961] Step 7:

[0962] Readjusting plans based on feedback

[0963] How it works: The server receives feedback from employees and uses the generative AI model to readjust the work plan, repeating this process until employees are satisfied.

[0964] Input: Employee feedback

[0965] Output: Reworked Work Plan

[0966] Step 8:

[0967] Finalize and save the plan

[0968] What it does: The server saves the finalized work plan to a database, allowing for future reference and optimization.

[0969] Input: Reworked Work Plan

[0970] Output: Final work plan stored in the database

[0971] Step 9:

[0972] Regular data collection and optimization

[0973] How it works: The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the work plan based on that data.

[0974] Input: Periodically collected work records and emotional data

[0975] Output: Optimized work plan

[0976] 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.

[0977] 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.

[0978] 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.

[0979] [Third embodiment]

[0980] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0981] 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.

[0982] 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).

[0983] 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.

[0984] 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.

[0985] 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).

[0986] 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. 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.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] 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.

[0991] 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."

[0992] The present invention is a system that automates the process of creating a care plan by providing a means for displaying a user input form for input by the care recipient or a relative. The system aims to reduce the workload of care managers and create care plans efficiently.

[0993] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[0994] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[0995] The generated care plan is sent from the server to the device and displayed to the user. The user can review the contents of the care plan through the device and request revisions as necessary. If revisions are requested, the server receives the request and again uses the generative AI model to generate a revised care plan. This process is repeated until a care plan that the user is finally satisfied with is created.

[0996] Once the final care plan is finalized, the server saves it in a database and the final version is displayed on the device. In addition, the server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This allows the system to flexibly respond to changes in the care recipient's situation and needs.

[0997] As a concrete example, consider the following scenario.

[0998] Enter the basic information of a care recipient, "Mr. Tanaka," as follows:

[0999] Name: Tanaka Jiro

[1000] Age: 85

[1001] Medical history: heart disease, diabetes

[1002] Family's wishes: Regular heart checkups, rehabilitation three times a week

[1003] 1. The terminal starts up and a user input form is displayed.

[1004] 2. The user enters Mr. Tanaka's basic information.

[1005] 3. The device sends the entered information to the server.

[1006] 4. The server receives the information and stores it in a database.

[1007] 5. The server launches the generative AI model and generates a care plan, including daily vital signs checks and three rehabilitation sessions per week.

[1008] 6. The care plan is sent to the device and the user reviews it.

[1009] 7. The user requests corrections as needed.

[1010] 8. The server processes the modification request and provides a regenerated care plan.

[1011] 9. The final care plan is finalized and saved in the database.

[1012] 10. The final plan will be displayed on your device.

[1013] 11. The server periodically collects care records and uses AI models to optimize the plan.

[1014] In this way, a care plan that accurately reflects the condition of the care recipient and the wishes of the relatives can be efficiently created.

[1015] The processing flow will be explained below.

[1016] Step 1:

[1017] The device starts up and displays a user input form, allowing the care manager or relative to enter information.

[1018] Step 2:

[1019] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[1020] Step 3:

[1021] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[1022] Step 4:

[1023] The server receives the transmitted information and first stores it in a database for later processing.

[1024] Step 5:

[1025] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[1026] Step 6:

[1027] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[1028] Step 7:

[1029] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[1030] Step 8:

[1031] Users can check the contents of the care plan through their terminal and can request revisions as needed.

[1032] Step 9:

[1033] When a user inputs a correction request, the device sends the information to the server, which receives the correction request and inputs it back into the generative AI model to correct the care plan.

[1034] Step 10:

[1035] The server generates a revised care plan and sends it back to the device, and this process is repeated until the user is satisfied.

[1036] Step 11:

[1037] Once the final care plan is finalized, the server stores it in a database and displays the final version of the care plan on the terminal.

[1038] Step 12:

[1039] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This process ensures that the most appropriate care plan is always provided based on the needs of the care recipient.

[1040] Example 1

[1041] 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."

[1042] In an aging society, there is a growing need to reduce the burden on care managers and relatives and to efficiently create care plans that are appropriate for the care recipients. However, with conventional methods, the manual input of information and creation of plans requires a great deal of time and effort, and the quality of the care plans varies.

[1043] 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.

[1044] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate prompts for generating a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's request for modification and re-inputting the modified care plan into the generative AI model, means for finalizing the care plan and storing it in a database, and means for periodically collecting care records and having the generative AI model automatically optimize the care plan based on them. This reduces the workload of care managers and relatives and enables the creation of an optimal care plan for the care recipient quickly and efficiently.

[1045] A "user input form" is an on-screen interface through which the care recipient or relative can input information.

[1046] "Basic information" refers to basic data about an individual, such as the care recipient's name, age, and medical history.

[1047] "Requests from relatives" refers to the hopes and demands of relatives regarding the care and support provided to the care recipient.

[1048] A "server" is a computer system that manages and processes data over a network.

[1049] "Database" means a digital storage device and system that can systematically store, search, and reference large amounts of information.

[1050] A "generative AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task (here, generating a care plan).

[1051] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[1052] A "care plan" is a written plan that outlines the specific care content and schedule, based on the health condition and needs of the person receiving care.

[1053] "Modification request" refers to a request made by a user to modify or change a generated care plan.

[1054] The present invention is a system that displays a user input form for input by a care recipient or their relatives and automates the process of creating a care plan. The system aims to reduce the workload of care managers and efficiently generate care plans. Detailed embodiments of this system are described below.

[1055] Hardware and software used

[1056] The system uses the following hardware and software:

[1057] Hardware: Devices (PCs, tablets, smartphones, etc.), servers

[1058] Software: Web browser, database (e.g., MySQL, PostgreSQL), generative AI model (e.g., machine learning algorithm such as GPT-4)

[1059] Initial Settings and Form Display

[1060] When the device is started, a user input form is displayed on the web browser. This form is an interface for the care recipient and their relatives to enter basic information.

[1061] Entering information

[1062] The user (care manager or relative) enters the following information into the form:

[1063] Name of the care recipient (e.g., Tanaka Jiro)

[1064] Age (e.g., 85 years old)

[1065] Medical history (e.g., heart disease, diabetes)

[1066] Family members' wishes (e.g., regular heart checkups, rehabilitation three times a week)

[1067] Once the input is complete, the user clicks the "Submit" button to send the data to the server.

[1068] Data transmission and storage

[1069] The device sends the input information as an HTTP request to the server, which then analyzes the received data and stores it in a database.

[1070] Generate a care plan

[1071] The server retrieves the necessary information from the database and launches the generative AI model. Using a machine learning algorithm (e.g., GPT-4), it analyzes the received data and generates a prompt to generate a care plan. For example, the prompt might be "Generate a care plan appropriate for a patient aged 85, with heart disease and diabetes."

[1072] Viewing and Modifying Care Plans

[1073] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan and make requests for revisions as necessary. Specifically, a request for revision such as "I want to reduce the frequency of rehabilitation from three times a week to two times a week" can be sent from the device to the server.

[1074] Regeneration and finalization

[1075] The server receives the modification request and generates a modified care plan using the generative AI model again. This process is repeated until a care plan that satisfies the user is generated. Once the final care plan is finalized, it is saved in the database. The finalized care plan is also displayed on the device.

[1076] Automatic optimization

[1077] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records, allowing for flexible responses to changes in the care recipient's situation and needs.

[1078] Specific operation example

[1079] As a concrete example, consider the case of a care recipient named "Jiro Tanaka." Below is an example of a series of specific actions.

[1080] 1. The terminal starts up and a user input form is displayed.

[1081] 2. The user enters Mr. Tanaka's basic information (name, age, medical history, and relatives' wishes).

[1082] 3. The device sends the entered information to the server.

[1083] 4. The server receives the information and stores it in a database.

[1084] 5. The server launches the generative AI model and inputs a prompt statement to generate a care plan.

[1085] 6. The generated care plan (e.g., daily vital signs checks, three rehabilitation sessions per week) is sent to the device and reviewed by the user.

[1086] 7. The user makes a request to modify the care plan and checks the regenerated plan.

[1087] 8. The final care plan is finalized and saved in the database.

[1088] 9. The confirmed care plan will be displayed on the device.

[1089] 10. The server periodically collects care records and optimizes the care plan using a generative AI model.

[1090] This system reduces the burden on care managers and relatives and makes it possible to efficiently generate care plans that are best suited to the situation of the person receiving care.

[1091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1092] Step 1: Launching a terminal and displaying a user input form

[1093] When the device starts up, it automatically opens a web browser and displays a user input form for the care recipient or relative to enter basic information, including fields for name, age, medical history, and relatives' preferences.

[1094] Input: The user turns on the device.

[1095] Output: A form for user input is displayed in the web browser.

[1096] Step 2: User Enters Basic Information

[1097] The user enters the basic information of the care recipient (name, age, medical history, and relatives' wishes) into the form. Once the information is complete, the user clicks the submit button.

[1098] Input: The user enters data into the form.

[1099] Output: By pressing the submit button, the entered data is saved in the form on the browser.

[1100] Step 3: Send data from the device to the server

[1101] The terminal sends the entered information to the server as an HTTP request. Here, the POST method is used.

[1102] Input: When the submit button is clicked, the form data is packaged into the HTTP request.

[1103] Output: The form data is sent to the server.

[1104] Step 4: Receiving information and storing it in a database

[1105] The server analyzes the received data and stores it in a database by parsing the data to extract the respective fields and inserting them into the database using SQL queries.

[1106] Input: Care recipient information extracted from the HTTP request.

[1107] Output: The information is stored in a database.

[1108] Step 5: Generate a care plan using a generative AI model

[1109] The server retrieves the care recipient's information from the database and launches the generative AI model. A prompt statement is generated and input into the generative AI model to generate a care plan. For example, "Generate a care plan appropriate for an 85-year-old patient with heart disease and diabetes."

[1110] Input: Information of the care recipient obtained from the database.

[1111] Output: A care plan from the generative AI model.

[1112] Step 6: Send the care plan to your device

[1113] The server generates a care plan and sends it to the terminal as an HTTP response, which the user can view in a web browser.

[1114] Input: Generated care plan.

[1115] Output: The care plan is displayed on the terminal.

[1116] Step 7: User reviews care plan and requests modifications

[1117] The user checks the care plan and, if any changes are necessary, sends a request for changes from the terminal to the server. For example, the user might input, "I want to change the frequency of rehabilitation from three times a week to two times a week."

[1118] Input: Request for amendment to care plan.

[1119] Output: A modification request is sent to the server.

[1120] Step 8: Process and regenerate correction requests

[1121] The server receives the modification request and generates a modified care plan by again using the generative AI model. The prompt text is also updated based on the modification request.

[1122] Input: A prompt containing a correction request and the care recipient's information, which is again input into the generative AI model.

[1123] Output: Revised care plan.

[1124] Step 9: Finalize and save the final care plan

[1125] The user checks and confirms the regenerated care plan. When the user presses the Confirm button, the confirmed care plan is sent to the server.

[1126] The server stores the finalized care plan in a database.

[1127] Input: Final care plan submitted with confirmation button pressed.

[1128] Output: Final care plan saved in database.

[1129] Step 10: Display the final plan on your device

[1130] The device displays the finalized care plan, which the user can review and use in their daily care.

[1131] Input: Final Care Plan.

[1132] Output: The final care plan is displayed on the terminal.

[1133] Step 11: Server collects care records and optimizes plans

[1134] The server periodically collects care records and uses a generative AI model to automatically optimize the care plan. Through regular data collection, the plan is adjusted to the latest condition of the care recipient.

[1135] Input: Routinely collected care records.

[1136] Output: Optimized care plan.

[1137] This flow allows for the efficient automatic generation and optimization of care plans, reducing the workload of care managers and relatives.

[1138] (Application example 1)

[1139] 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."

[1140] Conventional care plan creation systems have the problem of placing a heavy burden on care managers, making the creation process cumbersome and time-consuming. Furthermore, the actual selection and purchase of care products must be done separately, which adds to the time and effort. There is a need for a system that can solve these problems and create care plans and select appropriate care products efficiently and effectively.

[1141] 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.

[1142] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a database, means for periodically collecting care records and automatically optimizing the care plan based on the collected care records, means for generating a care product list at a care product store based on the information input by the care recipient or relative, means for displaying the generated care product list on the smart devices of store staff in real time, and means for linking the suggested care product list with in-store inventory information. This enables care recipients and relatives to efficiently create care plans and easily select and purchase appropriate care products.

[1143] "Care recipients" refers to people such as elderly people and people with disabilities who require care.

[1144] "Relatives" refers to people who are related by blood or marriage to the person receiving care, or people who support the person receiving care in a similar relationship.

[1145] "User input form" refers to the screen or interface provided for the care recipient or relative to enter the required information.

[1146] "Basic information" refers to personal data such as the care recipient's name, age, medical history, etc.

[1147] "Requests" refer to any particular wishes that the care recipient or their relatives have regarding the care plan.

[1148] A "server" refers to a computer system for processing and storing data over a network.

[1149] A "database" refers to a system that efficiently stores, manages, and searches large amounts of data.

[1150] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate results based on specific data.

[1151] A "care plan" refers to a plan that outlines the specific care content and schedule that should be provided to the person receiving care.

[1152] A "terminal" is a device used by a user to enter data and check results, such as a smartphone or tablet.

[1153] A "request for modification" refers to a user's request for desired changes to the generated care plan.

[1154] A "list of nursing care supplies" refers to a list of nursing care supplies required based on the care plan of the person receiving care.

[1155] "Store staff" refers to employees who select and sell nursing care products in physical stores.

[1156] "Smart devices" are electronic devices that can connect to the Internet, such as smartphones, tablets, and smart glasses.

[1157] "Inventory information" refers to the quantity and item information of products currently held in the store.

[1158] The present invention is a system for efficiently creating a care plan and selecting care products. The following describes the program and processing of the system that realizes the present invention.

[1159] Hardware and Software Used

[1160] Server: AWS EC2

[1161] Database: AWS RDS (MySQL)

[1162] Generative AI model: OpenAI GPT-4

[1163] Web frontend: React.js

[1164] Backend: Django

[1165] Communication protocol: REST API

[1166] User devices: smartphones, smart glasses

[1167] System program and processing explanation

[1168] 1. Displaying a user input form

[1169] The server uses React.js to display a user input form, in which the care recipient and their relatives enter basic information and requests.

[1170] 2. Data Transmission

[1171] The information sent from the device is received by the Django backend and stored in an AWS RDS database. The server receives this data and makes it available for analysis.

[1172] 3. Care plan generation

[1173] The server runs a generative AI model using OpenAI GPT-4 to analyze the information sent, and then uses machine learning algorithms to generate a care plan based on the care recipient's condition and the relatives' requests. This generative AI model runs in real time via a REST API.

[1174] 4. View care plans

[1175] The generated care plan is returned to the React.js front end and displayed on the device. The user can review the care plan and make corrections if necessary.

[1176] 5. Processing Amendment Requests

[1177] When the user issues a modification request, the server again uses the generative AI model to generate and display a modified care plan. This cycle is repeated until a care plan that satisfies the user is generated.

[1178] 6. Creating a list of care items

[1179] Once the care plan is finalized, the server generates a list of care products, and at this stage, the AI ​​model is used to analyze the information and select the necessary care products.

[1180] 7. Real-time display on smart devices

[1181] The generated list of care products is displayed in real time on the store staff's smart devices (smart glasses or smartphones), allowing them to quickly guide users to the most suitable products.

[1182] 8. Linking with inventory information

[1183] The suggestions on the nursing care product list are linked to the store's inventory information managed on the server, so it is possible to check in real time whether an item is in stock or if it can be reserved.

[1184] Specific examples

[1185] For example, the basic information and requests of the care recipient "Mr. Sato" are as follows:

[1186] Name: Hanako Sato

[1187] Age: 78

[1188] Medical history: high blood pressure, arthritis

[1189] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[1190] Based on this information, the following prompt sentence is input into the generative AI model:

[1191] Prompt Sentence Examples

[1192] "Based on the information below, please suggest the best care plan and necessary nursing care products.

[1193] The care recipient's information is as follows:

[1194] Name: Hanako Sato

[1195] Age: 78

[1196] Medical history: high blood pressure, arthritis

[1197] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[1198] thank you."

[1199] Based on these prompts, the generative AI model generates an optimal care plan and list of care supplies, which are then provided to the user and store staff.

[1200] In this way, the system of the present invention reduces the burden on the care recipient and their relatives, and allows for the creation of a care plan and the selection of care products to be carried out quickly and efficiently.

[1201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1202] Step 1:

[1203] The server uses React.js to display a user input form. The user enters basic information about the care recipient and the requests of their relatives. This input form includes information such as name, age, medical history, and care preferences. The entered data is temporarily stored in the device's memory.

[1204] Step 2:

[1205] The device sends the entered information to the Django backend for storage in the AWS RDS database. This data includes the care recipient's information and the relatives' requests entered by the user. The server receives this and stores it in the database.

[1206] Step 3:

[1207] The server uses the received information to launch a generative AI model using OpenAI GPT-4. Here, a prompt sentence is generated using a machine learning algorithm. Using this prompt sentence as input, the AI ​​model generates a care plan. In this generation process, the AI ​​calculates the optimal care plan based on the care recipient's condition and the relatives' requests.

[1208] Step 4:

[1209] The server returns the generated care plan to the React.js front end and displays it on the device. The user can then view the details of the care plan through the device. At this point, the care plan includes daily care details, regular checkups, and rehabilitation schedules.

[1210] Step 5:

[1211] If the user requests an amendment to the care plan, the device sends the amendment to the server, which then reactivates the generative AI model and regenerates the care plan based on the amendment request. This regeneration process is also performed using a machine learning algorithm.

[1212] Step 6:

[1213] The server returns the finalized care plan to the React.js front end and displays it on the terminal. At the same time, the care plan is saved in the AWS RDS database. The user can review the final care plan and confirm it if they are satisfied with its contents.

[1214] Step 7:

[1215] The server generates a list of care items based on the finalized care plan. This list is output by a generative AI model based on the prompt text. Here, the care items required for the care plan are calculated.

[1216] Step 8:

[1217] The server displays the generated list of care products in real time on the store staff's smart devices (smart glasses or smartphones), and the staff guides the user to the appropriate care products based on this information.

[1218] Step 9:

[1219] The server then links the nursing care product list suggestions to the store's inventory information. This communicates with the store's management system via API, updating inventory data in real time. This makes it possible to handle inventory availability and reserve items.

[1220] 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.

[1221] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[1222] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[1223] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[1224] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[1225] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[1226] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[1227] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[1228] As a concrete example, consider the following scenario.

[1229] Enter the basic information of a care recipient, "Mr. Sato," as follows:

[1230] Name: Sato Jiro

[1231] Age: 78

[1232] Medical history: high blood pressure, arthritis

[1233] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[1234] 1. The terminal starts up and a user input form is displayed.

[1235] 2. The user enters Mr. Sato's basic information.

[1236] 3. The device sends the entered information to the server.

[1237] 4. The server receives the information and stores it in a database.

[1238] 5. The server launches the generative AI model and generates a care plan, which may include, for example, daily morning blood pressure monitoring and twice-weekly physical therapy.

[1239] 6. The care plan is sent to the device and the user reviews it.

[1240] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[1241] 8. The user requests corrections as needed.

[1242] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[1243] 10. The final care plan is finalized and saved in the database.

[1244] 11. The final plan will be displayed on your device.

[1245] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[1246] In this way, this system reduces the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[1247] The processing flow will be explained below.

[1248] Step 1:

[1249] The terminal starts up and displays a user input form, allowing the user (care manager or relative) to enter information.

[1250] Step 2:

[1251] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[1252] Step 3:

[1253] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[1254] Step 4:

[1255] The server receives the transmitted information and stores it in a database for further processing.

[1256] Step 5:

[1257] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[1258] Step 6:

[1259] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[1260] Step 7:

[1261] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[1262] Step 8:

[1263] The user checks the contents of the care plan through the device and can make corrections as needed. At this point, the device collects the user's emotions using voice and text.

[1264] Step 9:

[1265] The emotion engine recognizes the user's emotions through voice and text analysis, for example, analyzing the user's text comments and voice tone to digitize their emotions.

[1266] Step 10:

[1267] The device sends the recognized emotion information to the server, which receives the correction request and the emotion information and re-evaluates it.

[1268] Step 11:

[1269] The server then reactivates the generative AI model and modifies the care plan based on the modification request and emotional information, generating a more personalized care plan taking into account the information from the emotional engine.

[1270] Step 12:

[1271] The server sends the modified care plan back to the device, which displays the modified care plan to the user, and this process is repeated until the user is satisfied.

[1272] Step 13:

[1273] Once the final care plan is finalized, the server stores it in the database and displays the final version of the care plan on the terminal.

[1274] Step 14:

[1275] The server periodically collects care records, and the generative AI model and emotion engine automatically optimize the care plan based on the records. This process provides an optimal care plan that continuously reflects the needs of the care recipient and the user's emotions.

[1276] Example 2

[1277] 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."

[1278] The current care plan creation process is often manual, requiring a great deal of effort and time from care managers and relatives. It is also difficult to properly reflect the feelings and wishes of the care recipient and relatives, making it difficult to provide personalized care. Conventional systems lack a mechanism to consider the user's feelings, and therefore care plans are not always satisfactory.

[1279] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1280] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to a central processing unit, means for receiving the transmitted information and storing it in a data storage device, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to an information display device and displaying it, means for an emotion engine to recognize the user's emotions through voice and text analysis, means for receiving a user's request for modification and inputting it back into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a data storage device, and means for periodically collecting care records and automatically optimizing the care plan based on the collected care records by the generative AI model. This automates the creation and modification of care plans, enabling the provision of personalized care plans that reflect the user's emotions and requests.

[1281] A "user input form" is a screen that is displayed on a terminal for the care recipient or relative to input information about care.

[1282] "Basic information of the care recipient" refers to personal data such as the care recipient's name, age, medical history, etc.

[1283] "Requests from relatives" refers to the specific wishes and demands regarding care provided to the person receiving care.

[1284] "Central Processing Unit" refers to a computer system that receives, analyzes, and processes information sent from a terminal.

[1285] "Data storage" refers to a database system for storing received information.

[1286] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate a care plan based on information about the care recipient and the requests of their relatives.

[1287] "Care plan" refers to a plan that describes the specific care to be provided to the care recipient.

[1288] An "information display device" is a terminal for displaying the generated care plan to the user.

[1289] An "emotion engine" is a system that recognizes a user's emotions by analyzing voice and text, and outputs them as analytical data.

[1290] A "modification request" refers to a user's request for a change or adjustment to a care plan.

[1291] "Care records" are data that record the status and results of care provided to care recipients.

[1292] "Optimization" refers to the process of improving care plans to make them more appropriate and effective based on collected data.

[1293] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[1294] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[1295] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[1296] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[1297] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[1298] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[1299] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[1300] To illustrate, consider the following scenario:

[1301] Enter the basic information of the care recipient "Sato-san" as follows:

[1302] Name: Jiro Sato

[1303] Age: 78

[1304] Medical history: high blood pressure, arthritis

[1305] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[1306] 1. The terminal starts up and a user input form is displayed.

[1307] 2. The user enters Mr. Sato's basic information.

[1308] 3. The device sends the entered information to the server.

[1309] 4. The server receives the information and stores it in a database.

[1310] 5. The server runs the generative AI model to generate a care plan, for example, one that includes blood pressure monitoring every morning and physical therapy twice a week.

[1311] 6. The care plan is sent to the device and the user reviews it.

[1312] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[1313] 8. The user requests corrections as needed.

[1314] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[1315] 10. The final care plan is finalized and saved in the database.

[1316] 11. The final plan will be displayed on your device.

[1317] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[1318] In this way, this system can reduce the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[1319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1320] Step 1:

[1321] Starts the terminal and displays a form for user input

[1322] The device starts up and launches a dedicated application. Next, a user input form is displayed on the screen, allowing the care recipient or relative to enter information. The form contains input fields for name, age, medical history, required care, and relatives' requests. The device provides the user with the initial data for input, and the data entered by the user is output.

[1323] Step 2:

[1324] Enter basic user information

[1325] The user (care manager or relative) enters basic information about the care recipient into the displayed user input form. Specifically, the user enters details such as the care recipient's name, age, medical history, required care, and the relative's requests. The input data is output and sent to the next processing step.

[1326] Step 3:

[1327] Sending information from the device to the server

[1328] The terminal sends the information entered by the user to the server. The terminal executes a sending process, packaging the input data from the user into packets and sending them to the server. The input is the user's input data, and the output is the data received by the server.

[1329] Step 4:

[1330] Receiving information from the server and storing it in the database

[1331] The server receives the information sent from the device. It then stores the received information in a database. Specifically, it issues a query to the database to store information such as name, age, medical history, and requests from relatives. The input is the data sent from the device, and the output is the data stored in the database.

[1332] Step 5:

[1333] Server generation: Launching the AI ​​model and generating care plans

[1334] The server launches the generative AI model, which then analyzes the basic information of the care recipient stored on the server and the wishes of their relatives to generate an appropriate care plan. The model uses machine learning algorithms to make predictions and optimizations based on the input data. The input is information from the database, and the output is the generated care plan.

[1335] Step 6:

[1336] Sending and displaying care plans to devices

[1337] The server sends the care plan to the terminal. The terminal displays the care plan it receives to the user. Specifically, the contents of the care plan are displayed on the screen so that the user can check them. The input is care plan data from the server, and the output is a care plan that is displayed to the user.

[1338] Step 7:

[1339] Emotional analysis of users using an emotion engine

[1340] The emotion engine analyzes the voice and text inputs made by the user when confirming the care plan. Using an emotion recognition algorithm, it analyzes the user's emotions (dissatisfaction, relief, etc.) and sends that information to the server. The input is the user's voice and text data, and the analyzed emotional information is generated as the output.

[1341] Step 8:

[1342] User correction request submission

[1343] The user submits a request to modify the care plan. The terminal transmits the modification request input by the user to the server. The input is the user's modification request data, and the modification data to be transmitted to the server is generated as the output.

[1344] Step 9:

[1345] Server-based correction requests, processing of emotional information, and reevaluation of care plans

[1346] The server receives the correction request and the user's emotional information, then launches the generative AI model again to reevaluate the care plan. A new care plan that takes the emotional information into account is generated. The inputs are the correction request and emotional information, and the output is a corrected care plan.

[1347] Step 10:

[1348] Final care plan database storage

[1349] The server saves the final care plan to a database. The database is queried and the final care plan is stored. The input is the modified care plan and the output is the care plan stored in the database.

[1350] Step 11:

[1351] Displaying the final plan on the device

[1352] The server sends the final care plan to the terminal, which then displays it to the user. The input is the final care plan, and the output is the care plan that is displayed to the user.

[1353] Step 12:

[1354] Periodic server-based care record collection and plan optimization

[1355] The server periodically collects care records and optimizes the care plan using a generative AI model. The data is analyzed together with an emotion engine to improve the care plan. The input is care record data, and the output is an optimized care plan.

[1356] (Application example 2)

[1357] 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."

[1358] In current manufacturing lines, employee fatigue and stress are not properly monitored, resulting in a decline in work efficiency and safety. Furthermore, uniform work plans are provided without considering employees' emotions or feedback, making it difficult to respond flexibly to individual situations. This leads to a decline in work quality and productivity.

[1359] 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 displaying a user input form for input by the care recipient or relative; means for inputting basic information about the care recipient and requests from the relative; means for transmitting the input information to the server; means for receiving the transmitted information and storing it in a database; means for activating a generative AI model to analyze the received information and generate a care plan; means for transmitting the generated care plan to a terminal and displaying it; means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan; means for finalizing the care plan and storing it in a database; means for analyzing the user's emotions using an emotion engine and adjusting the care plan based on the user's emotions; means for presenting the generated care plan to an employee's screen and collecting employee feedback; and means for periodically collecting care records and emotion data and having the generative AI model automatically optimize the care plan based on the collected data. This enables the provision of personalized work plans that take employee emotions and feedback into account, thereby improving work efficiency and safety.

[1360] "User Input Form" means a screen or interface through which an employee or associated person can enter basic information or requests.

[1361] "Basic information" refers to basic data about an individual, such as the employee's name, age, and shift times.

[1362] "Requests" are the desired conditions or requirements of employees or related persons.

[1363] A "server" is a computer system on a network that stores data and performs calculations and analysis.

[1364] A "database" is a storage system that systematically stores information and allows it to be searched and edited.

[1365] A "generative AI model" is an artificial intelligence algorithm that analyzes the information it receives and generates data and plans based on a specified task.

[1366] An "emotion engine" is software that recognizes a user's emotions through voice and text analysis and analyzes the results.

[1367] A "care plan" is a specific action plan created based on the user's basic information, requests, and feelings.

[1368] A "terminal" is a device or apparatus with which a user can interact, and which is used to input and display information.

[1369] "Feedback" refers to the responses and opinions received from employees and related parties, which are used to adjust and optimize the plan.

[1370] MODE FOR CARRYING OUT THE INVENTION

[1371] To implement this invention, the following system program is constructed: This system takes into account the emotions and feedback of employees and automatically adjusts the work plan to improve work efficiency and safety.

[1372] System Overview

[1373] 1. Display and fill in the user input form

[1374] The terminal displays a user input form where employees can enter basic information and requests, such as their name, age, and shift hours.

[1375] 2. Data transmission and storage

[1376] The terminal sends the entered information to the server, which receives it and stores it in a database.

[1377] 3. Analysis and plan generation using generative AI models

[1378] The server launches a generative AI model to analyze the received information and generate an initial work plan.

[1379] 4. Emotion analysis and plan adjustment using an emotion engine

[1380] The device uses a camera and voice recognition system to collect employee emotions in real time, and the server analyzes this data using an emotion engine. Based on the user's emotions, a generative AI model adjusts the work plan.

[1381] 5. Feedback and Recalibration

[1382] The adjusted work plan is displayed on the device and receives feedback from the employee. If necessary, the server adjusts the plan again using the generative AI model and emotion engine.

[1383] 6. Finalize and optimize the plan

[1384] The final work plan is finalized and saved in the database. The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the plan based on them.

[1385] Specific examples of hardware and software used

[1386] Camera: General surveillance camera

[1387] Speech recognition software: Google Cloud Speech-to-Text API

[1388] Sentiment analysis software: IBM Watson

[1389] Database: MySQL

[1390] Generative AI model: OpenAI GPT-4

[1391] Front-end framework: React

[1392] Backend framework: Django

[1393] Specific examples

[1394] For example, an employee named "A" is working an eight-hour shift. Before the shift begins, he or she enters basic information using a user input form on a device. During the shift, a camera analyzes A's facial expressions and voice data in real time using the Google Cloud Speech-to-Text API, and IBM Watson emotion analysis software detects "fatigue." This information is sent to a server, and a generative AI model (GPT-4) uses this information to readjust the work plan and insert breaks if necessary. The readjusted work plan is presented on A's device, and A's feedback is collected.

[1395] Prompt Sentence Examples

[1396] plain

[1397] def create_schedule_prompt(employee_info, current_emotion):

[1398] return f"""

[1399] Employee A, age, is on shift. A is currently in {current_emotion} state.

[1400] Use this information to come up with a work plan, including breaks where appropriate.

[1401] """

[1402] current_emotion = "fatigue"

[1403] employee_info = {"Name": "Mr. A", "Age": "50", "Shift": "8 hours"}

[1404] prompt = create_schedule_prompt(employee_info, current_emotion)

[1405] This system provides personalized work plans that take into account employee emotions and feedback, improving work efficiency and safety. The server also periodically collects data, and the generative AI model automatically optimizes the plans based on that data, enabling continuous improvement.

[1406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1407] Step 1:

[1408] Displaying a user input form and entering basic information

[1409] Specific operation: The terminal displays a user input form on the screen, and the employee enters basic information (name, age, shift time) and requests. In this way, the terminal collects basic information from the employee.

[1410] Input: User's basic information and requests

[1411] Output: Basic information and requests entered into the terminal

[1412] Step 2:

[1413] Sending and saving input data

[1414] Specific operation: The device sends the collected basic information and requests to the server, which receives this information and stores it in a database.

[1415] Input: Basic information and requests sent from the device

[1416] Output: Basic information and requests stored in the database

[1417] Step 3:

[1418] Generative AI model generates initial plans

[1419] Specific operation: The server launches the generative AI model, analyzes the stored basic information and requests, and generates an initial work plan. The generative AI model uses the prompt sentences to generate a work plan suitable for the employee.

[1420] Input: Basic information and requests stored in the database

[1421] Output: Generated initial work plan

[1422] Step 4:

[1423] Emotional data collection and analysis

[1424] How it works: The device uses a camera and voice recognition system to collect facial and voice data in real time, then sends it to a server, which then uses an emotion engine to analyze the data.

[1425] Input: Facial expression data and voice data collected in real time

[1426] Output: Parsed emotion data

[1427] Step 5:

[1428] Adjusting plans based on sentiment data

[1429] Specific operation: The server inputs the emotion data obtained by the emotion engine into the generative AI model and regenerates a work plan that takes into account the employee's emotions.

[1430] Input: Analyzed emotion data, generative AI model

[1431] Output: Adjusted work plan

[1432] Step 6:

[1433] View the adjusted plan and gather feedback

[1434] Specific operation: The terminal displays the adjusted work plan on the screen and collects feedback from the employee, which is then sent back to the server.

[1435] Input: Adjusted Work Plan

[1436] Output: Employee feedback

[1437] Step 7:

[1438] Readjusting plans based on feedback

[1439] How it works: The server receives feedback from employees and uses the generative AI model to readjust the work plan, repeating this process until employees are satisfied.

[1440] Input: Employee feedback

[1441] Output: Reworked Work Plan

[1442] Step 8:

[1443] Finalize and save the plan

[1444] What it does: The server saves the finalized work plan to a database, allowing for future reference and optimization.

[1445] Input: Reworked Work Plan

[1446] Output: Final work plan stored in the database

[1447] Step 9:

[1448] Regular data collection and optimization

[1449] How it works: The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the work plan based on that data.

[1450] Input: Periodically collected work records and emotional data

[1451] Output: Optimized work plan

[1452] 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.

[1453] 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.

[1454] 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.

[1455] [Fourth embodiment]

[1456] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1457] 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.

[1458] 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).

[1459] 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.

[1460] 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.

[1461] 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).

[1462] 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. 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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."

[1469] The present invention is a system that automates the process of creating a care plan by providing a means for displaying a user input form for input by the care recipient or a relative. The system aims to reduce the workload of care managers and create care plans efficiently.

[1470] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[1471] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[1472] The generated care plan is sent from the server to the device and displayed to the user. The user can review the contents of the care plan through the device and request revisions as necessary. If revisions are requested, the server receives the request and again uses the generative AI model to generate a revised care plan. This process is repeated until a care plan that the user is finally satisfied with is created.

[1473] Once the final care plan is finalized, the server saves it in a database and the final version is displayed on the device. In addition, the server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This allows the system to flexibly respond to changes in the care recipient's situation and needs.

[1474] As a concrete example, consider the following scenario.

[1475] Enter the basic information of a care recipient, "Mr. Tanaka," as follows:

[1476] Name: Tanaka Jiro

[1477] Age: 85

[1478] Medical history: heart disease, diabetes

[1479] Family's wishes: Regular heart checkups, rehabilitation three times a week

[1480] 1. The terminal starts up and a user input form is displayed.

[1481] 2. The user enters Mr. Tanaka's basic information.

[1482] 3. The device sends the entered information to the server.

[1483] 4. The server receives the information and stores it in a database.

[1484] 5. The server launches the generative AI model and generates a care plan, including daily vital signs checks and three rehabilitation sessions per week.

[1485] 6. The care plan is sent to the device and the user reviews it.

[1486] 7. The user requests corrections as needed.

[1487] 8. The server processes the modification request and provides a regenerated care plan.

[1488] 9. The final care plan is finalized and saved in the database.

[1489] 10. The final plan will be displayed on your device.

[1490] 11. The server periodically collects care records and uses AI models to optimize the plan.

[1491] In this way, a care plan that accurately reflects the condition of the care recipient and the wishes of the relatives can be efficiently created.

[1492] The processing flow will be explained below.

[1493] Step 1:

[1494] The device starts up and displays a user input form, allowing the care manager or relative to enter information.

[1495] Step 2:

[1496] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[1497] Step 3:

[1498] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[1499] Step 4:

[1500] The server receives the transmitted information and first stores it in a database for later processing.

[1501] Step 5:

[1502] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[1503] Step 6:

[1504] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[1505] Step 7:

[1506] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[1507] Step 8:

[1508] Users can check the contents of the care plan through their terminal and can request revisions as needed.

[1509] Step 9:

[1510] When a user inputs a correction request, the device sends the information to the server, which receives the correction request and inputs it back into the generative AI model to correct the care plan.

[1511] Step 10:

[1512] The server generates a revised care plan and sends it back to the device, and this process is repeated until the user is satisfied.

[1513] Step 11:

[1514] Once the final care plan is finalized, the server stores it in a database and displays the final version of the care plan on the terminal.

[1515] Step 12:

[1516] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records. This process ensures that the most appropriate care plan is always provided based on the needs of the care recipient.

[1517] Example 1

[1518] 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."

[1519] In an aging society, there is a growing need to reduce the burden on care managers and relatives and to efficiently create care plans that are appropriate for the care recipients. However, with conventional methods, the manual input of information and creation of plans requires a great deal of time and effort, and the quality of the care plans varies.

[1520] 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.

[1521] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate prompts for generating a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's request for modification and re-inputting the modified care plan into the generative AI model, means for finalizing the care plan and storing it in a database, and means for periodically collecting care records and having the generative AI model automatically optimize the care plan based on them. This reduces the workload of care managers and relatives and enables the creation of an optimal care plan for the care recipient quickly and efficiently.

[1522] A "user input form" is an on-screen interface through which the care recipient or relative can input information.

[1523] "Basic information" refers to basic data about an individual, such as the care recipient's name, age, and medical history.

[1524] "Requests from relatives" refers to the hopes and demands of relatives regarding the care and support provided to the care recipient.

[1525] A "server" is a computer system that manages and processes data over a network.

[1526] "Database" means a digital storage device and system that can systematically store, search, and reference large amounts of information.

[1527] A "generative AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task (here, generating a care plan).

[1528] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[1529] A "care plan" is a written plan that outlines the specific care content and schedule, based on the health condition and needs of the person receiving care.

[1530] "Modification request" refers to a request made by a user to modify or change a generated care plan.

[1531] The present invention is a system that displays a user input form for input by a care recipient or their relatives and automates the process of creating a care plan. The system aims to reduce the workload of care managers and efficiently generate care plans. Detailed embodiments of this system are described below.

[1532] Hardware and software used

[1533] The system uses the following hardware and software:

[1534] Hardware: Devices (PCs, tablets, smartphones, etc.), servers

[1535] Software: Web browser, database (e.g., MySQL, PostgreSQL), generative AI model (e.g., machine learning algorithm such as GPT-4)

[1536] Initial Settings and Form Display

[1537] When the device is started, a user input form is displayed on the web browser. This form is an interface for the care recipient and their relatives to enter basic information.

[1538] Entering information

[1539] The user (care manager or relative) enters the following information into the form:

[1540] Name of the care recipient (e.g., Tanaka Jiro)

[1541] Age (e.g., 85 years old)

[1542] Medical history (e.g., heart disease, diabetes)

[1543] Family members' wishes (e.g., regular heart checkups, rehabilitation three times a week)

[1544] Once the input is complete, the user clicks the "Submit" button to send the data to the server.

[1545] Data transmission and storage

[1546] The device sends the input information as an HTTP request to the server, which then analyzes the received data and stores it in a database.

[1547] Generate a care plan

[1548] The server retrieves the necessary information from the database and launches the generative AI model. Using a machine learning algorithm (e.g., GPT-4), it analyzes the received data and generates a prompt to generate a care plan. For example, the prompt might be "Generate a care plan appropriate for a patient aged 85, with heart disease and diabetes."

[1549] Viewing and Modifying Care Plans

[1550] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan and make requests for revisions as necessary. Specifically, a request for revision such as "I want to reduce the frequency of rehabilitation from three times a week to two times a week" can be sent from the device to the server.

[1551] Regeneration and finalization

[1552] The server receives the modification request and generates a modified care plan using the generative AI model again. This process is repeated until a care plan that satisfies the user is generated. Once the final care plan is finalized, it is saved in the database. The finalized care plan is also displayed on the device.

[1553] Automatic optimization

[1554] The server periodically collects care records, and the generative AI model automatically optimizes the care plan based on these records, allowing for flexible responses to changes in the care recipient's situation and needs.

[1555] Specific operation example

[1556] As a concrete example, consider the case of a care recipient named "Jiro Tanaka." Below is an example of a series of specific actions.

[1557] 1. The terminal starts up and a user input form is displayed.

[1558] 2. The user enters Mr. Tanaka's basic information (name, age, medical history, and relatives' wishes).

[1559] 3. The device sends the entered information to the server.

[1560] 4. The server receives the information and stores it in a database.

[1561] 5. The server launches the generative AI model and inputs a prompt statement to generate a care plan.

[1562] 6. The generated care plan (e.g., daily vital signs checks, three rehabilitation sessions per week) is sent to the device and reviewed by the user.

[1563] 7. The user makes a request to modify the care plan and checks the regenerated plan.

[1564] 8. The final care plan is finalized and saved in the database.

[1565] 9. The confirmed care plan will be displayed on the device.

[1566] 10. The server periodically collects care records and optimizes the care plan using a generative AI model.

[1567] This system reduces the burden on care managers and relatives and makes it possible to efficiently generate care plans that are best suited to the situation of the person receiving care.

[1568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1569] Step 1: Launching a terminal and displaying a user input form

[1570] When the device starts up, it automatically opens a web browser and displays a user input form for the care recipient or relative to enter basic information, including fields for name, age, medical history, and relatives' preferences.

[1571] Input: The user turns on the device.

[1572] Output: A form for user input is displayed in the web browser.

[1573] Step 2: User Enters Basic Information

[1574] The user enters the basic information of the care recipient (name, age, medical history, and relatives' wishes) into the form. Once the information is complete, the user clicks the submit button.

[1575] Input: The user enters data into the form.

[1576] Output: By pressing the submit button, the entered data is saved in the form on the browser.

[1577] Step 3: Send data from the device to the server

[1578] The terminal sends the entered information to the server as an HTTP request. Here, the POST method is used.

[1579] Input: When the submit button is clicked, the form data is packaged into the HTTP request.

[1580] Output: The form data is sent to the server.

[1581] Step 4: Receiving information and storing it in a database

[1582] The server analyzes the received data and stores it in a database by parsing the data to extract the respective fields and inserting them into the database using SQL queries.

[1583] Input: Care recipient information extracted from the HTTP request.

[1584] Output: The information is stored in a database.

[1585] Step 5: Generate a care plan using a generative AI model

[1586] The server retrieves the care recipient's information from the database and launches the generative AI model. A prompt statement is generated and input into the generative AI model to generate a care plan. For example, "Generate a care plan appropriate for an 85-year-old patient with heart disease and diabetes."

[1587] Input: Information of the care recipient obtained from the database.

[1588] Output: A care plan from the generative AI model.

[1589] Step 6: Send the care plan to your device

[1590] The server generates a care plan and sends it to the terminal as an HTTP response, which the user can view in a web browser.

[1591] Input: Generated care plan.

[1592] Output: The care plan is displayed on the terminal.

[1593] Step 7: User reviews care plan and requests modifications

[1594] The user checks the care plan and, if any changes are necessary, sends a request for changes from the terminal to the server. For example, the user might input, "I want to change the frequency of rehabilitation from three times a week to two times a week."

[1595] Input: Request for amendment to care plan.

[1596] Output: A modification request is sent to the server.

[1597] Step 8: Process and regenerate correction requests

[1598] The server receives the modification request and generates a modified care plan by again using the generative AI model. The prompt text is also updated based on the modification request.

[1599] Input: A prompt containing a correction request and the care recipient's information, which is again input into the generative AI model.

[1600] Output: Revised care plan.

[1601] Step 9: Finalize and save the final care plan

[1602] The user checks and confirms the regenerated care plan. When the user presses the Confirm button, the confirmed care plan is sent to the server.

[1603] The server stores the finalized care plan in a database.

[1604] Input: Final care plan submitted with confirmation button pressed.

[1605] Output: Final care plan saved in database.

[1606] Step 10: Display the final plan on your device

[1607] The device displays the finalized care plan, which the user can review and use in their daily care.

[1608] Input: Final Care Plan.

[1609] Output: The final care plan is displayed on the terminal.

[1610] Step 11: Server collects care records and optimizes plans

[1611] The server periodically collects care records and uses a generative AI model to automatically optimize the care plan. Through regular data collection, the plan is adjusted to the latest condition of the care recipient.

[1612] Input: Routinely collected care records.

[1613] Output: Optimized care plan.

[1614] This flow allows for the efficient automatic generation and optimization of care plans, reducing the workload of care managers and relatives.

[1615] (Application example 1)

[1616] 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."

[1617] Conventional care plan creation systems have the problem of placing a heavy burden on care managers, making the creation process cumbersome and time-consuming. Furthermore, the actual selection and purchase of care products must be done separately, which adds to the time and effort. There is a need for a system that can solve these problems and create care plans and select appropriate care products efficiently and effectively.

[1618] 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.

[1619] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to the server, means for receiving the transmitted information and storing it in a database, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to a terminal and displaying it, means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a database, means for periodically collecting care records and automatically optimizing the care plan based on the collected care records, means for generating a care product list at a care product store based on the information input by the care recipient or relative, means for displaying the generated care product list on the smart devices of store staff in real time, and means for linking the suggested care product list with in-store inventory information. This enables care recipients and relatives to efficiently create care plans and easily select and purchase appropriate care products.

[1620] "Care recipients" refers to people such as elderly people and people with disabilities who require care.

[1621] "Relatives" refers to people who are related by blood or marriage to the person receiving care, or people who support the person receiving care in a similar relationship.

[1622] "User input form" refers to the screen or interface provided for the care recipient or relative to enter the required information.

[1623] "Basic information" refers to personal data such as the care recipient's name, age, medical history, etc.

[1624] "Requests" refer to any particular wishes that the care recipient or their relatives have regarding the care plan.

[1625] A "server" refers to a computer system for processing and storing data over a network.

[1626] A "database" refers to a system that efficiently stores, manages, and searches large amounts of data.

[1627] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate results based on specific data.

[1628] A "care plan" refers to a plan that outlines the specific care content and schedule that should be provided to the person receiving care.

[1629] A "terminal" is a device used by a user to enter data and check results, such as a smartphone or tablet.

[1630] A "request for modification" refers to a user's request for desired changes to the generated care plan.

[1631] A "list of nursing care supplies" refers to a list of nursing care supplies required based on the care plan of the person receiving care.

[1632] "Store staff" refers to employees who select and sell nursing care products in physical stores.

[1633] "Smart devices" are electronic devices that can connect to the Internet, such as smartphones, tablets, and smart glasses.

[1634] "Inventory information" refers to the quantity and item information of products currently held in the store.

[1635] The present invention is a system for efficiently creating a care plan and selecting care products. The following describes the program and processing of the system that realizes the present invention.

[1636] Hardware and Software Used

[1637] Server: AWS EC2

[1638] Database: AWS RDS (MySQL)

[1639] Generative AI model: OpenAI GPT-4

[1640] Web frontend: React.js

[1641] Backend: Django

[1642] Communication protocol: REST API

[1643] User devices: smartphones, smart glasses

[1644] System program and processing explanation

[1645] 1. Displaying a user input form

[1646] The server uses React.js to display a user input form, in which the care recipient and their relatives enter basic information and requests.

[1647] 2. Data Transmission

[1648] The information sent from the device is received by the Django backend and stored in an AWS RDS database. The server receives this data and makes it available for analysis.

[1649] 3. Care plan generation

[1650] The server runs a generative AI model using OpenAI GPT-4 to analyze the information sent, and then uses machine learning algorithms to generate a care plan based on the care recipient's condition and the relatives' requests. This generative AI model runs in real time via a REST API.

[1651] 4. View care plans

[1652] The generated care plan is returned to the React.js front end and displayed on the device. The user can review the care plan and make corrections if necessary.

[1653] 5. Processing Amendment Requests

[1654] When the user issues a modification request, the server again uses the generative AI model to generate and display a modified care plan. This cycle is repeated until a care plan that satisfies the user is generated.

[1655] 6. Creating a list of care items

[1656] Once the care plan is finalized, the server generates a list of care products, and at this stage, the AI ​​model is used to analyze the information and select the necessary care products.

[1657] 7. Real-time display on smart devices

[1658] The generated list of care products is displayed in real time on the store staff's smart devices (smart glasses or smartphones), allowing them to quickly guide users to the most suitable products.

[1659] 8. Linking with inventory information

[1660] The suggestions on the nursing care product list are linked to the store's inventory information managed on the server, so it is possible to check in real time whether an item is in stock or if it can be reserved.

[1661] Specific examples

[1662] For example, the basic information and requests of the care recipient "Mr. Sato" are as follows:

[1663] Name: Hanako Sato

[1664] Age: 78

[1665] Medical history: high blood pressure, arthritis

[1666] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[1667] Based on this information, the following prompt sentence is input into the generative AI model:

[1668] Prompt Sentence Examples

[1669] "Based on the information below, please suggest the best care plan and necessary nursing care products.

[1670] The care recipient's information is as follows:

[1671] Name: Hanako Sato

[1672] Age: 78

[1673] Medical history: high blood pressure, arthritis

[1674] Family's wishes: Bath chair, non-slip carpet, blood pressure monitor

[1675] thank you."

[1676] Based on these prompts, the generative AI model generates an optimal care plan and list of care supplies, which are then provided to the user and store staff.

[1677] In this way, the system of the present invention reduces the burden on the care recipient and their relatives, and allows for the creation of a care plan and the selection of care products to be carried out quickly and efficiently.

[1678] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1679] Step 1:

[1680] The server uses React.js to display a user input form. The user enters basic information about the care recipient and the requests of their relatives. This input form includes information such as name, age, medical history, and care preferences. The entered data is temporarily stored in the device's memory.

[1681] Step 2:

[1682] The device sends the entered information to the Django backend for storage in the AWS RDS database. This data includes the care recipient's information and the relatives' requests entered by the user. The server receives this and stores it in the database.

[1683] Step 3:

[1684] The server uses the received information to launch a generative AI model using OpenAI GPT-4. Here, a prompt sentence is generated using a machine learning algorithm. Using this prompt sentence as input, the AI ​​model generates a care plan. In this generation process, the AI ​​calculates the optimal care plan based on the care recipient's condition and the relatives' requests.

[1685] Step 4:

[1686] The server returns the generated care plan to the React.js front end and displays it on the device. The user can then view the details of the care plan through the device. At this point, the care plan includes daily care details, regular checkups, and rehabilitation schedules.

[1687] Step 5:

[1688] If the user requests an amendment to the care plan, the device sends the amendment to the server, which then reactivates the generative AI model and regenerates the care plan based on the amendment request. This regeneration process is also performed using a machine learning algorithm.

[1689] Step 6:

[1690] The server returns the finalized care plan to the React.js front end and displays it on the terminal. At the same time, the care plan is saved in the AWS RDS database. The user can review the final care plan and confirm it if they are satisfied with its contents.

[1691] Step 7:

[1692] The server generates a list of care items based on the finalized care plan. This list is output by a generative AI model based on the prompt text. Here, the care items required for the care plan are calculated.

[1693] Step 8:

[1694] The server displays the generated list of care products in real time on the store staff's smart devices (smart glasses or smartphones), and the staff guides the user to the appropriate care products based on this information.

[1695] Step 9:

[1696] The server then links the nursing care product list suggestions to the store's inventory information. This communicates with the store's management system via API, updating inventory data in real time. This makes it possible to handle inventory availability and reserve items.

[1697] 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.

[1698] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[1699] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[1700] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[1701] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[1702] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[1703] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[1704] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[1705] As a concrete example, consider the following scenario.

[1706] Enter the basic information of a care recipient, "Mr. Sato," as follows:

[1707] Name: Sato Jiro

[1708] Age: 78

[1709] Medical history: high blood pressure, arthritis

[1710] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[1711] 1. The terminal starts up and a user input form is displayed.

[1712] 2. The user enters Mr. Sato's basic information.

[1713] 3. The device sends the entered information to the server.

[1714] 4. The server receives the information and stores it in a database.

[1715] 5. The server launches the generative AI model and generates a care plan, which may include, for example, daily morning blood pressure monitoring and twice-weekly physical therapy.

[1716] 6. The care plan is sent to the device and the user reviews it.

[1717] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[1718] 8. The user requests corrections as needed.

[1719] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[1720] 10. The final care plan is finalized and saved in the database.

[1721] 11. The final plan will be displayed on your device.

[1722] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[1723] In this way, this system reduces the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[1724] The processing flow will be explained below.

[1725] Step 1:

[1726] The terminal starts up and displays a user input form, allowing the user (care manager or relative) to enter information.

[1727] Step 2:

[1728] The user enters basic information about the care recipient (such as name, age, medical history, etc.) and the relative's requests (regular care details and special requests) into the input form.

[1729] Step 3:

[1730] The device sends the input information to the server, including the care recipient's health condition and the care they need.

[1731] Step 4:

[1732] The server receives the transmitted information and stores it in a database for further processing.

[1733] Step 5:

[1734] The server launches the generative AI model. The server inputs the received information into the generative AI model and starts the process of generating a care plan.

[1735] Step 6:

[1736] The generative AI model analyzes the care recipient's basic information and the relatives' requests to generate an initial care plan, which may include, for example, daily vital signs checks and several rehabilitation sessions per week.

[1737] Step 7:

[1738] The server sends the generated care plan to the terminal, which displays the care plan to the user.

[1739] Step 8:

[1740] The user checks the contents of the care plan through the device and can make corrections as needed. At this point, the device collects the user's emotions using voice and text.

[1741] Step 9:

[1742] The emotion engine recognizes the user's emotions through voice and text analysis, for example, analyzing the user's text comments and voice tone to digitize their emotions.

[1743] Step 10:

[1744] The device sends the recognized emotion information to the server, which receives the correction request and the emotion information and re-evaluates it.

[1745] Step 11:

[1746] The server then reactivates the generative AI model and modifies the care plan based on the modification request and emotional information, generating a more personalized care plan taking into account the information from the emotional engine.

[1747] Step 12:

[1748] The server sends the modified care plan back to the device, which displays the modified care plan to the user, and this process is repeated until the user is satisfied.

[1749] Step 13:

[1750] Once the final care plan is finalized, the server stores it in the database and displays the final version of the care plan on the terminal.

[1751] Step 14:

[1752] The server periodically collects care records, and the generative AI model and emotion engine automatically optimize the care plan based on the records. This process provides an optimal care plan that continuously reflects the needs of the care recipient and the user's emotions.

[1753] Example 2

[1754] 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."

[1755] The current care plan creation process is often manual, requiring a great deal of effort and time from care managers and relatives. It is also difficult to properly reflect the feelings and wishes of the care recipient and relatives, making it difficult to provide personalized care. Conventional systems lack a mechanism to consider the user's feelings, and therefore care plans are not always satisfactory.

[1756] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1757] In this invention, the server includes means for displaying a user input form for input by the care recipient or relative, means for inputting basic information about the care recipient and requests from the relative, means for transmitting the input information to a central processing unit, means for receiving the transmitted information and storing it in a data storage device, means for activating a generative AI model to analyze the received information and generate a care plan, means for transmitting the generated care plan to an information display device and displaying it, means for an emotion engine to recognize the user's emotions through voice and text analysis, means for receiving a user's request for modification and inputting it back into the generative AI model to generate a modified care plan, means for finalizing the care plan and storing it in a data storage device, and means for periodically collecting care records and automatically optimizing the care plan based on the collected care records by the generative AI model. This automates the creation and modification of care plans, enabling the provision of personalized care plans that reflect the user's emotions and requests.

[1758] A "user input form" is a screen that is displayed on a terminal for the care recipient or relative to input information about care.

[1759] "Basic information of the care recipient" refers to personal data such as the care recipient's name, age, medical history, etc.

[1760] "Requests from relatives" refers to the specific wishes and demands regarding care provided to the person receiving care.

[1761] "Central Processing Unit" refers to a computer system that receives, analyzes, and processes information sent from a terminal.

[1762] "Data storage" refers to a database system for storing received information.

[1763] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to generate a care plan based on information about the care recipient and the requests of their relatives.

[1764] "Care plan" refers to a plan that describes the specific care to be provided to the care recipient.

[1765] An "information display device" is a terminal for displaying the generated care plan to the user.

[1766] An "emotion engine" is a system that recognizes a user's emotions by analyzing voice and text, and outputs them as analytical data.

[1767] A "modification request" refers to a user's request for a change or adjustment to a care plan.

[1768] "Care records" are data that record the status and results of care provided to care recipients.

[1769] "Optimization" refers to the process of improving care plans to make them more appropriate and effective based on collected data.

[1770] This invention combines a system that automatically generates a care plan based on the requests of the care recipient and their relatives with an emotion engine that recognizes the user's emotions and adjusts the care plan based on those emotions. This system aims to provide more personalized care by automating the care plan creation process and taking the user's emotions into account.

[1771] First, the terminal starts up and displays a user input form for the care recipient or relative. The user (care manager or relative) enters basic information into the form, such as the care recipient's name, age, medical history, required care, and the relative's requests. The terminal then sends the entered information to the server.

[1772] The server receives the transmitted information and stores it in a database. It then activates a generative AI model to generate a care plan based on the received information. The generative AI model uses machine learning algorithms to create a specific care plan based on the care recipient's condition and the wishes of their relatives.

[1773] The system also incorporates an emotion engine, which recognizes the user's emotions through voice and text analysis. For example, if a user is dissatisfied with a care plan, the emotion engine analyzes that emotion and sends it to the server. This information is also used to modify and optimize the care plan.

[1774] The generated care plan is sent from the server to the device and displayed to the user. The user can check the contents of the care plan through the device and request revisions as necessary. The user's emotions are also analyzed and taken into consideration.

[1775] When a user submits a revision request, the device sends that information to the server. The server receives the revision request and emotion information, and again uses the generative AI model and emotion engine to revise the care plan. The revised care plan is presented to the user again, and this process is repeated until the user is satisfied.

[1776] Once the final care plan is finalized, the server saves it in a database and displays the final version on the device. The server periodically collects care records, which are then used by the generative AI model and emotion engine to automatically optimize the care plan. This process provides the most appropriate care plan, taking into account the care recipient's situation and needs, as well as the user's emotions.

[1777] To illustrate, consider the following scenario:

[1778] Enter the basic information of the care recipient "Sato-san" as follows:

[1779] Name: Sato Jiro

[1780] Age: 78

[1781] Medical history: high blood pressure, arthritis

[1782] Family's wishes: Daily blood pressure monitoring, physiotherapy twice a week

[1783] 1. The terminal starts up and a user input form is displayed.

[1784] 2. The user enters Mr. Sato's basic information.

[1785] 3. The device sends the entered information to the server.

[1786] 4. The server receives the information and stores it in a database.

[1787] 5. The server runs the generative AI model to generate a care plan, for example, one that includes blood pressure monitoring every morning and physical therapy twice a week.

[1788] 6. The care plan is sent to the device and the user reviews it.

[1789] 7. The emotion engine analyzes the user's emotions (e.g., frustration or relief) and suggests adjustments to the care plan as needed.

[1790] 8. The user requests corrections as needed.

[1791] 9. The server reassess based on the correction request and emotion information, and modifies the care plan using the generative AI model and emotion engine.

[1792] 10. The final care plan is finalized and saved in the database.

[1793] 11. The final plan will be displayed on your device.

[1794] 12. The server periodically collects care records and optimizes the plan together with the emotion engine.

[1795] In this way, this system can reduce the workload of care managers while providing care plans that take into consideration the feelings of the care recipient and their relatives.

[1796] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1797] Step 1:

[1798] Starts the terminal and displays a form for user input

[1799] The device starts up and launches a dedicated application. Next, a user input form is displayed on the screen, allowing the care recipient or relative to enter information. The form contains input fields for name, age, medical history, required care, and relatives' requests. The device provides the user with the initial data for input, and the data entered by the user is output.

[1800] Step 2:

[1801] Enter basic user information

[1802] The user (care manager or relative) enters basic information about the care recipient into the displayed user input form. Specifically, the user enters details such as the care recipient's name, age, medical history, required care, and the relative's requests. The input data is output and sent to the next processing step.

[1803] Step 3:

[1804] Sending information from the device to the server

[1805] The terminal sends the information entered by the user to the server. The terminal executes the sending process, packages the input data from the user into packets, and sends them to the server. The input is the user's input data, and the output is the data received by the server.

[1806] Step 4:

[1807] Receiving information from the server and storing it in the database

[1808] The server receives the information sent from the device. It then stores the received information in a database. Specifically, it issues a query to the database to store information such as name, age, medical history, and requests from relatives. The input is the data sent from the device, and the output is the data stored in the database.

[1809] Step 5:

[1810] Server generation: Launching the AI ​​model and generating care plans

[1811] The server launches the generative AI model, which then analyzes the basic information of the care recipient stored on the server and the wishes of their relatives to generate an appropriate care plan. The model uses machine learning algorithms to make predictions and optimizations based on the input data. The input is information from the database, and the output is the generated care plan.

[1812] Step 6:

[1813] Sending and displaying care plans to devices

[1814] The server sends the care plan to the terminal. The terminal displays the care plan it receives to the user. Specifically, the contents of the care plan are displayed on the screen so that the user can check them. The input is care plan data from the server, and the output is a care plan that is displayed to the user.

[1815] Step 7:

[1816] Emotional analysis of users using an emotion engine

[1817] The emotion engine analyzes the voice and text inputs made by the user when confirming the care plan. Using an emotion recognition algorithm, it analyzes the user's emotions (dissatisfaction, relief, etc.) and sends that information to the server. The input is the user's voice and text data, and the analyzed emotional information is generated as the output.

[1818] Step 8:

[1819] User correction request submission

[1820] The user submits a request to modify the care plan. The terminal transmits the modification request input by the user to the server. The input is the user's modification request data, and the modification data to be transmitted to the server is generated as the output.

[1821] Step 9:

[1822] Server-based correction requests, processing of emotional information, and reevaluation of care plans

[1823] The server receives the correction request and the user's emotional information, then launches the generative AI model again to reevaluate the care plan. A new care plan that takes the emotional information into account is generated. The inputs are the correction request and emotional information, and the output is a corrected care plan.

[1824] Step 10:

[1825] Final care plan database storage

[1826] The server saves the final care plan to a database. The database is queried and the final care plan is stored. The input is the modified care plan and the output is the care plan stored in the database.

[1827] Step 11:

[1828] Displaying the final plan on the device

[1829] The server sends the final care plan to the terminal, which then displays it to the user. The input is the final care plan, and the output is the care plan that is displayed to the user.

[1830] Step 12:

[1831] Periodic server-based care record collection and plan optimization

[1832] The server periodically collects care records and optimizes the care plan using a generative AI model. The data is analyzed together with an emotion engine to improve the care plan. The input is care record data, and the output is an optimized care plan.

[1833] (Application example 2)

[1834] 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."

[1835] In current manufacturing lines, employee fatigue and stress are not properly monitored, resulting in a decline in work efficiency and safety. Furthermore, uniform work plans are provided without considering employees' emotions or feedback, making it difficult to respond flexibly to individual situations. This leads to a decline in work quality and productivity.

[1836] 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 displaying a user input form for input by the care recipient or relative; means for inputting basic information about the care recipient and requests from the relative; means for transmitting the input information to the server; means for receiving the transmitted information and storing it in a database; means for activating a generative AI model to analyze the received information and generate a care plan; means for transmitting the generated care plan to a terminal and displaying it; means for receiving a user's modification request and re-inputting it into the generative AI model to generate a modified care plan; means for finalizing the care plan and storing it in a database; means for analyzing the user's emotions using an emotion engine and adjusting the care plan based on the user's emotions; means for presenting the generated care plan to an employee's screen and collecting employee feedback; and means for periodically collecting care records and emotion data and having the generative AI model automatically optimize the care plan based on the collected data. This enables the provision of personalized work plans that take employee emotions and feedback into account, thereby improving work efficiency and safety.

[1837] "User Input Form" means a screen or interface through which an employee or associated person can enter basic information or requests.

[1838] "Basic information" refers to basic data about an individual, such as the employee's name, age, and shift times.

[1839] "Requests" are the desired conditions or requirements of employees or related persons.

[1840] A "server" is a computer system on a network that stores data and performs calculations and analysis.

[1841] A "database" is a storage system that systematically stores information and allows it to be searched and edited.

[1842] A "generative AI model" is an artificial intelligence algorithm that analyzes the information it receives and generates data and plans based on a specified task.

[1843] An "emotion engine" is software that recognizes a user's emotions through voice and text analysis and analyzes the results.

[1844] A "care plan" is a specific action plan created based on the user's basic information, requests, and feelings.

[1845] A "terminal" is a device or apparatus with which a user can interact, and which is used to input and display information.

[1846] "Feedback" refers to the responses and opinions received from employees and related parties, which are used to adjust and optimize the plan.

[1847] MODE FOR CARRYING OUT THE INVENTION

[1848] To implement this invention, the following system program is constructed: This system takes into account the emotions and feedback of employees and automatically adjusts the work plan to improve work efficiency and safety.

[1849] System Overview

[1850] 1. Display and fill in the user input form

[1851] The terminal displays a user input form where employees can enter basic information and requests, such as their name, age, and shift hours.

[1852] 2. Data transmission and storage

[1853] The terminal sends the entered information to the server, which receives it and stores it in a database.

[1854] 3. Analysis and plan generation using generative AI models

[1855] The server launches a generative AI model to analyze the received information and generate an initial work plan.

[1856] 4. Emotion analysis and plan adjustment using an emotion engine

[1857] The device uses a camera and voice recognition system to collect employee emotions in real time, and the server analyzes this data using an emotion engine. Based on the user's emotions, a generative AI model adjusts the work plan.

[1858] 5. Feedback and Recalibration

[1859] The adjusted work plan is displayed on the device and receives feedback from the employee. If necessary, the server adjusts the plan again using the generative AI model and emotion engine.

[1860] 6. Finalize and optimize the plan

[1861] The final work plan is finalized and saved in the database. The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the plan based on them.

[1862] Specific examples of hardware and software used

[1863] Camera: General surveillance camera

[1864] Speech recognition software: Google Cloud Speech-to-Text API

[1865] Sentiment analysis software: IBM Watson

[1866] Database: MySQL

[1867] Generative AI model: OpenAI GPT-4

[1868] Front-end framework: React

[1869] Backend framework: Django

[1870] Specific examples

[1871] For example, an employee named "A" is working an eight-hour shift. Before the shift begins, he or she enters basic information using a user input form on a device. During the shift, a camera analyzes A's facial expressions and voice data in real time using the Google Cloud Speech-to-Text API, and IBM Watson emotion analysis software detects "fatigue." This information is sent to a server, and a generative AI model (GPT-4) uses this information to readjust the work plan and insert breaks if necessary. The readjusted work plan is presented on A's device, and A's feedback is collected.

[1872] Prompt Sentence Examples

[1873] plain

[1874] def create_schedule_prompt(employee_info, current_emotion):

[1875] return f"""

[1876] Employee A, age, is on shift. A is currently in {current_emotion} state.

[1877] Use this information to come up with a work plan, including breaks where appropriate.

[1878] """

[1879] current_emotion = "fatigue"

[1880] employee_info = {"Name": "Mr. A", "Age": "50", "Shift": "8 hours"}

[1881] prompt = create_schedule_prompt(employee_info, current_emotion)

[1882] This system provides personalized work plans that take into account employee emotions and feedback, improving work efficiency and safety. The server also periodically collects data, and the generative AI model automatically optimizes the plans based on that data, enabling continuous improvement.

[1883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1884] Step 1:

[1885] Displaying a user input form and entering basic information

[1886] Specific operation: The terminal displays a user input form on the screen, and the employee enters basic information (name, age, shift time) and requests. In this way, the terminal collects basic information from the employee.

[1887] Input: User's basic information and requests

[1888] Output: Basic information and requests entered into the terminal

[1889] Step 2:

[1890] Sending and saving input data

[1891] Specific operation: The device sends the collected basic information and requests to the server, which receives this information and stores it in a database.

[1892] Input: Basic information and requests sent from the device

[1893] Output: Basic information and requests stored in the database

[1894] Step 3:

[1895] Generative AI model generates initial plans

[1896] Specific operation: The server launches the generative AI model, analyzes the stored basic information and requests, and generates an initial work plan. The generative AI model uses the prompt sentences to generate a work plan suitable for the employee.

[1897] Input: Basic information and requests stored in the database

[1898] Output: Generated initial work plan

[1899] Step 4:

[1900] Emotional data collection and analysis

[1901] How it works: The device uses a camera and voice recognition system to collect facial and voice data in real time, then sends it to a server, which then uses an emotion engine to analyze the data.

[1902] Input: Facial expression data and voice data collected in real time

[1903] Output: Parsed emotion data

[1904] Step 5:

[1905] Adjusting plans based on sentiment data

[1906] Specific operation: The server inputs the emotion data obtained by the emotion engine into the generative AI model and regenerates a work plan that takes into account the employee's emotions.

[1907] Input: Analyzed emotion data, generative AI model

[1908] Output: Adjusted work plan

[1909] Step 6:

[1910] View the adjusted plan and gather feedback

[1911] Specific operation: The terminal displays the adjusted work plan on the screen and collects feedback from the employee, which is then sent back to the server.

[1912] Input: Adjusted Work Plan

[1913] Output: Employee feedback

[1914] Step 7:

[1915] Readjusting plans based on feedback

[1916] How it works: The server receives feedback from employees and uses the generative AI model to readjust the work plan, repeating this process until employees are satisfied.

[1917] Input: Employee feedback

[1918] Output: Reworked Work Plan

[1919] Step 8:

[1920] Finalize and save the plan

[1921] What it does: The server saves the finalized work plan to a database, allowing for future reference and optimization.

[1922] Input: Reworked Work Plan

[1923] Output: Final work plan stored in the database

[1924] Step 9:

[1925] Regular data collection and optimization

[1926] How it works: The server periodically collects work records and emotion data, and the generative AI model automatically optimizes the work plan based on that data.

[1927] Input: Periodically collected work records and emotional data

[1928] Output: Optimized work plan

[1929] 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.

[1930] 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.

[1931] 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.

[1932] 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.

[1933] 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.

[1934] 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.

[1935] 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).

[1936] 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.

[1937] 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."

[1938] 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.

[1939] 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).

[1940] 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.

[1941] 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.

[1942] 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.

[1943] 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.

[1944] 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.

[1945] 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.

[1946] 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.

[1947] 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.

[1948] 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.

[1949] 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.

[1950] The following is further disclosed regarding the above embodiment.

[1951] (Claim 1)

[1952] a means for displaying a user input form for input by the care recipient or a relative;

[1953] A means for inputting basic information of the care recipient and requests of relatives;

[1954] means for transmitting the input information to a server;

[1955] means for receiving and storing the transmitted information in a database;

[1956] means for activating a generative AI model to analyze the received information and generate a care plan;

[1957] A means for transmitting the generated care plan to a terminal and displaying it;

[1958] A means for receiving a user's modification request and inputting it into the generative AI model again to generate a modified care plan;

[1959] A means to finalize and store the final care plan in a database;

[1960] A means to periodically collect care records and use them to automatically optimize care plans using generative AI models; and

[1961] A system including:

[1962] (Claim 2)

[1963] 10. The system of claim 1, wherein the generative AI model uses a machine learning algorithm to generate the care plan.

[1964] (Claim 3)

[1965] 10. The system of claim 1, wherein the terminal further comprises means for displaying the final care plan to the user.

[1966] "Example 1"

[1967] (Claim 1)

[1968] a means for displaying a user input form for input by the care recipient or a relative;

[1969] A means for inputting basic information of the care recipient and requests of relatives;

[1970] means for transmitting the input information to a server;

[1971] means for receiving and storing the transmitted information in a database;

[1972] means for activating a generative AI model to analyze the received information and generate prompt sentences for generating a care plan;

[1973] A means for transmitting the generated care plan to a terminal and displaying it;

[1974] A means for receiving a user's modification request and inputting it into the generative AI model again to generate a modified care plan;

[1975] A means to finalize and store the final care plan in a database;

[1976] A means to periodically collect care records and use them to automatically optimize care plans using generative AI models; and

[1977] A system including:

[1978] (Claim 2)

[1979] 10. The system of claim 1, wherein the generative AI model uses a machine learning algorithm to generate the care plan.

[1980] (Claim 3)

[1981] 10. The system of claim 1, wherein the terminal further comprises means for displaying the final care plan to the user.

[1982] "Application Example 1"

[1983] (Claim 1)

[1984] a means for displaying a user input form for input by the care recipient or a relative;

[1985] A means for inputting basic information of the care recipient and requests of relatives;

[1986] means for transmitting the input information to a server;

[1987] means for receiving and storing the transmitted information in a database;

[1988] means for activating a generative AI model to analyze the received information and generate a care plan;

[1989] A means for transmitting the generated care plan to a terminal and displaying it;

[1990] A means for receiving a user's modification request and inputting it into the generative AI model again to generate a modified care plan;

[1991] A means to finalize and store the final care plan in a database;

[1992] A means to periodically collect care records and use them to automatically optimize care plans using generative AI models; and

[1993] a means for generating a list of care products based on information input by the care recipient or a relative at a care product store;

[1994] A means for displaying the generated nursing care product list on the store staff's smart devices in real time;

[1995] A means to link the proposed nursing care product list with in-store inventory information,

[1996] A system including:

[1997] (Claim 2)

[1998] 10. The system of claim 1, wherein the generative AI model uses a machine learning algorithm to generate the care plan and supply list.

[1999] (Claim 3)

[2000] 10. The system of claim 1, wherein the terminal further comprises means for displaying a final care plan and a list of care items to the user.

[2001] "Example 2: Combining Emotion Engines"

[2002] (Claim 1)

[2003] a means for displaying a user input form for input by the care recipient or a relative;

[2004] A means for inputting basic information of the care recipient and requests of relatives;

[2005] means for transmitting the input information to a central processing unit;

[2006] means for receiving and storing the transmitted information in a data storage device;

[2007] means for activating a generative AI model to analyze the received information and generate a care plan;

[2008] means for transmitting the generated care plan to an information display device and displaying it;

[2009] A means for the emotion engine to recognize user emotions through voice and text analysis;

[2010] a means for receiving a user's modification request and inputting it back into the generative AI model to generate a modified care plan;

[2011] means for determining and storing a final care plan in a data store;

[2012] A means to periodically collect care records and use them to automatically optimize care plans using generative AI models; and

[2013] A system including:

[2014] (Claim 2)

[2015] 10. The system of claim 1, wherein the generative AI model uses a machine learning algorithm to generate the care plan.

[2016] (Claim 3)

[2017] 10. The system of claim 1, wherein the information display further comprises means for displaying the final care plan to the user.

[2018] "Application example 2 when combining emotion engines"

[2019] (Claim 1)

[2020] a means for displaying a user input form for input by the care recipient or a relative;

[2021] A means for inputting basic information of the care recipient and requests of relatives;

[2022] means for transmitting the input information to a server;

[2023] means for receiving and storing the transmitted information in a database;

[2024] means for activating a generative AI model to analyze the received information and generate a care plan;

[2025] A means for transmitting the generated care plan to a terminal and displaying it;

[2026] A means for receiving a user's modification request and inputting it into the generative AI model again to generate a modified care plan;

[2027] A means to finalize and store the final care plan in a database;

[2028] means for analyzing a user's emotions using an emotion engine and adjusting a care plan based on the emotions;

[2029] a means for presenting the generated care plan to the employee's screen and collecting employee feedback;

[2030] A means to periodically collect care records and emotional data, and a generative AI model to automatically optimize care plans based on that data;

[2031] A system including:

[2032] (Claim 2)

[2033] 10. The system of claim 1, wherein the generative AI model uses a machine learning algorithm to generate the care plan.

[2034] (Claim 3)

[2035] 10. The system of claim 1, wherein the terminal further comprises means for displaying the final care plan to the user. [Explanation of symbols]

[2036] 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 displaying a user input form for input by the care recipient or a relative; A means for inputting basic information of the care recipient and requests of relatives; means for transmitting the input information to a server; means for receiving and storing the transmitted information in a database; means for activating a generative AI model to analyze the received information and generate a care plan; A means for transmitting the generated care plan to a terminal and displaying it; A means for receiving a user's modification request and inputting it into the generative AI model again to generate a modified care plan; A means to finalize and store the final care plan in a database; A means to periodically collect care records and use them to automatically optimize care plans using generative AI models; and A system including:

2. The system of claim 1 , wherein the generative AI model generates the care plan using a machine learning algorithm.

3. The system of claim 1 , wherein the terminal further comprises means for displaying the final care plan to the user.

Images