system

Abstract

We provide the system. [Solution] Means of collecting data based on the care recipient's situation, A means of analyzing collected data to generate an optimal care plan, A means of providing the generated care plan to the user and receiving feedback, A means of updating the care plan based on the feedback received, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society, the increasing burden and shortage of caregivers have become serious problems. In particular, family caregivers lack professional knowledge and it is difficult to obtain information for providing appropriate care. In addition, the accompanying stress and physical burden are factors that damage the health of the caregivers themselves. Furthermore, there is a demand for an efficient system that can improve the quality of care while reducing the burden on caregivers.

Means for Solving the Problems

[0005] This invention provides a system for developing optimal care plans tailored to the individual needs of the person receiving care. The system first collects data about the person receiving care and analyzes it using a machine learning algorithm. Based on the analysis results, it generates a care plan and provides it to the user. Furthermore, it receives feedback from the user and updates the care plan as needed to continuously provide appropriate care. In addition, it aims to improve the knowledge of caregivers by providing practical information through instruction on care techniques using videos and diagrams.

[0006] "Persons receiving care" refers to elderly people, people with disabilities, and others who require care.

[0007] "Data collection" refers to the act of gathering information about the health status and lifestyle habits of those receiving care.

[0008] "Analysis" is the process of analyzing collected data to derive meaningful information and patterns.

[0009] A "care plan" is a plan that specifically outlines the necessary care content and schedule for the person receiving care.

[0010] A "machine learning algorithm" is a software process that analyzes data, learns from it, and automatically improves.

[0011] "User" refers to family caregivers or care workers who implement care plans.

[0012] "Feedback" is the act of sending opinions or reactions to the information or services provided.

[0013] "Updating" refers to the process of readjusting the care plan based on the latest information and creating an optimized plan.

[0014] "Videos and diagrams" are media formats used to convey information visually and are used in education and instruction. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the 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.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0029] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is implemented as an information processing system for generating and providing optimal care plans tailored to the care recipient's situation. The system mainly consists of three components: a server, a terminal, and a user.

[0037] server

[0038] The server collects data about the person receiving care and analyzes it using machine learning algorithms. This allows for the creation of individualized care plans for each person. For example, if a person receiving care needs regular walking exercise, the server will generate a plan that takes into account the frequency and duration of that exercise.

[0039] terminal

[0040] The terminal is responsible for receiving care plans generated on the server and providing them visually to the user. This includes features that display the plan details in a user interface to make it easy for the user to review. It also provides an interface for the user to input feedback.

[0041] User

[0042] Users participate in the system by executing care plans provided through their devices and sending feedback to the server as needed. For example, they can carry out planned care while referring to videos and diagrams about specific care methods. Feedback on care plans is used to improve the system and is reflected in the generation of future plans.

[0043] These components enable the system to provide an efficient and flexible mechanism for delivering appropriate care to those receiving care.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user inputs data about the care recipient's health information and lifestyle into the terminal. This includes meal times, medication schedules, and exercise levels.

[0047] Step 2:

[0048] The terminal sends the entered data to the server. The data is formatted and securely transferred.

[0049] Step 3:

[0050] The server stores the received data in a database and performs analysis using machine learning algorithms. It identifies the individual needs of the care recipient and generates an optimal care plan.

[0051] Step 4:

[0052] The server sends the generated care plan to the terminal. The plan includes daily care tasks and health management recommendations.

[0053] Step 5:

[0054] The terminal is designed to display received care plans on a user interface, allowing users to review them. The display will be in a visually easy-to-understand format.

[0055] Step 6:

[0056] Users carry out daily care based on the provided care plan and input feedback into the device based on their actual living situation and perceived progress.

[0057] Step 7:

[0058] The terminal sends user feedback back to the server, which then identifies areas for improvement in the plan.

[0059] Step 8:

[0060] The server adjusts and improves the care plan based on the feedback received. The optimized new plan is then provided to the user in the next cycle.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In recent years, the needs of those receiving care have diversified in our super-aging society, and there is a demand for flexible and effective care plans tailored to individual circumstances. However, the current system often fails to adequately analyze the detailed circumstances of those receiving care, resulting in uniform plans. This creates a challenge in providing optimal care to those receiving care.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information and generating an optimal support plan, and means for providing the support plan generated using a generation AI model to the user and receiving feedback. This enables the efficient generation of flexible support plans tailored to the individual circumstances of the care recipient, and improves the provision of care.

[0066] "Means of collecting information" refers to the processes and technologies used to acquire various data related to the person receiving care, including health data and lifestyle data.

[0067] "Means of analyzing information and generating optimal support plans" refers to the process and techniques of using collected data to perform data analysis and create care plans that are best suited to the needs of those receiving care.

[0068] "A means of providing support plans generated using a generative AI model to users and receiving their feedback" refers to the process and technology for presenting support plans created using artificial intelligence to caregivers or those receiving care in an appropriate format, and incorporating the feedback obtained into the system.

[0069] This invention is an information processing system that efficiently generates and provides support plans tailored to the individual circumstances of care recipients. The system mainly consists of three components: a server, a terminal, and a user.

[0070] server

[0071] The server is responsible for collecting and analyzing data related to the person receiving care. To this end, the server acquires data from data collection devices (e.g., biosensors and wearable devices) and stores it in a database. For analysis, machine learning algorithms using Python's Scikit-learn and TENSORFLOW® are applied to perform data analysis and generate the most appropriate support plan for the person receiving care. For example, if the person receiving care needs light exercise, the server analyzes the data to determine the appropriate frequency and intensity.

[0072] terminal

[0073] The device receives the support plan generated by the server and presents it to the user visually. The device, using a tablet or smartphone, displays the plan through an intuitive user interface. This interface includes a daily schedule, recommended activities, and steps to follow the plan, and allows the user to edit and provide feedback as needed.

[0074] User

[0075] Users act based on the support plan provided through their device and input feedback on what they did and their impressions. This feedback is sent to the server and used to generate the next support plan. For example, a user might submit feedback such as, "I went for a walk, but I'm a little tired, so I'd like to increase my rest time."

[0076] As a result, the system enables personalized and flexible support for those receiving care, leading to continuous improvement in care. An example of a prompt for the generated AI model is text such as, "Based on the care recipient's health data, please suggest an exercise plan suitable for next week."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects information from the person receiving care. This includes data input from wearable devices and environmental sensors. Specifically, the server performs a daily data collection process and stores biometric information such as heart rate and walking distance in a database. It receives health data as input, normalizes it, and stores it to obtain organized data as output.

[0080] Step 2:

[0081] The server analyzes the collected data. It uses machine learning algorithms to analyze the health status and activity patterns of those receiving care. Specifically, the server uses Python libraries (e.g., Scikit-learn) to perform anomaly detection and clustering, classifying the data into clusters. It takes historical health data as input, evaluates the health status based on that data, and generates analysis results as output.

[0082] Step 3:

[0083] The server uses an AI model based on the analysis results to create a care plan suitable for the person receiving care. During this process, prompts are used to instruct the AI ​​model to generate the plan. Specifically, the server inputs a prompt to the AI ​​such as, "Please create a weekly exercise plan tailored to the person receiving care's activity level." The analysis results are passed to the AI ​​as input, and the individualized care plan is obtained as output.

[0084] Step 4:

[0085] The terminal provides the generated care plan to the user through a user interface. Here, the specific details of the plan are displayed on the screen, allowing the user to easily review it. Specifically, the terminal application uses a UI framework to display schedules and activities in a calendar format. This results in a visually easy-to-understand care plan being displayed as output for the user.

[0086] Step 5:

[0087] Users carry out activities based on the provided care plan and send feedback to the server via their device. Specifically, users input their activity progress and feelings into a dedicated form on their device and provide feedback by pressing the submit button. The system receives the user's activity report as input and saves the feedback data as output, which serves as the basis for generating the next care plan.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In today's aging society, providing personalized and efficient support plans tailored to each individual care recipient is a major challenge. Furthermore, because the circumstances of care recipients change daily, flexible updates to support plans and intuitive guidance for users are essential. This invention aims to address these challenges and provide high-quality support to care recipients.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information to generate an optimal support plan, means for providing the generated support plan to the user and receiving feedback, and means for providing activity reminders and guides based on the support situation. This enables the provision of flexible support plans tailored to the individual circumstances of the care recipient and clear guidance to the user.

[0093] "Persons receiving care" refers to people who need support or care, and this particularly includes the elderly and people with disabilities.

[0094] "Information" refers to data concerning the health status, lifestyle, and environment of the person receiving care.

[0095] "Analysis" refers to the process of finding patterns and trends based on collected information, and is carried out using computational methods and algorithms.

[0096] A "support plan" refers to a set of action guidelines and activity schedules created based on the individual needs of the person receiving care.

[0097] "Users" refers to individuals who accept support plans and have the role of incorporating them into their daily lives.

[0098] "Opinions" refers to feedback and suggestions for improvement regarding the support plan provided by the user.

[0099] A "reminder" refers to an alarm or message that is sent to remind you of a specific activity or time.

[0100] A "guide" refers to a set of instructions or guidance provided to support actions or activities.

[0101] An "intelligent machine algorithm" is a computational method used for data analysis and decision-making, utilizing machine learning and artificial intelligence technologies.

[0102] The embodiments for carrying out this invention will be described in detail below.

[0103] server

[0104] The server operates on a cloud platform such as Amazon Web Services (AWS®) and collects and analyzes information about the health status and lifestyle of those receiving care. Data collection uses information transmitted from mobile devices such as smartphones and smartwatches. Python is used for analysis, employing intelligent machine algorithms with data analysis libraries such as pandas and scikit-learn. The support plans generated by these algorithms are optimized for each individual receiving care.

[0105] terminal

[0106] The terminal is a device that runs a smartphone application developed with ANDROID® Studio or Xcode. The terminal receives support plans generated from the server and provides them to the user. Specifically, it notifies activity reminders and provides support guidance using an intuitively understandable user interface. It also has a means of sending user feedback to the server via the interface.

[0107] User

[0108] Users use their devices to carry out activities based on the provided support plan. For example, the device sends reminders to achieve walking goals set based on the care recipient's health condition. At that time, the user receives a prompt such as, "Today's goal is 6,000 steps. Let's take a stroll in a nearby park," and can start a specific activity. Such prompts are automatically generated by the app, promoting intuitive user behavior.

[0109] By implementing the present invention in accordance with this configuration, a support plan optimized for each individual care recipient can be continuously provided without interruption, resulting in efficient care.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects health data from the person receiving care. It receives sensor information and daily activity logs transmitted from smartphones and smartwatches as input. Based on this data, it performs preprocessing to create a dataset aligned with the time axis. The output is a dataset formatted for analysis by machine learning models.

[0113] Step 2:

[0114] The server uses intelligent machine algorithms to analyze the collected data. These algorithms are used to assess the care recipient's condition and generate an optimal support plan. They accept a formatted dataset as input and perform trend analysis and predictions. The output is a personalized support plan tailored to the care recipient.

[0115] Step 3:

[0116] Once a support plan is generated, the server sends it to the terminal. The input is the generated support plan. The server converts this plan into an appropriate format and notifies the terminal. The output is support plan data in a format that is easy for the user to understand.

[0117] Step 4:

[0118] The terminal presents the received support plan to the user. It receives support plan data sent from the server as input. The terminal visually displays this plan through the user interface and creates reminders and notifications. The output is a specific activity guide presented to the user.

[0119] Step 5:

[0120] The user performs activities based on the support plan presented on the device. The input is the content of the support plan and reminders displayed on the device. The user modifies their daily activities according to the instructions and sends feedback on the support plan to the server via the device as needed. The output is the content of the feedback and a record of the activities.

[0121] Step 6:

[0122] The server receives and analyzes user feedback. The input is the feedback data received from the user. The server analyzes this data to identify areas for improvement in the support plan. The output provides evaluation information useful for generating the next support plan.

[0123] In this way, it is possible to continuously provide the most suitable support plan for each care recipient throughout the entire system.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is a system that generates an optimal care plan based on the care recipient's situation, and further incorporates an emotion engine to recognize the user's emotional state and reflect it in the plan. It also provides guidance on care techniques to the user.

[0126] server

[0127] The server receives extensive data on the user's and care recipient's conditions and analyzes it using machine learning algorithms. Furthermore, it leverages an emotion engine to evaluate the user's emotional data and incorporate emotional adjustments into the care plan. For example, if the server detects that the user is experiencing stress, it generates care suggestions that reflect that emotional state and recommends activities aimed at stress reduction.

[0128] terminal

[0129] The terminal visually displays the care plan generated on the server in the user interface. It functions as an interface for users to review the care plan and input feedback and emotional data. It also provides users with instruction on care techniques through videos and diagrams as needed. For example, if an explanation of a care technique is deemed difficult, it will present a more detailed explanation or a video of an appropriate difficulty level.

[0130] User

[0131] Users execute care plans provided through their devices and send feedback based on their actual progress and emotions. The emotion engine analyzes the user's emotional state, and if it recognizes, for example, that the user is feeling tired, it suggests relaxation methods and makes adjustments to reduce their burden.

[0132] This configuration enables a system that provides flexible care plans that take emotional states into consideration, allowing for meticulous attention to both the user and the person receiving care.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user inputs information about the person being cared for, daily care details, and their own emotional state into the device. This includes dietary information, exercise status, and emotional feedback (e.g., stress levels, fatigue levels).

[0136] Step 2:

[0137] The terminal transmits the entered data and the user's emotional state to the server. The data is transferred in real time and precisely formatted.

[0138] Step 3:

[0139] The server analyzes the received data and uses an emotion engine to evaluate the user's emotional state. It then uses machine learning algorithms to generate the optimal care plan for both the care recipient and the user.

[0140] Step 4:

[0141] The server incorporates the results of the emotion analysis into the care plan. For example, if the user is experiencing stress, it creates a care plan that includes stress reduction activities.

[0142] Step 5:

[0143] The server sends the adjusted care plan to the terminal. The plan includes specific care tasks, recommended activities, and emotionally-based suggestions.

[0144] Step 6:

[0145] The terminal displays the received care plan on its user interface, allowing the user to review it. It also provides instruction on care techniques tailored to the user's emotional state through videos and illustrations.

[0146] Step 7:

[0147] The user implements the provided care plan and provides feedback as their emotional state changes during the plan's progress. This allows the server to understand the user's current emotional state.

[0148] Step 8:

[0149] Feedback is sent from the terminal to the server, which then readjusts the care plan based on that feedback. The system always provides a plan that is adapted to the circumstances of the care recipient and the user, and responds flexibly.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Current care systems struggle to generate flexible care plans that are tailored to the care recipient's situation and the user's emotions. Furthermore, the reliance on manual or static materials for teaching care techniques makes it difficult to provide personalized plans. Additionally, the lack of features to dynamically update plans based on user feedback can hinder prompt responses.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for collecting diverse information based on the care recipient's situation, means for analyzing the collected information and generating an optimal plan using a machine learning algorithm, and means for visually presenting the generated plan on a user interface and accepting input. This makes it possible to generate and provide flexible and dynamic care plans that respond to the care recipient's situation and the user's emotions.

[0155] "Situational information" refers to a variety of data related to the care recipient and the user, including health status, emotional state, and living environment.

[0156] A "machine learning algorithm" is an artificial intelligence technology used in the data analysis process, a method for making predictions and classifications through learning from past data.

[0157] "Plan generation" is the process of formulating appropriate care activities and guidance schedules based on the collected information.

[0158] A "user interface" refers to the screens and operating methods that a user uses to access a system and input or retrieve information.

[0159] "Emotion analysis" is a process that uses natural language processing technology to determine a user's emotional state and utilizes that information to adjust action plans.

[0160] "Instructional items" refer to the specific procedures and learning content of caregiving techniques and activities that users receive.

[0161] The system of this invention includes a series of processes for dynamically generating care plans and optimizing them according to individual needs. Specific embodiments thereof are described below.

[0162] server

[0163] The server continuously collects situational information from care recipients and users and stores it in a database. Network-connected sensors and wearable devices are used to acquire this information. Python libraries such as TensorFlow and PyTorch are used to execute machine learning algorithms. This allows the collected information to be analyzed and care plans to be generated.

[0164] As a concrete example, the server predicts the user's stress level for the day based on data such as heart rate and activity level, and recommends appropriate relaxation activities. By utilizing a generative AI model to analyze emotions and extracting "anxiety" from the user's text feedback, corresponding care is suggested.

[0165] terminal

[0166] The terminal displays the care plan received from the server, allowing users to easily access the plan. A dedicated application running on Android and iOS devices is used for this purpose. The user interface is implemented using React Native, resulting in a visually intuitive design.

[0167] The system also includes a function to collect user feedback and incorporate it into future plans. The device can play videos and diagrams to help users learn caregiving techniques. For example, it can present 3D animations of complex caregiving techniques to support user understanding.

[0168] User

[0169] Users perform daily care activities based on the care plan displayed on their device. This includes implementing suggested stretches, meditations, or activity plans tailored to the person being cared for. Users input their progress on the plan and their own feelings on the device and send this feedback to the server.

[0170] For example, the effectiveness of a plan can be evaluated by having a user perform a "deep breathing exercise to relax" and then provide feedback that they "felt refreshed."

[0171] Example of a prompt

[0172] The prompt text used to input into the generating AI model is something like, "Please suggest an activity plan suitable for a person who is active during the day. It is important that the plan be flexible and adaptable to changes in their emotions."

[0173] In this way, the invention can provide flexible and adaptive care plans based on the needs of the user and the person being cared for, thereby improving the quality of daily life.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server acquires data on users and those receiving care. This data collection involves inputting data such as heart rate, activity level, and environmental conditions via wearable devices and sensors. This biometric data is stored in a database on the server and processed into a format usable for subsequent processing.

[0177] Step 2:

[0178] The server analyzes the collected data using machine learning algorithms to infer the user's current state and emotional condition. Specifically, it preprocesses the data using Python libraries and calculates stress level predictions and emotion scores based on the algorithms. This analysis result is obtained as output, which forms the basis for generating the next plan.

[0179] Step 3:

[0180] The server generates a care plan based on the analysis results. Utilizing a generation AI model, the plan includes the care recipient's schedule and recommended activities. Specifically, it generates text about the activities, and the artificial intelligence model outputs suggestions tailored to individual needs.

[0181] Step 4:

[0182] The terminal visually displays the care plan received from the server in a user interface. Here, a schedule format and task list are presented for easy user understanding. Furthermore, an interface is provided for the user to input feedback on the plan.

[0183] Step 5:

[0184] Users perform care activities based on the care plan displayed on the terminal and input feedback into the terminal. Examples of user input include feedback on the results of the activities and their feelings, which are entered through digital forms. This information is sent to the server for subsequent analysis.

[0185] Step 6:

[0186] The server receives feedback from the user and uses it to update the care plan. It re-evaluates the user's condition using an emotion analysis algorithm and modifies the plan based on the new analysis results. This revised plan is then sent back to the terminal and prepared for the user to receive.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In modern society, the mental and physical burden on both those receiving care and those providing care is increasing. Under these circumstances, creating appropriate support plans tailored to the individual's condition is not easy, and adjusting support to accommodate the caregiver's stress and emotions is difficult. Furthermore, there is a need for flexible plans that take emotional aspects into account, but the current system is insufficient to address this. To solve these problems, it is necessary to grasp the individual's situation and the caregiver's emotional state in real time and dynamically adjust support plans based on this information.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for collecting information based on the state of the person being supported, means for analyzing the collected information to generate an optimal support plan, and means for analyzing the user's emotional state in real time from camera and voice input. This makes it possible to provide a flexible and optimal support plan that is tailored to the state of the person being supported and the emotions of the supporter.

[0192] "Recipients of support" refers to individuals or groups who are eligible for support, and specifically refers to those who require assistance or support.

[0193] "Information" refers to all data obtained from the person being supported and related circumstances, including data on physical condition, environmental information, and emotional state.

[0194] "Means of collection" refers to the devices or methods used to acquire information, and this includes data acquisition methods using sensor devices and networks.

[0195] "Means of analysis" refers to devices or methods for analyzing collected information to obtain useful insights, and this includes machine learning algorithms and data processing software.

[0196] A "support plan" refers to a detailed plan of the support activities to be carried out for the person receiving support, and this includes the activity schedule, equipment to be used, and necessary personnel.

[0197] "User" refers to the person who uses the system to implement and adjust the support plan, and generally refers to a support worker or care provider.

[0198] "Emotional state" refers to the psychological or emotional state that a user experiences, and includes feelings such as joy, sadness, and stress.

[0199] "Means of real-time analysis" refers to devices or methods that instantly evaluate and analyze a user's emotional state, and this includes voice analysis software and image recognition technology.

[0200] The system for carrying out the present invention consists of two main components: a client device and a server.

[0201] First, the server plays a central role in data processing, collecting and analyzing information related to the person being supported and the supporter. This includes state data, environmental data, and emotional data. The server analyzes this data using machine learning algorithms (e.g., TensorFlow or PyTorch) to generate an optimal support plan for the person being supported. Camera footage and audio input are used to analyze emotional states, and if a specific emotion (e.g., stress) is detected, the plan is adjusted accordingly.

[0202] Next, the client device provides information to the user and collects feedback through the interface. Specifically, a dedicated application is installed on the user's smartphone or tablet, visually displaying the support plan generated on the server. It also has a function to input user emotion data in real time. By utilizing cloud services and keeping the data constantly up-to-date, flexible and rapid responses are possible.

[0203] In this model, as a concrete example, if the caregiver experiences stress while the person being supported is going about their daily life, "stress" is detected through emotion analysis. In this situation, the cloud server immediately suggests relaxation methods to reduce stress and sends a relaxation video to the user's smartphone. For example, a prompt such as "Please suggest ways to reduce the stress a caregiver feels while preparing dinner" might be used. Based on this prompt, the generating AI model provides appropriate advice.

[0204] With the above configuration, a system is provided that enables meticulous support that takes into account the feelings of the supporters, and contributes to reducing the burden on both those receiving support and those providing it.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives status data from both the person being supported and the supporter. Inputs include information on the person being supported's physical condition and the supporter's environment and emotional data. Data collection devices (sensors and smart devices) are used to acquire this data and transmit it to the server. Here, the data is centrally managed and prepared for analysis.

[0208] Step 2:

[0209] The server uses machine learning algorithms to analyze the input state data. This analysis includes calculations in which the algorithm receives the state data and generates an optimal support plan based on the recipient's health status and activity patterns. As a result of the analysis, a customized support plan specifically for the recipient is generated.

[0210] Step 3:

[0211] The server analyzes the user's emotional state in real time via camera and voice input. Camera video and audio data are received from the user as input. An emotion analysis engine analyzes this data to evaluate the user's emotional state (e.g., stress level). The analysis results are used to adjust the support plan.

[0212] Step 4:

[0213] The server dynamically adjusts the support plan based on the user's emotional state and generates new instructions and advice. Input includes the emotional analysis results and the original support plan. Using a generative AI model, the server outputs the optimal action plan in response to a prompt (e.g., "Please suggest advice based on the current emotional state").

[0214] Step 5:

[0215] The terminal presents the user with the adjusted support plan and advice received from the server through a user interface. The input includes the adjusted support plan and advice from the server. The terminal displays this visually, making it easy for the user to understand.

[0216] Step 6:

[0217] Users input feedback on the support plan into a terminal during actual support activities. This input includes the status of the support activities and additional emotional data. This allows for an evaluation of the plan's effectiveness, which is then sent to the server as feedback and used to generate the next support plan.

[0218] Through the steps described above, the present invention makes it possible to provide optimal support adapted to the circumstances of both the person being supported and the person providing the support.

[0219] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0235] This invention is implemented as an information processing system for generating and providing optimal care plans tailored to the care recipient's situation. The system mainly consists of three components: a server, a terminal, and a user.

[0236] server

[0237] The server collects data about the person receiving care and analyzes it using machine learning algorithms. This allows for the creation of individualized care plans for each person. For example, if a person receiving care needs regular walking exercise, the server will generate a plan that takes into account the frequency and duration of that exercise.

[0238] terminal

[0239] The terminal is responsible for receiving care plans generated on the server and providing them visually to the user. This includes features that display the plan details in a user interface to make it easy for the user to review. It also provides an interface for the user to input feedback.

[0240] User

[0241] Users participate in the system by executing care plans provided through their devices and sending feedback to the server as needed. For example, they can carry out planned care while referring to videos and diagrams about specific care methods. Feedback on care plans is used to improve the system and is reflected in the generation of future plans.

[0242] These components enable the system to provide an efficient and flexible mechanism for delivering appropriate care to those receiving care.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user inputs data about the care recipient's health information and lifestyle into the terminal. This includes meal times, medication schedules, and exercise levels.

[0246] Step 2:

[0247] The terminal sends the entered data to the server. The data is formatted and securely transferred.

[0248] Step 3:

[0249] The server stores the received data in a database and performs analysis using machine learning algorithms. It identifies the individual needs of the care recipient and generates an optimal care plan.

[0250] Step 4:

[0251] The server sends the generated care plan to the terminal. The plan includes daily care tasks and health management recommendations.

[0252] Step 5:

[0253] The terminal is designed to display received care plans on a user interface, allowing users to review them. The display will be in a visually easy-to-understand format.

[0254] Step 6:

[0255] Users carry out daily care based on the provided care plan and input feedback into the device based on their actual living situation and perceived progress.

[0256] Step 7:

[0257] The terminal sends user feedback back to the server, which then identifies areas for improvement in the plan.

[0258] Step 8:

[0259] The server adjusts and improves the care plan based on the feedback received. The optimized new plan is then provided to the user in the next cycle.

[0260] (Example 1)

[0261] Next, we will describe Example 1. 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."

[0262] In recent years, the needs of those receiving care have diversified in our super-aging society, and there is a demand for flexible and effective care plans tailored to individual circumstances. However, the current system often fails to adequately analyze the detailed circumstances of those receiving care, resulting in uniform plans. This creates a challenge in providing optimal care to those receiving care.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information and generating an optimal support plan, and means for providing the support plan generated using a generation AI model to the user and receiving feedback. This enables the efficient generation of flexible support plans tailored to the individual circumstances of the care recipient, and improves the provision of care.

[0265] "Means of collecting information" refers to the processes and technologies used to acquire various data related to the person receiving care, including health data and lifestyle data.

[0266] "Means of analyzing information and generating optimal support plans" refers to the process and techniques of using collected data to perform data analysis and create care plans that are best suited to the needs of those receiving care.

[0267] "A means of providing support plans generated using a generative AI model to users and receiving their feedback" refers to the process and technology for presenting support plans created using artificial intelligence to caregivers or those receiving care in an appropriate format, and incorporating the feedback obtained into the system.

[0268] This invention is an information processing system that efficiently generates and provides support plans tailored to the individual circumstances of care recipients. The system mainly consists of three components: a server, a terminal, and a user.

[0269] server

[0270] The server is responsible for collecting and analyzing data related to the person receiving care. To this end, the server acquires data from data collection devices (e.g., biosensors and wearable devices) and stores it in a database. For analysis, machine learning algorithms using Python's Scikit-learn and TensorFlow are applied to perform data analysis and generate the most appropriate support plan for the person receiving care. For example, if the person receiving care needs light exercise, the server analyzes the data to determine the appropriate frequency and intensity.

[0271] terminal

[0272] The device receives the support plan generated by the server and presents it to the user visually. The device, using a tablet or smartphone, displays the plan through an intuitive user interface. This interface includes a daily schedule, recommended activities, and steps to follow the plan, and allows the user to edit and provide feedback as needed.

[0273] User

[0274] Users act based on the support plan provided through their device and input feedback on what they did and their impressions. This feedback is sent to the server and used to generate the next support plan. For example, a user might submit feedback such as, "I went for a walk, but I'm a little tired, so I'd like to increase my rest time."

[0275] As a result, the system enables personalized and flexible support for those receiving care, leading to continuous improvement in care. An example of a prompt for the generated AI model is text such as, "Based on the care recipient's health data, please suggest an exercise plan suitable for next week."

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server collects information from the person receiving care. This includes data input from wearable devices and environmental sensors. Specifically, the server performs a daily data collection process and stores biometric information such as heart rate and walking distance in a database. It receives health data as input, normalizes it, and stores it to obtain organized data as output.

[0279] Step 2:

[0280] The server analyzes the collected data. It uses machine learning algorithms to analyze the health status and activity patterns of those receiving care. Specifically, the server uses Python libraries (e.g., Scikit-learn) to perform anomaly detection and clustering, classifying the data into clusters. It takes historical health data as input, evaluates the health status based on that data, and generates analysis results as output.

[0281] Step 3:

[0282] The server uses an AI model based on the analysis results to create a care plan suitable for the person receiving care. During this process, prompts are used to instruct the AI ​​model to generate the plan. Specifically, the server inputs a prompt to the AI ​​such as, "Please create a weekly exercise plan tailored to the person receiving care's activity level." The analysis results are passed to the AI ​​as input, and the individualized care plan is obtained as output.

[0283] Step 4:

[0284] The terminal provides the generated care plan to the user through the user interface. Here, the specific content of the plan is displayed on the screen so that the user can easily check the plan. As a specific operation, the terminal application uses the UI framework to display schedules and activities in a calendar format. As a result, a visually easy-to-understand care plan is displayed for the user as output.

[0285] Step 5:

[0286] The user carries out activities based on the provided care plan and sends feedback to the server through the terminal. As a specific operation, the user inputs the progress of the activity and what they felt into the dedicated form of the terminal and presses the send button to provide feedback. The user's implementation report is received as input, and feedback data is saved as output, which becomes the basis for the next plan generation.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In modern aging societies, continuously and efficiently providing an optimized support plan for each care recipient is a major challenge. In addition, since the situation of care recipients changes daily, flexible updates to the support plan and intuitive guidance to users are required. The present invention aims to solve such problems and provide high-quality support for care recipients.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information to generate an optimal support plan, means for providing the generated support plan to the user and receiving feedback, and means for providing activity reminders and guides based on the support situation. This enables the provision of flexible support plans tailored to the individual circumstances of the care recipient and clear guidance to the user.

[0292] "Persons receiving care" refers to people who need support or care, and this particularly includes the elderly and people with disabilities.

[0293] "Information" refers to data concerning the health status, lifestyle, and environment of the person receiving care.

[0294] "Analysis" refers to the process of finding patterns and trends based on collected information, and is carried out using computational methods and algorithms.

[0295] A "support plan" refers to a set of action guidelines and activity schedules created based on the individual needs of the person receiving care.

[0296] "Users" refers to individuals who accept support plans and have the role of incorporating them into their daily lives.

[0297] "Opinions" refers to feedback and suggestions for improvement regarding the support plan provided by the user.

[0298] A "reminder" refers to an alarm or message that is sent to remind you of a specific activity or time.

[0299] A "guide" refers to a set of instructions or guidance provided to support actions or activities.

[0300] An "intelligent machine algorithm" is a computational method used for data analysis and decision-making, utilizing machine learning and artificial intelligence technologies.

[0301] The embodiments for implementing this invention will be described in detail below.

[0302] Server

[0303] The server operates on a cloud platform such as Amazon Web Services (AWS) and collects and analyzes information regarding the health status and living habits of care recipients. For data collection, information transmitted from mobile devices such as smartphones and smartwatches is used. For analysis, Python is used, and data analysis libraries such as pandas and scikit - learn are utilized to execute intelligent machine algorithms. The support plans generated by this algorithm are optimized for each individual care recipient.

[0304] Terminal

[0305] The terminal is a device that runs a smartphone application developed with Android Studio or Xcode. The terminal receives the support plan generated by the server and provides it to the user. Specifically, it uses a user interface to notify activity reminders in an intuitively understandable manner and provides guides regarding support. It also has a means to transmit the user's opinions to the server via the interface.

[0306] User

[0307] The user uses the terminal to perform activities based on the provided support plan. For example, a reminder for achieving a walking goal set based on the health status of the care recipient is notified from the terminal. At that time, the user can receive prompts such as "Today's goal is 6,000 steps. Let's take a walk in the nearby park." and start specific activities. Such prompt texts are automatically generated from the application and promote intuitive actions by the user.

[0308] By implementing the present invention in accordance with this configuration, a support plan optimized for each individual care recipient can be continuously provided without interruption, resulting in efficient care.

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] The server collects health data from the person receiving care. It receives sensor information and daily activity logs transmitted from smartphones and smartwatches as input. Based on this data, it performs preprocessing to create a dataset aligned with the time axis. The output is a dataset formatted for analysis by machine learning models.

[0312] Step 2:

[0313] The server uses intelligent machine algorithms to analyze the collected data. These algorithms are used to assess the care recipient's condition and generate an optimal support plan. They accept a formatted dataset as input and perform trend analysis and predictions. The output is a personalized support plan tailored to the care recipient.

[0314] Step 3:

[0315] Once a support plan is generated, the server sends it to the terminal. The input is the generated support plan. The server converts this plan into an appropriate format and notifies the terminal. The output is support plan data in a format that is easy for the user to understand.

[0316] Step 4:

[0317] The terminal presents the received support plan to the user. It receives support plan data sent from the server as input. The terminal visually displays this plan through the user interface and creates reminders and notifications. The output is a specific activity guide presented to the user.

[0318] Step 5:

[0319] The user performs activities based on the support plan presented on the device. The input is the content of the support plan and reminders displayed on the device. The user modifies their daily activities according to the instructions and sends feedback on the support plan to the server via the device as needed. The output is the content of the feedback and a record of the activities.

[0320] Step 6:

[0321] The server receives and analyzes user feedback. The input is the feedback data received from the user. The server analyzes this data to identify areas for improvement in the support plan. The output provides evaluation information useful for generating the next support plan.

[0322] In this way, it is possible to continuously provide the most suitable support plan for each care recipient throughout the entire system.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] This invention is a system that generates an optimal care plan based on the care recipient's situation, and further incorporates an emotion engine to recognize the user's emotional state and reflect it in the plan. It also provides guidance on care techniques to the user.

[0325] server

[0326] The server receives extensive data on the user's and care recipient's conditions and analyzes it using machine learning algorithms. Furthermore, it leverages an emotion engine to evaluate the user's emotional data and incorporate emotional adjustments into the care plan. For example, if the server detects that the user is experiencing stress, it generates care suggestions that reflect that emotional state and recommends activities aimed at stress reduction.

[0327] terminal

[0328] The terminal visually displays the care plan generated on the server in the user interface. It functions as an interface for users to review the care plan and input feedback and emotional data. It also provides users with instruction on care techniques through videos and diagrams as needed. For example, if an explanation of a care technique is deemed difficult, it will present a more detailed explanation or a video of an appropriate difficulty level.

[0329] User

[0330] Users execute care plans provided through their devices and send feedback based on their actual progress and emotions. The emotion engine analyzes the user's emotional state, and if it recognizes, for example, that the user is feeling tired, it suggests relaxation methods and makes adjustments to reduce their burden.

[0331] This configuration enables a system that provides flexible care plans that take emotional states into consideration, allowing for meticulous attention to both the user and the person receiving care.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] The user inputs information about the person being cared for, daily care details, and their own emotional state into the device. This includes dietary information, exercise status, and emotional feedback (e.g., stress levels, fatigue levels).

[0335] Step 2:

[0336] The terminal transmits the entered data and the user's emotional state to the server. The data is transferred in real time and precisely formatted.

[0337] Step 3:

[0338] The server analyzes the received data and uses an emotion engine to evaluate the user's emotional state. It then uses machine learning algorithms to generate the optimal care plan for both the care recipient and the user.

[0339] Step 4:

[0340] The server incorporates the results of the emotion analysis into the care plan. For example, if the user is experiencing stress, it creates a care plan that includes stress reduction activities.

[0341] Step 5:

[0342] The server sends the adjusted care plan to the terminal. The plan includes specific care tasks, recommended activities, and emotionally-based suggestions.

[0343] Step 6:

[0344] The terminal displays the received care plan on its user interface, allowing the user to review it. It also provides instruction on care techniques tailored to the user's emotional state through videos and illustrations.

[0345] Step 7:

[0346] The user implements the provided care plan and provides feedback as their emotional state changes during the plan's progress. This allows the server to understand the user's current emotional state.

[0347] Step 8:

[0348] Feedback is sent from the terminal to the server, which then readjusts the care plan based on that feedback. The system always provides a plan that is adapted to the circumstances of the care recipient and the user, and responds flexibly.

[0349] (Example 2)

[0350] Next, we will describe Example 2. 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".

[0351] Current care systems struggle to generate flexible care plans that are tailored to the care recipient's situation and the user's emotions. Furthermore, the reliance on manual or static materials for teaching care techniques makes it difficult to provide personalized plans. Additionally, the lack of features to dynamically update plans based on user feedback can hinder prompt responses.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for collecting diverse information based on the care recipient's situation, means for analyzing the collected information and generating an optimal plan using a machine learning algorithm, and means for visually presenting the generated plan on a user interface and accepting input. This makes it possible to generate and provide flexible and dynamic care plans that respond to the care recipient's situation and the user's emotions.

[0354] "Situational information" refers to a variety of data related to the care recipient and the user, including health status, emotional state, and living environment.

[0355] A "machine learning algorithm" is an artificial intelligence technology used in the data analysis process, a method for making predictions and classifications through learning from past data.

[0356] "Plan generation" is the process of formulating appropriate care activities and guidance schedules based on the collected information.

[0357] A "user interface" refers to the screens and operating methods that a user uses to access a system and input or retrieve information.

[0358] "Emotion analysis" is a process that uses natural language processing technology to determine a user's emotional state and utilizes that information to adjust action plans.

[0359] "Instructional items" refer to the specific procedures and learning content of caregiving techniques and activities that users receive.

[0360] The system of this invention includes a series of processes for dynamically generating care plans and optimizing them according to individual needs. Specific embodiments thereof are described below.

[0361] server

[0362] The server continuously collects situational information from care recipients and users and stores it in a database. Network-connected sensors and wearable devices are used to acquire this information. Python libraries such as TensorFlow and PyTorch are used to execute machine learning algorithms. This allows the collected information to be analyzed and care plans to be generated.

[0363] As a concrete example, the server predicts the user's stress level for the day based on data such as heart rate and activity level, and recommends appropriate relaxation activities. By utilizing a generative AI model to analyze emotions and extracting "anxiety" from the user's text feedback, corresponding care is suggested.

[0364] terminal

[0365] The terminal displays the care plan received from the server, allowing users to easily access the plan. A dedicated application running on Android and iOS devices is used for this purpose. The user interface is implemented using React Native, resulting in a visually intuitive design.

[0366] The system also includes a function to collect user feedback and incorporate it into future plans. The device can play videos and diagrams to help users learn caregiving techniques. For example, it can present 3D animations of complex caregiving techniques to support user understanding.

[0367] User

[0368] Users perform daily care activities based on the care plan displayed on their device. This includes implementing suggested stretches, meditations, or activity plans tailored to the person being cared for. Users input their progress on the plan and their own feelings on the device and send this feedback to the server.

[0369] For example, the effectiveness of a plan can be evaluated by having a user perform a "deep breathing exercise to relax" and then provide feedback that they "felt refreshed."

[0370] Example of a prompt

[0371] The prompt text used to input into the generating AI model is something like, "Please suggest an activity plan suitable for a person who is active during the day. It is important that the plan be flexible and adaptable to changes in their emotions."

[0372] In this way, the invention can provide flexible and adaptive care plans based on the needs of the user and the person being cared for, thereby improving the quality of daily life.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The server acquires data on users and those receiving care. This data collection involves inputting data such as heart rate, activity level, and environmental conditions via wearable devices and sensors. This biometric data is stored in a database on the server and processed into a format usable for subsequent processing.

[0376] Step 2:

[0377] The server analyzes the collected data using machine learning algorithms to infer the user's current state and emotional condition. Specifically, it preprocesses the data using Python libraries and calculates stress level predictions and emotion scores based on the algorithms. This analysis result is obtained as output, which forms the basis for generating the next plan.

[0378] Step 3:

[0379] The server generates a care plan based on the analysis results. Utilizing a generation AI model, the plan includes the care recipient's schedule and recommended activities. Specifically, it generates text about the activities, and the artificial intelligence model outputs suggestions tailored to individual needs.

[0380] Step 4:

[0381] The terminal visually displays the care plan received from the server in a user interface. Here, a schedule format and task list are presented for easy user understanding. Furthermore, an interface is provided for the user to input feedback on the plan.

[0382] Step 5:

[0383] Users perform care activities based on the care plan displayed on the terminal and input feedback into the terminal. Examples of user input include feedback on the results of the activities and their feelings, which are entered through digital forms. This information is sent to the server for subsequent analysis.

[0384] Step 6:

[0385] The server receives feedback from the user and uses it to update the care plan. It re-evaluates the user's condition using an emotion analysis algorithm and modifies the plan based on the new analysis results. This revised plan is then sent back to the terminal and prepared for the user to receive.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] In modern society, the mental and physical burden on both those receiving care and those providing care is increasing. Under these circumstances, creating appropriate support plans tailored to the individual's condition is not easy, and adjusting support to accommodate the caregiver's stress and emotions is difficult. Furthermore, there is a need for flexible plans that take emotional aspects into account, but the current system is insufficient to address this. To solve these problems, it is necessary to grasp the individual's situation and the caregiver's emotional state in real time and dynamically adjust support plans based on this information.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for collecting information based on the state of the person being supported, means for analyzing the collected information to generate an optimal support plan, and means for analyzing the user's emotional state in real time from camera and voice input. This makes it possible to provide a flexible and optimal support plan that is tailored to the state of the person being supported and the emotions of the supporter.

[0391] "Recipients of support" refers to individuals or groups who are eligible for support, and specifically refers to those who require assistance or support.

[0392] "Information" refers to all data obtained from the person being supported and related circumstances, including data on physical condition, environmental information, and emotional state.

[0393] "Means of collection" refers to the devices or methods used to acquire information, and this includes data acquisition methods using sensor devices and networks.

[0394] "Means of analysis" refers to devices or methods for analyzing collected information to obtain useful insights, and this includes machine learning algorithms and data processing software.

[0395] A "support plan" refers to a detailed plan of the support activities to be carried out for the person receiving support, and this includes the activity schedule, equipment to be used, and necessary personnel.

[0396] "User" refers to the person who uses the system to implement and adjust the support plan, and generally refers to a support worker or care provider.

[0397] "Emotional state" refers to the psychological or emotional state that a user experiences, and includes feelings such as joy, sadness, and stress.

[0398] "Means of real-time analysis" refers to devices or methods that instantly evaluate and analyze a user's emotional state, and this includes voice analysis software and image recognition technology.

[0399] The system for carrying out the present invention consists of two main components: a client device and a server.

[0400] First, the server plays a central role in data processing, collecting and analyzing information related to the person being supported and the supporter. This includes state data, environmental data, and emotional data. The server analyzes this data using machine learning algorithms (e.g., TensorFlow or PyTorch) to generate an optimal support plan for the person being supported. Camera footage and audio input are used to analyze emotional states, and if a specific emotion (e.g., stress) is detected, the plan is adjusted accordingly.

[0401] Next, the client device provides information to the user and collects feedback through the interface. Specifically, a dedicated application is installed on the user's smartphone or tablet, visually displaying the support plan generated on the server. It also has a function to input user emotion data in real time. By utilizing cloud services and keeping the data constantly up-to-date, flexible and rapid responses are possible.

[0402] In this model, as a concrete example, if the caregiver experiences stress while the person being supported is going about their daily life, "stress" is detected through emotion analysis. In this situation, the cloud server immediately suggests relaxation methods to reduce stress and sends a relaxation video to the user's smartphone. For example, a prompt such as "Please suggest ways to reduce the stress a caregiver feels while preparing dinner" might be used. Based on this prompt, the generating AI model provides appropriate advice.

[0403] With the above configuration, a system is provided that enables meticulous support that takes into account the feelings of the supporters, and contributes to reducing the burden on both those receiving support and those providing it.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server receives status data from both the person being supported and the supporter. Inputs include information on the person being supported's physical condition and the supporter's environment and emotional data. Data collection devices (sensors and smart devices) are used to acquire this data and transmit it to the server. Here, the data is centrally managed and prepared for analysis.

[0407] Step 2:

[0408] The server uses machine learning algorithms to analyze the input state data. This analysis includes calculations in which the algorithm receives the state data and generates an optimal support plan based on the recipient's health status and activity patterns. As a result of the analysis, a customized support plan specifically for the recipient is generated.

[0409] Step 3:

[0410] The server analyzes the user's emotional state in real time via camera and voice input. Camera video and audio data are received from the user as input. An emotion analysis engine analyzes this data to evaluate the user's emotional state (e.g., stress level). The analysis results are used to adjust the support plan.

[0411] Step 4:

[0412] The server dynamically adjusts the support plan based on the user's emotional state and generates new instructions and advice. Input includes the emotional analysis results and the original support plan. Using a generative AI model, the server outputs the optimal action plan in response to a prompt (e.g., "Please suggest advice based on the current emotional state").

[0413] Step 5:

[0414] The terminal presents the user with the adjusted support plan and advice received from the server through a user interface. The input includes the adjusted support plan and advice from the server. The terminal displays this visually, making it easy for the user to understand.

[0415] Step 6:

[0416] Users input feedback on the support plan into a terminal during actual support activities. This input includes the status of the support activities and additional emotional data. This allows for an evaluation of the plan's effectiveness, which is then sent to the server as feedback and used to generate the next support plan.

[0417] Through the steps described above, the present invention makes it possible to provide optimal support adapted to the circumstances of both the person being supported and the person providing the support.

[0418] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0433] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0434] This invention is implemented as an information processing system for generating and providing optimal care plans tailored to the care recipient's situation. The system mainly consists of three components: a server, a terminal, and a user.

[0435] server

[0436] The server collects data about the person receiving care and analyzes it using machine learning algorithms. This allows for the creation of individualized care plans for each person. For example, if a person receiving care needs regular walking exercise, the server will generate a plan that takes into account the frequency and duration of that exercise.

[0437] terminal

[0438] The terminal is responsible for receiving care plans generated on the server and providing them visually to the user. This includes features that display the plan details in a user interface to make it easy for the user to review. It also provides an interface for the user to input feedback.

[0439] User

[0440] Users participate in the system by executing care plans provided through their devices and sending feedback to the server as needed. For example, they can carry out planned care while referring to videos and diagrams about specific care methods. Feedback on care plans is used to improve the system and is reflected in the generation of future plans.

[0441] These components enable the system to provide an efficient and flexible mechanism for delivering appropriate care to those receiving care.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The user inputs data about the care recipient's health information and lifestyle into the terminal. This includes meal times, medication schedules, and exercise levels.

[0445] Step 2:

[0446] The terminal sends the entered data to the server. The data is formatted and securely transferred.

[0447] Step 3:

[0448] The server stores the received data in a database and performs analysis using machine learning algorithms. It identifies the individual needs of the care recipient and generates an optimal care plan.

[0449] Step 4:

[0450] The server sends the generated care plan to the terminal. The plan includes daily care tasks and health management recommendations.

[0451] Step 5:

[0452] The terminal is designed to display received care plans on a user interface, allowing users to review them. The display will be in a visually easy-to-understand format.

[0453] Step 6:

[0454] Users carry out daily care based on the provided care plan and input feedback into the device based on their actual living situation and perceived progress.

[0455] Step 7:

[0456] The terminal sends user feedback back to the server, which then identifies areas for improvement in the plan.

[0457] Step 8:

[0458] The server adjusts and improves the care plan based on the feedback received. The optimized new plan is then provided to the user in the next cycle.

[0459] (Example 1)

[0460] Next, we will describe Example 1. 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."

[0461] In recent years, the needs of those receiving care have diversified in our super-aging society, and there is a demand for flexible and effective care plans tailored to individual circumstances. However, the current system often fails to adequately analyze the detailed circumstances of those receiving care, resulting in uniform plans. This creates a challenge in providing optimal care to those receiving care.

[0462] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0463] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information and generating an optimal support plan, and means for providing the support plan generated using a generation AI model to the user and receiving feedback. This enables the efficient generation of flexible support plans tailored to the individual circumstances of the care recipient, and improves the provision of care.

[0464] "Means of collecting information" refers to the processes and technologies used to acquire various data related to the person receiving care, including health data and lifestyle data.

[0465] "Means of analyzing information and generating optimal support plans" refers to the process and techniques of using collected data to perform data analysis and create care plans that are best suited to the needs of those receiving care.

[0466] "A means of providing support plans generated using a generative AI model to users and receiving their feedback" refers to the process and technology for presenting support plans created using artificial intelligence to caregivers or those receiving care in an appropriate format, and incorporating the feedback obtained into the system.

[0467] This invention is an information processing system that efficiently generates and provides support plans tailored to the individual circumstances of care recipients. The system mainly consists of three components: a server, a terminal, and a user.

[0468] server

[0469] The server is responsible for collecting and analyzing data related to the person receiving care. To this end, the server acquires data from data collection devices (e.g., biosensors and wearable devices) and stores it in a database. For analysis, machine learning algorithms using Python's Scikit-learn and TensorFlow are applied to perform data analysis and generate the most appropriate support plan for the person receiving care. For example, if the person receiving care needs light exercise, the server analyzes the data to determine the appropriate frequency and intensity.

[0470] terminal

[0471] The device receives the support plan generated by the server and presents it to the user visually. The device, using a tablet or smartphone, displays the plan through an intuitive user interface. This interface includes a daily schedule, recommended activities, and steps to follow the plan, and allows the user to edit and provide feedback as needed.

[0472] User

[0473] Users act based on the support plan provided through their device and input feedback on what they did and their impressions. This feedback is sent to the server and used to generate the next support plan. For example, a user might submit feedback such as, "I went for a walk, but I'm a little tired, so I'd like to increase my rest time."

[0474] As a result, the system enables personalized and flexible support for those receiving care, leading to continuous improvement in care. An example of a prompt for the generated AI model is text such as, "Based on the care recipient's health data, please suggest an exercise plan suitable for next week."

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The server collects information from the person receiving care. This includes data input from wearable devices and environmental sensors. Specifically, the server performs a daily data collection process and stores biometric information such as heart rate and walking distance in a database. It receives health data as input, normalizes it, and stores it to obtain organized data as output.

[0478] Step 2:

[0479] The server analyzes the collected data. It uses machine learning algorithms to analyze the health status and activity patterns of those receiving care. Specifically, the server uses Python libraries (e.g., Scikit-learn) to perform anomaly detection and clustering, classifying the data into clusters. It takes historical health data as input, evaluates the health status based on that data, and generates analysis results as output.

[0480] Step 3:

[0481] The server uses an AI model based on the analysis results to create a care plan suitable for the person receiving care. During this process, prompts are used to instruct the AI ​​model to generate the plan. Specifically, the server inputs a prompt to the AI ​​such as, "Please create a weekly exercise plan tailored to the person receiving care's activity level." The analysis results are passed to the AI ​​as input, and the individualized care plan is obtained as output.

[0482] Step 4:

[0483] The terminal provides the generated care plan to the user through a user interface. Here, the specific details of the plan are displayed on the screen, allowing the user to easily review it. Specifically, the terminal application uses a UI framework to display schedules and activities in a calendar format. This results in a visually easy-to-understand care plan being displayed as output for the user.

[0484] Step 5:

[0485] Users carry out activities based on the provided care plan and send feedback to the server via their device. Specifically, users input their activity progress and feelings into a dedicated form on their device and provide feedback by pressing the submit button. The system receives the user's activity report as input and saves the feedback data as output, which serves as the basis for generating the next care plan.

[0486] (Application Example 1)

[0487] Next, we will explain Application Example 1. In the following explanation, 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."

[0488] In today's aging society, providing personalized and efficient support plans tailored to each individual care recipient is a major challenge. Furthermore, because the circumstances of care recipients change daily, flexible updates to support plans and intuitive guidance for users are essential. This invention aims to address these challenges and provide high-quality support to care recipients.

[0489] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0490] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information to generate an optimal support plan, means for providing the generated support plan to the user and receiving feedback, and means for providing activity reminders and guides based on the support situation. This enables the provision of flexible support plans tailored to the individual circumstances of the care recipient and clear guidance to the user.

[0491] "Persons receiving care" refers to people who need support or care, and this particularly includes the elderly and people with disabilities.

[0492] "Information" refers to data concerning the health status, lifestyle, and environment of the person receiving care.

[0493] "Analysis" refers to the process of finding patterns and trends based on collected information, and is carried out using computational methods and algorithms.

[0494] A "support plan" refers to a set of action guidelines and activity schedules created based on the individual needs of the person receiving care.

[0495] "Users" refers to individuals who accept support plans and have the role of incorporating them into their daily lives.

[0496] "Opinions" refers to feedback and suggestions for improvement regarding the support plan provided by the user.

[0497] A "reminder" refers to an alarm or message that is sent to remind you of a specific activity or time.

[0498] A "guide" refers to a set of instructions or guidance provided to support actions or activities.

[0499] An "intelligent machine algorithm" is a computational method used for data analysis and decision-making, utilizing machine learning and artificial intelligence technologies.

[0500] The embodiments for carrying out this invention will be described in detail below.

[0501] server

[0502] The server runs on a cloud platform such as Amazon Web Services (AWS) and collects and analyzes information about the health status and lifestyle of those receiving care. Data collection uses information transmitted from mobile devices such as smartphones and smartwatches. Python is used for analysis, and intelligent machine algorithms are executed using data analysis libraries such as pandas and scikit-learn. The support plans generated by these algorithms are optimized for each individual receiving care.

[0503] terminal

[0504] The device is a smartphone application developed with Android Studio or Xcode. The device receives support plans generated from the server and provides them to the user. Specifically, it uses an intuitive user interface to notify activity reminders and provide support guidance. It also includes a means for users to send feedback to the server via the interface.

[0505] User

[0506] Users use their devices to carry out activities based on the provided support plan. For example, the device sends reminders to achieve walking goals set based on the care recipient's health condition. At that time, the user receives a prompt such as, "Today's goal is 6,000 steps. Let's take a stroll in a nearby park," and can start a specific activity. Such prompts are automatically generated by the app, promoting intuitive user behavior.

[0507] By implementing the present invention in accordance with this configuration, a support plan optimized for each individual care recipient can be continuously provided without interruption, resulting in efficient care.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The server collects health data from the person receiving care. It receives sensor information and daily activity logs transmitted from smartphones and smartwatches as input. Based on this data, it performs preprocessing to create a dataset aligned with the time axis. The output is a dataset formatted for analysis by machine learning models.

[0511] Step 2:

[0512] The server uses intelligent machine algorithms to analyze the collected data. These algorithms are used to assess the care recipient's condition and generate an optimal support plan. They accept a formatted dataset as input and perform trend analysis and predictions. The output is a personalized support plan tailored to the care recipient.

[0513] Step 3:

[0514] Once a support plan is generated, the server sends it to the terminal. The input is the generated support plan. The server converts this plan into an appropriate format and notifies the terminal. The output is support plan data in a format that is easy for the user to understand.

[0515] Step 4:

[0516] The terminal presents the received support plan to the user. It receives support plan data sent from the server as input. The terminal visually displays this plan through the user interface and creates reminders and notifications. The output is a specific activity guide presented to the user.

[0517] Step 5:

[0518] The user performs activities based on the support plan presented on the device. The input is the content of the support plan and reminders displayed on the device. The user modifies their daily activities according to the instructions and sends feedback on the support plan to the server via the device as needed. The output is the content of the feedback and a record of the activities.

[0519] Step 6:

[0520] The server receives and analyzes user feedback. The input is the feedback data received from the user. The server analyzes this data to identify areas for improvement in the support plan. The output provides evaluation information useful for generating the next support plan.

[0521] In this way, it is possible to continuously provide the most suitable support plan for each care recipient throughout the entire system.

[0522] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0523] This invention is a system that generates an optimal care plan based on the care recipient's situation, and further incorporates an emotion engine to recognize the user's emotional state and reflect it in the plan. It also provides guidance on care techniques to the user.

[0524] server

[0525] The server receives extensive data on the user's and care recipient's conditions and analyzes it using machine learning algorithms. Furthermore, it leverages an emotion engine to evaluate the user's emotional data and incorporate emotional adjustments into the care plan. For example, if the server detects that the user is experiencing stress, it generates care suggestions that reflect that emotional state and recommends activities aimed at stress reduction.

[0526] terminal

[0527] The terminal visually displays the care plan generated on the server in the user interface. It functions as an interface for users to review the care plan and input feedback and emotional data. It also provides users with instruction on care techniques through videos and diagrams as needed. For example, if an explanation of a care technique is deemed difficult, it will present a more detailed explanation or a video of an appropriate difficulty level.

[0528] User

[0529] Users execute care plans provided through their devices and send feedback based on their actual progress and emotions. The emotion engine analyzes the user's emotional state, and if it recognizes, for example, that the user is feeling tired, it suggests relaxation methods and makes adjustments to reduce their burden.

[0530] This configuration enables a system that provides flexible care plans that take emotional states into consideration, allowing for meticulous attention to both the user and the person receiving care.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user inputs information about the person being cared for, daily care details, and their own emotional state into the device. This includes dietary information, exercise status, and emotional feedback (e.g., stress levels, fatigue levels).

[0534] Step 2:

[0535] The terminal transmits the entered data and the user's emotional state to the server. The data is transferred in real time and precisely formatted.

[0536] Step 3:

[0537] The server analyzes the received data and uses an emotion engine to evaluate the user's emotional state. It then uses machine learning algorithms to generate the optimal care plan for both the care recipient and the user.

[0538] Step 4:

[0539] The server incorporates the results of the emotion analysis into the care plan. For example, if the user is experiencing stress, it creates a care plan that includes stress reduction activities.

[0540] Step 5:

[0541] The server sends the adjusted care plan to the terminal. The plan includes specific care tasks, recommended activities, and emotionally-based suggestions.

[0542] Step 6:

[0543] The terminal displays the received care plan on its user interface, allowing the user to review it. It also provides instruction on care techniques tailored to the user's emotional state through videos and illustrations.

[0544] Step 7:

[0545] The user implements the provided care plan and provides feedback as their emotional state changes during the plan's progress. This allows the server to understand the user's current emotional state.

[0546] Step 8:

[0547] Feedback is sent from the terminal to the server, which then readjusts the care plan based on that feedback. The system always provides a plan that is adapted to the circumstances of the care recipient and the user, and responds flexibly.

[0548] (Example 2)

[0549] Next, we will describe Example 2. 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."

[0550] Current care systems struggle to generate flexible care plans that are tailored to the care recipient's situation and the user's emotions. Furthermore, the reliance on manual or static materials for teaching care techniques makes it difficult to provide personalized plans. Additionally, the lack of features to dynamically update plans based on user feedback can hinder prompt responses.

[0551] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0552] In this invention, the server includes means for collecting diverse information based on the care recipient's situation, means for analyzing the collected information and generating an optimal plan using a machine learning algorithm, and means for visually presenting the generated plan on a user interface and accepting input. This makes it possible to generate and provide flexible and dynamic care plans that respond to the care recipient's situation and the user's emotions.

[0553] "Situational information" refers to a variety of data related to the care recipient and the user, including health status, emotional state, and living environment.

[0554] A "machine learning algorithm" is an artificial intelligence technology used in the data analysis process, a method for making predictions and classifications through learning from past data.

[0555] "Plan generation" is the process of formulating appropriate care activities and guidance schedules based on the collected information.

[0556] A "user interface" refers to the screens and operating methods that a user uses to access a system and input or retrieve information.

[0557] "Emotion analysis" is a process that uses natural language processing technology to determine a user's emotional state and utilizes that information to adjust action plans.

[0558] "Instructional items" refer to the specific procedures and learning content of caregiving techniques and activities that users receive.

[0559] The system of this invention includes a series of processes for dynamically generating care plans and optimizing them according to individual needs. Specific embodiments thereof are described below.

[0560] server

[0561] The server continuously collects situational information from care recipients and users and stores it in a database. Network-connected sensors and wearable devices are used to acquire this information. Python libraries such as TensorFlow and PyTorch are used to execute machine learning algorithms. This allows the collected information to be analyzed and care plans to be generated.

[0562] As a concrete example, the server predicts the user's stress level for the day based on data such as heart rate and activity level, and recommends appropriate relaxation activities. By utilizing a generative AI model to analyze emotions and extracting "anxiety" from the user's text feedback, corresponding care is suggested.

[0563] terminal

[0564] The terminal displays the care plan received from the server, allowing users to easily access the plan. A dedicated application running on Android and iOS devices is used for this purpose. The user interface is implemented using React Native, resulting in a visually intuitive design.

[0565] The system also includes a function to collect user feedback and incorporate it into future plans. The device can play videos and diagrams to help users learn caregiving techniques. For example, it can present 3D animations of complex caregiving techniques to support user understanding.

[0566] User

[0567] Users perform daily care activities based on the care plan displayed on their device. This includes implementing suggested stretches, meditations, or activity plans tailored to the person being cared for. Users input their progress on the plan and their own feelings on the device and send this feedback to the server.

[0568] For example, the effectiveness of a plan can be evaluated by having a user perform a "deep breathing exercise to relax" and then provide feedback that they "felt refreshed."

[0569] Example of a prompt

[0570] The prompt text used to input into the generating AI model is something like, "Please suggest an activity plan suitable for a person who is active during the day. It is important that the plan be flexible and adaptable to changes in their emotions."

[0571] In this way, the invention can provide flexible and adaptive care plans based on the needs of the user and the person being cared for, thereby improving the quality of daily life.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The server acquires data on users and those receiving care. This data collection involves inputting data such as heart rate, activity level, and environmental conditions via wearable devices and sensors. This biometric data is stored in a database on the server and processed into a format usable for subsequent processing.

[0575] Step 2:

[0576] The server analyzes the collected data using machine learning algorithms to infer the user's current state and emotional condition. Specifically, it preprocesses the data using Python libraries and calculates stress level predictions and emotion scores based on the algorithms. This analysis result is obtained as output, which forms the basis for generating the next plan.

[0577] Step 3:

[0578] The server generates a care plan based on the analysis results. Utilizing a generation AI model, the plan includes the care recipient's schedule and recommended activities. Specifically, it generates text about the activities, and the artificial intelligence model outputs suggestions tailored to individual needs.

[0579] Step 4:

[0580] The terminal visually displays the care plan received from the server in a user interface. Here, a schedule format and task list are presented for easy user understanding. Furthermore, an interface is provided for the user to input feedback on the plan.

[0581] Step 5:

[0582] Users perform care activities based on the care plan displayed on the terminal and input feedback into the terminal. Examples of user input include feedback on the results of the activities and their feelings, which are entered through digital forms. This information is sent to the server for subsequent analysis.

[0583] Step 6:

[0584] The server receives feedback from the user and uses it to update the care plan. It re-evaluates the user's condition using an emotion analysis algorithm and modifies the plan based on the new analysis results. This revised plan is then sent back to the terminal and prepared for the user to receive.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, 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."

[0587] In modern society, the mental and physical burden on both those receiving care and those providing care is increasing. Under these circumstances, creating appropriate support plans tailored to the individual's condition is not easy, and adjusting support to accommodate the caregiver's stress and emotions is difficult. Furthermore, there is a need for flexible plans that take emotional aspects into account, but the current system is insufficient to address this. To solve these problems, it is necessary to grasp the individual's situation and the caregiver's emotional state in real time and dynamically adjust support plans based on this information.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes means for collecting information based on the state of the person being supported, means for analyzing the collected information to generate an optimal support plan, and means for analyzing the user's emotional state in real time from camera and voice input. This makes it possible to provide a flexible and optimal support plan that is tailored to the state of the person being supported and the emotions of the supporter.

[0590] "Recipients of support" refers to individuals or groups who are eligible for support, and specifically refers to those who require assistance or support.

[0591] "Information" refers to all data obtained from the person being supported and related circumstances, including data on physical condition, environmental information, and emotional state.

[0592] "Means of collection" refers to the devices or methods used to acquire information, and this includes data acquisition methods using sensor devices and networks.

[0593] "Means of analysis" refers to devices or methods for analyzing collected information to obtain useful insights, and this includes machine learning algorithms and data processing software.

[0594] A "support plan" refers to a detailed plan of the support activities to be carried out for the person receiving support, and this includes the activity schedule, equipment to be used, and necessary personnel.

[0595] "User" refers to the person who uses the system to implement and adjust the support plan, and generally refers to a support worker or care provider.

[0596] "Emotional state" refers to the psychological or emotional state that a user experiences, and includes feelings such as joy, sadness, and stress.

[0597] "Means of real-time analysis" refers to devices or methods that instantly evaluate and analyze a user's emotional state, and this includes voice analysis software and image recognition technology.

[0598] The system for carrying out the present invention consists of two main components: a client device and a server.

[0599] First, the server plays a central role in data processing, collecting and analyzing information related to the person being supported and the supporter. This includes state data, environmental data, and emotional data. The server analyzes this data using machine learning algorithms (e.g., TensorFlow or PyTorch) to generate an optimal support plan for the person being supported. Camera footage and audio input are used to analyze emotional states, and if a specific emotion (e.g., stress) is detected, the plan is adjusted accordingly.

[0600] Next, the client device provides information to the user and collects feedback through the interface. Specifically, a dedicated application is installed on the user's smartphone or tablet, visually displaying the support plan generated on the server. It also has a function to input user emotion data in real time. By utilizing cloud services and keeping the data constantly up-to-date, flexible and rapid responses are possible.

[0601] In this model, as a concrete example, if the caregiver experiences stress while the person being supported is going about their daily life, "stress" is detected through emotion analysis. In this situation, the cloud server immediately suggests relaxation methods to reduce stress and sends a relaxation video to the user's smartphone. For example, a prompt such as "Please suggest ways to reduce the stress a caregiver feels while preparing dinner" might be used. Based on this prompt, the generating AI model provides appropriate advice.

[0602] With the above configuration, a system is provided that enables meticulous support that takes into account the feelings of the supporters, and contributes to reducing the burden on both those receiving support and those providing it.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The server receives status data from both the person being supported and the supporter. Inputs include information on the person being supported's physical condition and the supporter's environment and emotional data. Data collection devices (sensors and smart devices) are used to acquire this data and transmit it to the server. Here, the data is centrally managed and prepared for analysis.

[0606] Step 2:

[0607] The server uses machine learning algorithms to analyze the input state data. This analysis includes calculations in which the algorithm receives the state data and generates an optimal support plan based on the recipient's health status and activity patterns. As a result of the analysis, a customized support plan specifically for the recipient is generated.

[0608] Step 3:

[0609] The server analyzes the user's emotional state in real time via camera and voice input. Camera video and audio data are received from the user as input. An emotion analysis engine analyzes this data to evaluate the user's emotional state (e.g., stress level). The analysis results are used to adjust the support plan.

[0610] Step 4:

[0611] The server dynamically adjusts the support plan based on the user's emotional state and generates new instructions and advice. Input includes the emotional analysis results and the original support plan. Using a generative AI model, the server outputs the optimal action plan in response to a prompt (e.g., "Please suggest advice based on the current emotional state").

[0612] Step 5:

[0613] The terminal presents the user with the adjusted support plan and advice received from the server through a user interface. The input includes the adjusted support plan and advice from the server. The terminal displays this visually, making it easy for the user to understand.

[0614] Step 6:

[0615] Users input feedback on the support plan into a terminal during actual support activities. This input includes the status of the support activities and additional emotional data. This allows for an evaluation of the plan's effectiveness, which is then sent to the server as feedback and used to generate the next support plan.

[0616] Through the steps described above, the present invention makes it possible to provide optimal support adapted to the circumstances of both the person being supported and the person providing the support.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] This invention is implemented as an information processing system for generating and providing optimal care plans tailored to the care recipient's situation. The system mainly consists of three components: a server, a terminal, and a user.

[0635] server

[0636] The server collects data about the person receiving care and analyzes it using machine learning algorithms. This allows for the creation of individualized care plans for each person. For example, if a person receiving care needs regular walking exercise, the server will generate a plan that takes into account the frequency and duration of that exercise.

[0637] terminal

[0638] The terminal is responsible for receiving care plans generated on the server and providing them visually to the user. This includes features that display the plan details in a user interface to make it easy for the user to review. It also provides an interface for the user to input feedback.

[0639] User

[0640] Users participate in the system by executing care plans provided through their devices and sending feedback to the server as needed. For example, they can carry out planned care while referring to videos and diagrams about specific care methods. Feedback on care plans is used to improve the system and is reflected in the generation of future plans.

[0641] These components enable the system to provide an efficient and flexible mechanism for delivering appropriate care to those receiving care.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The user inputs data about the care recipient's health information and lifestyle into the terminal. This includes meal times, medication schedules, and exercise levels.

[0645] Step 2:

[0646] The terminal sends the entered data to the server. The data is formatted and securely transferred.

[0647] Step 3:

[0648] The server stores the received data in a database and performs analysis using machine learning algorithms. It identifies the individual needs of the care recipient and generates an optimal care plan.

[0649] Step 4:

[0650] The server sends the generated care plan to the terminal. The plan includes daily care tasks and health management recommendations.

[0651] Step 5:

[0652] The terminal is designed to display received care plans on a user interface, allowing users to review them. The display will be in a visually easy-to-understand format.

[0653] Step 6:

[0654] Users carry out daily care based on the provided care plan and input feedback into the device based on their actual living situation and perceived progress.

[0655] Step 7:

[0656] The terminal sends user feedback back to the server, which then identifies areas for improvement in the plan.

[0657] Step 8:

[0658] The server adjusts and improves the care plan based on the feedback received. The optimized new plan is then provided to the user in the next cycle.

[0659] (Example 1)

[0660] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In recent years, the needs of those receiving care have diversified in our super-aging society, and there is a demand for flexible and effective care plans tailored to individual circumstances. However, the current system often fails to adequately analyze the detailed circumstances of those receiving care, resulting in uniform plans. This creates a challenge in providing optimal care to those receiving care.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0663] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information and generating an optimal support plan, and means for providing the support plan generated using a generation AI model to the user and receiving feedback. This enables the efficient generation of flexible support plans tailored to the individual circumstances of the care recipient, and improves the provision of care.

[0664] "Means of collecting information" refers to the processes and technologies used to acquire various data related to the person receiving care, including health data and lifestyle data.

[0665] "Means of analyzing information and generating optimal support plans" refers to the process and techniques of using collected data to perform data analysis and create care plans that are best suited to the needs of those receiving care.

[0666] "A means of providing support plans generated using a generative AI model to users and receiving their feedback" refers to the process and technology for presenting support plans created using artificial intelligence to caregivers or those receiving care in an appropriate format, and incorporating the feedback obtained into the system.

[0667] This invention is an information processing system that efficiently generates and provides support plans tailored to the individual circumstances of care recipients. The system mainly consists of three components: a server, a terminal, and a user.

[0668] server

[0669] The server is responsible for collecting and analyzing data related to the person receiving care. To this end, the server acquires data from data collection devices (e.g., biosensors and wearable devices) and stores it in a database. For analysis, machine learning algorithms using Python's Scikit-learn and TensorFlow are applied to perform data analysis and generate the most appropriate support plan for the person receiving care. For example, if the person receiving care needs light exercise, the server analyzes the data to determine the appropriate frequency and intensity.

[0670] terminal

[0671] The device receives the support plan generated by the server and presents it to the user visually. The device, using a tablet or smartphone, displays the plan through an intuitive user interface. This interface includes a daily schedule, recommended activities, and steps to follow the plan, and allows the user to edit and provide feedback as needed.

[0672] User

[0673] Users act based on the support plan provided through their device and input feedback on what they did and their impressions. This feedback is sent to the server and used to generate the next support plan. For example, a user might submit feedback such as, "I went for a walk, but I'm a little tired, so I'd like to increase my rest time."

[0674] As a result, the system enables personalized and flexible support for those receiving care, leading to continuous improvement in care. An example of a prompt for the generated AI model is text such as, "Based on the care recipient's health data, please suggest an exercise plan suitable for next week."

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server collects information from the person receiving care. This includes data input from wearable devices and environmental sensors. Specifically, the server performs a daily data collection process and stores biometric information such as heart rate and walking distance in a database. It receives health data as input, normalizes it, and stores it to obtain organized data as output.

[0678] Step 2:

[0679] The server analyzes the collected data. It uses machine learning algorithms to analyze the health status and activity patterns of those receiving care. Specifically, the server uses Python libraries (e.g., Scikit-learn) to perform anomaly detection and clustering, classifying the data into clusters. It takes historical health data as input, evaluates the health status based on that data, and generates analysis results as output.

[0680] Step 3:

[0681] The server uses an AI model based on the analysis results to create a care plan suitable for the person receiving care. During this process, prompts are used to instruct the AI ​​model to generate the plan. Specifically, the server inputs a prompt to the AI ​​such as, "Please create a weekly exercise plan tailored to the person receiving care's activity level." The analysis results are passed to the AI ​​as input, and the individualized care plan is obtained as output.

[0682] Step 4:

[0683] The terminal provides the generated care plan to the user through a user interface. Here, the specific details of the plan are displayed on the screen, allowing the user to easily review it. Specifically, the terminal application uses a UI framework to display schedules and activities in a calendar format. This results in a visually easy-to-understand care plan being displayed as output for the user.

[0684] Step 5:

[0685] Users carry out activities based on the provided care plan and send feedback to the server via their device. Specifically, users input their activity progress and feelings into a dedicated form on their device and provide feedback by pressing the submit button. The system receives the user's activity report as input and saves the feedback data as output, which serves as the basis for generating the next care plan.

[0686] (Application Example 1)

[0687] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In today's aging society, providing personalized and efficient support plans tailored to each individual care recipient is a major challenge. Furthermore, because the circumstances of care recipients change daily, flexible updates to support plans and intuitive guidance for users are essential. This invention aims to address these challenges and provide high-quality support to care recipients.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0690] In this invention, the server includes means for collecting information based on the care recipient's situation, means for analyzing the collected information to generate an optimal support plan, means for providing the generated support plan to the user and receiving feedback, and means for providing activity reminders and guides based on the support situation. This enables the provision of flexible support plans tailored to the individual circumstances of the care recipient and clear guidance to the user.

[0691] "Persons receiving care" refers to people who need support or care, and this particularly includes the elderly and people with disabilities.

[0692] "Information" refers to data concerning the health status, lifestyle, and environment of the person receiving care.

[0693] "Analysis" refers to the process of finding patterns and trends based on collected information, and is carried out using computational methods and algorithms.

[0694] A "support plan" refers to a set of action guidelines and activity schedules created based on the individual needs of the person receiving care.

[0695] "Users" refers to individuals who accept support plans and have the role of incorporating them into their daily lives.

[0696] "Opinions" refers to feedback and suggestions for improvement regarding the support plan provided by the user.

[0697] A "reminder" refers to an alarm or message that is sent to remind you of a specific activity or time.

[0698] A "guide" refers to a set of instructions or guidance provided to support actions or activities.

[0699] An "intelligent machine algorithm" is a computational method used for data analysis and decision-making, utilizing machine learning and artificial intelligence technologies.

[0700] The embodiments for carrying out this invention will be described in detail below.

[0701] server

[0702] The server runs on a cloud platform such as Amazon Web Services (AWS) and collects and analyzes information about the health status and lifestyle of those receiving care. Data collection uses information transmitted from mobile devices such as smartphones and smartwatches. Python is used for analysis, and intelligent machine algorithms are executed using data analysis libraries such as pandas and scikit-learn. The support plans generated by these algorithms are optimized for each individual receiving care.

[0703] terminal

[0704] The device is a smartphone application developed with Android Studio or Xcode. The device receives support plans generated from the server and provides them to the user. Specifically, it uses an intuitive user interface to notify activity reminders and provide support guidance. It also includes a means for users to send feedback to the server via the interface.

[0705] User

[0706] Users use their devices to carry out activities based on the provided support plan. For example, the device sends reminders to achieve walking goals set based on the care recipient's health condition. At that time, the user receives a prompt such as, "Today's goal is 6,000 steps. Let's take a stroll in a nearby park," and can start a specific activity. Such prompts are automatically generated by the app, promoting intuitive user behavior.

[0707] By implementing the present invention in accordance with this configuration, a support plan optimized for each individual care recipient can be continuously provided without interruption, resulting in efficient care.

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The server collects health data from the person receiving care. It receives sensor information and daily activity logs transmitted from smartphones and smartwatches as input. Based on this data, it performs preprocessing to create a dataset aligned with the time axis. The output is a dataset formatted for analysis by machine learning models.

[0711] Step 2:

[0712] The server uses intelligent machine algorithms to analyze the collected data. These algorithms are used to assess the care recipient's condition and generate an optimal support plan. They accept a formatted dataset as input and perform trend analysis and predictions. The output is a personalized support plan tailored to the care recipient.

[0713] Step 3:

[0714] Once a support plan is generated, the server sends it to the terminal. The input is the generated support plan. The server converts this plan into an appropriate format and notifies the terminal. The output is support plan data in a format that is easy for the user to understand.

[0715] Step 4:

[0716] The terminal presents the received support plan to the user. It receives support plan data sent from the server as input. The terminal visually displays this plan through the user interface and creates reminders and notifications. The output is a specific activity guide presented to the user.

[0717] Step 5:

[0718] The user performs activities based on the support plan presented on the device. The input is the content of the support plan and reminders displayed on the device. The user modifies their daily activities according to the instructions and sends feedback on the support plan to the server via the device as needed. The output is the content of the feedback and a record of the activities.

[0719] Step 6:

[0720] The server receives and analyzes user feedback. The input is the feedback data received from the user. The server analyzes this data to identify areas for improvement in the support plan. The output provides evaluation information useful for generating the next support plan.

[0721] In this way, it is possible to continuously provide the most suitable support plan for each care recipient throughout the entire system.

[0722] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0723] This invention is a system that generates an optimal care plan based on the care recipient's situation, and further incorporates an emotion engine to recognize the user's emotional state and reflect it in the plan. It also provides guidance on care techniques to the user.

[0724] server

[0725] The server receives extensive data on the user's and care recipient's conditions and analyzes it using machine learning algorithms. Furthermore, it leverages an emotion engine to evaluate the user's emotional data and incorporate emotional adjustments into the care plan. For example, if the server detects that the user is experiencing stress, it generates care suggestions that reflect that emotional state and recommends activities aimed at stress reduction.

[0726] terminal

[0727] The terminal visually displays the care plan generated on the server in the user interface. It functions as an interface for users to review the care plan and input feedback and emotional data. It also provides users with instruction on care techniques through videos and diagrams as needed. For example, if an explanation of a care technique is deemed difficult, it will present a more detailed explanation or a video of an appropriate difficulty level.

[0728] User

[0729] Users execute care plans provided through their devices and send feedback based on their actual progress and emotions. The emotion engine analyzes the user's emotional state, and if it recognizes, for example, that the user is feeling tired, it suggests relaxation methods and makes adjustments to reduce their burden.

[0730] This configuration enables a system that provides flexible care plans that take emotional states into consideration, allowing for meticulous attention to both the user and the person receiving care.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The user inputs information about the person being cared for, daily care details, and their own emotional state into the device. This includes dietary information, exercise status, and emotional feedback (e.g., stress levels, fatigue levels).

[0734] Step 2:

[0735] The terminal transmits the entered data and the user's emotional state to the server. The data is transferred in real time and precisely formatted.

[0736] Step 3:

[0737] The server analyzes the received data and uses an emotion engine to evaluate the user's emotional state. It then uses machine learning algorithms to generate the optimal care plan for both the care recipient and the user.

[0738] Step 4:

[0739] The server incorporates the results of the emotion analysis into the care plan. For example, if the user is experiencing stress, it creates a care plan that includes stress reduction activities.

[0740] Step 5:

[0741] The server sends the adjusted care plan to the terminal. The plan includes specific care tasks, recommended activities, and emotionally-based suggestions.

[0742] Step 6:

[0743] The terminal displays the received care plan on its user interface, allowing the user to review it. It also provides instruction on care techniques tailored to the user's emotional state through videos and illustrations.

[0744] Step 7:

[0745] The user implements the provided care plan and provides feedback as their emotional state changes during the plan's progress. This allows the server to understand the user's current emotional state.

[0746] Step 8:

[0747] Feedback is sent from the terminal to the server, which then readjusts the care plan based on that feedback. The system always provides a plan that is adapted to the circumstances of the care recipient and the user, and responds flexibly.

[0748] (Example 2)

[0749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] Current care systems struggle to generate flexible care plans that are tailored to the care recipient's situation and the user's emotions. Furthermore, the reliance on manual or static materials for teaching care techniques makes it difficult to provide personalized plans. Additionally, the lack of features to dynamically update plans based on user feedback can hinder prompt responses.

[0751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0752] In this invention, the server includes means for collecting diverse information based on the care recipient's situation, means for analyzing the collected information and generating an optimal plan using a machine learning algorithm, and means for visually presenting the generated plan on a user interface and accepting input. This makes it possible to generate and provide flexible and dynamic care plans that respond to the care recipient's situation and the user's emotions.

[0753] "Situational information" refers to a variety of data related to the care recipient and the user, including health status, emotional state, and living environment.

[0754] A "machine learning algorithm" is an artificial intelligence technology used in the data analysis process, a method for making predictions and classifications through learning from past data.

[0755] "Plan generation" is the process of formulating appropriate care activities and guidance schedules based on the collected information.

[0756] A "user interface" refers to the screens and operating methods that a user uses to access a system and input or retrieve information.

[0757] "Emotion analysis" is a process that uses natural language processing technology to determine a user's emotional state and utilizes that information to adjust action plans.

[0758] "Instructional items" refer to the specific procedures and learning content of caregiving techniques and activities that users receive.

[0759] The system of this invention includes a series of processes for dynamically generating care plans and optimizing them according to individual needs. Specific embodiments thereof are described below.

[0760] server

[0761] The server continuously collects situational information from care recipients and users and stores it in a database. Network-connected sensors and wearable devices are used to acquire this information. Python libraries such as TensorFlow and PyTorch are used to execute machine learning algorithms. This allows the collected information to be analyzed and care plans to be generated.

[0762] As a concrete example, the server predicts the user's stress level for the day based on data such as heart rate and activity level, and recommends appropriate relaxation activities. By utilizing a generative AI model to analyze emotions and extracting "anxiety" from the user's text feedback, corresponding care is suggested.

[0763] terminal

[0764] The terminal displays the care plan received from the server, allowing users to easily access the plan. A dedicated application running on Android and iOS devices is used for this purpose. The user interface is implemented using React Native, resulting in a visually intuitive design.

[0765] The system also includes a function to collect user feedback and incorporate it into future plans. The device can play videos and diagrams to help users learn caregiving techniques. For example, it can present 3D animations of complex caregiving techniques to support user understanding.

[0766] User

[0767] Users perform daily care activities based on the care plan displayed on their device. This includes implementing suggested stretches, meditations, or activity plans tailored to the person being cared for. Users input their progress on the plan and their own feelings on the device and send this feedback to the server.

[0768] For example, the effectiveness of a plan can be evaluated by having a user perform a "deep breathing exercise to relax" and then provide feedback that they "felt refreshed."

[0769] Example of a prompt

[0770] The prompt text used to input into the generating AI model is something like, "Please suggest an activity plan suitable for a person who is active during the day. It is important that the plan be flexible and adaptable to changes in their emotions."

[0771] In this way, the invention can provide flexible and adaptive care plans based on the needs of the user and the person being cared for, thereby improving the quality of daily life.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The server acquires data on users and those receiving care. This data collection involves inputting data such as heart rate, activity level, and environmental conditions via wearable devices and sensors. This biometric data is stored in a database on the server and processed into a format usable for subsequent processing.

[0775] Step 2:

[0776] The server analyzes the collected data using machine learning algorithms to infer the user's current state and emotional condition. Specifically, it preprocesses the data using Python libraries and calculates stress level predictions and emotion scores based on the algorithms. This analysis result is obtained as output, which forms the basis for generating the next plan.

[0777] Step 3:

[0778] The server generates a care plan based on the analysis results. Utilizing a generation AI model, the plan includes the care recipient's schedule and recommended activities. Specifically, it generates text about the activities, and the artificial intelligence model outputs suggestions tailored to individual needs.

[0779] Step 4:

[0780] The terminal visually displays the care plan received from the server in a user interface. Here, a schedule format and task list are presented for easy user understanding. Furthermore, an interface is provided for the user to input feedback on the plan.

[0781] Step 5:

[0782] Users perform care activities based on the care plan displayed on the terminal and input feedback into the terminal. Examples of user input include feedback on the results of the activities and their feelings, which are entered through digital forms. This information is sent to the server for subsequent analysis.

[0783] Step 6:

[0784] The server receives feedback from the user and uses it to update the care plan. It re-evaluates the user's condition using an emotion analysis algorithm and modifies the plan based on the new analysis results. This revised plan is then sent back to the terminal and prepared for the user to receive.

[0785] (Application Example 2)

[0786] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0787] In modern society, the mental and physical burden on both those receiving care and those providing care is increasing. Under these circumstances, creating appropriate support plans tailored to the individual's condition is not easy, and adjusting support to accommodate the caregiver's stress and emotions is difficult. Furthermore, there is a need for flexible plans that take emotional aspects into account, but the current system is insufficient to address this. To solve these problems, it is necessary to grasp the individual's situation and the caregiver's emotional state in real time and dynamically adjust support plans based on this information.

[0788] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0789] In this invention, the server includes means for collecting information based on the state of the person being supported, means for analyzing the collected information to generate an optimal support plan, and means for analyzing the user's emotional state in real time from camera and voice input. This makes it possible to provide a flexible and optimal support plan that is tailored to the state of the person being supported and the emotions of the supporter.

[0790] "Recipients of support" refers to individuals or groups who are eligible for support, and specifically refers to those who require assistance or support.

[0791] "Information" refers to all data obtained from the person being supported and related circumstances, including data on physical condition, environmental information, and emotional state.

[0792] "Means of collection" refers to the devices or methods used to acquire information, and this includes data acquisition methods using sensor devices and networks.

[0793] "Means of analysis" refers to devices or methods for analyzing collected information to obtain useful insights, and this includes machine learning algorithms and data processing software.

[0794] A "support plan" refers to a detailed plan of the support activities to be carried out for the person receiving support, and this includes the activity schedule, equipment to be used, and necessary personnel.

[0795] "User" refers to the person who uses the system to implement and adjust the support plan, and generally refers to a support worker or care provider.

[0796] "Emotional state" refers to the psychological or emotional state that a user experiences, and includes feelings such as joy, sadness, and stress.

[0797] "Means of real-time analysis" refers to devices or methods that instantly evaluate and analyze a user's emotional state, and this includes voice analysis software and image recognition technology.

[0798] The system for carrying out the present invention consists of two main components: a client device and a server.

[0799] First, the server plays a central role in data processing, collecting and analyzing information related to the person being supported and the supporter. This includes state data, environmental data, and emotional data. The server analyzes this data using machine learning algorithms (e.g., TensorFlow or PyTorch) to generate an optimal support plan for the person being supported. Camera footage and audio input are used to analyze emotional states, and if a specific emotion (e.g., stress) is detected, the plan is adjusted accordingly.

[0800] Next, the client device provides information to the user and collects feedback through the interface. Specifically, a dedicated application is installed on the user's smartphone or tablet, visually displaying the support plan generated on the server. It also has a function to input user emotion data in real time. By utilizing cloud services and keeping the data constantly up-to-date, flexible and rapid responses are possible.

[0801] In this model, as a concrete example, if the caregiver experiences stress while the person being supported is going about their daily life, "stress" is detected through emotion analysis. In this situation, the cloud server immediately suggests relaxation methods to reduce stress and sends a relaxation video to the user's smartphone. For example, a prompt such as "Please suggest ways to reduce the stress a caregiver feels while preparing dinner" might be used. Based on this prompt, the generating AI model provides appropriate advice.

[0802] With the above configuration, a system is provided that enables meticulous support that takes into account the feelings of the supporters, and contributes to reducing the burden on both those receiving support and those providing it.

[0803] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0804] Step 1:

[0805] The server receives status data from both the person being supported and the supporter. Inputs include information on the person being supported's physical condition and the supporter's environment and emotional data. Data collection devices (sensors and smart devices) are used to acquire this data and transmit it to the server. Here, the data is centrally managed and prepared for analysis.

[0806] Step 2:

[0807] The server uses machine learning algorithms to analyze the input state data. This analysis includes calculations in which the algorithm receives the state data and generates an optimal support plan based on the recipient's health status and activity patterns. As a result of the analysis, a customized support plan specifically for the recipient is generated.

[0808] Step 3:

[0809] The server analyzes the user's emotional state in real time via camera and voice input. Camera video and audio data are received from the user as input. An emotion analysis engine analyzes this data to evaluate the user's emotional state (e.g., stress level). The analysis results are used to adjust the support plan.

[0810] Step 4:

[0811] The server dynamically adjusts the support plan based on the user's emotional state and generates new instructions and advice. Input includes the emotional analysis results and the original support plan. Using a generative AI model, the server outputs the optimal action plan in response to a prompt (e.g., "Please suggest advice based on the current emotional state").

[0812] Step 5:

[0813] The terminal presents the user with the adjusted support plan and advice received from the server through a user interface. The input includes the adjusted support plan and advice from the server. The terminal displays this visually, making it easy for the user to understand.

[0814] Step 6:

[0815] Users input feedback on the support plan into a terminal during actual support activities. This input includes the status of the support activities and additional emotional data. This allows for an evaluation of the plan's effectiveness, which is then sent to the server as feedback and used to generate the next support plan.

[0816] Through the steps described above, the present invention makes it possible to provide optimal support adapted to the circumstances of both the person being supported and the person providing the support.

[0817] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0818] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0819] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0821] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0822] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0823] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0824] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0825] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0826] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0827] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0828] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0829] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0830] 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.

[0831] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0832] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0833] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0834] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0835] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0836] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0837] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] Means of collecting data based on the care recipient's situation,

[0841] A means of analyzing collected data to generate an optimal care plan,

[0842] A means of providing the generated care plan to the user and receiving feedback,

[0843] A means of updating the care plan based on the feedback received,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which uses a machine learning algorithm to generate a care plan.

[0847] (Claim 3)

[0848] The system according to claim 1, which provides instruction on nursing care techniques to users using videos and diagrams.

[0849] "Example 1"

[0850] (Claim 1)

[0851] Means of collecting information based on the care recipient's situation,

[0852] A means of analyzing collected information to generate an optimal support plan,

[0853] A means of providing users with support plans generated using a generative AI model and receiving their feedback,

[0854] A means of updating the support plan based on the feedback received,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which uses a machine learning algorithm to generate a support plan.

[0858] (Claim 3)

[0859] The system according to claim 1, which provides instruction on assistive technologies to users using video and illustrations.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] Means of collecting information based on the care recipient's situation,

[0863] A means of analyzing collected information to generate an optimal support plan,

[0864] A means of providing the generated support plan to the user and receiving their feedback,

[0865] A means of updating the support plan based on the feedback received,

[0866] A means of providing activity reminders and guides based on the support situation,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which uses an intelligent machine algorithm to generate a support plan.

[0870] (Claim 3)

[0871] The system according to claim 1, which provides instruction on assistive technologies to users using videos and diagrams.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] Means for collecting diverse information based on the circumstances of the person receiving care,

[0875] A means of analyzing collected information and generating an optimal plan using machine learning algorithms,

[0876] A means of visually presenting the generated plan on a user interface and accepting input,

[0877] A means of performing sentiment analysis based on the input information and updating the plan accordingly.

[0878] A means of presenting instructions to users based on the updated plan,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which uses a generative algorithm to generate a plan.

[0882] (Claim 3)

[0883] The system according to claim 1, which provides user instruction using visual content.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] Means of collecting information based on the condition of the person receiving support,

[0887] A means of analyzing collected information to generate an optimal support plan,

[0888] A means of analyzing the user's emotional state in real time from camera and voice input,

[0889] A means of adjusting and providing support plans to users based on their analyzed emotional states,

[0890] A means of receiving feedback on the generated support plan,

[0891] A means of updating the support plan based on the feedback received,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, which uses a machine learning algorithm to generate a support plan.

[0895] (Claim 3)

[0896] The system according to claim 1, which makes adjustments according to the user's emotional state and proposes a relaxation method. [Explanation of symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting data based on the care recipient's situation, A means of analyzing collected data to generate an optimal care plan, A means of providing the generated care plan to the user and receiving feedback, A means of updating the care plan based on the feedback received, A system that includes this.

2. The system according to claim 1, which uses a machine learning algorithm to generate a care plan.

3. The system according to claim 1, which provides instruction on nursing care techniques to users using videos and diagrams.

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