system

Abstract

We provide the system. [Solution] Means for collecting individual user information, Based on the information collected above, a means of analyzing user needs, A means for automatically generating an optimal care plan based on the aforementioned analysis results, A means for visualizing the generated care plan and providing an interface that can be edited by the user's caregiver, 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, the method 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, it is an important issue to improve the work efficiency of care managers while maintaining the quality of care. Conventionally, creating a care plan requires a great deal of time and labor, increasing the burden on care managers. Also, it has been difficult to quickly provide a care plan that accurately reflects the differences in medical resources in each region and the diverse needs of individual users. In such a situation, there is a demand for quickly providing uniform and appropriate care.

Means for Solving the Problems

[0005] This invention improves work efficiency by providing a system that collects individual user information and uses AI to analyze user needs based on that information. This enables the automatic generation of data-driven optimal care plans and provides a user-friendly interface that visualizes and allows caregivers to easily edit these plans. Furthermore, by monitoring user health indicator data in real time and integrating local medical resource information to analyze availability, it enables the provision of appropriate care services to users. In this way, it reduces the burden on care managers and promotes the standardization and speed of care delivery.

[0006] "Users" refer to individuals who are eligible to receive care services and who require individualized care based on their health condition and lifestyle.

[0007] "Individual information" refers to data that includes the user's unique health condition, living environment, family wishes, and past care history, and is the information that forms the basis for creating a care plan.

[0008] "Needs" refer to the demands and support that users should have met when receiving care.

[0009] A "care plan" is a detailed plan formulated to provide care services tailored to the user's needs, and it includes implementation procedures and schedules.

[0010] The term "interface" refers to the screens and operating environments that users can use to review and edit care plans, and plays a role in facilitating interaction with the system.

[0011] "Real-time monitoring" refers to continuously monitoring users' health indicator data and immediately reflecting that information in the system.

[0012] "Medical resource information" is data that collects information on medical institutions and care service providers in the region, and is useful for developing care plans.

[0013] "Visualization" refers to clearly displaying the care plan generated by the system, making it easy for staff to understand and utilize its contents. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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), etc.

[0018] 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.

[0019] 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] In an embodiment of the present invention, a system is constructed that collects individual user information, analyzes that information using AI, and automatically generates a care plan. This system mainly consists of two components: a server and a terminal, and the user operates it through its interface.

[0036] The server aggregates and integrates user health information, past care history, and local medical resource information from various data sources. Next, it analyzes this data using AI algorithms to extract user needs. The analysis utilizes natural language processing (NLP) to extract key information from text data and machine learning models to predict the user's condition. Based on the analysis results, the server automatically generates a care plan optimized for the user. This care plan includes specific care services, their timing, and recommended local medical services.

[0037] The terminal resides on a device operated by the care manager, who is responsible for the user, and receives care plans sent from the server. The terminal displays this plan through a visually intuitive interface, allowing the user to easily review and edit it. Editing allows for schedule adjustments and the addition or deletion of specific care items, with final adjustments made based on the user's expertise.

[0038] As a concrete example, consider the case of Mr. A, an elderly person with heart disease. He inputs user information using a terminal. The server analyzes this information and generates a care plan that includes, for example, "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." This allows the care manager to quickly identify a service plan suitable for Mr. A and efficiently transition to its implementation.

[0039] Thus, the invention according to this embodiment reduces the workload of care managers while improving the quality of care provided to users.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches an application on their device and collects information about themselves. Specifically, they input information such as their health status, daily life, and family wishes in an interview format. The device saves this information as text data and transmits it to the server in real time.

[0043] Step 2:

[0044] The server receives user information sent from the terminal and stores it in an existing database. This includes the user's past care history and information on medical resources in their region. The server integrates this data and prepares it as a preliminary step for analysis.

[0045] Step 3:

[0046] The server executes AI algorithms on the aggregated data. Data analysis uses natural language processing (NLP) to extract key information and machine learning models to identify user needs. This process generates a list of the most suitable care items for each user.

[0047] Step 4:

[0048] The server automatically generates a care plan based on the analysis results. The generated care plan includes specific details of care services, a schedule, and recommended local medical services. This plan is optimized for the user's health condition and local medical resources.

[0049] Step 5:

[0050] The server sends the generated care plan to the terminal. The terminal provides an interface for the user to review this plan, displaying it in an easy-to-understand format.

[0051] Step 6:

[0052] Users can review and edit their care plans using the interface on their devices. Editing allows for schedule adjustments, addition and removal of specific services, and changes in priority. The plan is finalized after the user has completed final review and adjustments.

[0053] Step 7:

[0054] The edited final care plan is sent back from the terminal to the server, where it is ready for implementation with the user. The server shares the plan with other stakeholders as needed, establishing a collaborative system to improve the quality of service delivery.

[0055] (Example 1)

[0056] 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."

[0057] The current care plan creation process is problematic because it requires a lot of manual work to address the individual needs of users, making it time-consuming and labor-intensive. Furthermore, real-time monitoring of health status and the rapid updating of care plans based on that data are difficult. In addition, effectively utilizing local medical resources is challenging, hindering the optimization of care services.

[0058] 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.

[0059] In this invention, the server includes means for collecting individual user information from various data sources, means for integrating and analyzing the collected information using natural language processing and machine learning models, and means for automatically generating an optimized care plan using a generative AI model based on the analysis results. This enables the real-time and efficient generation and updating of care plans for individual users. Furthermore, by integrating local medical resource information, the effective use of care services can also be realized.

[0060] "Personalized information" refers to data identified for each user, including information about their health status, care history, and medical resources.

[0061] "Data sources" refer to the origins and channels for providing information, such as hospital databases and APIs of healthcare service providers.

[0062] Natural language processing is a technology that understands and analyzes human language, and is used to extract important information from text data.

[0063] A "machine learning model" is an algorithm that learns from data and finds patterns, and is used to predict future states and trends.

[0064] A "generative AI model" is a model that uses artificial intelligence to generate new content and data, and is used to automatically generate optimal care plans.

[0065] A "user interface" refers to the operation screen or display screen that allows users to interact with a system, providing information visually and enabling editing operations.

[0066] In an embodiment of this invention, a system is constructed in which two main components, a server and a terminal, work in cooperation with each other.

[0067] The server functions as an information gathering device, collecting individual user information from multiple data sources. This includes databases of healthcare institutions and APIs of healthcare service providers. The server runs on a computer system with a high-performance processor and sufficient storage to perform data integration and analysis.

[0068] The server utilizes natural language processing technology to extract important information from text data. It also uses machine learning models to predict the user's health status and care needs. Based on this analysis process, the server uses a generative AI model to automatically generate an optimized care plan. The generated care plan includes specific care services, their timing, and recommendations for local medical services.

[0069] The generated care plan is sent to a terminal. This terminal is operated by the care manager and visualizes the care plan through its user interface. The terminal's interface is designed for ease of use, allowing users to easily review and edit the plan as needed. Users can adjust schedules, add or remove care items, and finally save the customized plan.

[0070] As a concrete example, consider the case of an elderly person with heart disease. The care manager would use a terminal to input user information. The server would then analyze this information and generate a care plan that includes "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." An example of a prompt message might be, "Please generate the optimal care plan based on the elderly person's health condition and care history."

[0071] This allows the system to efficiently provide care tailored to each user and optimize the entire care process.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server collects individual user information from various data sources. Inputs include information from healthcare institution databases and APIs provided by healthcare service providers. The server then stores the collected data in storage, using it as foundational data for subsequent analysis steps. Specifically, this includes the user's health status and past care history.

[0075] Step 2:

[0076] The server integrates the collected individual information and performs data preprocessing. The input is the raw data collected in step 1. The server standardizes data in different formats and imputes missing data. It removes noise and generates a clean dataset. As output, data in a format suitable for analysis is obtained.

[0077] Step 3:

[0078] The server extracts important information from text data using natural language processing techniques. The input is the clean dataset generated in step 2. The server runs a language analysis algorithm to extract important keywords and phrases related to health status and care needs. The extracted information forms the basis for analysis in the next step.

[0079] Step 4:

[0080] The server uses a machine learning model to predict the user's health status. The input is the key information extracted in step 3. The server feeds the data into the trained model and predicts health trends and potential risks. The output provides insights into the user's future health status.

[0081] Step 5:

[0082] The server automatically generates an optimized care plan using a generative AI model. The input is the predicted health status data obtained in step 4. The generative AI model uses this information to create a specific care plan. This plan includes the types and schedules of recommended care services. The output is the care plan, usually expressed in text format.

[0083] Step 6:

[0084] The terminal receives the care plan sent from the server and displays it through the user interface. The input is the care plan generated in step 5. The terminal visually presents the plan on the interface, allowing the care manager to review its contents. The output is an editable care plan displayed on the screen.

[0085] Step 7:

[0086] The user (care manager) edits and customizes the care plan via the terminal. The input is the care plan displayed in step 6. The user adjusts the schedule and adds or removes necessary care items based on their expertise. Finally, the customized care plan is completed and saved. The output is the adjusted care plan.

[0087] (Application Example 1)

[0088] 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."

[0089] In the fields of nursing care and medical care, there is a need for the generation of optimized care plans based on individual user information and for safe and efficient information presentation methods. However, current systems have limitations in real-time data analysis, making it difficult to quickly check information or adjust care plans, especially when users are away from home. Furthermore, conventional technologies that rely on visual interfaces have the problem of making it difficult to perform the necessary flexible information editing.

[0090] 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.

[0091] In this invention, the server includes means for collecting individual user information, means for analyzing the user's needs based on the collected information, means for automatically generating an optimal care plan based on the analysis results, means for displaying the care plan in augmented reality format via smart glasses, and means for adjusting the care plan using voice input. This allows the user's caregiver to quickly and flexibly check the care plan and adjust it as needed, even when away from the office.

[0092] "Individual user information" refers to data about the health status and living environment of individuals who are the subjects of care or medical treatment.

[0093] "Needs analysis" is the process of evaluating and identifying the necessary care and medical needs based on the user's current situation and past data.

[0094] "Automatic care plan generation" is a process that uses AI technology to mechanically create a plan of optimal care or medical services for the user.

[0095] "Displaying in augmented reality format" is a technology that uses devices such as smart glasses to overlay computer-generated information onto the real environment and present it to the visual senses.

[0096] "Adjusting the care plan using voice input" refers to a method of operation that uses voice recognition technology to analyze the caregiver's statements and modify or update the content of the care plan.

[0097] A "service provider" is a professional who directly interacts with service users to provide care and medical services, offering support and coordinating their plans.

[0098] The system that implements this application primarily consists of a server and a terminal including smart glasses. The server collects individual user information, analyzes needs using AI algorithms, and automatically generates an optimal care plan based on that analysis. This utilizes natural language processing (NLP) technology for analyzing text data and machine learning models for predicting the user's health data.

[0099] The device uses smart glasses to display the generated care plan in augmented reality (AR) format. This allows caregivers to visually confirm the information. The care plan can be adjusted as needed using voice input technology. Google® Cloud Speech-to-Text API is used for speech recognition.

[0100] As a concrete example, consider a care plan to effectively manage the weight gain of elderly person B. In this case, the server collects B's past dietary and exercise data and generates a plan through AI analysis, such as "walking three times a week" and "regular delivery of low-salt meals." The caregiver can view this plan by wearing smart glasses and adjust things like the date and time of walking using voice input.

[0101] The generative AI model can be provided with prompts such as:

[0102] "Please propose the next care actions based on Ms. B's health data for the past week."

[0103] "Please analyze the exercise history for the past two weeks and identify the issues that should be discussed during the next care visit."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects individual user information. Specifically, it retrieves the user's health status, care history, and local medical resource information from a database. Inputs include user ID and past health data, and output is the generation of an integrated user information dataset.

[0107] Step 2:

[0108] The server analyzes user needs using AI algorithms. Based on the collected data, it performs text data analysis using NLP techniques and predicts health status using machine learning models. An integrated dataset is used as input, and the output is an analysis result that clearly identifies user needs. Specifically, an AI model (e.g., scikit-learn or TENSORFLOW®) processes the data.

[0109] Step 3:

[0110] The server automatically generates a care plan based on the analysis results. The generating AI model creates a plan that includes care content, implementation timing, and recommended medical services. The input is the analysis results obtained in the previous step, and the output is an optimized care plan. Specific examples include weekly exercise plans and dietary suggestions.

[0111] Step 4:

[0112] The device displays the generated care plan in augmented reality (AR) through smart glasses. Plan information is provided visually in real time. The care plan is sent from the server as input, and the user can view the information via the AR display as output. Specifically, the plan is overlaid on the smart glasses' display.

[0113] Step 5:

[0114] The user (caregiver) adjusts the care plan using voice input. Voice recognition technology analyzes instructions for changes and modifications to the plan. Voice data is taken as input, and the care plan is updated as output. Specifically, this includes actions such as changing appointments or adding new tasks via voice commands.

[0115] 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.

[0116] This invention is a system for recognizing users' emotions in real time and reflecting them in adjusting care plans. The system consists of three main components: a server, a terminal, and an emotion engine. Users can use this system to provide efficient and effective care services.

[0117] The server manages individual user information, health indicator data, and local healthcare resource information, and analyzes user needs based on this data. In addition, it incorporates a function to evaluate the user's stress level and emotional state based on emotional data transmitted from the emotion engine. This evaluation enables the optimization of care plans.

[0118] The terminal displays a care plan, including emotion recognition, to the user and provides an interface for editing it as needed. The terminal displays emotion data acquired by the emotion engine in real time and provides the user with alerts in response to changes in emotions. This enables the user to provide more appropriate care services more quickly.

[0119] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize their emotional state at that moment. The resulting emotional data is sent to the server as an indicator of stress levels and emotional changes. This data is used to improve the user's sense of security and satisfaction when receiving care services.

[0120] As a concrete example, consider the case of an elderly person, Mr. B. Using an application on his device, the emotion engine recognizes that Mr. B's recent stress level is high. This information is sent to the server in real time, and the server uses this to generate a care plan that includes suggestions such as "adding relaxing activities" and "ensuring time for communication with family." The care manager, as the user, can then fine-tune the plan based on these suggestions and provide customized care services for Mr. B.

[0121] Thus, by incorporating the user's emotional state into the care plan, the present invention makes it possible to provide individualized care, further improving the quality of care.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user launches an application on their device and enters basic information about themselves. This information includes details about their health and daily life. The device also transmits audio and video data obtained through interaction with the user to the emotion engine.

[0125] Step 2:

[0126] The emotion engine analyzes audio data, facial images, and body movements received from the device to recognize the user's emotional state in real time. This analysis uses machine learning models to calculate emotional changes and stress levels numerically.

[0127] Step 3:

[0128] The device displays emotional data received from the emotion engine, informing the user of their current emotional state. If a significant change in emotion is detected, a notification function is activated on the device to alert the user.

[0129] Step 4:

[0130] The server integrates user information and emotional data transmitted from the terminal. Based on this, an AI algorithm analyzes the user's needs and generates a care plan that takes into account their emotions and health status. Past care history and stress levels are also considered in the analysis.

[0131] Step 5:

[0132] The server sends the newly generated care plan to the terminal. The terminal provides an interface that allows the user to review the plan in detail. The plan includes specific action items that reflect emotional data.

[0133] Step 6:

[0134] Users can review and edit care plans using the interface on their devices. For example, if a user is experiencing high stress levels, relaxation activities can be added. After adjustments, the plan is finalized.

[0135] Step 7:

[0136] Based on the established care plan, the user provides specific care services to the client. The server stores emotional data and care implementation logs, preparing them for use in improving future plans.

[0137] (Example 2)

[0138] 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".

[0139] The current care system makes it difficult to instantly reflect the emotional state of the user and adjust the care plan accordingly. Furthermore, there is a lack of individualized care that takes the user's emotional state into account, highlighting the need for effective means to improve the quality of care services.

[0140] 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.

[0141] In this invention, the server includes means for recognizing the user's emotional state in real time and analyzing emotional data; means for collecting individual information including the emotional data and analyzing the user's needs based on that information; and means for automatically generating an optimal care plan using an AI model based on the analysis results. This makes it possible to automatically generate a care plan that takes the user's emotional state into consideration and to provide appropriate care services.

[0142] A "user" is an individual who receives care services through the system.

[0143] "Emotional state" refers to information that indicates the user's stress level and momentary psychological state.

[0144] "Real-time" refers to a state in which processing or a response occurs immediately or almost immediately.

[0145] "Emotional data" refers to information related to emotions recognized from the user's voice, facial expressions, and body movements.

[0146] "Personalized information" refers to information specific to each user, such as their health status, past history, and living environment.

[0147] "Analyzing needs" is the process of identifying the optimal care requirements and services based on the individual user's information and emotional state.

[0148] A "care plan" is a set of care activities and policies formulated to optimize the health and well-being of the user.

[0149] A "generative AI model" is a mathematical model developed to perform data processing and decision-making using artificial intelligence technology.

[0150] An "interface" is the point of contact through which a user accesses a system and manipulates or displays information.

[0151] This invention is a system that enables emotion recognition and real-time care plan generation. The following describes how the server, terminal, and user components of the system implement this invention.

[0152] First, the server stores individual user information and uses this to analyze the user's needs. Specifically, the server receives emotional data from an emotion engine and integrates it with other health indicator data. This data is updated in real time using a database management system. The server also creates care plans using a generative AI model, utilizing natural language processing technology in this process. This generated care plan is then used to provide the most appropriate care services to each individual user through appropriate feedback.

[0153] Next, the terminal functions as an interface that provides the user with information about the care plan. The terminal displays data acquired from the emotion engine in real time, visualizing the generated care plan. This allows the user to easily understand the plan and edit it as needed. The interface needs to be intuitively designed and is typically operated on mobile devices such as tablets and smartphones.

[0154] Users can quickly adjust care plans to suit the emotional state of the user using the provided interface. This process enables personalized services and improves the quality of care. Care managers and care staff can use this system to provide more attentive care.

[0155] As a concrete example, in the case of an elderly person, if the emotional engine assesses the user's stress level as high, this information is sent to the server. Based on this, the server generates a care plan such as "adding relaxing activities" or "ensuring time for communication with family." This suggestion is visualized to the user via the terminal, and the user can adjust the plan according to their situation.

[0156] An example of a prompt based on the generated AI model is: "Please use the elderly person's recent emotional data and health indicators to generate a new care plan."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server collects individual user information and health indicator data from a database. Input includes the user's ID and sensor data, and based on this, a database management system is used to extract relevant health indicator data. The extracted data is compiled into individual profiles.

[0160] Step 2:

[0161] The emotion engine analyzes the user's voice, facial expressions, and body movements. The input is sensor data collected in real time, and stress levels and emotional states are evaluated through an emotion recognition algorithm. This evaluation result is output as emotion data to the subsequent steps.

[0162] Step 3:

[0163] The server integrates the health indicator data collected in Step 1 with the emotional data obtained in Step 2. Using this integrated data as input, the server performs data calculations using analytical software to analyze the user's needs. The output is an analytical report that reflects the user's needs and current emotional state.

[0164] Step 4:

[0165] The server automatically generates care plans using an AI model based on the analysis results. The input is an analysis report, and the AI ​​model uses this data to generate appropriate care activities and improvement suggestions. The output is a care plan optimized for the user.

[0166] Step 5:

[0167] The terminal visualizes the generated care plan on the user interface. It receives the care plan sent from the server as input and performs format conversion for visual display. The output is a graphical display of the care plan that the user can refer to.

[0168] Step 6:

[0169] The user reviews the care plan displayed on the terminal and edits it as needed. Input is care plan information from the terminal, and the user interface allows for fine-tuning of the plan. Output is the edited, customized care plan.

[0170] (Application Example 2)

[0171] 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".

[0172] In today's service industry, maintaining customer satisfaction requires service providers to quickly and appropriately understand customers' emotional states and respond flexibly. However, traditional systems struggle to recognize customer emotions in real time and provide appropriate responses, which limits the optimization of the customer experience.

[0173] 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.

[0174] In this invention, the server includes means for collecting individual user information, means for analyzing user requests, and means for automatically generating an optimal plan. This enables immediate recognition of the customer's emotional state and real-time notification to the service provider, thereby improving the customer experience.

[0175] "Users" refers to customers who use the system or individuals who receive care services.

[0176] "Personalized information" refers to specific data about a user, including their name, age, and past behavioral history.

[0177] "Requirements" refer to the results or needs that users want to obtain through the system, and can also refer to specific services or support.

[0178] A "plan" refers to a series of measures and proposals created to provide the best possible service to users.

[0179] "Information processing means" refers to technical functions that generate and display information useful to users using collected data.

[0180] "Audio and image data" refers to information including recordings of the user's voice, as well as media formats such as photographs and videos.

[0181] "Emotional state" is a concept that describes the user's emotional state and psychological condition, and is inferred from their voice and facial expressions.

[0182] "Real-time" refers to a state where processing is done instantly in accordance with real-world time, with virtually no time lag.

[0183] A "notification" is information or an alert message sent from a system to a user or service provider.

[0184] "Service provider" refers to a person or organization that is responsible for providing services to users through a system.

[0185] To implement this invention, a system is constructed that analyzes the emotional state of customers in real time and sends necessary notifications to service providers. This system mainly consists of a server, a terminal, and an emotion recognition engine.

[0186] The server collects and manages individual user information and analyzes user requests based on that data. It also receives sentiment data in real time and automatically generates appropriate service plans. General database management systems and statistical analysis software are used for data processing in this process.

[0187] The terminal provides the operating screen for mobile devices and smart glasses used by store staff, displaying generated plans and notifications. The interface on the terminal visually displays information based on the user's emotional state, allowing staff to flexibly adjust their responses as needed.

[0188] The emotion recognition engine collects user voice and image data and infers their emotional state in real time from that data. Machine learning models and voice / image processing algorithms are used for analysis, such as OpenCV or dedicated emotion recognition software. The analysis results are sent to a server and used to optimize the service.

[0189] As a concrete example, consider a scenario in a cafe. If a customer appears dissatisfied at the register, the emotion recognition engine identifies this state and sends the information to the server. Based on this data, the server sends a notification to the staff member's terminal prompting them to take immediate action. This allows the staff to improve their service to customers and increase their satisfaction.

[0190] An example of a prompt for a generating AI model is, "Please tell me what to do when a customer looks dissatisfied." This prompt is used to complement the recommendations automatically generated by the system and to suggest specific actions that staff should take.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server collects individual user information and health indicator data and stores it in a database. Inputs are basic user profile information and health data, and outputs are database entries built based on that data. This process uses a data storage and management system to maintain data integrity and process data efficiently.

[0194] Step 2:

[0195] The emotion recognition engine receives audio and image data transmitted from the device and analyzes the user's emotional state. The input is real-time audio and facial expression data, and the output is digital data of the analyzed emotional state. It utilizes machine learning algorithms and employs speech processing and computer vision technologies to identify emotions.

[0196] Step 3:

[0197] The server analyzes user requests using emotional data provided by the emotion recognition engine. The input is digital data of emotional states, and the analysis results output countermeasures tailored to the user's needs. At this stage, statistical analysis tools are used to comprehensively evaluate the data and automatically generate an optimal care plan.

[0198] Step 4:

[0199] The terminal receives care plans and related notifications sent from the server and displays them to the service provider. Input is notification data and plan information from the server, and output is a visual interface display for the service provider. A simple display method using user interface design is employed via mobile devices and smart glasses.

[0200] Step 5:

[0201] Based on information on their device, users adjust service responses as needed to provide the best possible service to their customers. Inputs are notifications and plans displayed on the device, while outputs are the adjusted customer service actions. The service provider's judgment is crucial, and quick and effective action is required.

[0202] Step 6:

[0203] The system uses a generative AI model as input to provide prompts and supplements the suggestions that support the system's optimal response. The input consists of prompts tailored to the user's situation, while the output is additional response suggestions generated by the AI. Effective instructions are generated by a generative AI based on an expert system.

[0204] 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.

[0205] 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 those described above. 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 shown 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] In an embodiment of the present invention, a system is constructed that collects individual user information, analyzes that information using AI, and automatically generates a care plan. This system mainly consists of two components: a server and a terminal, and the user operates it through its interface.

[0221] The server aggregates and integrates user health information, past care history, and local medical resource information from various data sources. Next, it analyzes this data using AI algorithms to extract user needs. The analysis utilizes natural language processing (NLP) to extract key information from text data and machine learning models to predict the user's condition. Based on the analysis results, the server automatically generates a care plan optimized for the user. This care plan includes specific care services, their timing, and recommended local medical services.

[0222] The terminal resides on a device operated by the care manager, who is responsible for the user, and receives care plans sent from the server. The terminal displays this plan through a visually intuitive interface, allowing the user to easily review and edit it. Editing allows for schedule adjustments and the addition or deletion of specific care items, with final adjustments made based on the user's expertise.

[0223] As a concrete example, consider the case of Mr. A, an elderly person with heart disease. He inputs user information using a terminal. The server analyzes this information and generates a care plan that includes, for example, "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." This allows the care manager to quickly identify a service plan suitable for Mr. A and efficiently transition to its implementation.

[0224] Thus, the invention according to this embodiment reduces the workload of care managers while improving the quality of care provided to users.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user launches an application on their device and collects information about themselves. Specifically, they input information such as their health status, daily life, and family wishes in an interview format. The device saves this information as text data and transmits it to the server in real time.

[0228] Step 2:

[0229] The server receives user information sent from the terminal and stores it in an existing database. This includes the user's past care history and information on medical resources in their region. The server integrates this data and prepares it as a preliminary step for analysis.

[0230] Step 3:

[0231] The server executes AI algorithms on the aggregated data. Data analysis uses natural language processing (NLP) to extract key information and machine learning models to identify user needs. This process generates a list of the most suitable care items for each user.

[0232] Step 4:

[0233] The server automatically generates a care plan based on the analysis results. The generated care plan includes specific details of care services, a schedule, and recommended local medical services. This plan is optimized for the user's health condition and local medical resources.

[0234] Step 5:

[0235] The server sends the generated care plan to the terminal. The terminal provides an interface for the user to review this plan, displaying it in an easy-to-understand format.

[0236] Step 6:

[0237] Users can review and edit their care plans using the interface on their devices. Editing allows for schedule adjustments, addition and removal of specific services, and changes in priority. The plan is finalized after the user has completed final review and adjustments.

[0238] Step 7:

[0239] The edited final care plan is sent back from the terminal to the server, where it is ready for implementation with the user. The server shares the plan with other stakeholders as needed, establishing a collaborative system to improve the quality of service delivery.

[0240] (Example 1)

[0241] 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."

[0242] The current care plan creation process is problematic because it requires a lot of manual work to address the individual needs of users, making it time-consuming and labor-intensive. Furthermore, real-time monitoring of health status and the rapid updating of care plans based on that data are difficult. In addition, effectively utilizing local medical resources is challenging, hindering the optimization of care services.

[0243] 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.

[0244] In this invention, the server includes means for collecting individual user information from various data sources, means for integrating and analyzing the collected information using natural language processing and machine learning models, and means for automatically generating an optimized care plan using a generative AI model based on the analysis results. This enables the real-time and efficient generation and updating of care plans for individual users. Furthermore, by integrating local medical resource information, the effective use of care services can also be realized.

[0245] "Personalized information" refers to data identified for each user, including information about their health status, care history, and medical resources.

[0246] "Data sources" refer to the origins and channels for providing information, such as hospital databases and APIs of healthcare service providers.

[0247] Natural language processing is a technology that understands and analyzes human language, and is used to extract important information from text data.

[0248] A "machine learning model" is an algorithm that learns from data and finds patterns, and is used to predict future states and trends.

[0249] A "generative AI model" is a model that uses artificial intelligence to generate new content and data, and is used to automatically generate optimal care plans.

[0250] A "user interface" refers to the operation screen or display screen that allows users to interact with a system, providing information visually and enabling editing operations.

[0251] In an embodiment of this invention, a system is constructed in which two main components, a server and a terminal, work in cooperation with each other.

[0252] The server functions as an information gathering device, collecting individual user information from multiple data sources. This includes databases of healthcare institutions and APIs of healthcare service providers. The server runs on a computer system with a high-performance processor and sufficient storage to perform data integration and analysis.

[0253] The server utilizes natural language processing technology to extract important information from text data. It also uses machine learning models to predict the user's health status and care needs. Based on this analysis process, the server uses a generative AI model to automatically generate an optimized care plan. The generated care plan includes specific care services, their timing, and recommendations for local medical services.

[0254] The generated care plan is sent to a terminal. This terminal is operated by the care manager and visualizes the care plan through its user interface. The terminal's interface is designed for ease of use, allowing users to easily review and edit the plan as needed. Users can adjust schedules, add or remove care items, and finally save the customized plan.

[0255] As a concrete example, consider the case of an elderly person with heart disease. The care manager would use a terminal to input user information. The server would then analyze this information and generate a care plan that includes "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." An example of a prompt message might be, "Please generate the optimal care plan based on the elderly person's health condition and care history."

[0256] This allows the system to efficiently provide care tailored to each user and optimize the entire care process.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server collects individual user information from various data sources. Inputs include information from healthcare institution databases and APIs provided by healthcare service providers. The server then stores the collected data in storage, using it as foundational data for subsequent analysis steps. Specifically, this includes the user's health status and past care history.

[0260] Step 2:

[0261] The server integrates the collected individual information and performs data preprocessing. The input is the raw data collected in step 1. The server standardizes data in different formats and imputes missing data. It removes noise and generates a clean dataset. As output, data in a format suitable for analysis is obtained.

[0262] Step 3:

[0263] The server extracts important information from text data using natural language processing techniques. The input is the clean dataset generated in step 2. The server runs a language analysis algorithm to extract important keywords and phrases related to health status and care needs. The extracted information forms the basis for analysis in the next step.

[0264] Step 4:

[0265] The server uses a machine learning model to predict the user's health status. The input is the key information extracted in step 3. The server feeds the data into the trained model and predicts health trends and potential risks. The output provides insights into the user's future health status.

[0266] Step 5:

[0267] The server automatically generates an optimized care plan using a generative AI model. The input is the predicted health status data obtained in step 4. The generative AI model uses this information to create a specific care plan. This plan includes the types and schedules of recommended care services. The output is the care plan, usually expressed in text format.

[0268] Step 6:

[0269] The terminal receives the care plan sent from the server and displays it through the user interface. The input is the care plan generated in step 5. The terminal visually presents the plan on the interface, allowing the care manager to review its contents. The output is an editable care plan displayed on the screen.

[0270] Step 7:

[0271] The user (care manager) edits and customizes the care plan via the terminal. The input is the care plan displayed in step 6. The user adjusts the schedule and adds or removes necessary care items based on their expertise. Finally, the customized care plan is completed and saved. The output is the adjusted care plan.

[0272] (Application Example 1)

[0273] 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 glasses 214 will be referred to as the "terminal."

[0274] In the fields of nursing care and medical care, there is a need for the generation of optimized care plans based on individual user information and for safe and efficient information presentation methods. However, current systems have limitations in real-time data analysis, making it difficult to quickly check information or adjust care plans, especially when users are away from home. Furthermore, conventional technologies that rely on visual interfaces have the problem of making it difficult to perform the necessary flexible information editing.

[0275] 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.

[0276] In this invention, the server includes means for collecting individual user information, means for analyzing the user's needs based on the collected information, means for automatically generating an optimal care plan based on the analysis results, means for displaying the care plan in augmented reality format via smart glasses, and means for adjusting the care plan using voice input. This allows the user's caregiver to quickly and flexibly check the care plan and adjust it as needed, even when away from the office.

[0277] "Individual user information" refers to data about the health status and living environment of individuals who are the subjects of care or medical treatment.

[0278] "Needs analysis" is the process of evaluating and identifying the necessary care and medical needs based on the user's current situation and past data.

[0279] "Automatic care plan generation" is a process that uses AI technology to mechanically create a plan of optimal care or medical services for the user.

[0280] "Displaying in augmented reality format" is a technology that uses devices such as smart glasses to overlay computer-generated information onto the real environment and present it to the visual senses.

[0281] "Adjusting the care plan using voice input" refers to a method of operation that uses voice recognition technology to analyze the caregiver's statements and modify or update the content of the care plan.

[0282] A "service provider" is a professional who directly interacts with service users to provide care and medical services, offering support and coordinating their plans.

[0283] The system that realizes this application example has a server and a terminal including smart glasses as main components. The server collects the user's individual information, analyzes the needs using AI algorithms, and automatically generates an optimal care plan based on this. For this, natural language processing (NLP) technology is utilized for parsing text data, and a machine learning model is used for predicting the user's health data.

[0284] The terminal prepares smart glasses and displays the generated care plan in augmented reality (AR) format. Thus, the person in charge of the user can visually confirm the information. Using voice input technology, the care plan can be adjusted as needed. Google Cloud Speech-to-Text API is used for voice recognition.

[0285] As a specific example, consider a care plan for effectively managing the increased weight of elderly person B. In this case, the server collects B's past diet and exercise data and generates plans such as "walking three times a week" and "regular delivery of low-salt food" through AI analysis. The person in charge of the user can wear the smart glasses to confirm this plan and adjust it by voice input, such as changing the date and time of walking.

[0286] The following prompt sentences can be provided to the generated AI model:

[0287] "Please propose the next care actions based on B's one-week health data."

[0288] "Analyze the two-week exercise history and show the issues to be discussed in the next care visit."

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The server collects individual user information. Specifically, it retrieves the user's health status, care history, and local medical resource information from a database. Inputs include user ID and past health data, and output is the generation of an integrated user information dataset.

[0292] Step 2:

[0293] The server analyzes user needs using AI algorithms. Based on the collected data, it performs text data analysis using NLP techniques and predicts health status using machine learning models. An integrated dataset is used as input, and the output is an analysis result that clearly identifies user needs. Specifically, an AI model (e.g., scikit-learn or TensorFlow) processes the data.

[0294] Step 3:

[0295] The server automatically generates a care plan based on the analysis results. The generating AI model creates a plan that includes care content, implementation timing, and recommended medical services. The input is the analysis results obtained in the previous step, and the output is an optimized care plan. Specific examples include weekly exercise plans and dietary suggestions.

[0296] Step 4:

[0297] The device displays the generated care plan in augmented reality (AR) through smart glasses. Plan information is provided visually in real time. The care plan is sent from the server as input, and the user can view the information via the AR display as output. Specifically, the plan is overlaid on the smart glasses' display.

[0298] Step 5:

[0299] The user (caregiver) adjusts the care plan using voice input. Voice recognition technology analyzes instructions for changes and modifications to the plan. Voice data is taken as input, and the care plan is updated as output. Specifically, this includes actions such as changing appointments or adding new tasks via voice commands.

[0300] 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.

[0301] This invention is a system for recognizing users' emotions in real time and reflecting them in adjusting care plans. The system consists of three main components: a server, a terminal, and an emotion engine. Users can use this system to provide efficient and effective care services.

[0302] The server manages individual user information, health indicator data, and local healthcare resource information, and analyzes user needs based on this data. In addition, it incorporates a function to evaluate the user's stress level and emotional state based on emotional data transmitted from the emotion engine. This evaluation enables the optimization of care plans.

[0303] The terminal displays a care plan, including emotion recognition, to the user and provides an interface for editing it as needed. The terminal displays emotion data acquired by the emotion engine in real time and provides the user with alerts in response to changes in emotions. This enables the user to provide more appropriate care services more quickly.

[0304] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize their emotional state at that moment. The resulting emotional data is sent to the server as an indicator of stress levels and emotional changes. This data is used to improve the user's sense of security and satisfaction when receiving care services.

[0305] As a specific example, considering the case of an elderly person, Mr. B, the emotion engine recognizes that Mr. B's recent stress level is high using an application on the terminal. This information is sent to the server in real time, and based on this, the server generates a care plan such as "adding relaxing activities" or "ensuring communication time with family members". The user, who is the care manager, can fine-tune the plan based on this proposal and provide a customized care service for Mr. B.

[0306] Thus, according to the present invention, by incorporating the user's emotional state into the care plan, it becomes possible to provide individualized care, further improving the quality of care.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The user launches an application on the terminal and enters basic information about the user. This information includes details about the health condition and daily life. Also, the terminal sends voice and video data obtained through the interaction with the user to the emotion engine.

[0310] Step 2:

[0311] The emotion engine analyzes the voice data, facial expression images, and body movements received from the terminal and recognizes the user's emotional state in real time. For this analysis, a machine learning model is used, and changes in emotions and stress levels are calculated as numerical values.

[0312] Step 3:

[0313] The terminal displays the emotion data received from the emotion engine and notifies the user of the current emotional state. When a significant change in emotion is recognized, a function to notify the user on the terminal works to arouse attention.

[0314] Step 4:

[0315] The server integrates user information and emotional data transmitted from the terminal. Based on this, an AI algorithm analyzes the user's needs and generates a care plan that takes into account their emotions and health status. Past care history and stress levels are also considered in the analysis.

[0316] Step 5:

[0317] The server sends the newly generated care plan to the terminal. The terminal provides an interface that allows the user to review the plan in detail. The plan includes specific action items that reflect emotional data.

[0318] Step 6:

[0319] Users can review and edit care plans using the interface on their devices. For example, if a user is experiencing high stress levels, relaxation activities can be added. After adjustments, the plan is finalized.

[0320] Step 7:

[0321] Based on the established care plan, the user provides specific care services to the client. The server stores emotional data and care implementation logs, preparing them for use in improving future plans.

[0322] (Example 2)

[0323] 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".

[0324] The current care system makes it difficult to instantly reflect the emotional state of the user and adjust the care plan accordingly. Furthermore, there is a lack of individualized care that takes the user's emotional state into account, highlighting the need for effective means to improve the quality of care services.

[0325] 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.

[0326] In this invention, the server includes means for recognizing the user's emotional state in real time and analyzing emotional data; means for collecting individual information including the emotional data and analyzing the user's needs based on that information; and means for automatically generating an optimal care plan using an AI model based on the analysis results. This makes it possible to automatically generate a care plan that takes the user's emotional state into consideration and to provide appropriate care services.

[0327] A "user" is an individual who receives care services through the system.

[0328] "Emotional state" refers to information that indicates the user's stress level and momentary psychological state.

[0329] "Real-time" refers to a state in which processing or a response occurs immediately or almost immediately.

[0330] "Emotional data" refers to information related to emotions recognized from the user's voice, facial expressions, and body movements.

[0331] "Personalized information" refers to information specific to each user, such as their health status, past history, and living environment.

[0332] "Analyzing needs" is the process of identifying the optimal care requirements and services based on the individual user's information and emotional state.

[0333] A "care plan" is a set of care activities and policies formulated to optimize the health and well-being of the user.

[0334] A "generative AI model" is a mathematical model developed to perform data processing and decision-making using artificial intelligence technology.

[0335] An "interface" is the point of contact through which a user accesses a system and manipulates or displays information.

[0336] This invention is a system that enables emotion recognition and real-time care plan generation. The following describes how the server, terminal, and user components of the system implement this invention.

[0337] First, the server stores individual user information and uses this to analyze the user's needs. Specifically, the server receives emotional data from an emotion engine and integrates it with other health indicator data. This data is updated in real time using a database management system. The server also creates care plans using a generative AI model, utilizing natural language processing technology in this process. This generated care plan is then used to provide the most appropriate care services to each individual user through appropriate feedback.

[0338] Next, the terminal functions as an interface that provides the user with information about the care plan. The terminal displays data acquired from the emotion engine in real time, visualizing the generated care plan. This allows the user to easily understand the plan and edit it as needed. The interface needs to be intuitively designed and is typically operated on mobile devices such as tablets and smartphones.

[0339] Users can quickly adjust care plans to suit the emotional state of the user using the provided interface. This process enables personalized services and improves the quality of care. Care managers and care staff can use this system to provide more attentive care.

[0340] As a concrete example, in the case of an elderly person, if the emotional engine assesses the user's stress level as high, this information is sent to the server. Based on this, the server generates a care plan such as "adding relaxing activities" or "ensuring time for communication with family." This suggestion is visualized to the user via the terminal, and the user can adjust the plan according to their situation.

[0341] An example of a prompt based on the generated AI model is: "Please use the elderly person's recent emotional data and health indicators to generate a new care plan."

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The server collects individual user information and health indicator data from a database. Input includes the user's ID and sensor data, and based on this, a database management system is used to extract relevant health indicator data. The extracted data is compiled into individual profiles.

[0345] Step 2:

[0346] The emotion engine analyzes the user's voice, facial expressions, and body movements. The input is sensor data collected in real time, and stress levels and emotional states are evaluated through an emotion recognition algorithm. This evaluation result is output as emotion data to the subsequent steps.

[0347] Step 3:

[0348] The server integrates the health indicator data collected in Step 1 with the emotional data obtained in Step 2. Using this integrated data as input, the server performs data calculations using analytical software to analyze the user's needs. The output is an analytical report that reflects the user's needs and current emotional state.

[0349] Step 4:

[0350] The server automatically generates care plans using an AI model based on the analysis results. The input is an analysis report, and the AI ​​model uses this data to generate appropriate care activities and improvement suggestions. The output is a care plan optimized for the user.

[0351] Step 5:

[0352] The terminal visualizes the generated care plan on the user interface. It receives the care plan sent from the server as input and performs format conversion for visual display. The output is a graphical display of the care plan that the user can refer to.

[0353] Step 6:

[0354] The user reviews the care plan displayed on the terminal and edits it as needed. Input is care plan information from the terminal, and the user interface allows for fine-tuning of the plan. Output is the edited, customized care plan.

[0355] (Application Example 2)

[0356] 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."

[0357] In today's service industry, maintaining customer satisfaction requires service providers to quickly and appropriately understand customers' emotional states and respond flexibly. However, traditional systems struggle to recognize customer emotions in real time and provide appropriate responses, which limits the optimization of the customer experience.

[0358] 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.

[0359] In this invention, the server includes means for collecting individual user information, means for analyzing user requests, and means for automatically generating an optimal plan. This enables immediate recognition of the customer's emotional state and real-time notification to the service provider, thereby improving the customer experience.

[0360] "Users" refers to customers who use the system or individuals who receive care services.

[0361] "Personalized information" refers to specific data about a user, including their name, age, and past behavioral history.

[0362] "Requirements" refer to the results or needs that users want to obtain through the system, and can also refer to specific services or support.

[0363] A "plan" refers to a series of measures and proposals created to provide the best possible service to users.

[0364] "Information processing means" refers to technical functions that generate and display information useful to users using collected data.

[0365] "Audio and image data" refers to information including recordings of the user's voice, as well as media formats such as photographs and videos.

[0366] "Emotional state" is a concept that describes the user's emotional state and psychological condition, and is inferred from their voice and facial expressions.

[0367] "Real-time" refers to a state where processing is done instantly in accordance with real-world time, with virtually no time lag.

[0368] A "notification" is information or an alert message sent from a system to a user or service provider.

[0369] "Service provider" refers to a person or organization that is responsible for providing services to users through a system.

[0370] To implement this invention, a system is constructed that analyzes the emotional state of customers in real time and sends necessary notifications to service providers. This system mainly consists of a server, a terminal, and an emotion recognition engine.

[0371] The server collects and manages individual user information and analyzes user requests based on that data. It also receives sentiment data in real time and automatically generates appropriate service plans. General database management systems and statistical analysis software are used for data processing in this process.

[0372] The terminal provides the operating screen for mobile devices and smart glasses used by store staff, displaying generated plans and notifications. The interface on the terminal visually displays information based on the user's emotional state, allowing staff to flexibly adjust their responses as needed.

[0373] The emotion recognition engine collects user voice and image data and infers their emotional state in real time from that data. Machine learning models and voice / image processing algorithms are used for analysis, such as OpenCV or dedicated emotion recognition software. The analysis results are sent to a server and used to optimize the service.

[0374] As a concrete example, consider a scenario in a cafe. If a customer appears dissatisfied at the register, the emotion recognition engine identifies this state and sends the information to the server. Based on this data, the server sends a notification to the staff member's terminal prompting them to take immediate action. This allows the staff to improve their service to customers and increase their satisfaction.

[0375] An example of a prompt for a generating AI model is, "Please tell me what to do when a customer looks dissatisfied." This prompt is used to complement the recommendations automatically generated by the system and to suggest specific actions that staff should take.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The server collects individual user information and health indicator data and stores it in a database. Inputs are basic user profile information and health data, and outputs are database entries built based on that data. This process uses a data storage and management system to maintain data integrity and process data efficiently.

[0379] Step 2:

[0380] The emotion recognition engine receives audio and image data transmitted from the device and analyzes the user's emotional state. The input is real-time audio and facial expression data, and the output is digital data of the analyzed emotional state. It utilizes machine learning algorithms and employs speech processing and computer vision technologies to identify emotions.

[0381] Step 3:

[0382] The server analyzes user requests using emotional data provided by the emotion recognition engine. The input is digital data of emotional states, and the analysis results output countermeasures tailored to the user's needs. At this stage, statistical analysis tools are used to comprehensively evaluate the data and automatically generate an optimal care plan.

[0383] Step 4:

[0384] The terminal receives care plans and related notifications sent from the server and displays them to the service provider. Input is notification data and plan information from the server, and output is a visual interface display for the service provider. A simple display method using user interface design is employed via mobile devices and smart glasses.

[0385] Step 5:

[0386] Based on information on their device, users adjust service responses as needed to provide the best possible service to their customers. Inputs are notifications and plans displayed on the device, while outputs are the adjusted customer service actions. The service provider's judgment is crucial, and quick and effective action is required.

[0387] Step 6:

[0388] The system uses a generative AI model as input to provide prompts and supplements the suggestions that support the system's optimal response. The input consists of prompts tailored to the user's situation, while the output is additional response suggestions generated by the AI. Effective instructions are generated by a generative AI based on an expert system.

[0389] 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.

[0390] 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 those described above. 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 shown 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] In an embodiment of the present invention, a system is constructed that collects individual user information, analyzes that information using AI, and automatically generates a care plan. This system mainly consists of two components: a server and a terminal, and the user operates it through its interface.

[0406] The server aggregates and integrates user health information, past care history, and local medical resource information from various data sources. Next, it analyzes this data using AI algorithms to extract user needs. The analysis utilizes natural language processing (NLP) to extract key information from text data and machine learning models to predict the user's condition. Based on the analysis results, the server automatically generates a care plan optimized for the user. This care plan includes specific care services, their timing, and recommended local medical services.

[0407] The terminal resides on a device operated by the care manager, who is responsible for the user, and receives care plans sent from the server. The terminal displays this plan through a visually intuitive interface, allowing the user to easily review and edit it. Editing allows for schedule adjustments and the addition or deletion of specific care items, with final adjustments made based on the user's expertise.

[0408] As a concrete example, consider the case of Mr. A, an elderly person with heart disease. He inputs user information using a terminal. The server analyzes this information and generates a care plan that includes, for example, "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." This allows the care manager to quickly identify a service plan suitable for Mr. A and efficiently transition to its implementation.

[0409] Thus, the invention according to this embodiment reduces the workload of care managers while improving the quality of care provided to users.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The user launches an application on their device and collects information about themselves. Specifically, they input information such as their health status, daily life, and family wishes in an interview format. The device saves this information as text data and transmits it to the server in real time.

[0413] Step 2:

[0414] The server receives user information sent from the terminal and stores it in an existing database. This includes the user's past care history and information on medical resources in their region. The server integrates this data and prepares it as a preliminary step for analysis.

[0415] Step 3:

[0416] The server executes AI algorithms on the aggregated data. Data analysis uses natural language processing (NLP) to extract key information and machine learning models to identify user needs. This process generates a list of the most suitable care items for each user.

[0417] Step 4:

[0418] The server automatically generates a care plan based on the analysis results. The generated care plan includes specific details of care services, a schedule, and recommended local medical services. This plan is optimized for the user's health condition and local medical resources.

[0419] Step 5:

[0420] The server sends the generated care plan to the terminal. The terminal provides an interface for the user to review this plan, displaying it in an easy-to-understand format.

[0421] Step 6:

[0422] Users can review and edit their care plans using the interface on their devices. Editing allows for schedule adjustments, addition and removal of specific services, and changes in priority. The plan is finalized after the user has completed final review and adjustments.

[0423] Step 7:

[0424] The edited final care plan is sent back from the terminal to the server, where it is ready for implementation with the user. The server shares the plan with other stakeholders as needed, establishing a collaborative system to improve the quality of service delivery.

[0425] (Example 1)

[0426] 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."

[0427] The current care plan creation process is problematic because it requires a lot of manual work to address the individual needs of users, making it time-consuming and labor-intensive. Furthermore, real-time monitoring of health status and the rapid updating of care plans based on that data are difficult. In addition, effectively utilizing local medical resources is challenging, hindering the optimization of care services.

[0428] 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.

[0429] In this invention, the server includes means for collecting individual user information from various data sources, means for integrating and analyzing the collected information using natural language processing and machine learning models, and means for automatically generating an optimized care plan using a generative AI model based on the analysis results. This enables the real-time and efficient generation and updating of care plans for individual users. Furthermore, by integrating local medical resource information, the effective use of care services can also be realized.

[0430] "Personalized information" refers to data identified for each user, including information about their health status, care history, and medical resources.

[0431] "Data sources" refer to the origins and channels for providing information, such as hospital databases and APIs of healthcare service providers.

[0432] Natural language processing is a technology that understands and analyzes human language, and is used to extract important information from text data.

[0433] A "machine learning model" is an algorithm that learns from data and finds patterns, and is used to predict future states and trends.

[0434] A "generative AI model" is a model that uses artificial intelligence to generate new content and data, and is used to automatically generate optimal care plans.

[0435] A "user interface" refers to the operation screen or display screen that allows users to interact with a system, providing information visually and enabling editing operations.

[0436] In an embodiment of this invention, a system is constructed in which two main components, a server and a terminal, work in cooperation with each other.

[0437] The server functions as an information gathering device, collecting individual user information from multiple data sources. This includes databases of healthcare institutions and APIs of healthcare service providers. The server runs on a computer system with a high-performance processor and sufficient storage to perform data integration and analysis.

[0438] The server utilizes natural language processing technology to extract important information from text data. It also uses machine learning models to predict the user's health status and care needs. Based on this analysis process, the server uses a generative AI model to automatically generate an optimized care plan. The generated care plan includes specific care services, their timing, and recommendations for local medical services.

[0439] The generated care plan is sent to a terminal. This terminal is operated by the care manager and visualizes the care plan through its user interface. The terminal's interface is designed for ease of use, allowing users to easily review and edit the plan as needed. Users can adjust schedules, add or remove care items, and finally save the customized plan.

[0440] As a concrete example, consider the case of an elderly person with heart disease. The care manager would use a terminal to input user information. The server would then analyze this information and generate a care plan that includes "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." An example of a prompt message might be, "Please generate the optimal care plan based on the elderly person's health condition and care history."

[0441] This allows the system to efficiently provide care tailored to each user and optimize the entire care process.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The server collects individual user information from various data sources. Inputs include information from healthcare institution databases and APIs provided by healthcare service providers. The server then stores the collected data in storage, using it as foundational data for subsequent analysis steps. Specifically, this includes the user's health status and past care history.

[0445] Step 2:

[0446] The server integrates the collected individual information and performs data preprocessing. The input is the raw data collected in step 1. The server standardizes data in different formats and imputes missing data. It removes noise and generates a clean dataset. As output, data in a format suitable for analysis is obtained.

[0447] Step 3:

[0448] The server extracts important information from text data using natural language processing techniques. The input is the clean dataset generated in step 2. The server runs a language analysis algorithm to extract important keywords and phrases related to health status and care needs. The extracted information forms the basis for analysis in the next step.

[0449] Step 4:

[0450] The server uses a machine learning model to predict the user's health status. The input is the key information extracted in step 3. The server feeds the data into the trained model and predicts health trends and potential risks. The output provides insights into the user's future health status.

[0451] Step 5:

[0452] The server automatically generates an optimized care plan using a generative AI model. The input is the predicted health status data obtained in step 4. The generative AI model uses this information to create a specific care plan. This plan includes the types and schedules of recommended care services. The output is the care plan, usually expressed in text format.

[0453] Step 6:

[0454] The terminal receives the care plan sent from the server and displays it through the user interface. The input is the care plan generated in step 5. The terminal visually presents the plan on the interface, allowing the care manager to review its contents. The output is an editable care plan displayed on the screen.

[0455] Step 7:

[0456] The user (care manager) edits and customizes the care plan via the terminal. The input is the care plan displayed in step 6. The user adjusts the schedule and adds or removes necessary care items based on their expertise. Finally, the customized care plan is completed and saved. The output is the adjusted care plan.

[0457] (Application Example 1)

[0458] 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."

[0459] In the fields of nursing care and medical care, there is a need for the generation of optimized care plans based on individual user information and for safe and efficient information presentation methods. However, current systems have limitations in real-time data analysis, making it difficult to quickly check information or adjust care plans, especially when users are away from home. Furthermore, conventional technologies that rely on visual interfaces have the problem of making it difficult to perform the necessary flexible information editing.

[0460] 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.

[0461] In this invention, the server includes means for collecting individual user information, means for analyzing the user's needs based on the collected information, means for automatically generating an optimal care plan based on the analysis results, means for displaying the care plan in augmented reality format via smart glasses, and means for adjusting the care plan using voice input. This allows the user's caregiver to quickly and flexibly check the care plan and adjust it as needed, even when away from the office.

[0462] "Individual user information" refers to data about the health status and living environment of individuals who are the subjects of care or medical treatment.

[0463] "Needs analysis" is the process of evaluating and identifying the necessary care and medical needs based on the user's current situation and past data.

[0464] "Automatic care plan generation" is a process that uses AI technology to mechanically create a plan of optimal care or medical services for the user.

[0465] "Displaying in augmented reality format" is a technology that uses devices such as smart glasses to overlay computer-generated information onto the real environment and present it to the visual senses.

[0466] "Adjusting the care plan using voice input" refers to a method of operation that uses voice recognition technology to analyze the caregiver's statements and modify or update the content of the care plan.

[0467] A "service provider" is a professional who directly interacts with service users to provide care and medical services, offering support and coordinating their plans.

[0468] The system that implements this application primarily consists of a server and a terminal including smart glasses. The server collects individual user information, analyzes needs using AI algorithms, and automatically generates an optimal care plan based on that analysis. This utilizes natural language processing (NLP) technology for analyzing text data and machine learning models for predicting the user's health data.

[0469] The device uses smart glasses to display the generated care plan in augmented reality (AR) format. This allows caregivers to visually confirm the information. Voice input technology allows for adjustments to the care plan as needed. Google Cloud Speech-to-Text API is used for speech recognition.

[0470] As a concrete example, consider a care plan to effectively manage the weight gain of elderly person B. In this case, the server collects B's past dietary and exercise data and generates a plan through AI analysis, such as "walking three times a week" and "regular delivery of low-salt meals." The caregiver can view this plan by wearing smart glasses and adjust things like the date and time of walking using voice input.

[0471] The generative AI model can be provided with prompts such as:

[0472] "Please propose the next care actions based on Ms. B's health data for the past week."

[0473] "Please analyze the exercise history for the past two weeks and identify the issues that should be discussed during the next care visit."

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The server collects individual user information. Specifically, it retrieves the user's health status, care history, and local medical resource information from a database. Inputs include user ID and past health data, and output is the generation of an integrated user information dataset.

[0477] Step 2:

[0478] The server analyzes user needs using AI algorithms. Based on the collected data, it performs text data analysis using NLP techniques and predicts health status using machine learning models. An integrated dataset is used as input, and the output is an analysis result that clearly identifies user needs. Specifically, an AI model (e.g., scikit-learn or TensorFlow) processes the data.

[0479] Step 3:

[0480] The server automatically generates a care plan based on the analysis results. The generating AI model creates a plan that includes care content, implementation timing, and recommended medical services. The input is the analysis results obtained in the previous step, and the output is an optimized care plan. Specific examples include weekly exercise plans and dietary suggestions.

[0481] Step 4:

[0482] The device displays the generated care plan in augmented reality (AR) through smart glasses. Plan information is provided visually in real time. The care plan is sent from the server as input, and the user can view the information via the AR display as output. Specifically, the plan is overlaid on the smart glasses' display.

[0483] Step 5:

[0484] The user (caregiver) adjusts the care plan using voice input. Voice recognition technology analyzes instructions for changes and modifications to the plan. Voice data is taken as input, and the care plan is updated as output. Specifically, this includes actions such as changing appointments or adding new tasks via voice commands.

[0485] 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.

[0486] This invention is a system for recognizing users' emotions in real time and reflecting them in adjusting care plans. The system consists of three main components: a server, a terminal, and an emotion engine. Users can use this system to provide efficient and effective care services.

[0487] The server manages individual user information, health indicator data, and local healthcare resource information, and analyzes user needs based on this data. In addition, it incorporates a function to evaluate the user's stress level and emotional state based on emotional data transmitted from the emotion engine. This evaluation enables the optimization of care plans.

[0488] The terminal displays a care plan, including emotion recognition, to the user and provides an interface for editing it as needed. The terminal displays emotion data acquired by the emotion engine in real time and provides the user with alerts in response to changes in emotions. This enables the user to provide more appropriate care services more quickly.

[0489] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize their emotional state at that moment. The resulting emotional data is sent to the server as an indicator of stress levels and emotional changes. This data is used to improve the user's sense of security and satisfaction when receiving care services.

[0490] As a concrete example, consider the case of an elderly person, Mr. B. Using an application on his device, the emotion engine recognizes that Mr. B's recent stress level is high. This information is sent to the server in real time, and the server uses this to generate a care plan that includes suggestions such as "adding relaxing activities" and "ensuring time for communication with family." The care manager, as the user, can then fine-tune the plan based on these suggestions and provide customized care services for Mr. B.

[0491] Thus, by incorporating the user's emotional state into the care plan, the present invention makes it possible to provide individualized care, further improving the quality of care.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The user launches an application on their device and enters basic information about themselves. This information includes details about their health and daily life. The device also transmits audio and video data obtained through interaction with the user to the emotion engine.

[0495] Step 2:

[0496] The emotion engine analyzes audio data, facial images, and body movements received from the device to recognize the user's emotional state in real time. This analysis uses machine learning models to calculate emotional changes and stress levels numerically.

[0497] Step 3:

[0498] The device displays emotional data received from the emotion engine, informing the user of their current emotional state. If a significant change in emotion is detected, a notification function is activated on the device to alert the user.

[0499] Step 4:

[0500] The server integrates user information and emotional data transmitted from the terminal. Based on this, an AI algorithm analyzes the user's needs and generates a care plan that takes into account their emotions and health status. Past care history and stress levels are also considered in the analysis.

[0501] Step 5:

[0502] The server sends the newly generated care plan to the terminal. The terminal provides an interface that allows the user to review the plan in detail. The plan includes specific action items that reflect emotional data.

[0503] Step 6:

[0504] Users can review and edit care plans using the interface on their devices. For example, if a user is experiencing high stress levels, relaxation activities can be added. After adjustments, the plan is finalized.

[0505] Step 7:

[0506] Based on the established care plan, the user provides specific care services to the client. The server stores emotional data and care implementation logs, preparing them for use in improving future plans.

[0507] (Example 2)

[0508] 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."

[0509] The current care system makes it difficult to instantly reflect the emotional state of the user and adjust the care plan accordingly. Furthermore, there is a lack of individualized care that takes the user's emotional state into account, highlighting the need for effective means to improve the quality of care services.

[0510] 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.

[0511] In this invention, the server includes means for recognizing the user's emotional state in real time and analyzing emotional data; means for collecting individual information including the emotional data and analyzing the user's needs based on that information; and means for automatically generating an optimal care plan using an AI model based on the analysis results. This makes it possible to automatically generate a care plan that takes the user's emotional state into consideration and to provide appropriate care services.

[0512] A "user" is an individual who receives care services through the system.

[0513] "Emotional state" refers to information that indicates the user's stress level and momentary psychological state.

[0514] "Real-time" refers to a state in which processing or a response occurs immediately or almost immediately.

[0515] "Emotional data" refers to information related to emotions recognized from the user's voice, facial expressions, and body movements.

[0516] "Personalized information" refers to information specific to each user, such as their health status, past history, and living environment.

[0517] "Analyzing needs" is the process of identifying the optimal care requirements and services based on the individual user's information and emotional state.

[0518] A "care plan" is a set of care activities and policies formulated to optimize the health and well-being of the user.

[0519] A "generative AI model" is a mathematical model developed to perform data processing and decision-making using artificial intelligence technology.

[0520] An "interface" is the point of contact through which a user accesses a system and manipulates or displays information.

[0521] This invention is a system that enables emotion recognition and real-time care plan generation. The following describes how the server, terminal, and user components of the system implement this invention.

[0522] First, the server stores individual user information and uses this to analyze the user's needs. Specifically, the server receives emotional data from an emotion engine and integrates it with other health indicator data. This data is updated in real time using a database management system. The server also creates care plans using a generative AI model, utilizing natural language processing technology in this process. This generated care plan is then used to provide the most appropriate care services to each individual user through appropriate feedback.

[0523] Next, the terminal functions as an interface that provides the user with information about the care plan. The terminal displays data acquired from the emotion engine in real time, visualizing the generated care plan. This allows the user to easily understand the plan and edit it as needed. The interface needs to be intuitively designed and is typically operated on mobile devices such as tablets and smartphones.

[0524] Users can quickly adjust care plans to suit the emotional state of the user using the provided interface. This process enables personalized services and improves the quality of care. Care managers and care staff can use this system to provide more attentive care.

[0525] As a concrete example, in the case of an elderly person, if the emotional engine assesses the user's stress level as high, this information is sent to the server. Based on this, the server generates a care plan such as "adding relaxing activities" or "ensuring time for communication with family." This suggestion is visualized to the user via the terminal, and the user can adjust the plan according to their situation.

[0526] An example of a prompt based on the generated AI model is: "Please use the elderly person's recent emotional data and health indicators to generate a new care plan."

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The server collects individual user information and health indicator data from a database. Input includes the user's ID and sensor data, and based on this, a database management system is used to extract relevant health indicator data. The extracted data is compiled into individual profiles.

[0530] Step 2:

[0531] The emotion engine analyzes the user's voice, facial expressions, and body movements. The input is sensor data collected in real time, and stress levels and emotional states are evaluated through an emotion recognition algorithm. This evaluation result is output as emotion data to the subsequent steps.

[0532] Step 3:

[0533] The server integrates the health indicator data collected in Step 1 with the emotional data obtained in Step 2. Using this integrated data as input, the server performs data calculations using analytical software to analyze the user's needs. The output is an analytical report that reflects the user's needs and current emotional state.

[0534] Step 4:

[0535] The server automatically generates care plans using an AI model based on the analysis results. The input is an analysis report, and the AI ​​model uses this data to generate appropriate care activities and improvement suggestions. The output is a care plan optimized for the user.

[0536] Step 5:

[0537] The terminal visualizes the generated care plan on the user interface. It receives the care plan sent from the server as input and performs format conversion for visual display. The output is a graphical display of the care plan that the user can refer to.

[0538] Step 6:

[0539] The user reviews the care plan displayed on the terminal and edits it as needed. Input is care plan information from the terminal, and the user interface allows for fine-tuning of the plan. Output is the edited, customized care plan.

[0540] (Application Example 2)

[0541] 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."

[0542] In today's service industry, maintaining customer satisfaction requires service providers to quickly and appropriately understand customers' emotional states and respond flexibly. However, traditional systems struggle to recognize customer emotions in real time and provide appropriate responses, which limits the optimization of the customer experience.

[0543] 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.

[0544] In this invention, the server includes means for collecting individual user information, means for analyzing user requests, and means for automatically generating an optimal plan. This enables immediate recognition of the customer's emotional state and real-time notification to the service provider, thereby improving the customer experience.

[0545] "Users" refers to customers who use the system or individuals who receive care services.

[0546] "Personalized information" refers to specific data about a user, including their name, age, and past behavioral history.

[0547] "Requirements" refer to the results or needs that users want to obtain through the system, and can also refer to specific services or support.

[0548] A "plan" refers to a series of measures and proposals created to provide the best possible service to users.

[0549] "Information processing means" refers to technical functions that generate and display information useful to users using collected data.

[0550] "Audio and image data" refers to information including recordings of the user's voice, as well as media formats such as photographs and videos.

[0551] "Emotional state" is a concept that describes the user's emotional state and psychological condition, and is inferred from their voice and facial expressions.

[0552] "Real-time" refers to a state where processing is done instantly in accordance with real-world time, with virtually no time lag.

[0553] A "notification" is information or an alert message sent from a system to a user or service provider.

[0554] "Service provider" refers to a person or organization that is responsible for providing services to users through a system.

[0555] To implement this invention, a system is constructed that analyzes the emotional state of customers in real time and sends necessary notifications to service providers. This system mainly consists of a server, a terminal, and an emotion recognition engine.

[0556] The server collects and manages individual user information and analyzes user requests based on that data. It also receives sentiment data in real time and automatically generates appropriate service plans. General database management systems and statistical analysis software are used for data processing in this process.

[0557] The terminal provides the operating screen for mobile devices and smart glasses used by store staff, displaying generated plans and notifications. The interface on the terminal visually displays information based on the user's emotional state, allowing staff to flexibly adjust their responses as needed.

[0558] The emotion recognition engine collects user voice and image data and infers their emotional state in real time from that data. Machine learning models and voice / image processing algorithms are used for analysis, such as OpenCV or dedicated emotion recognition software. The analysis results are sent to a server and used to optimize the service.

[0559] As a concrete example, consider a scenario in a cafe. If a customer appears dissatisfied at the register, the emotion recognition engine identifies this state and sends the information to the server. Based on this data, the server sends a notification to the staff member's terminal prompting them to take immediate action. This allows the staff to improve their service to customers and increase their satisfaction.

[0560] An example of a prompt for a generating AI model is, "Please tell me what to do when a customer looks dissatisfied." This prompt is used to complement the recommendations automatically generated by the system and to suggest specific actions that staff should take.

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The server collects individual user information and health indicator data and stores it in a database. Inputs are basic user profile information and health data, and outputs are database entries built based on that data. This process uses a data storage and management system to maintain data integrity and process data efficiently.

[0564] Step 2:

[0565] The emotion recognition engine receives audio and image data transmitted from the device and analyzes the user's emotional state. The input is real-time audio and facial expression data, and the output is digital data of the analyzed emotional state. It utilizes machine learning algorithms and employs speech processing and computer vision technologies to identify emotions.

[0566] Step 3:

[0567] The server analyzes user requests using emotional data provided by the emotion recognition engine. The input is digital data of emotional states, and the analysis results output countermeasures tailored to the user's needs. At this stage, statistical analysis tools are used to comprehensively evaluate the data and automatically generate an optimal care plan.

[0568] Step 4:

[0569] The terminal receives care plans and related notifications sent from the server and displays them to the service provider. Input is notification data and plan information from the server, and output is a visual interface display for the service provider. A simple display method using user interface design is employed via mobile devices and smart glasses.

[0570] Step 5:

[0571] Based on information on their device, users adjust service responses as needed to provide the best possible service to their customers. Inputs are notifications and plans displayed on the device, while outputs are the adjusted customer service actions. The service provider's judgment is crucial, and quick and effective action is required.

[0572] Step 6:

[0573] The system uses a generative AI model as input to provide prompts and supplements the suggestions that support the system's optimal response. The input consists of prompts tailored to the user's situation, while the output is additional response suggestions generated by the AI. Effective instructions are generated by a generative AI based on an expert system.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] In an embodiment of the present invention, a system is constructed that collects individual user information, analyzes that information using AI, and automatically generates a care plan. This system mainly consists of two components: a server and a terminal, and the user operates it through its interface.

[0592] The server aggregates and integrates user health information, past care history, and local medical resource information from various data sources. Next, it analyzes this data using AI algorithms to extract user needs. The analysis utilizes natural language processing (NLP) to extract key information from text data and machine learning models to predict the user's condition. Based on the analysis results, the server automatically generates a care plan optimized for the user. This care plan includes specific care services, their timing, and recommended local medical services.

[0593] The terminal resides on a device operated by the care manager, who is responsible for the user, and receives care plans sent from the server. The terminal displays this plan through a visually intuitive interface, allowing the user to easily review and edit it. Editing allows for schedule adjustments and the addition or deletion of specific care items, with final adjustments made based on the user's expertise.

[0594] As a concrete example, consider the case of Mr. A, an elderly person with heart disease. He inputs user information using a terminal. The server analyzes this information and generates a care plan that includes, for example, "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." This allows the care manager to quickly identify a service plan suitable for Mr. A and efficiently transition to its implementation.

[0595] Thus, the invention according to this embodiment reduces the workload of care managers while improving the quality of care provided to users.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The user launches an application on their device and collects information about themselves. Specifically, they input information such as their health status, daily life, and family wishes in an interview format. The device saves this information as text data and transmits it to the server in real time.

[0599] Step 2:

[0600] The server receives user information sent from the terminal and stores it in an existing database. This includes the user's past care history and information on medical resources in their region. The server integrates this data and prepares it as a preliminary step for analysis.

[0601] Step 3:

[0602] The server executes AI algorithms on the aggregated data. Data analysis uses natural language processing (NLP) to extract key information and machine learning models to identify user needs. This process generates a list of the most suitable care items for each user.

[0603] Step 4:

[0604] The server automatically generates a care plan based on the analysis results. The generated care plan includes specific care services, a schedule, and recommended local medical services. This plan is optimized for the user's health condition and local medical resources.

[0605] Step 5:

[0606] The server sends the generated care plan to the terminal. The terminal provides an interface for the user to review this plan, displaying it in an easy-to-understand format.

[0607] Step 6:

[0608] Users can review and edit their care plans using the interface on their devices. Editing allows for schedule adjustments, addition and removal of specific services, and changes in priority. The plan is finalized after the user has completed final review and adjustments.

[0609] Step 7:

[0610] The edited final care plan is sent back from the terminal to the server, where it is ready for implementation with the user. The server shares the plan with other stakeholders as needed, establishing a collaborative system to improve the quality of service delivery.

[0611] (Example 1)

[0612] 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".

[0613] The current care plan creation process is problematic because it requires a lot of manual work to address the individual needs of users, making it time-consuming and labor-intensive. Furthermore, real-time monitoring of health status and the rapid updating of care plans based on that data are difficult. In addition, effectively utilizing local medical resources is challenging, hindering the optimization of care services.

[0614] 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.

[0615] In this invention, the server includes means for collecting individual user information from various data sources, means for integrating and analyzing the collected information using natural language processing and machine learning models, and means for automatically generating an optimized care plan using a generative AI model based on the analysis results. This enables the real-time and efficient generation and updating of care plans for individual users. Furthermore, by integrating local medical resource information, the effective use of care services can also be realized.

[0616] "Personalized information" refers to data identified for each user, including information about their health status, care history, and medical resources.

[0617] "Data sources" refer to the origins and channels for providing information, such as hospital databases and APIs of healthcare service providers.

[0618] Natural language processing is a technology that understands and analyzes human language, and is used to extract important information from text data.

[0619] A "machine learning model" is an algorithm that learns from data and finds patterns, and is used to predict future states and trends.

[0620] A "generative AI model" is a model that uses artificial intelligence to generate new content and data, and is used to automatically generate optimal care plans.

[0621] A "user interface" refers to the operation screen or display screen that allows users to interact with a system, providing information visually and enabling editing operations.

[0622] In an embodiment of this invention, a system is constructed in which two main components, a server and a terminal, work in cooperation with each other.

[0623] The server functions as an information gathering device, collecting individual user information from multiple data sources. This includes databases of healthcare institutions and APIs of healthcare service providers. The server runs on a computer system with a high-performance processor and sufficient storage to perform data integration and analysis.

[0624] The server utilizes natural language processing technology to extract important information from text data. It also uses machine learning models to predict the user's health status and care needs. Based on this analysis process, the server uses a generative AI model to automatically generate an optimized care plan. The generated care plan includes specific care services, their timing, and recommendations for local medical services.

[0625] The generated care plan is sent to a terminal. This terminal is operated by the care manager and visualizes the care plan through its user interface. The terminal's interface is designed for ease of use, allowing users to easily review and edit the plan as needed. Users can adjust schedules, add or remove care items, and finally save the customized plan.

[0626] As a concrete example, consider the case of an elderly person with heart disease. The care manager would use a terminal to input user information. The server would then analyze this information and generate a care plan that includes "daily blood pressure checks," "two nurse visits per week," and "one specialist consultation per month." An example of a prompt message might be, "Please generate the optimal care plan based on the elderly person's health condition and care history."

[0627] This allows the system to efficiently provide care tailored to each user and optimize the entire care process.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The server collects individual user information from various data sources. Inputs include information from healthcare institution databases and APIs provided by healthcare service providers. The server then stores the collected data in storage, using it as foundational data for subsequent analysis steps. Specifically, this includes the user's health status and past care history.

[0631] Step 2:

[0632] The server integrates the collected individual information and performs data preprocessing. The input is the raw data collected in step 1. The server standardizes data in different formats and imputes missing data. It removes noise and generates a clean dataset. As output, data in a format suitable for analysis is obtained.

[0633] Step 3:

[0634] The server extracts important information from text data using natural language processing techniques. The input is the clean dataset generated in step 2. The server runs a language analysis algorithm to extract important keywords and phrases related to health status and care needs. The extracted information forms the basis for analysis in the next step.

[0635] Step 4:

[0636] The server uses a machine learning model to predict the user's health status. The input is the key information extracted in step 3. The server feeds the data into the trained model and predicts health trends and potential risks. The output provides insights into the user's future health status.

[0637] Step 5:

[0638] The server automatically generates an optimized care plan using a generative AI model. The input is the predicted health status data obtained in step 4. The generative AI model uses this information to create a specific care plan. This plan includes the types and schedules of recommended care services. The output is the care plan, usually expressed in text format.

[0639] Step 6:

[0640] The terminal receives the care plan sent from the server and displays it through the user interface. The input is the care plan generated in step 5. The terminal visually presents the plan on the interface, allowing the care manager to review its contents. The output is an editable care plan displayed on the screen.

[0641] Step 7:

[0642] The user (care manager) edits and customizes the care plan via the terminal. The input is the care plan displayed in step 6. The user adjusts the schedule and adds or removes necessary care items based on their expertise. Finally, the customized care plan is completed and saved. The output is the adjusted care plan.

[0643] (Application Example 1)

[0644] 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".

[0645] In the fields of nursing care and medical care, there is a need for the generation of optimized care plans based on individual user information and for safe and efficient information presentation methods. However, current systems have limitations in real-time data analysis, making it difficult to quickly check information or adjust care plans, especially when users are away from home. Furthermore, conventional technologies that rely on visual interfaces have the problem of making it difficult to perform the necessary flexible information editing.

[0646] 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.

[0647] In this invention, the server includes means for collecting individual user information, means for analyzing the user's needs based on the collected information, means for automatically generating an optimal care plan based on the analysis results, means for displaying the care plan in augmented reality format via smart glasses, and means for adjusting the care plan using voice input. This allows caregivers to quickly and flexibly check the care plan and adjust it as needed, even when away from the office.

[0648] "Individual user information" refers to data about the health status and living environment of individuals who are the subjects of care or medical treatment.

[0649] "Needs analysis" is the process of evaluating and identifying the necessary care and medical needs based on the user's current situation and past data.

[0650] "Automatic care plan generation" is a process that uses AI technology to mechanically create a plan of optimal care or medical services for the user.

[0651] "Displaying in augmented reality format" is a technology that uses devices such as smart glasses to overlay computer-generated information onto the real environment and present it to the visual senses.

[0652] "Adjusting the care plan using voice input" refers to a method of operation that uses voice recognition technology to analyze the caregiver's statements and modify or update the content of the care plan.

[0653] A "service provider" is a professional who directly interacts with service users to provide care and medical services, offering support and coordinating their plans.

[0654] The system that implements this application primarily consists of a server and a terminal including smart glasses. The server collects individual user information, analyzes needs using AI algorithms, and automatically generates an optimal care plan based on that analysis. This utilizes natural language processing (NLP) technology for analyzing text data and machine learning models for predicting the user's health data.

[0655] The device uses smart glasses to display the generated care plan in augmented reality (AR) format. This allows caregivers to visually confirm the information. Voice input technology allows for adjustments to the care plan as needed. Google Cloud Speech-to-Text API is used for speech recognition.

[0656] As a concrete example, consider a care plan to effectively manage the weight gain of elderly person B. In this case, the server collects B's past dietary and exercise data and generates a plan through AI analysis, such as "walking three times a week" and "regular delivery of low-salt meals." The caregiver can view this plan by wearing smart glasses and adjust things like the date and time of walking using voice input.

[0657] The generative AI model can be provided with prompts such as:

[0658] "Please propose the next care actions based on Ms. B's health data for the past week."

[0659] "Please analyze the exercise history for the past two weeks and identify the issues that should be discussed during the next care visit."

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The server collects individual user information. Specifically, it retrieves the user's health status, care history, and local medical resource information from a database. Inputs include user ID and past health data, and output is the generation of an integrated user information dataset.

[0663] Step 2:

[0664] The server analyzes user needs using AI algorithms. Based on the collected data, it performs text data analysis using NLP techniques and predicts health status using machine learning models. An integrated dataset is used as input, and the output is an analysis result that clearly identifies user needs. Specifically, an AI model (e.g., scikit-learn or TensorFlow) processes the data.

[0665] Step 3:

[0666] The server automatically generates a care plan based on the analysis results. The generating AI model creates a plan that includes care content, implementation timing, and recommended medical services. The input is the analysis results obtained in the previous step, and the output is an optimized care plan. Specific examples include weekly exercise plans and dietary suggestions.

[0667] Step 4:

[0668] The device displays the generated care plan in augmented reality (AR) through smart glasses. Plan information is provided visually in real time. The care plan is sent from the server as input, and the user can view the information via the AR display as output. Specifically, the plan is overlaid on the smart glasses' display.

[0669] Step 5:

[0670] The user (caregiver) adjusts the care plan using voice input. Voice recognition technology analyzes instructions for changes and modifications to the plan. Voice data is taken as input, and the care plan is updated as output. Specifically, this includes actions such as changing appointments or adding new tasks via voice commands.

[0671] 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.

[0672] This invention is a system for recognizing users' emotions in real time and reflecting them in adjusting care plans. The system consists of three main components: a server, a terminal, and an emotion engine. Users can use this system to provide efficient and effective care services.

[0673] The server manages individual user information, health indicator data, and local healthcare resource information, and analyzes user needs based on this data. In addition, it incorporates a function to evaluate the user's stress level and emotional state based on emotional data transmitted from the emotion engine. This evaluation enables the optimization of care plans.

[0674] The terminal displays a care plan, including emotion recognition, to the user and provides an interface for editing it as needed. The terminal displays emotion data acquired by the emotion engine in real time and provides the user with alerts in response to changes in emotions. This enables the user to provide more appropriate care services more quickly.

[0675] The emotion engine analyzes the user's voice, facial expressions, and body movements to recognize their emotional state at that moment. The resulting emotional data is sent to the server as an indicator of stress levels and emotional changes. This data is used to improve the user's sense of security and satisfaction when receiving care services.

[0676] As a concrete example, consider the case of an elderly person, Mr. B. Using an application on his device, the emotion engine recognizes that Mr. B's recent stress level is high. This information is sent to the server in real time, and the server uses this to generate a care plan that includes suggestions such as "adding relaxing activities" and "ensuring time for communication with family." The care manager, as the user, can then fine-tune the plan based on these suggestions and provide customized care services for Mr. B.

[0677] Thus, by incorporating the user's emotional state into the care plan, the present invention makes it possible to provide individualized care, further improving the quality of care.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user launches an application on their device and enters basic information about themselves. This information includes details about their health and daily life. The device also transmits audio and video data obtained through interaction with the user to the emotion engine.

[0681] Step 2:

[0682] The emotion engine analyzes audio data, facial images, and body movements received from the device to recognize the user's emotional state in real time. This analysis uses machine learning models to calculate emotional changes and stress levels numerically.

[0683] Step 3:

[0684] The device displays emotional data received from the emotion engine, informing the user of their current emotional state. If a significant change in emotion is detected, a notification function is activated on the device to alert the user.

[0685] Step 4:

[0686] The server integrates user information and emotional data transmitted from the terminal. Based on this, an AI algorithm analyzes the user's needs and generates a care plan that takes into account their emotions and health status. Past care history and stress levels are also considered in the analysis.

[0687] Step 5:

[0688] The server sends the newly generated care plan to the terminal. The terminal provides an interface that allows the user to review the plan in detail. The plan includes specific action items that reflect emotional data.

[0689] Step 6:

[0690] Users can review and edit care plans using the interface on their devices. For example, if a user is experiencing high stress levels, relaxation activities can be added. After adjustments, the plan is finalized.

[0691] Step 7:

[0692] Based on the established care plan, the user provides specific care services to the client. The server stores emotional data and care implementation logs, preparing them for use in improving future plans.

[0693] (Example 2)

[0694] 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".

[0695] The current care system makes it difficult to instantly reflect the emotional state of the user and adjust the care plan accordingly. Furthermore, there is a lack of individualized care that takes the user's emotional state into account, highlighting the need for effective means to improve the quality of care services.

[0696] 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.

[0697] In this invention, the server includes means for recognizing the user's emotional state in real time and analyzing emotional data; means for collecting individual information including the emotional data and analyzing the user's needs based on that information; and means for automatically generating an optimal care plan using an AI model based on the analysis results. This makes it possible to automatically generate a care plan that takes the user's emotional state into consideration and to provide appropriate care services.

[0698] A "user" is an individual who receives care services through the system.

[0699] "Emotional state" refers to information that indicates the user's stress level and momentary psychological state.

[0700] "Real-time" refers to a state in which processing or a response occurs immediately or almost immediately.

[0701] "Emotional data" refers to information related to emotions recognized from the user's voice, facial expressions, and body movements.

[0702] "Personalized information" refers to information specific to each user, such as their health status, past history, and living environment.

[0703] "Analyzing needs" is the process of identifying the optimal care requirements and services based on the individual user's information and emotional state.

[0704] A "care plan" is a set of care activities and policies formulated to optimize the health and well-being of the user.

[0705] A "generative AI model" is a mathematical model developed to perform data processing and decision-making using artificial intelligence technology.

[0706] An "interface" is the point of contact through which a user accesses a system and manipulates or displays information.

[0707] This invention is a system that enables emotion recognition and real-time care plan generation. The following describes how the server, terminal, and user components of the system implement this invention.

[0708] First, the server stores individual user information and uses this to analyze the user's needs. Specifically, the server receives emotional data from an emotion engine and integrates it with other health indicator data. This data is updated in real time using a database management system. The server also creates care plans using a generative AI model, utilizing natural language processing technology in this process. This generated care plan is then used to provide the most appropriate care services to each individual user through appropriate feedback.

[0709] Next, the terminal functions as an interface that provides the user with information about the care plan. The terminal displays data acquired from the emotion engine in real time, visualizing the generated care plan. This allows the user to easily understand the plan and edit it as needed. The interface needs to be intuitively designed and is typically operated on mobile devices such as tablets and smartphones.

[0710] Users can quickly adjust care plans to suit the emotional state of the user using the provided interface. This process enables personalized services and improves the quality of care. Care managers and care staff can use this system to provide more attentive care.

[0711] As a concrete example, in the case of an elderly person, if the emotional engine assesses the user's stress level as high, this information is sent to the server. Based on this, the server generates a care plan such as "adding relaxing activities" or "ensuring time for communication with family." This suggestion is visualized to the user via the terminal, and the user can adjust the plan according to their situation.

[0712] An example of a prompt based on the generated AI model is: "Please use the elderly person's recent emotional data and health indicators to generate a new care plan."

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The server collects individual user information and health indicator data from a database. Input includes the user's ID and sensor data, and based on this, a database management system is used to extract relevant health indicator data. The extracted data is compiled into individual profiles.

[0716] Step 2:

[0717] The emotion engine analyzes the user's voice, facial expressions, and body movements. The input is sensor data collected in real time, and stress levels and emotional states are evaluated through an emotion recognition algorithm. This evaluation result is output as emotion data to the subsequent steps.

[0718] Step 3:

[0719] The server integrates the health indicator data collected in Step 1 with the emotional data obtained in Step 2. Using this integrated data as input, the server performs data calculations using analytical software to analyze the user's needs. The output is an analytical report that reflects the user's needs and current emotional state.

[0720] Step 4:

[0721] The server automatically generates care plans using an AI model based on the analysis results. The input is an analysis report, and the AI ​​model uses this data to generate appropriate care activities and improvement suggestions. The output is a care plan optimized for the user.

[0722] Step 5:

[0723] The terminal visualizes the generated care plan on the user interface. It receives the care plan sent from the server as input and performs format conversion for visual display. The output is a graphical display of the care plan that the user can refer to.

[0724] Step 6:

[0725] The user reviews the care plan displayed on the terminal and edits it as needed. Input is care plan information from the terminal, and the user interface allows for fine-tuning of the plan. Output is the edited, customized care plan.

[0726] (Application Example 2)

[0727] 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".

[0728] In today's service industry, maintaining customer satisfaction requires service providers to quickly and appropriately understand customers' emotional states and respond flexibly. However, traditional systems struggle to recognize customer emotions in real time and provide appropriate responses, which limits the optimization of the customer experience.

[0729] 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.

[0730] In this invention, the server includes means for collecting individual user information, means for analyzing user requests, and means for automatically generating an optimal plan. This enables immediate recognition of the customer's emotional state and real-time notification to the service provider, thereby improving the customer experience.

[0731] "Users" refers to customers who use the system or individuals who receive care services.

[0732] "Personalized information" refers to specific data about a user, including their name, age, and past behavioral history.

[0733] "Requirements" refer to the results or needs that users want to obtain through the system, and can also refer to specific services or support.

[0734] A "plan" refers to a series of measures and proposals created to provide the best possible service to users.

[0735] "Information processing means" refers to technical functions that generate and display information useful to users using collected data.

[0736] "Audio and image data" refers to information including recordings of the user's voice, as well as media formats such as photographs and videos.

[0737] "Emotional state" is a concept that describes the user's emotional state and psychological condition, and is inferred from their voice and facial expressions.

[0738] "Real-time" refers to a state where processing is done instantly in accordance with real-world time, with virtually no time lag.

[0739] A "notification" is information or an alert message sent from a system to a user or service provider.

[0740] "Service provider" refers to a person or organization that is responsible for providing services to users through a system.

[0741] To implement this invention, a system is constructed that analyzes the emotional state of customers in real time and sends necessary notifications to service providers. This system mainly consists of a server, a terminal, and an emotion recognition engine.

[0742] The server collects and manages individual user information and analyzes user requests based on that data. It also receives sentiment data in real time and automatically generates appropriate service plans. General database management systems and statistical analysis software are used for data processing in this process.

[0743] The terminal provides the operating screen for mobile devices and smart glasses used by store staff, displaying generated plans and notifications. The interface on the terminal visually displays information based on the user's emotional state, allowing staff to flexibly adjust their responses as needed.

[0744] The emotion recognition engine collects user voice and image data and infers their emotional state in real time from that data. Machine learning models and voice / image processing algorithms are used for analysis, such as OpenCV or dedicated emotion recognition software. The analysis results are sent to a server and used to optimize the service.

[0745] As a concrete example, consider a scenario in a cafe. If a customer appears dissatisfied at the register, the emotion recognition engine identifies this state and sends the information to the server. Based on this data, the server sends a notification to the staff member's terminal prompting them to take immediate action. This allows the staff to improve their service to customers and increase their satisfaction.

[0746] An example of a prompt for a generating AI model is, "Please tell me what to do when a customer looks dissatisfied." This prompt is used to complement the recommendations automatically generated by the system and to suggest specific actions that staff should take.

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] The server collects individual user information and health indicator data and stores it in a database. Inputs are basic user profile information and health data, and outputs are database entries built based on that data. This process uses a data storage and management system to maintain data integrity and process data efficiently.

[0750] Step 2:

[0751] The emotion recognition engine receives audio and image data transmitted from the device and analyzes the user's emotional state. The input is real-time audio and facial expression data, and the output is digital data of the analyzed emotional state. It utilizes machine learning algorithms and employs speech processing and computer vision technologies to identify emotions.

[0752] Step 3:

[0753] The server analyzes user requests using emotional data provided by the emotion recognition engine. The input is digital data of emotional states, and the analysis results output countermeasures tailored to the user's needs. At this stage, statistical analysis tools are used to comprehensively evaluate the data and automatically generate an optimal care plan.

[0754] Step 4:

[0755] The terminal receives care plans and related notifications sent from the server and displays them to the service provider. Input is notification data and plan information from the server, and output is a visual interface display for the service provider. A simple display method using user interface design is employed via mobile devices and smart glasses.

[0756] Step 5:

[0757] Based on information on their device, users adjust service responses as needed to provide the best possible service to their customers. Inputs are notifications and plans displayed on the device, while outputs are the adjusted customer service actions. The service provider's judgment is crucial, and quick and effective action is required.

[0758] Step 6:

[0759] The system uses a generative AI model as input to provide prompts and supplements the suggestions that support the system's optimal response. The input consists of prompts tailored to the user's situation, while the output is additional response suggestions generated by the AI. Effective instructions are generated by a generative AI based on an expert system.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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."

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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 as being incorporated by reference.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] Means for collecting individual user information,

[0784] Based on the information collected above, a means of analyzing user needs,

[0785] A means for automatically generating an optimal care plan based on the aforementioned analysis results,

[0786] A means for visualizing the generated care plan and providing an interface that can be edited by the user's caregiver,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, further comprising means for monitoring the user's health indicator data in real time and updating the care plan as necessary.

[0790] (Claim 3)

[0791] The system according to claim 1, further comprising means for integrating regional medical resource information and analyzing the availability of care services in the region.

[0792] "Example 1"

[0793] (Claim 1)

[0794] A means of collecting individual user information from diverse data sources,

[0795] A means of integrating and analyzing the aforementioned collected information using natural language processing and machine learning models,

[0796] A means for automatically generating an optimized care plan using a generated AI model based on the aforementioned analysis results,

[0797] A means for visualizing the generated care plan and providing a user interface that can be edited by the person in charge of the service user,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, which monitors the user's health indicator data in real time by predicting their health status and updates the care plan as needed.

[0801] (Claim 3)

[0802] The system according to claim 1, which integrates regional medical resource information and analyzes the availability of care services in the region.

[0803] "Application Example 1"

[0804] (Claim 1)

[0805] Means for collecting individual user information,

[0806] Based on the information collected above, a means of analyzing user needs,

[0807] A means for automatically generating an optimal care plan based on the aforementioned analysis results,

[0808] A means for visualizing the generated care plan and providing an interface that can be edited by the user's caregiver,

[0809] A means of displaying care plans in augmented reality format via smart glasses,

[0810] A means of adjusting care plans using voice input,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means for monitoring the user's health indicator data in real time and updating the care plan as necessary.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for integrating regional medical resource information and analyzing the availability of care services in the region.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] A means to recognize the emotional state of users in real time and analyze emotional data,

[0819] A means for collecting individual information, including the aforementioned emotional data, and for analyzing user needs based on that information,

[0820] A means for automatically generating an optimal care plan using an AI model based on the aforementioned analysis results,

[0821] A means for visualizing the generated care plan and providing a user-editable interface,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for monitoring the user's health indicator data in real time and updating the generated care plan as necessary.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for integrating regional medical resource information and analyzing the availability of care services in the region.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] Means for collecting individual user information,

[0830] Based on the information collected, a means for analyzing user requests,

[0831] A means for automatically generating an optimal plan based on the aforementioned analysis results,

[0832] The generated plan is visualized and the information processing means allows the user to edit it.

[0833] A means of recognizing the emotional state by analyzing the user's voice and image data,

[0834] A means for sending a notification to the service provider in real time based on the aforementioned emotional state,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for monitoring user health indicators in real time and updating the plan as necessary.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for integrating information on local medical resources and analyzing the feasibility of providing services in the region. [Explanation of symbols]

[0840] 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 for collecting individual user information, Based on the information collected above, a means of analyzing user needs, A means for automatically generating an optimal care plan based on the aforementioned analysis results, A means for visualizing the generated care plan and providing an interface that can be edited by the user's caregiver, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the user's health indicator data in real time and updating the care plan as necessary.

3. The system according to claim 1, further comprising means for integrating regional medical resource information and analyzing the availability of care services in the region.

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