system

Abstract

We provide the system. [Solution] The present invention relates to an integrated business support system that includes an attendance management unit, an engagement input unit, a physical and mental condition input unit, an expense management unit, a schedule linkage unit, a generative AI diagnostic unit, an administrator dashboard unit, etc., allowing employees to centrally input and manage attendance information, work motivation, physical and mental state, expense applications, schedules, etc. from a terminal, while a generative AI on the server comprehensively analyzes this data and automatically generates individually optimized mental support, work efficiency advice, anomaly detection reports, etc., notifying and visualizing this information to the employee and administrator in real time, thereby simultaneously achieving employee health management, work efficiency improvement, and advanced organizational management by administrators.

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 executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence associated with a description relating to a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 traditional workplaces, employee attendance management systems, work schedule management systems, expense application systems, and mental health support systems are operated independently. Due to insufficient information sharing between these multiple systems, employees must individually clock in and out, report their motivation and physical condition, submit expense applications, and check their schedules. This leads to challenges such as cumbersome data entry, fragmented information, delays in managers understanding employee status and detecting anomalies, difficulty in early detection and response to mental health issues, and difficulty in visualizing and analyzing the overall health management status of the organization. Furthermore, with the advancement of diverse work styles and a stressful society in recent years, there is a strong demand to grasp employees' physical and mental health status and engagement in real time and from multiple perspectives, and to realize individually optimized mental support and work efficiency using advanced technologies such as AI. However, conventional technologies do not have a system that can achieve this in an integrated and automatic manner. This project aims to solve that problem. [Means for solving the problem]

[0005] To address the above challenges, we have adopted a business support system that includes an attendance management unit, an engagement input unit, a physical and mental condition input unit, an expense management unit, a schedule linkage unit, a generation-based AI diagnostic unit, and an administrator dashboard unit. Specifically, the attendance management unit records each employee's arrival and departure times; the engagement input unit allows employees to quantitatively and qualitatively input their work motivation and satisfaction levels upon arrival; the physical and mental condition input unit allows employees to input their daily physical and mental scores and comments; the expense management unit allows employees to input and centrally manage expense claims such as transportation expenses, along with the date, amount, and purpose; and the schedule linkage unit manages the calendar The system integrates with data to manage employee schedules, meetings, and tasks. A generative AI diagnostic unit receives input data on employees' physical and mental condition, uses generative AI to automatically diagnose their mental state, and notifies both the employee and their manager if an abnormality is detected. A manager dashboard allows managers to view a list of attendance information, engagement scores, physical and mental condition, expense claim status, schedule information, and diagnostic results from the generative AI for multiple employees under their management, enabling visualization of past trends and patterns. By linking these units via a network, the system provides an integrated and automated means of improving employee work efficiency, health management, and managerial efficiency in member management. [Brief explanation of the drawing]

[0006] [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. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the storage with a reference sign is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of the non-volatile storage device include a flash memory (SSD (Solid State Drive)), a magnetic disk (for example, a hard disk), a magnetic tape, and the like.

[0012] In the following embodiments, the communication I / F (Interface) with a reference sign is an interface including a communication processor, an antenna, and the like. The communication I / F governs communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like.

[0013] 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 only A, may be only B, or may be a combination of A and B. Further, in the present specification, even when three or more items are expressed in a linked manner by "and / or", the same concept as that for "A and / or B" is applied.

[0014] [First Embodiment]

[0015] FIG. 1 illustrates an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. An example of the data processing apparatus 12 includes a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of the "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network), and the like.

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, camera 42, and communication I / F 44 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact of an indicator (for example, a pen, a finger, or the like), thereby receiving user input by the contact of the indicator. The microphone 38B detects a user's voice, thereby receiving voice-based user input. A control unit 46A in the processor 46 transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 acquires the data indicating the user input.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0025] 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 the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models. 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.

[0026] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0027] (Example 1)

[0028] The flow of a specific process in Example 1 will now be described. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0029] (Embodiments for carrying out the present invention)

[0030] Embodiments of the present invention are integrated business support systems realized by linking a server and a terminal, which organically integrate the functions of employee attendance management, engagement input, physical and mental condition input, expense management, schedule linkage, generative AI diagnosis, and an administrator dashboard, and by dividing the functions of each part between the server and the terminal, the system streamlines the daily work and health management of employees and the organizational management by administrators. The configuration and processing of each part, the method of utilizing generative AI, and the division of functions between the server and the terminal will be described in detail below with specific examples.

[0031] First, terminals are information devices such as personal computers, smartphones, and tablets that employees and administrators use on a daily basis, while servers consist of high-performance computers in a cloud environment or on the company's internal network. The terminal side is mainly responsible for displaying the user interface, data input, receiving notifications, and simple local processing, while the server side is responsible for advanced processing such as centralized data management, analysis and diagnosis using generative AI, data aggregation and visualization, automatic report generation, and integration with external systems.

[0032] The attendance management department provides a screen for employees to enter their clock-in and clock-out times from their terminals, and the entered data is sent to a server for centralized management. For example, when an employee presses the "Clock In" button on a smartphone app, time information, GPS location information, and terminal identification information are sent to the server. This allows for remote work, satellite office work, and clocking in from outside the office. Furthermore, by combining biometric authentication such as facial recognition, fingerprint authentication, and IC card authentication with physical authentication, it is possible to prevent impersonation and proxy clocking in. On the server side, attendance history for each employee is compiled daily, weekly, and monthly, and it is possible to automatically determine situations such as tardiness, early departure, absence, and overtime, and send alert notifications to administrators and HR personnel.

[0033] The engagement input section provides an interface on the terminal for employees to input their daily work motivation and satisfaction, evaluation of the work environment, and expectations and anxieties about their work. For example, they can select their "motivation level for today" on a scale from 1 to 10 and enter specific comments in free-text fields such as "expectations for work," "relationships with superiors and colleagues," and "anxieties about future career." On the server side, this engagement data is accumulated daily, and trends for each employee and each department are analyzed. For example, if the engagement score is continuously declining in a particular department, a generative AI can extract the cause from the comments and automatically extract keywords such as "excessive workload," "deteriorating interpersonal relationships," and "dissatisfaction with the evaluation system" and notify the administrator.

[0034] The mental and physical condition input unit provides employees with a screen on their terminal where they can input detailed information about their physical and mental state, including physical and mental scores, specific symptoms and stressors, sleep duration, dietary habits, exercise routines, and personal events. For example, it can include options such as "headache," "stomach ache," "fatigue," "insomnia," and "irritability," as well as specific questions such as "sleep duration yesterday," "recent stressors," and "family health status." Employees input these items daily, weekly, or monthly and send them to the server. The server stores this data chronologically and uses it for analysis in the generative AI diagnostic unit.

[0035] The expense management department provides an input screen for employees to submit expense claims such as transportation, business trip, equipment purchase, and entertainment expenses from their terminals. Employees input and attach detailed information such as date, amount, purpose, receipt image, destination, travel route, and mode of transportation used, and then send it to the server. For example, employees can take a picture of a receipt with their smartphone camera and attach the image to the application data. The server centrally manages this expense claim data, enabling approval flows, integration with accounting systems, and automatic detection of expense fraud. For instance, if multiple high-value claims are submitted on the same day, or if an claim deviates significantly from past averages, the generation AI can detect anomalies and send an alert to the administrator.

[0036] The scheduling integration unit connects with employees' calendar information, allowing them to view and register their daily schedules, meetings, and tasks on their devices. For example, it can integrate with external calendar services via API to automatically retrieve and display employee schedules, and send meeting reminder notifications to devices. It can also implement functions for inputting and managing the progress and priority of work tasks. For instance, by registering tasks for each project and managing progress rates, assigned personnel, and deadlines, work can be made more visible and efficient.

[0037] The Generative AI Diagnostic Unit is the core of this invention. It utilizes a generative AI running on a server to comprehensively analyze employee mental and physical condition input data, engagement data, past attendance history, expense claim status, schedule information, etc. For example, if an employee's mental score has been declining over the past few days, or if there are frequent reports of poor health, the Generative AI automatically generates diagnostic comments such as "Mental support is needed" or "Adjustment of workload is recommended" using natural language processing, time-series analysis, and anomaly detection algorithms. Furthermore, based on the employee's input and past trends, the Generative AI generates individually optimized advice and self-care suggestions, such as "Get enough rest is recommended," "Self-care methods for stress relief," or "Consider consulting an industrial physician," and displays them as feedback on the terminal. In addition, the Generative AI also automatically generates anomaly detection reports for managers, such as "Subordinate A's mental score has dropped sharply," "Subordinate B's expense claims are higher than usual," or "Subordinate C's meeting schedule is too busy," as well as trend analysis reports on the overall health status of the organization, and sends them to the manager's dashboard. Furthermore, the AI ​​can extract stressors and work-related challenges from employees' free-form comments, automatically classifying and summarizing statements such as "I'm exhausted from the heavy workload lately," "I'm having trouble communicating with my boss," and "I can't concentrate due to family matters," and then notifying managers. The generative AI can also analyze the correlation between employees' expense claims, schedule information, and their physical and mental condition, automatically performing trend analyses such as "poor health increases after business trips," "mental scores tend to decrease on days with many meetings," and "engagement decreases when overtime continues."

[0038] The administrator dashboard provides an accessible dashboard screen for administrators from their terminals, based on employee data aggregated and analyzed on the server side. For example, administrators can view a list of attendance status, engagement scores, mental and physical condition, expense claim status, schedule information, and diagnostic results generated by the generative AI for all employees under their management. Furthermore, visualization tools such as graphs, heatmaps, and time-series charts allow for an intuitive understanding of individual employee status trends and overall organizational trends. For instance, if an employee's mental score drops sharply, an alert is displayed on the dashboard, allowing administrators to quickly take action such as individual consultations or work adjustments. In addition, health management reports automatically generated by the generative AI, individual employee self-care history, and expense claim approval status can all be centrally managed on the dashboard. Moreover, practical support can be provided, such as when an administrator checks a graph of a specific employee's mental and physical condition trends over the past six months, and if abnormal values ​​persist, a notification automatically recommending consultation with an industrial physician is displayed.

[0039] Furthermore, this system can flexibly accommodate diverse work styles for employees, including remote work, flextime, part-time work, working at multiple locations, and working at overseas locations. For example, it can automatically adjust time zones and calendar settings on the server side so that employees at overseas locations can input their attendance in local time and be managed according to local holidays and work systems. In addition, if an employee chooses a special work arrangement such as long-term leave, childcare leave, or family care leave, the attendance management department and schedule coordination department will automatically respond and notify managers and HR personnel of the necessary information.

[0040] In this embodiment, the terminal side is primarily responsible for displaying the user interface, data input, notification reception, and simple local processing, while the server side is responsible for advanced processing such as centralized data management, analysis and diagnosis by generative AI, data aggregation and visualization, automatic generation of various reports, and integration with external systems. For example, data entered by an employee from the terminal is immediately sent to the server, where the generative AI analyzes it in real time, and the diagnostic results and advice are fed back to the terminal. This allows employees to instantly understand their own status, and managers to monitor and respond to the overall organizational situation in real time.

[0041] Furthermore, generative AI can extract stressors and work-related challenges from employees' free-form comments and report them to managers in natural language. For example, the AI ​​can automatically classify and summarize comments such as "I'm exhausted from the heavy workload lately" or "I'm having trouble communicating with my boss" and notify managers. Generative AI can also analyze the correlation between employees' expense claims and schedule information and their physical and mental condition, automatically performing trend analysis such as "Employees tend to experience more health problems after business trips" or "Mental health scores tend to decrease on days with many meetings."

[0042] As described above, the embodiments of the present invention, through the functional division between a server and a terminal, centrally and in real time manage employee attendance, work, health, expenses, and schedule information, and automatically realize individually optimized mental support, improved work efficiency, and promotion of health management throughout the organization by utilizing generative AI. For example, it can flexibly accommodate diverse working styles of employees, such as remote work, flextime, part-time work, and working at multiple locations, and it can also support early problem detection and rapid decision-making by making it easier for managers and executives to grasp the overall health and work status of the organization.

[0043] (System Configuration)

[0044] The system according to this embodiment includes an attendance management unit, an engagement input unit, a physical and mental condition input unit, an expense management unit, a schedule linkage unit, a generation-based AI diagnostic unit, an administrator dashboard unit, a database unit, a notification control unit, and an external linkage unit. The attendance management unit provides a screen for employees to input their arrival and departure times from terminals such as PCs, smartphones, and tablets, and has the function of transmitting the entered time information to a server for centralized recording and management. For example, when an employee starts remote work from home, pressing the "Clock In" button on a smartphone app automatically transmits the time and location information of the terminal. In other words, information stamped at an IC card reader installed at the office entrance / exit gate is immediately reflected on the server. In other words, attendance is recorded simultaneously with identity verification using a facial recognition camera. In other words, attendance is entered from outside using a tablet terminal for direct commutes. Furthermore, the attendance management unit also has the function of automatically aggregating each employee's attendance history on a daily, weekly, and monthly basis, and detecting abnormal values ​​such as tardiness, early departures, overtime, and holiday work, and notifying administrators and HR personnel with alerts.

[0045] The engagement input unit displays an interface on the terminal for employees to input their daily work motivation and satisfaction, evaluation of the work environment, expectations and anxieties about their work, and changes in motivation, and has the function of sending the input content to the server. For example, it can be used to select "Today's Motivation Level" on a scale from 1 to 10, to input specific comments in free-text fields such as "What I felt a sense of accomplishment in this week's work," "Comments on workplace relationships," and "Anxieties about future careers," to input engagement scores for each project, and to visualize engagement trends aggregated by department. The engagement input unit accumulates this data on a daily basis and stores it on the server as basic data for analyzing trends for each employee and each department.

[0046] The mental and physical condition input unit provides a screen on the terminal where employees can input detailed information about their physical and mental state, including physical and mental scores, specific symptoms and stressors, sleep duration, dietary habits, exercise routines, and personal events, and has the function of sending this data to a server. Examples include selecting the appropriate symptom from options such as "headache," "stomach ache," "fatigue," "insomnia," and "irritability," setting specific questions such as "enter yesterday's sleep duration," "describe recent stressors," and "enter information about family health," automatically acquiring and linking vital data such as body temperature and blood pressure from wearable devices, and displaying changes in physical and mental state on a weekly or monthly basis in graph form. The mental and physical condition input unit stores detailed data on the server to comprehensively understand the health status of employees.

[0047] The Expense Management Department provides an input screen for employees to submit expense claims such as transportation expenses, business trip expenses, equipment purchase expenses, and entertainment expenses from their terminals. It has a function to input and attach detailed information such as date, amount, purpose, receipt image, destination, travel route, and mode of transportation used, and send it to the server. For example, it can take a picture of a receipt with a smartphone camera and attach the image to the application data, automatically acquire map information of the destination when submitting an expense claim, automatically link and reflect the usage history of transportation IC cards in the expense claim, and automatically check for errors in the expense claim and prompt re-entry if there are any deficiencies in the expense claim content. The Expense Management Department centrally manages this expense claim data and realizes approval flows, integration with accounting systems, and automatic detection of expense fraud.

[0048] The Schedule Integration Unit has the functionality to link with employees' calendar information, allowing them to check and register their daily schedules, meetings, and tasks on their devices. Examples include automatically retrieving and displaying employee schedules by linking with external calendar services via API, sending meeting reminder notifications to devices, inputting and managing the progress and priority of work tasks, registering tasks for each project and managing progress rates, assigned personnel, and deadlines, and managing schedules in local time by linking with calendars at multiple locations or overseas offices. The Schedule Integration Unit stores basic information on its server to improve the visibility and efficiency of business operations.

[0049] The Generative AI Diagnostic Unit utilizes a generative AI running on a server to comprehensively analyze employee mental and physical condition input data, engagement data, past attendance history, expense claim status, schedule information, etc., and has the function of automatically generating individually optimized diagnostic comments and advice, anomaly detection reports, and trend analysis reports. For example, the system may diagnose an employee as needing mental support if their mental score has been declining over the past few days; generate a comment recommending workload adjustment if employees frequently report feeling unwell; extract stressors from free-text comments such as "I'm exhausted from the heavy workload lately" or "I'm having trouble communicating with my supervisor" and notify managers; analyze the correlation between expense claims and schedule information and physical and mental condition to identify trends such as "fewer health issues after business trips" or "mental scores tend to decrease on days with many meetings"; generate individual advice for employees such as "get enough rest," "self-care methods for stress relief," or "consider consulting an industrial physician" and provide feedback to their devices; and automatically generate anomaly detection reports for managers, such as "subordinate A's mental score has dropped sharply," "subordinate B's expense claims are higher than usual," or "subordinate C's meeting schedule is too packed." In the generative AI diagnostics department, for example, by providing the generative AI with specific prompts such as "Summarize stress factors and workload trends in natural language based on employee mental and physical condition input data and engagement data, and generate individually optimized advice," "Detect anomalies from the mental score trends over the past 30 days and create warning messages for managers," "Extract negative emotional expressions from free-text comments and classify stress factors," and "Analyze the correlation between expense claim data and health scores and report on the impact of business trips and meetings on health status," more accurate diagnoses and report generation can be achieved.

[0050] The administrator dashboard section has the function of providing a dashboard screen that administrators can access from their terminals, based on employee data aggregated and analyzed on the server side. For example, administrators can view a list of attendance status, engagement scores, mental and physical condition, expense claim status, schedule information, and diagnostic results from generative AI for all employees under their management. They can also intuitively grasp the changes in each employee's condition and the overall organizational trends using visualization methods such as graphs, heatmaps, and time-series charts. If an employee's mental score drops sharply, an alert is displayed on the dashboard, allowing administrators to quickly take action such as individual interviews or work adjustments. Generative AI-generated health management reports, self-care history, and expense claim approval status can be centrally managed. Furthermore, administrators can view a graph of a specific employee's mental and physical condition changes over the past six months, and if abnormal values ​​persist, a notification recommending consultation with an industrial physician is automatically displayed.

[0051] The database unit has the function of centrally storing and managing various data from the entire system, including attendance information, engagement data, physical and mental condition data, expense claim data, schedule information, diagnostic results from generative AI, and administrator response history. Examples include storing daily, weekly, and monthly attendance history for each employee in chronological order, accumulating raw data for graphing the trends in engagement and physical and mental condition, long-term storage of expense claim approval history and receipt images, and storing diagnostic results and advice history from generative AI linked to each employee.

[0052] The notification control unit has the function of notifying employees' and administrators' terminals in real time of various events and abnormal values ​​that occur within the system, diagnostic results from generative AI, and instructions from administrators. Examples include push notifications to employees' smartphones for missed attendance clock-ins, tardiness, or early departures; notifications to administrators via email or dashboard for abnormal values ​​in engagement or physical and mental condition; immediate notification to applicants of the approval or rejection of expense claims; and delivery of self-care advice recommended by generative AI to employees in chat format.

[0053] The external integration unit has the functionality to automatically acquire and exchange data with other business systems, calendar services, accounting systems, wearable devices, chat tools, etc., via APIs and other means. Examples include automatically acquiring schedule information from an external calendar service and reflecting it in the schedule integration unit, automatically transferring expense application data in conjunction with the accounting system, automatically integrating vital data acquired from wearable devices into the physical and mental condition input unit, and distributing advice and alerts to employees using generative AI in conjunction with a chat tool.

[0054] As described above, the system according to this embodiment has its various components working together to centrally and in real time manage employee attendance, work, health, expenses, and schedule information, and automatically realizes individually optimized mental support, improved work efficiency, and promotion of health management throughout the organization by utilizing generative AI. For example, by providing the generative AI with specific prompts such as "Summarize stress factors and workload trends in natural language based on employee mental and physical condition input data and engagement data, and generate individually optimized advice," "Detect anomalies from the mental score trends over the past 30 days and create a warning message for managers," "Extract negative emotional expressions from free-text comments and classify stress factors," and "Analyze the correlation between expense application data and health scores, and report on the impact of business trips and meetings on health status," more accurate diagnoses and report generation can be achieved.

[0055] (Implementation Steps)

[0056] Step 1: Enter and record attendance information. Employees enter their arrival and departure times using devices such as PCs, smartphones, and tablets, and this information is sent to a server for centralized recording. For example, when working remotely, employees record their time and location by pressing the clock-in button on a smartphone app from home, while when working in the office, arrival and departure times are recorded using IC card readers or facial recognition terminals. The system also supports direct commutes to and from work and clocking in from outside the office. All attendance data is compiled on the server side on a daily, weekly, and monthly basis, and abnormal values ​​such as tardiness, early departures, overtime, and holiday work are automatically detected.

[0057] Step 2: Input and store engagement information Employees input their daily work motivation and satisfaction, evaluation of the work environment, and expectations and anxieties about their work through an interface on their terminals and send the data to the server. For example, they select their motivation level on a scale from 1 to 10 and enter things like "What did I feel I accomplished in my work this week?", "Comments on workplace relationships," and "Anxieties about my future career" in the free-text field. This data is accumulated daily and serves as the basis for analyzing trends for each employee and departments.

[0058] Step 3: Input and store information on physical and mental condition. Employees input information such as their physical and mental health scores, specific symptoms and stressors, sleep duration, diet, exercise habits, and personal events on a terminal and send it to the server. This includes selecting symptoms such as "headache," "stomach ache," "fatigue," "insomnia," and "irritability," inputting details such as "sleep duration yesterday," "recent stressors," and "family health status," and automatically acquiring vital data from wearable devices. This information is stored on the server in chronological order.

[0059] Step 4: Entering and managing expense application information Employees submit expense claims for transportation, business trips, supplies, entertainment, etc., from their terminals, entering and attaching detailed information such as date, amount, purpose, receipt image, destination, travel route, and mode of transportation used, and sending it to the server. For example, employees can take photos of receipts with their smartphones and attach them to the application data, automatically link transportation IC card usage history, and perform automatic error checks if there are any deficiencies in the expense claim. On the server side, expense claim data is centrally managed, and approval flows, integration with accounting systems, and automatic detection of expense fraud are also realized.

[0060] Step 5: Linking and managing schedule information The system integrates with employee calendar information, allowing them to view and register their daily schedules, meetings, and tasks on their devices. For example, it can automatically retrieve schedules via API integration with external calendar services and send meeting reminder notifications to devices, input and manage the progress and priority of work tasks, register tasks for each project and manage progress rates, assignees, and deadlines, and manage schedules in local time by integrating with calendars at multiple locations and overseas offices.

[0061] Step 6: Analysis and diagnosis of mental, physical, and work-related conditions using generative AI. The AI-powered diagnostic unit on the server comprehensively analyzes employee mental and physical condition input data, engagement data, attendance history, expense claim status, schedule information, etc., and automatically generates individually optimized diagnostic comments and advice, anomaly detection reports, and trend analysis reports. For example, if the mental score is on a downward trend, it will diagnose "mental support is needed," and if reports of poor health are frequent, it will generate a comment such as "adjustment of workload is recommended." It extracts stress factors from free-text comments, analyzes the correlation between expense claims and schedule information and mental and physical condition, and reports on the impact of business trips and meetings on health status. Specific examples of prompts to be fed into the generating AI include: "Based on employee mental and physical condition input data and engagement data, summarize stress factors and workload trends in natural language and generate individually optimized advice," "Detect anomalies from the mental score trends over the past 30 days and create a warning message for managers," "Extract negative emotional expressions from free-text comments and classify stress factors," and "Analyze the correlation between expense claim data and health scores and report on the impact of business trips and meetings on health status."

[0062] Step 7: Notification and visualization of diagnostic results, advice, and reports. The diagnostic comments, advice, anomaly detection reports, and trend analysis reports generated by the AI ​​diagnostic system are displayed in a list on the administrator's terminal via the administrator dashboard. For example, visualization methods such as graphs, heatmaps, and time-series charts can be used to intuitively grasp the status changes of each employee and the overall trends of the organization. Individual advice and self-care suggestions are fed back to the employee on their terminal, and anomalies and warnings are notified to the administrator in real time. If a specific employee's mental score drops sharply, an alert is displayed, allowing the administrator to quickly take action such as individual interviews or work adjustments.

[0063] Step 8: Integration with external systems and devices The External Integration Department connects with other business systems, calendar services, accounting systems, wearable devices, chat tools, etc., via APIs, etc., to enable automatic data acquisition and mutual integration. For example, it can automatically acquire schedule information from external calendar services and reflect it in the Schedule Integration Department, automatically transfer expense application data in conjunction with the accounting system, automatically link vital data acquired from wearable devices to the Physical and Mental Condition Input Department, and deliver advice and alerts to employees using generative AI in conjunction with chat tools.

[0064] (Specific use cases)

[0065] For example, an employee could enter their arrival time from a computer terminal, simultaneously select their motivation level for the day on a scale of 1 to 10, enter a free-text comment such as "I'm highly motivated today because a new project is starting," enter a physical health score of 8 and a mental health score of 7, and also enter specific physical health comments such as "I slept well last night, but I have a bit of shoulder stiffness." In addition, to claim transportation expenses incurred that day, an employee could take a picture of the receipt with their smartphone, enter detailed information such as the amount, purpose, destination, and mode of transportation used on the expense claim screen, and send it to the server. Employee calendar information is automatically retrieved in conjunction with an external calendar service, and meeting schedules and tasks for the day are displayed on the terminal. Employees can also receive reminder notifications before meetings begin and enter the progress of their work tasks.

[0066] On the server side, attendance information, engagement data, mental and physical condition data, expense claim data, and schedule information are centrally stored. The generation AI diagnostic unit comprehensively analyzes this data, and if, for example, an employee's mental score has been gradually declining over the past week, or if free-text comments such as "I'm tired because of the heavy workload lately" or "I'm having trouble communicating with my boss" are entered multiple times, it uses an anomaly detection algorithm and natural language processing to extract stress factors and automatically generates individually optimized advice such as "We recommend adjusting your workload," "Take sufficient rest as self-care," or "Consider consulting an industrial physician." Furthermore, by analyzing the correlation between expense claim data and health scores, if, for example, reports of poor health increase in weeks with many business trips or meetings, it can also generate specific advice such as "We recommend taking sufficient rest after business trips" or "We recommend stretching between meetings."

[0067] The administrator dashboard allows administrators to view a list of attendance records, engagement scores, mental and physical condition, expense claim status, schedule information, and diagnostic results generated by the generative AI for all employees under their management. For example, visualization tools such as graphs, heatmaps, and time-series charts can be used to intuitively grasp the changes in each employee's condition and the overall trends of the organization. If an employee's mental score drops sharply, an alert is displayed on the dashboard, allowing administrators to quickly take action such as individual interviews or work adjustments. Furthermore, health management reports, self-care history, and expense claim approval status automatically generated by the generative AI can also be centrally managed on the dashboard.

[0068] In such use cases, specific examples of prompts to be fed into the generating AI include: "Analyze the trend of mental scores over the past 7 days based on employee mental and physical condition input data, engagement data, attendance history, expense claim data, and schedule information, extract stressors if abnormal values ​​or downward trends are found, and generate individually optimized advice in natural language," "Extract negative emotional expressions and work-related challenges from free-text comments and create warning messages for managers," "Analyze the correlation between expense claim data and health scores and report on the impact of business trips and meetings on health status," and "Combine employee calendar information and mental and physical condition data to analyze the impact of busy schedules on mental scores and generate self-care suggestions as needed."

[0069] Thus, the system of the present invention grasps the diverse working styles, work situations, and health status of employees in real time and from multiple perspectives, and automatically realizes individually optimized mental support, improved work efficiency, and promotion of health management throughout the organization by utilizing generative AI.

[0070] (Application Example 1) The flow of a specific process in Application Example 1 will be explained below. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0071] (Embodiments for carrying out the present invention)

[0072] Embodiments of the present invention are configured as an advanced integrated care support system that grasps the health, work, and care status of caregivers and those receiving care in a multidimensional and real-time manner, and automates individually optimized support, care plan optimization, anomaly detection, and organizational risk management using generative AI. This system comprises multiple functional units, including a caregiver management unit, a care recipient management unit, a care record management unit, an expense management unit, a schedule management unit, a generative AI analysis unit, an administrator dashboard unit, an external linkage unit, a database unit, and a notification control unit, and is configured so that the server and terminals are linked via a network.

[0073] First, the care worker management system has a function that allows care workers to input and record shift records, work motivation scores, physical condition scores, mental scores, and work comments from their devices. For example, when a care worker starts their shift, they can input shift information such as "June 1, 2024, 8:00 AM to 5:00 PM" from their smartphone or tablet, and at the same time select "Today's work motivation (1-5)", "Physical condition score (1-5)", and "Mental score (1-5)", and also record free comments such as "I didn't get enough sleep last night and feel a little tired", "I feel stressed from yesterday's night shift", or "My back pain has gotten worse recently". Furthermore, it is designed to flexibly accommodate various work patterns, such as night shifts, early morning shifts, consecutive shifts, working at multiple locations, short-time shifts, team care by multiple workers, and temporary participation of external helpers. Shift records and physical / mental scores are automatically compiled daily, weekly, and monthly, allowing managers to understand trends and abnormal values ​​for each worker. For example, it can automatically generate an alert if you work for three consecutive days or more, or if your health score declines for three consecutive days.

[0074] The care recipient management unit has the function of inputting and accumulating chronological data for each care recipient, including physical health scores, mental health scores, specific symptoms (e.g., decreased appetite, fever, anxiety, risk of falls, risk of pressure ulcers, constipation, diarrhea, sleep disorders, progression of dementia symptoms, etc.), care records, and special notes during care. For example, a caregiver can input a physical health score of 2 / 5 and a mental health score of 4 / 5 for a care recipient, select symptoms such as "decreased appetite," "fever," "anxiety," and "nocturnal delirium," and record detailed comments such as "couldn't sleep last night," "decreased food intake," "wandered around at night," and "increased frequency of bowel movements." Furthermore, it is possible to automatically acquire and link vital data (temperature, blood pressure, pulse, SpO2, etc.) from wearable devices and measuring instruments. For example, temperature and blood pressure data can be automatically sent to the server at scheduled times, and if abnormal values ​​are detected, the administrator or attending physician will be immediately notified.

[0075] The Care Record Management Department has the function of recording in detail the content, results, and special notes of various care provided to the person receiving care, such as meal assistance, bathing assistance, medication management, rehabilitation support, toileting assistance, transfer assistance, outing support, participation in recreation, oral care, nail trimming, pressure ulcer care, dementia care, and support for family visits. For example, it can record specific content, results, and special notes for each care, such as "June 1, 2024 Meal assistance: Unable to finish eating," "Bathing assistance: No problems," "Medication: Refusal," "Rehabilitation: Increased walking distance," "Toileting assistance: Incontinence," "Oral care: Independent," and "Recreation: Decreased motivation to participate." Even when multiple caregivers take turns providing care, care records can be centrally managed chronologically, facilitating smooth handovers and information sharing. For example, if the caregiver in the morning records "Meal assistance: Decreased appetite, half left," the caregiver in the afternoon can refer to that information and adjust the care provided at dinnertime.

[0076] The expense management department has a function to allow care workers to input and centrally manage expense applications for consumables, transportation costs, external service usage fees, medical supplies, recreation expenses, and external expert dispatch fees, along with the date, amount, purpose, and receipt images. For example, "June 1, 2024, 500 yen, consumables purchase, receipt image attached," "June 2, 2024, 1000 yen, external rehabilitation service usage," "June 3, 2024, 200 yen, transportation expenses (accompanying the care recipient to a medical appointment)," etc. Detailed information is recorded for each expense and centrally managed on the server. If there are any deficiencies in the expense application content, the system automatically checks for errors and prompts re-entry, and it is also possible to implement automatic approval and automatic journal entries through integration with the accounting system. For example, if multiple high-value applications occur in a short period of time from the same care worker, or if there are applications that significantly exceed the normal amount of consumables used, the AI ​​will detect the anomaly and issue an alert to the administrator.

[0077] The Schedule Management Department has functions that allow care workers to check, register, and manage the progress of their care schedules and tasks on their terminals. For example, care schedules such as "bathing assistance," "meal assistance," "medication management," "rehabilitation support," "excretion assistance," "outing support," and "recreation" can be displayed in a calendar format, and progress and completion reports for each schedule can be entered. It can also integrate with external calendar services via API to automatically retrieve and display individual staff schedules and facility-wide event information. It also supports schedule adjustments across multiple locations and teams, streamlining shift adjustments and task distribution. For example, if a staff member is suddenly absent, the AI ​​can automatically analyze the shifts and workloads of other staff members and suggest the optimal replacement personnel and task assignments.

[0078] The Generative AI Analysis Unit is the core of this invention. It comprehensively analyzes the physical and mental state of caregivers and those receiving care, care records, shift history, expense claims, schedule information, etc., and uses generative AI to automatically perform functions such as providing mental support to caregivers, optimizing the health condition and care plans of those receiving care, detecting anomalies, generating individual advice, and managing organizational risks. For example, if a caregiver's mental score is low at 2 / 5, the system will refer to past input history and comments and automatically generate feedback such as, "You have been working consecutive night shifts recently, so mental support is needed," "We recommend securing opportunities for rest and consultation," and "Your back pain is worsening, so we recommend adjusting your workload," and notify the caregiver and their manager. For those receiving care, if their health score is 2 / 5 and they have experienced loss of appetite or fever for several days, the AI ​​will generate diagnostic comments such as, "Your health is deteriorating," "We recommend seeing a medical professional," "You need to review your care plan," and "You are at increased risk of dehydration, so strengthen your fluid intake," and notify the manager and attending physician. The system comprehensively analyzes care records and changes in the care recipient's condition to extract positive trends such as "improved health score after meal assistance," "reduced anxiety after bathing assistance," and "increased walking distance after rehabilitation," as well as negative trends such as "continued refusal to take medication and worsening health" and "increased failures in toileting assistance," and proposes optimized care plans. For example, it automatically generates specific improvement suggestions such as "consider changing medication assistance methods," "recommend reviewing meal content," "adjusting rehabilitation frequency would be effective," and "recommend reviewing toileting care." It also aggregates the shifts and health status of multiple staff members to evaluate the overall workload and mental risks of the team, and generates organizational advice such as "increased overall workload for the team," "recommend shift adjustments and rest," and "strengthen support for new staff members."

[0079] The administrator dashboard provides administrators with the ability to view the health, work, care status, expenses, and AI diagnostic results of all staff and care recipients in a list, graph, and alert format. For example, administrators can view the shift status, health and mental status, expense claim status, care records, and AI diagnostic results of all staff members in a list format on the dashboard. Visualization methods such as graphs, heatmaps, and time-series charts allow for an intuitive understanding of the changes in each staff member's condition and the overall organizational trends. If a staff member's mental score drops sharply, an alert is displayed on the dashboard, allowing administrators to quickly take action such as individual consultations or work adjustments. Health management reports, self-care history, and expense claim approval status automatically generated by the generative AI can also be centrally managed on the dashboard. If a particular care recipient's health continues to deteriorate, the AI ​​automatically notifies administrators of "recommendation to visit a medical institution" or "suggestion to review care content," allowing administrators and attending physicians to respond immediately. For example, if multiple care recipients simultaneously develop fever or diarrhea, the AI ​​can automatically detect the risk of infection and suggest to administrators that they issue a warning to the entire facility and strengthen infection control measures.

[0080] The External Collaboration Department connects with medical institutions' electronic medical record systems, family communication apps, government care reporting systems, wearable devices, and remote consultation systems for external experts via APIs, etc., to enable automatic data acquisition and mutual exchange. For example, it can automatically acquire the care recipient's medical visit history and prescription drug information and reflect it in the care plan; automatically distribute summaries of the care recipient's health status and care records to families; automatically generate periodic reports for government agencies using AI; automatically link vital data acquired from wearable devices to the physical and mental condition input department; and reflect remote advice from external experts in care records. A wide range of collaborations are possible.

[0081] The database department centrally stores and manages various data from across the entire system, including attendance information, engagement data, physical and mental condition data, expense claim data, schedule information, diagnostic results from generative AI, administrator response history, and external collaboration data. For example, it stores daily, weekly, and monthly attendance history for each employee in chronological order, accumulates raw data for graphing the trends in engagement and physical and mental condition, stores approval history of expense claims and receipt images for the long term, stores diagnostic results and advice history from generative AI linked to each employee and care recipient, and records collaboration history with external medical institutions and families.

[0082] The notification control unit has the function of notifying employees and administrators in real time of various events and abnormal values ​​that occur within the system, diagnostic results from generative AI, and instructions from administrators. For example, it can push notifications to employees' smartphones for missed attendance clock-ins, tardiness, or early departures, notify administrators via email or dashboard of abnormal values ​​regarding engagement and physical and mental condition, immediately notify applicants of the approval or rejection of expense claims, deliver self-care advice recommended by generative AI to employees in chat format, and send simultaneous notifications to all employees regarding infectious disease risks or emergency contact in the event of a disaster.

[0083] Thus, the embodiments of the present invention enable real-time and multifaceted monitoring of the health, work, and care status of caregivers and those receiving care in care settings. By automating individualized support and care plan optimization, anomaly detection, and organizational risk management using generative AI, the system simultaneously improves the quality of care services and reduces the burden on caregivers. It can flexibly adapt to diverse operational forms of care settings, such as multi-site deployment, mixed day and night shifts, short working hours, team care by multiple caregivers, information sharing with external medical institutions and families, infectious disease control, disaster response, and automated administrative reporting. Furthermore, the generative AI comprehensively provides advanced functions that were not possible with conventional care support systems, such as extracting stress factors and care challenges from free-text comments of caregivers and those receiving care and reporting them to administrators in natural language, analyzing the correlation between expense application content, schedule information, and physical and mental condition to automatically analyze trends in workload and health risks, and optimizing care plans and work assignments using externally linked data.

[0084] (System Configuration)

[0085] The system according to this embodiment includes a caregiver management unit, a care recipient management unit, a care record management unit, an expense management unit, a schedule management unit, a generational AI analysis unit, an administrator dashboard unit, an external linkage unit, a database unit, and a notification control unit. The caregiver management unit allows caregivers to input and record shift records, work motivation scores, physical condition scores, mental scores, work comments, etc., from a terminal. For example, when a caregiver starts work, they can input shift information such as "June 1, 2024, from 8:00 to 17:00" on their smartphone or tablet, and at the same time select "Today's work motivation (1-5)", "Physical condition score (1-5)", and "Mental score (1-5)", and also record free comments such as "I didn't get enough sleep last night and feel a little tired", "I feel stressed from yesterday's night shift", and "My back pain has gotten worse recently". Furthermore, the system can flexibly accommodate various work arrangements, including night shifts, early morning shifts, consecutive shifts, work at multiple locations, part-time work, team care by multiple staff members, and temporary participation of external helpers. Shift records and physical / mental health scores are automatically compiled daily, weekly, and monthly, and an alert can be automatically generated if consecutive shifts continue for three days or if the physical health score declines for three consecutive days.

[0086] The care recipient management department inputs and stores information for each care recipient in chronological order, including physical health scores, mental health scores, specific symptoms, care records, and special notes during care. For example, it can input a physical health score of 2 / 5 and a mental health score of 4 / 5, select symptoms such as "decreased appetite," "fever," "anxiety," and "nocturnal delirium," and record detailed comments such as "couldn't sleep last night," "decreased food intake," "wandered around at night," and "increased frequency of bowel movements." Furthermore, vital data (temperature, blood pressure, pulse, SpO2, etc.) is automatically acquired and linked from wearable devices and measuring instruments. Temperature and blood pressure data are automatically sent to the server at scheduled times, and if abnormal values ​​are detected, administrators and attending physicians are immediately notified.

[0087] The Care Record Management Department meticulously records various care services provided to care recipients, including meal assistance, bathing assistance, medication management, rehabilitation support, toileting assistance, transfer assistance, outing support, recreation participation, oral care, nail trimming, pressure ulcer care, dementia care, and family visit support. For each care service, it can record specific details, results, and special notes, such as "June 1, 2024 Meal assistance: Unable to finish meal," "Bathing assistance: No problems," "Medication: Refusal," "Rehabilitation: Increased walking distance," "Toileting assistance: Incontinence," "Oral care: Independent," and "Recreation: Decreased motivation to participate." Even when multiple staff members provide care in shifts, care records are centrally managed chronologically. If the morning staff member records "Meal assistance: Decreased appetite, half left," the afternoon staff member can refer to that information and adjust the care provided during dinner.

[0088] The expense management department allows care workers to input and centrally manage expense applications for consumables, transportation, external service fees, medical supplies, recreation expenses, and external expert dispatch fees, along with the date, amount, purpose, and receipt images. Detailed information is recorded for each expense, such as "June 1, 2024, 500 yen, consumables purchase, receipt image attached," "June 2, 2024, 1000 yen, external rehabilitation service usage," and "June 3, 2024, 200 yen, transportation expenses (accompanying the care recipient to a medical appointment)." The system automatically checks for errors in expense applications and prompts re-entry, and can also implement automatic approval and journal entries through integration with the accounting system. If multiple high-value applications occur from the same care worker in a short period, or if an application significantly exceeds the normal amount of consumables used, the AI ​​detects the anomaly and issues an alert to the administrator.

[0089] The schedule management system allows caregivers to view, register, and manage the progress of their care schedules and tasks on their devices. Care schedules such as "bathing assistance," "meal assistance," "medication management," "rehabilitation support," "excretion assistance," "outing support," and "recreation" are displayed in a calendar format, and progress and completion reports for each schedule can be entered. By integrating with external calendar services via API, it automatically retrieves and displays individual staff schedules and facility-wide event information, supports schedule adjustments across multiple locations and teams, and in the event of a sudden staff absence, AI automatically analyzes the shifts and workloads of other staff members and can propose the most suitable replacement personnel and task assignments.

[0090] The Generative AI Analysis Unit comprehensively analyzes the physical and mental state of caregivers and those receiving care, care records, shift history, expense claims, schedule information, etc., and uses generative AI to automatically perform functions such as mental support for caregivers, optimization of the health condition and care plans of those receiving care, anomaly detection, generation of individual advice, and organizational risk management. For example, if a caregiver's mental score is low at 2 / 5, the unit will refer to past input history and comments and automatically generate feedback such as "Mental support is needed because night shifts have been continuing recently," "We recommend securing rest and opportunities for consultation," and "We recommend adjusting the workload because lower back pain is worsening," and notify the caregiver and their manager. For those receiving care, if their health score is 2 / 5 and they have experienced decreased appetite or fever for several days, the AI ​​will generate diagnostic comments such as "Deteriorating health trend," "We recommend consulting a medical institution," "Care content needs to be reviewed," and "Increased risk of dehydration, so strengthen hydration," and notify the manager and attending physician. The system comprehensively analyzes care records and changes in the care recipient's condition to extract positive trends such as "improved health score after meal assistance," "reduced anxiety after bathing assistance," and "increased walking distance after rehabilitation," as well as negative trends such as "continued refusal to take medication and worsening health" and "increased failures in toileting assistance." Based on these findings, it proposes optimized care plans and automatically generates specific improvement suggestions such as "consider changing medication assistance methods," "recommend reviewing meal content," "adjusting rehabilitation frequency would be effective," and "recommend reviewing toileting care." It also aggregates the shifts and health status of multiple staff members to evaluate the overall workload and mental risks of the team, and generates organizational advice such as "increased overall workload for the team," "recommend shift adjustments and rest," and "strengthen support for new staff members." Specific examples of prompts to be fed into the generating AI include: "Analyze the physical and mental scores and comments of care workers over the past 7 days, extract stressors and workload trends, and generate individually optimized mental support advice," "Based on the care recipient's physical score, care records, and symptom input, diagnose signs of worsening health and the need to revise the care plan, and generate specific improvement suggestions," and "Collect the shift and health status of multiple staff members, evaluate the overall workload and mental risk of the team, and create a warning message and countermeasures for managers."

[0091] The administrator dashboard allows administrators to view the health, work, care status, expenses, and AI diagnostic results of all staff and care recipients in a list, graph, and alert format. Using visualization tools such as graphs, heatmaps, and time-series charts, administrators can intuitively grasp the progression of each staff member's condition and overall organizational trends. If a staff member's mental score drops sharply, an alert is displayed on the dashboard, allowing administrators to quickly take action such as individual consultations or work adjustments. The dashboard also centrally manages health management reports, self-care history, and expense application approval status automatically generated by the AI. If a particular care recipient's health continues to deteriorate, the AI ​​automatically notifies administrators of "recommendation for medical consultation" or "suggestion for review of care content," enabling immediate action by administrators and attending physicians. If multiple care recipients simultaneously experience fever or diarrhea, the AI ​​automatically detects the risk of infection and can suggest to administrators that they raise awareness throughout the facility and strengthen infection control measures.

[0092] The External Collaboration Department connects with medical institutions' electronic medical record systems, family communication apps, government care reporting systems, wearable devices, and remote consultation systems for external experts via APIs, etc., enabling automatic data acquisition and mutual exchange. This allows for diverse collaborations, such as automatically acquiring the care recipient's medical visit history and prescription drug information and reflecting it in the care plan, automatically distributing summaries of the care recipient's health status and care records to families, having AI automatically generate periodic reports for the government, automatically linking vital data acquired from wearable devices to the physical and mental condition input department, and reflecting remote advice from external experts in care records.

[0093] The database department centrally stores and manages various system data, including attendance information, engagement data, physical and mental condition data, expense claim data, schedule information, diagnostic results from generative AI, administrator response history, and external collaboration data. It stores daily, weekly, and monthly attendance history for each employee in chronological order, accumulates raw data for graphing the trends in engagement and physical and mental condition, stores expense claim approval history and receipt images long-term, stores diagnostic results and advice history from generative AI linked to each employee and care recipient, and records collaboration history with external medical institutions and families.

[0094] The notification control unit has the function of notifying employees and administrators in real time of various events and abnormal values ​​that occur within the system, diagnostic results from the generative AI, and instructions from administrators. Examples include pushing notifications to employees' smartphones for missed clock-ins, tardiness, or early departures, notifying administrators of abnormal values ​​in engagement and physical / mental condition via email or dashboard, immediately notifying applicants of the approval or rejection of expense claims, delivering self-care advice recommended by the generative AI to employees in chat format, and simultaneously notifying all employees of infectious disease risks or emergency contact information in the event of a disaster.

[0095] As described above, the system according to this embodiment has its various components working together to grasp the health, work, and care status of caregivers and care recipients in real time and from multiple perspectives. By automating individualized support and care plan optimization using generative AI, anomaly detection, and organizational risk management, it simultaneously improves the quality of care services and reduces the burden on staff. It can also flexibly adapt to diverse operational forms of care settings, such as multi-site deployment, mixed day and night shifts, short working hours, team care by multiple caregivers, information sharing with external medical institutions and families, infectious disease control, disaster response, and automated administrative reporting. Specific examples of prompts to be fed into the generating AI include: "Analyze the physical and mental scores and comments of care workers over the past 7 days, extract stressors and workload trends, and generate individually optimized mental support advice," "Based on the care recipient's physical score, care records, and symptom input, diagnose signs of worsening health and the need to revise the care plan, and generate specific improvement suggestions," and "Collect the shift and health status of multiple staff members, evaluate the overall workload and mental risk of the team, and create a warning message and countermeasures for managers."

[0096] (Implementation Steps)

[0097] Step 1: Enter the shift schedule and health status of care workers. Care workers enter their shift records via a terminal at the start of their shift, selecting their work motivation score, physical condition score, and mental state score on a scale of 1 to 5, and also entering free-form comments such as "I'm a little tired from lack of sleep last night," "I'm feeling stressed from yesterday's night shift," or "My back pain has been getting worse lately." The system supports a variety of work arrangements, including night shifts, early morning shifts, consecutive shifts, working at multiple locations, short shifts, and temporary participation of external helpers. This information is sent to a server and automatically compiled on a daily, weekly, and monthly basis.

[0098] Step 2: Enter the care recipient's health, symptoms, and care records. Caregivers input physical and mental health scores, specific symptoms (such as decreased appetite, fever, anxiety, nocturnal delirium, fall risk, pressure ulcer risk, constipation, diarrhea, sleep disturbances, and progression of dementia symptoms), vital data (temperature, blood pressure, pulse, SpO2, etc.), care records (such as assistance with meals, bathing, medication management, rehabilitation support, toileting, transfer assistance, outing support, participation in recreation, oral care, nail trimming, pressure ulcer care, dementia care, and support for family visits) from a terminal and send them to the server. Automatic data acquisition from wearable devices and measuring instruments is also possible, and the system supports multiple recording sessions (morning, afternoon, and night) and shift recording by multiple caregivers.

[0099] Step 3: Expense Application and Management Caregivers submit expense claims for consumables, transportation, external service fees, medical supplies, recreation expenses, and fees for dispatching external specialists, including the date, amount, purpose, and receipt images, via a terminal. These claims are then centrally managed on a server. If there are any deficiencies in the expense claim, an automatic error check is performed, prompting the user to re-enter the information. If high-value claims or an unusual frequency of claims are detected, an AI-powered anomaly detection system is activated, and an alert is sent to the administrator.

[0100] Step 4: Schedule registration and progress management Caregivers register and check care schedules and tasks such as "bathing assistance," "meal assistance," "medication management," "rehabilitation support," "toileting assistance," "outing support," and "recreation" in a calendar format on their terminals, and input progress and completion reports. Personal schedules and facility events are automatically retrieved through API integration with external calendar services, and in the event of sudden absences or shift changes, AI suggests the most suitable replacement staff and task assignments.

[0101] Step 5: Analysis of caregiver and care recipient conditions and feedback using generated AI. The AI ​​analysis unit on the server comprehensively analyzes the physical and mental health scores of care workers, work comments, shift history, the physical and mental health scores of care recipients, symptoms, vital data, care records, expense claims, schedule information, etc., and automatically generates individually optimized feedback, anomaly detection, care plan optimization, and organizational risk management. For example, if a care worker's mental health score is declining, it generates diagnostic comments such as "Mental support is needed because they have been working consecutive night shifts recently" or "We recommend securing opportunities for rest and consultation," and if a care recipient's health deteriorates, it generates diagnostic comments such as "We recommend consulting a medical institution" or "The care plan needs to be reviewed," and notifies the manager or attending physician. Specific examples of prompts to be fed into the generating AI include: "Analyze the physical and mental scores and comments of care workers over the past 7 days, extract stressors and workload trends, and generate individually optimized mental support advice," "Based on the care recipient's physical score, care records, and symptom input, diagnose signs of worsening health and the need to revise the care plan, and generate specific improvement suggestions," and "Collect the shift and health status of multiple staff members, evaluate the overall workload and mental risk of the team, and create a warning message and countermeasures for managers."

[0102] Step 6: Understand the situation and take action using the administrator dashboard. On the dashboard, administrators can view the health, work, care status, expenses, and AI diagnostic results of all staff and care recipients in a list, graph, and alert format. Visualization tools such as graphs, heatmaps, and time-series charts allow for an intuitive understanding of individual staff member status changes and overall organizational trends. If there are abnormal values ​​or sudden changes in specific staff members or care recipients, an alert is displayed, allowing administrators to quickly take action such as individual interviews, work adjustments, care plan changes, and coordination with medical institutions.

[0103] Step 7: Automate external integration, notifications, and recording. The External Collaboration Department connects with medical institutions' electronic medical record systems, family communication apps, government care reporting systems, wearable devices, and remote consultation systems for external experts via APIs, etc. This enables diverse collaborations such as automatically acquiring the care recipient's medical visit history and prescription drug information and reflecting it in the care plan, automatically distributing summaries of health status and care records to families, having AI automatically generate periodic reports for the government, automatically linking vital data acquired from wearable devices to the physical and mental condition input department, and reflecting remote advice from external experts in care records. The Notification Control Unit notifies caregivers and administrators in real time of various events, abnormal values, AI diagnostic results, and instructions from administrators.

[0104] (Specific use cases)

[0105] For example, when applying the system of the present invention to a nursing care facility where multiple caregivers take turns providing care to residents on a daily basis, each caregiver enters their shift record from a terminal at the start of their shift, selects a work motivation score, physical condition score, and mental score on a scale of 1 to 5, and enters free comments such as "I didn't get enough sleep last night and feel a little tired," "My back pain has worsened," or "I feel stressed from yesterday's night shift." For each resident, they select a physical condition score, mental score, specific symptoms such as decreased appetite, fever, anxiety, nocturnal delirium, fall risk, pressure ulcer risk, constipation, diarrhea, sleep disorder, and progression of dementia symptoms. Vital data such as body temperature, blood pressure, pulse, and SpO2 are automatically acquired from wearable devices or measuring instruments, and care records detail the content and results of care provided, such as assistance with meals, bathing, medication management, rehabilitation assistance, excretion assistance, transfer assistance, outing support, participation in recreation, oral care, nail trimming, pressure ulcer care, dementia care, and support for family visits, along with any special notes. For example, specific records are kept such as "Meal assistance: Unable to finish eating," "Medication: Refusal," "Rehabilitation: Increased walking distance," "Excretion assistance: Incontinence," and "Recreation: Decreased motivation to participate." For expense claims, consumables, transportation costs, external service fees, medical supplies, recreation expenses, and fees for dispatching external specialists are entered along with the date, amount, purpose, and receipt images, and are centrally managed in conjunction with the accounting system. For schedule management, each staff member registers and checks care schedules and tasks in a calendar format on their terminal, enters progress and completion reports, and personal schedules and facility events are automatically retrieved through API integration with external calendar services.

[0106] The Generative AI Analysis Unit comprehensively analyzes this multidimensional data. For example, if a caregiver's mental score is continuously declining, or if comments such as "I'm feeling very tired due to consecutive night shifts" or "My back pain is getting worse" are entered multiple times in the work comments, the AI ​​automatically generates individually optimized feedback such as "Mental support is needed because you've been working consecutive night shifts recently," "We recommend securing opportunities for rest and consultation," or "We recommend adjusting the workload because your back pain is getting worse," and notifies the caregiver and their manager. For care recipients, if their health score is 2 / 5, or if they have had a loss of appetite or fever for several days, the AI ​​generates diagnostic comments such as "Your health is deteriorating," "We recommend seeing a doctor," "You need to review your care plan," or "You're at high risk of dehydration, so strengthen your fluid intake," and notifies the manager and attending physician. The system comprehensively analyzes care records and changes in the care recipient's condition to extract positive trends such as "improved health score after meal assistance," "reduced anxiety after bathing assistance," and "increased walking distance after rehabilitation," as well as negative trends such as "continued refusal to take medication and worsening health" and "increased failures in toileting assistance." It then automatically generates specific care plan optimization suggestions such as "consider changing medication assistance methods," "recommend reviewing meal content," "adjusting rehabilitation frequency would be effective," and "recommend reviewing toileting care." It also aggregates the shifts and health status of multiple staff members to evaluate the overall workload and mental risks of the team, and generates organizational advice such as "increased overall workload for the team," "recommend shift adjustments and rest," and "strengthen support for new staff members."

[0107] On the dashboard, administrators can view the health, work, care status, expenses, and AI diagnostic results of all staff and care recipients in a list, graph, and alert format. Visualization tools such as graphs, heatmaps, and time-series charts allow for an intuitive understanding of individual staff member status changes and overall organizational trends. Alerts are displayed for abnormal values ​​or sudden changes in specific staff members or care recipients, enabling administrators to quickly take action such as individual consultations, work adjustments, care plan changes, and coordination with medical institutions. The external collaboration department connects with medical institutions' electronic medical record systems, family communication apps, government care reporting systems, wearable devices, and remote consultation systems for external experts via APIs, etc. This enables diverse collaborations such as automatically acquiring care recipients' medical visit history and prescription drug information and reflecting it in care plans, automatically distributing summaries of health status and care records to families, having AI automatically generate periodic reports for government, automatically linking vital data acquired from wearable devices to the physical and mental condition input department, and reflecting remote advice from external experts in care records.

[0108] Specific examples of prompts to be fed into the generating AI include: "Analyze the physical and mental scores and comments of care workers over the past 7 days, extract stress factors and workload trends, and generate individually optimized mental support advice," "Based on the care recipient's physical score, care records, and symptom input, diagnose signs of worsening health and the need to revise the care plan, and generate specific improvement suggestions," "Collate the shift and health status of multiple care workers, evaluate the overall workload and mental risk of the team, and create a warning message and countermeasures for managers," and "Analyze the care recipient's vital data and care records in chronological order, and generate a report recommending medical consultation or revision of care content if there are abnormal values ​​or sudden changes."

[0109] Thus, the system of the present invention grasps the health, work, and care status of caregivers and care recipients in care settings in real time and from multiple perspectives, and automates everything from individually optimized support and care plan optimization using generative AI to anomaly detection and organizational risk management, thereby simultaneously improving the quality of care services and reducing the burden on caregivers.

[0110] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0111] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0114] 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.

[0115] [Second Embodiment]

[0116] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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).

[0119] 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, camera 42, and communication interface 44 are also connected to the bus 52.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] 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. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the identification processing unit 290 using these models.

[0127] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is realized by the data processing device 12 and the smart glasses 214. 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".

[0128] (Example 1)

[0129] Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0130] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0131] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Furthermore, the processing by the data processing system 10 described above is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0134] 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.

[0135] [Third Embodiment]

[0136] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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).

[0139] 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, display 343, and communication interface 44 are also connected to the bus 52.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the headset terminal 314. 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".

[0148] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0149] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0150] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 and display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] 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.

[0154] [Fourth Embodiment]

[0155] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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).

[0158] 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, controlled object 443, and communication interface 44 are also connected to the bus 52.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0168] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0169] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0170] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0171] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0172] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the robot 414 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the speaker 240 and controlled object 443 of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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 this memory.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] Furthermore, the following additional information is disclosed regarding the above explanation.

[0193] (Claim 1)

[0194] A care support system comprising a care worker management department, a care recipient management department, a care record management department, an expense management department, a schedule management department, a generative AI analysis department, and an administrator dashboard department. The care worker management department allows care workers to input and record shift records, work motivation scores, physical condition scores, mental scores, work comments, etc., via a terminal. The expense management department allows care workers to input and centrally manage expense applications for consumables purchases, transportation costs, etc., along with the date, amount, purpose, and receipt images. The schedule management department provides functions for care workers to check, register, and manage the progress of their care schedules and tasks on a terminal. The care recipient management department inputs and stores physical condition scores, mental scores, specific symptoms, care records, special notes during care, etc., chronologically for each care recipient. The care record management department records care details and results such as meal assistance, bathing assistance, medication management, and rehabilitation assistance. This care support system features detailed recording of special notes, a generative AI analysis unit that comprehensively analyzes the physical and mental state of caregivers and those receiving care, care records, shift history, expense claims, schedule information, etc., and uses generative AI to automatically perform mental support for caregivers, optimization of the health condition and care plans of those receiving care, anomaly detection, generation of individual advice, and organizational risk management. The administrator dashboard unit has functions that allow administrators to understand the health, work, care status, expenses, and AI diagnostic results of all staff and those receiving care in a list, graph, and alert display. These units are linked via a network to grasp the health, work, and care status of staff and those receiving care in the care setting in real time and from multiple perspectives, and automates individually optimized support, care plan optimization, anomaly detection, and organizational risk management using generative AI. (Claim 2)

[0195] The care support system according to claim 1, comprising a generation AI analysis unit that analyzes the past physical condition scores, mental scores, work comments, shift history, etc. of care workers in chronological order, extracts stress factors and workload trends, and automatically generates individually optimized mental support advice, recommendations for rest and consultation opportunities, and organizational workload evaluations and shift adjustment proposals, and notifies managers and the care workers themselves. (Claim 3)

[0196] The care support system according to claim 1, comprising a generation AI analysis unit that comprehensively analyzes the care recipient's physical condition score, mental score, symptom input, care records, expense claims, schedule information, etc., automatically diagnoses signs of deterioration in physical condition and the need to revise the care plan, automatically generates individually optimized suggestions for improving care content, recommending visits to medical institutions, and proposed changes to care methods, and notifies administrators and care workers. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. This business support system comprises an attendance management unit, an engagement input unit, a physical and mental condition input unit, an expense management unit, a schedule linkage unit, a generation-based AI diagnostic unit, and an administrator dashboard unit. The attendance management unit records the arrival and departure times for each employee; the engagement input unit allows employees to quantitatively and qualitatively input their work motivation and satisfaction levels upon arrival at work; the physical and mental condition input unit allows employees to input their daily physical and mental scores and comments; the expense management unit allows employees to input and centrally manage expense claims such as transportation expenses, along with the date, amount, and purpose; and the schedule linkage unit links with calendar information to manage employee schedules and meetings. This business support system features a task management function, a generative AI diagnostic unit that receives input data on employees' physical and mental condition and uses generative AI to automatically diagnose their mental state, and notifies the employee and manager if an abnormality is detected, and a manager dashboard unit that allows managers to view a list of attendance information, engagement scores, physical and mental condition, expense application status, schedule information, and diagnosis results from the generative AI for multiple employees under their management, and visualizes past trends and tendencies. These units are linked via a network to improve the efficiency of employee work and health management, as well as the efficiency of member management by managers.

2. The business support system according to claim 1, comprising a generation-based AI diagnostic unit that analyzes past history and company-wide trend data of each employee's mental and physical condition input data, and when it detects a declining trend or abnormal values ​​in the mental score, it automatically generates and notifies the employee and their manager of individually optimized mental support suggestions and self-care advice.

3. The business support system according to claim 1, comprising an administrator dashboard that aggregates diagnostic results from a generative AI diagnostic unit, as well as attendance information, engagement scores, physical and mental condition, expense claim status, and schedule information for each employee in chronological order, and enables administrators to grasp at a glance any abnormalities or trends in the overall health and work status of the organization using visualization means such as graphs and heatmaps, and further comprising a function to automatically generate an organizational health management report using generative AI and provide it to management and human resources personnel.

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