system

Abstract

We provide the system. [Solution] The present invention relates to an automated in-house event system that includes an employee information management department, an event planning and management department, and an effectiveness analysis department. Using generative AI, it comprehensively analyzes employee attribute information and past event participation history, etc., and automatically plans and manages optimal in-house events based on employee interests, departmental structure, the status of new employees, etc. It automates event announcement, registration, and on-site management, and after the event, it quantitatively analyzes and visualizes satisfaction levels and participation rates, etc., and automatically feeds this back into future event planning, thereby simultaneously reducing the burden on staff, improving employee satisfaction, promoting the retention of new employees, and strengthening inter-departmental collaboration.

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] The problems that this invention aims to solve are: the heavy workload on those in charge of planning and managing in-house events, the fact that the themes and content of events do not adequately match the interests and concerns of employees, and as a result, the tendency for participant satisfaction to decrease; the insufficient means of quantitatively and objectively measuring and analyzing the effects of events, making it difficult to incorporate effective feedback into future events; the problem that new employees do not have enough opportunities to interact with existing employees, making it difficult for them to quickly settle into the organization and leading to isolation; and the problem that inter-departmental interaction is limited, making it difficult to activate collaboration and communication throughout the organization. In order to solve these multiple problems simultaneously, this invention aims to reduce the burden on those in charge, improve employee satisfaction, facilitate the early retention of new employees, strengthen inter-departmental collaboration, and continuously improve the quality of events by analyzing diverse data such as employee attribute information and past event participation history using generative AI and consistently automating the planning, operation, effectiveness measurement, and feedback of optimal events. [Means for solving the problem]

[0005] The means for solving the problems according to the present invention comprises an employee information management department, an event planning and management department, and an effectiveness analysis department. The employee information management department records attribute information of each employee, including their name, department, areas of interest, date of joining the company, and past event participation history, in a database. The event planning and management department uses a generative AI to analyze the employee attribute information recorded in the employee information management department and automatically generates an optimal event theme, target employees, target departments, event content, event title, and description based on the employee's areas of interest, department structure, status of new employees, and past event participation trends. Furthermore, the event... The goal is to automate the planning, operation, effectiveness measurement, and feedback processes for internal events. This will reduce the burden on staff, improve employee satisfaction, facilitate the early retention of new employees, strengthen inter-departmental collaboration, and continuously improve the quality of events. This will be achieved by automating announcements, participant recruitment, registration, on-site preparation, and progress management. After the event, the effectiveness analysis department will use a generative AI to collect satisfaction scores, participation rates, participation trends by department, and evaluation results for each event. These results will be output as a visualized report, and the analysis results will be automatically reflected in future event planning. [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 labeled storage is one or a plurality of 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, or the like.

[0012] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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. In addition, in the present specification, when three or more matters are expressed in combination with "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 is 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).

[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 receives user input via contact of an indicator by detecting the contact of the indicator (for example, a pen or a finger). The microphone 38B receives voice-based user input by detecting the user's voice. 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 will be described in more specific and detail. The system of the present invention consists of a server and terminals, with an employee information management department, an event planning and management department, and an effectiveness analysis department implemented on the server, and the terminals function as a user interface for use by employees and personnel. The server may be a cloud-based server, an on-premise server, or a hybrid configuration thereof. The terminals consist of various devices such as personal computers, smartphones, tablets, and wearable devices, and communicate with the server via the company network or the internet.

[0031] First, the Employee Information Management Department operates on a server and records and manages a wide range of attribute information for each employee in a database, including name, department, position, date of joining the company, areas of interest, skill set, work style (remote / in-office), past event participation history, past survey responses, company activity history, qualifications, language skills, hobbies, self-introduction, history of interaction on the company's internal social media, health status, work hour trends, project participation history, and company awards. For example, it accumulates detailed profiles for each employee, such as Employee A, who belongs to the sales department, is interested in "sports," "board games," and "outdoors," has been with the company for six months, has participated in three past events, and works remotely most of the time; Employee B, who belongs to the development department, is interested in "music," "movies," and "programming," has been with the company for less than a year, has strong language skills, and is active on the company's internal social media; and Employee C, who belongs to the management department, is interested in "cooking," "reading," and "travel," holds multiple qualifications, and has received company awards. This information is collected automatically or manually through a variety of means, including direct input from terminals, API integration with the internal HR system, periodic survey forms, analysis of posts on internal social media, and acquisition of health data from wearable devices.

[0032] The Event Planning and Management Department operates on a server and utilizes generative AI to comprehensively analyze the attribute information of all employees recorded in the Employee Information Management Department's database. The generative AI combines natural language processing models, large-scale language models, clustering algorithms, recommendation systems, and image recognition models to perform analyses of employee interests and preferences, extract patterns from past event participation history, analyze participation rates and satisfaction levels by department, analyze interaction trends on the company's internal social networking service, and analyze trends in employee work patterns and working hours. For example, if an employee's interests are diverse, such as "sports," "music," "board games," "outdoors," "cooking," "movies," and "programming," the generative AI automatically generates multiple event proposals combining these themes (e.g., sports viewing party, music concert, board game tournament, outdoor experience tour, cooking class, movie screening, programming workshop). Furthermore, considering employee work patterns and working hours, it automatically proposes online events that can be attended remotely, as well as proposals for multiple events for employees with flexible working hours.

[0033] Furthermore, if participation rates are low among specific departments or new employees, the generative AI estimates the cause based on past surveys, participation history, free-response comments, and posts on the company's internal social media platform. It then automatically suggests improvement plans such as "setting a self-introduction time for new employees," "making it a departmental competition," "increasing interaction time for remote participants," and "flexibly setting event times." In addition, it utilizes employees' skill sets and qualification information to automatically generate events that leverage expertise and experience, such as "English conversation events led by employees with strong language skills," "study sessions led by certified professionals," and "know-how sharing sessions by project experience holders."

[0034] The AI ​​automatically generates the event details, title, description, date and time, potential venues, list of necessary equipment, schedule, assignment of MCs and presenters, participant grouping, online streaming options, recording and archiving settings, and participant reminder notifications. For example, it automatically creates titles such as "An exchange event themed around sports, which was of the most interest to employees," and descriptions such as "A cross-departmental team-based board game tournament to promote interaction among people meeting for the first time." Event announcements are automatically distributed from the server to terminals via internal social media, email, chat tools, calendar integration, push notifications, etc., and employees can register to participate with a single click from their terminals. Registration status is aggregated on the server in real time, and a participant list is automatically generated. For example, if there are many people who want to participate in the event, the AI ​​automatically performs a lottery, first-come, first-served basis, or adjusts the balance by department to create the optimal participant list.

[0035] Event operations on the day of the event are also automated through the coordination of servers and terminals. For example, a generation AI automatically generates QR codes or barcodes for registration, and attendance is confirmed by scanning them with a terminal. The generation AI also automatically optimizes the schedule and timetable, and pushes notifications to terminals. The AI ​​also acts as the facilitator for activities and games during the event, displaying announcements on terminals such as "Next is self-introduction time," "Let's start the team quiz," or "The group discussion topic is XX." For online events, the generation AI automatically issues URLs for video conferencing tools, distributes them to participants, and automatically handles breakout room assignments and provides assistance through chatbots. Furthermore, it is possible to conduct surveys, polls, quizzes, and collect feedback in real time during the event, and compile and display the results on the spot.

[0036] After the event ends, the effectiveness analysis department operates on a server and uses generative AI to collect and analyze diverse data from participants via their devices, including satisfaction questionnaires, free-response comments, participation rates, participation trends by department, evaluation results for each event, content of comments and chat logs during the event, reactions on social media, and changes in internal communication after the event. For example, analysis results such as "the sales department's participation rate was lower than other departments," "new employees had high satisfaction levels," "many free-response comments mentioned 'the event ran smoothly' and 'it was easy to interact online'," and "interaction on internal social media increased after the event" are automatically generated into reports and distributed to the terminals of those in charge and management. Furthermore, the generative AI uses these analysis results to inform the planning of future events. For example, it automatically generates feedback such as "Since satisfaction levels were high for the sports event last time, we will propose a similar theme next time," "To improve the participation rate of new employees, we will increase the frequency of welcome parties," and "Since there is a lack of inter-departmental interaction, we will increase cross-departmental events."

[0037] Furthermore, for example, in the case of events for new employees, a generative AI automatically extracts employees who have joined the company within a certain period of time and automatically plans and manages welcome parties, lunch meetings, workshops, self-introduction events, mentor matching events, and online social gatherings to promote interaction with existing employees. For example, it generates easy-to-participate event proposals such as a "New Employee Welcome Board Game Tournament," a "Cross-Departmental Lunch Social Gathering," a "New Employee Self-Introduction Presentation Competition," and a "Mentor-to-New Employee One-on-One Meeting Event," and analyzes participation and satisfaction levels to support the prevention of isolation among new employees and their early retention. In addition, it automatically monitors the number of posts by new employees on the company's internal social media after events and changes in the frequency of interaction with existing employees, visualizing the retention status.

[0038] Furthermore, in the case of cross-departmental events, for example, the generative AI analyzes employee information from multiple departments and automatically plans team-based events combining departments that don't usually interact much, as well as departmental problem-solving workshops, ideathons, joint study sessions, inter-industry networking events, project presentations, and internal hackathons. For example, it generates events that promote interaction across departmental boundaries, such as "an ideathon by mixed teams from the development and sales departments," "a joint quiz competition between the management and human resources departments," and "a joint project results presentation by multiple departments." After the event, it analyzes the participation rate and satisfaction level of each department and automatically provides feedback that contributes to strengthening collaboration throughout the organization. In addition, it automatically monitors the increase in project collaboration after the event and changes in the frequency of information sharing between departments, quantitatively evaluating the effect of strengthened collaboration.

[0039] In this way, the system of the present invention, with its various components centered on a generative AI on a server working in coordination and interacting with employees and personnel through terminals, comprehensively and automatically performs a series of tasks related to internal events, from collecting and managing employee information to planning, operating, announcing, registering participants, managing the event on the day, measuring its effectiveness, and providing feedback. This significantly reduces the burden on personnel and simultaneously achieves improved employee satisfaction, enhanced communication, increased retention rates for new employees, strengthened inter-departmental collaboration, and continuous improvement of event quality. Furthermore, by utilizing generative AI, it realizes advanced personalization, real-time optimization, quantitative effectiveness measurement, and a continuous improvement cycle that were not possible with conventional manual or person-dependent operations, greatly contributing to the growth and revitalization of the organization.

[0040] (System Configuration)

[0041] The system according to this embodiment comprises an Employee Information Management Department, an Event Planning and Management Department, an Effectiveness Analysis Department, a User Interface Department, a Notification Distribution Department, a Data Integration Department, and a Security Management Department. The Employee Information Management Department records and manages a wide range of attribute information for each employee in a database, including name, department, position, date of joining the company, areas of interest, skill set, work style, past event participation history, survey responses, interaction history on the company's internal social networking service, health status, work hour trends, project participation history, qualifications, language skills, hobbies, self-introduction, and company awards. For example, it accumulates detailed profiles for each employee, such as an employee in the sales department who is interested in "sports" and "outdoors" and works remotely frequently within the first six months of employment; a new employee in the development department who is interested in "music" and "movies," has strong language skills, and frequently participates in the company's internal social networking service; or an employee in the management department who is interested in "cooking" and "travel," holds multiple qualifications, and has received company awards. The Employee Information Management Department collects information automatically or manually through a variety of means, including direct input from terminals, API integration with the internal HR system, periodic survey forms, analysis of posts on internal social media, and acquisition of health data from wearable devices. It provides AI with prompts such as "Please enter the employee's hobbies and interests," "Please select the events they have recently attended," "Please enter information about their health status," and "We will automatically acquire their interaction history on internal social media," ensuring the efficiency and comprehensiveness of information collection.

[0042] The Event Planning and Management Department utilizes generative AI to comprehensively analyze the attribute information of all employees recorded in the Employee Information Management Department's database. This analysis includes analyzing employee interests and areas of interest, extracting patterns from past event participation history, analyzing participation rates and satisfaction levels by department, analyzing interaction trends on the company's internal social networking service, and analyzing employee work patterns and working hours. For example, if an employee's interests are diverse, such as "sports," "music," "board games," "outdoors," "cooking," "movies," and "programming," the generative AI will automatically generate multiple event proposals combining these themes, such as "sports viewing party," "music concert viewing party," "board game tournament," "outdoor experience tour," "cooking class," "movie screening," and "programming workshop." It also takes into account employees' work patterns and working hours, automatically suggesting online events that can be attended remotely and multiple event options for employees with flexible working hours. Furthermore, if participation rates are low among specific departments or new employees, the generative AI estimates the cause based on past surveys, participation history, free-response comments, and posts on the company's internal social media platform, and automatically proposes improvement plans such as "setting a self-introduction time for new employees," "making it a departmental competition," "increasing interaction time for remote participants," and "flexibly setting event times." By utilizing employees' skill sets and qualification information, the system also automatically generates events that leverage expertise and experience, such as "English conversation events led by employees with strong language skills," "study sessions led by certified individuals," and "know-how sharing sessions by project experience holders." The AI ​​automatically generates specific event details, titles, descriptions, dates and times, potential venues, lists of necessary equipment, schedules, assignments of MCs and presenters, participant groupings, online streaming options, recording and archiving settings, and participant reminder notifications. For example, prompts such as "Plan a social event themed around sports, which is of the most interest to employees," "Create a schedule for a cross-departmental team-based board game tournament," "Generate a title and description for a new employee welcome event," and "Create an invitation email for an online event" are given to the AI, enabling the automation and optimization of event planning.

[0043] The effectiveness analysis department collects and analyzes diverse data after an event using generative AI, including satisfaction questionnaires, free-response comments, participation rates, participation trends by department, evaluation results for each event, content of comments and chat logs during the event, reactions on social media, and changes in internal communication after the event. For example, it automatically generates reports of analysis results such as "the sales department's participation rate is lower than other departments," "new employees have high satisfaction levels," "many free-response comments mentioned 'the event ran smoothly' and 'it was easy to interact online'," and "interaction on internal social media increased after the event," and distributes these reports to the terminals of those in charge and management. Furthermore, the generative AI uses these analysis results to inform future event planning, providing prompts such as "satisfaction levels were high with the sports event last time, so please suggest a similar theme for the next event," "to improve new employee participation, please suggest increasing the frequency of welcome parties," and "inter-departmental interaction is lacking, so please propose increasing cross-departmental events," thereby realizing a continuous improvement cycle.

[0044] The user interface section provides screens and operating systems for employees and staff to access the system from their terminals to input information, register for events, answer questionnaires, view event details, check notifications, and view reports. For example, it provides a variety of interfaces, such as a screen for employees to input their interests and skills, a screen displaying event details and participation buttons, a form screen for answering questionnaires, a screen for event progress guidance on the day of the event, and a screen for viewing feedback and reports after the event. The user interface section reflects event titles, descriptions, questionnaire questions, and notification content generated by the generative AI in real time, and improves the user experience by providing prompts to the generative AI such as "Please enter your interests," "Please select whether you will participate in the next event," "Please rate your satisfaction with the event on a scale of 1 to 5," and "Please freely write your impressions of the event."

[0045] The notification distribution unit automatically delivers various notifications to employees' and staff's devices, including event announcements, reminders, registration confirmations, event day instructions, survey requests, and analytical report distributions. For example, it uses a variety of methods such as internal social media, email, chat tools, calendar integration, and push notifications to generate prompts such as "Create an event announcement," "Send a registration deadline reminder," "Generate meeting place instructions for the event day," and "Create a survey request email," thereby ensuring timely and appropriate information delivery.

[0046] The Data Integration Department integrates with internal systems such as the HR system, attendance management system, internal social networking service, wearable devices, and external calendar services via APIs to automatically acquire and update employee information, event information, participation history, health data, work status, and internal communication history. For example, it provides prompts to the AI ​​such as "Please acquire the latest employee information from the HR system," "Please integrate work style data from the attendance system," "Please analyze the communication history from the internal social networking service," and "Please acquire health data from wearable devices," ensuring data consistency and up-to-dateness across the entire system.

[0047] The Security Management Department is responsible for securely managing confidential data such as employee information, event information, survey results, and analysis reports, and for implementing access control, data encryption, unauthorized access detection, log monitoring, privacy protection, data backup, and disaster recovery measures. For example, it provides prompts to a generating AI such as "Set access permissions for the employee information database," "Encrypt event participation history data," "Generate an alert message if unauthorized access is detected," and "Create data backup procedures" to ensure the security and reliability of the system.

[0048] As described above, the system according to this embodiment comprehensively and automatically performs a series of tasks related to internal company events, from collecting and managing employee information, to planning, operating, announcing, registering participants, managing the event on the day, measuring its effectiveness and providing feedback, providing a user interface, distributing notifications, linking data, and managing security, all through the cooperation of each component. By utilizing a variety of prompt examples generated by generative AI as needed, it achieves a high level of automation and optimization that surpasses conventional manual and person-dependent operations.

[0049] (Implementation Steps)

[0050] Step 1: Collect and register employee information The Employee Information Management Department records and manages a wide range of attribute information for each employee in a database, including name, department, position, date of joining the company, areas of interest, skill set, work style, past event participation history, survey responses, internal SNS interaction history, health status, work time trends, project participation history, qualifications, language skills, hobbies, self-introduction, and company awards. Information is collected through various means, including direct input from terminals, API integration with the internal HR system, periodic survey forms, analysis of internal SNS posts, and acquisition of health data from wearable devices. For example, prompts such as "Please enter the employee's hobbies and interests," "Please select the events recently attended," and "Please enter information regarding your health status" are given to the generating AI to facilitate input and automate completion.

[0051] Step 2: Analyze employee information and extract event themes. The Event Planning and Management Department uses a generating AI to comprehensively analyze the attribute information of all employees accumulated in the Employee Information Management Department. This analysis will include analyzing employee interests and preferences, extracting patterns from past event participation history, analyzing participation rates and satisfaction levels by department, analyzing interaction trends on the company's internal social networking service, and analyzing trends in work styles and working hours. Examples of prompts given to the generating AI include: "Based on employee interest data, extract the most popular event themes," "Analyze the characteristics of events with high participation rates based on past event participation history," and "Visualize participation trends by department."

[0052] Step 3: Automatic generation and optimization of event proposals The Event Planning and Management Department automatically generates multiple event proposals based on themes and trends extracted using a generation AI. For example, it presents specific event proposals such as "sports viewing party," "music concert viewing party," "board game tournament," "outdoor experience tour," "cooking class," "movie screening party," and "programming workshop," and automatically suggests online events that can be attended remotely, taking into account employees' work styles and hours, as well as proposals for multiple events for employees with flexible working hours. Examples of prompts given to the generation AI include "Generate 10 event proposals that take into account employees' interests and work styles," "Create a title and description for a welcome event for new employees," and "Create a progress schedule for a cross-departmental event."

[0053] Step 4: Automatically create event details and prepare announcements. The event planning and management department uses a generation AI to automatically generate specific event details, titles, descriptions, dates and times, potential venues, lists of necessary equipment, schedules, assignments of MCs and presenters, participant groupings, whether or not online streaming is available, recording and archiving settings, and reminder notifications for participants. Examples of prompts given to the generation AI include "Please create a detailed description of a sports event," "Please generate an invitation email for an online event," and "Please create a reminder notification for event participants."

[0054] Step 5: Event announcement and registration The notification distribution department automatically distributes event announcements from the server to terminals using various methods such as internal social networking services, email, chat tools, calendar integration, and push notifications, and employees can register to participate with a single click from their terminals. The registration status is aggregated on the server in real time, and a participant list is automatically generated. Examples of prompts given to the generation AI include "Generate an event announcement," "Create a notification for accepting participants," and "Automatically create a participant list."

[0055] Step 6: Automate event day operations The event planning and management department uses a generation AI to automatically generate QR codes and barcodes for registration, which attendees scan with their devices to confirm their attendance. The generation AI also automatically optimizes the event schedule and timetable, sending push notifications to the devices. The AI ​​also acts as the facilitator for activities and games during the event, displaying announcements such as "Next up is self-introduction time," "Let's start the team quiz," and "The group discussion topic is XX" on the devices. For online events, the generation AI automatically issues URLs for video conferencing tools, assigns breakout rooms, and provides chatbot support. Examples of prompts given to the generation AI include "Generate a QR code for event registration," "Optimize the event schedule," and "Create an announcement for the online event."

[0056] Step 7: Post-event effectiveness measurement and analysis The effectiveness analysis department uses a generation AI to collect and analyze diverse data from participants via their devices, including satisfaction questionnaires, free-response comments, participation rates, participation trends by department, evaluation results for each event, content of comments and chat logs during the event, reactions on social media, and changes in internal communication after the event. Examples of prompts given to the generation AI include "Please analyze the aggregated results of the event satisfaction questionnaire," "Please extract positive and negative opinions from the free-response comments," and "Please visualize the changes in the frequency of internal social media interaction after the event."

[0057] Step 8: Incorporate feedback into the next event The effectiveness analysis department uses a generation AI to analyze the results and incorporate them into future event planning. For example, prompts such as "Satisfaction with the sports event was high last time, so please suggest a similar theme for the next event," "To improve the participation rate of new employees, please suggest increasing the frequency of welcome parties," and "There is a lack of interdepartmental interaction, so please suggest increasing cross-departmental events" are given to the generation AI, automatically realizing a continuous improvement cycle.

[0058] (Specific use cases)

[0059] For example, in an organization with several hundred employees, if the goal is to automatically plan and manage internal events aimed at revitalizing communication among employees and accelerating the retention of new employees, the employee information management department would first record and manage attribute information in a database, such as each employee's name, department, position, date of joining the company, areas of interest, skill set, work style, past event participation history, survey responses, interaction history on the internal social networking service, health status, work schedule trends, project participation history, qualifications, language skills, hobbies, self-introduction, and company awards. Employee information is collected through various means, including direct input from terminals, API integration with the internal HR system, periodic survey forms, analysis of internal social networking service posts, and acquisition of health data from wearable devices. For example, an employee may have multiple areas of interest registered, such as "sports," "board games," "music," "movies," "outdoors," "cooking," and "programming." Examples of prompts to give to the generating AI include, "Please enter the employee's hobbies and interests," "Please select the events they recently participated in," and "Please enter information regarding their health status."

[0060] Next, the Event Planning and Management Department uses a generating AI to comprehensively analyze the attribute information of all employees accumulated in the Employee Information Management Department. This analysis includes analyzing employee interests and preferences, extracting patterns from past event participation history, analyzing participation rates and satisfaction levels by department, analyzing interaction trends on the company's internal social networking service, and analyzing work patterns and working hours. For example, if employees' interests are concentrated in "sports," "music," and "board games," the generating AI will automatically generate event proposals that combine these themes. Examples of prompts given to the generating AI include: "Based on employee interest data, please extract the most popular event themes," "Based on past event participation history, please analyze the characteristics of events with high participation rates," and "Please visualize participation trends by department."

[0061] Furthermore, the generating AI automatically generates multiple event proposals based on extracted themes and trends. For example, it presents specific event proposals such as "sports viewing party," "music concert viewing party," "board game tournament," "outdoor experience tour," "cooking class," "movie screening party," and "programming workshop," and automatically suggests online events that can be attended remotely, taking into account employees' work styles and hours, as well as proposals for multiple events for employees with flexible working hours. Examples of prompts to give to the generating AI include, "Generate 10 event proposals that take into account employees' interests and work styles," "Create a title and description for a welcome event for new employees," and "Create a progress schedule for a cross-departmental event."

[0062] The event planning and management department uses a generation AI to automatically generate specific event details, titles, descriptions, dates and times, potential venues, lists of necessary equipment, schedules, assignments of MCs and presenters, participant groupings, whether or not online streaming is available, recording and archiving settings, and reminder notifications for participants. For example, prompts such as "Create a detailed description for a sports event," "Generate an invitation email for an online event," and "Create a reminder notification for event participants" are given to the generation AI.

[0063] The notification distribution unit automatically distributes event announcements from the server to terminals using various methods such as internal social networking services, email, chat tools, calendar integration, and push notifications, allowing employees to register with a single click from their terminals. For example, prompts such as "Generate an event announcement," "Create a registration confirmation message," and "Automatically create a participant list" are given to the generation AI.

[0064] On the day of the event, the event planning and management department will use a generation AI to automatically generate QR codes or barcodes for registration, which attendees will scan with their devices to confirm their attendance. The generation AI will also automatically optimize the schedule and timetable, and send push notifications to the devices. The AI ​​will also act as the facilitator for activities and games during the event, displaying announcements on the devices such as "Next is self-introduction time," "Let's start the team quiz," and "The group discussion topic is XX." For online events, the generation AI will automatically issue URLs for video conferencing tools, assign breakout rooms, and provide support through a chatbot. For example, prompts such as "Generate a QR code for event registration," "Optimize the schedule," and "Create an announcement for the online event" can be given to the generation AI.

[0065] After the event, the effectiveness analysis department uses a generation AI to collect and analyze various data from participants via their devices, including satisfaction questionnaires, open-ended comments, participation rates, participation trends by department, evaluation results for each event, content of comments and chat logs during the event, reactions on social media, and changes in internal communication after the event. For example, prompts such as "Please analyze the results of the event satisfaction questionnaire," "Please extract positive and negative opinions from the open-ended comments," and "Please visualize the changes in the frequency of internal social media interaction after the event" are given to the generation AI.

[0066] Furthermore, the effectiveness analysis department uses a generating AI to analyze the results and incorporate them into future event planning. For example, they provide the generating AI with prompts such as, "Since satisfaction with the sports event was high last time, please suggest a similar theme for the next event," "To improve the participation rate of new employees, please suggest increasing the frequency of welcome parties," and "Since there is a lack of interdepartmental interaction, please suggest increasing the number of cross-departmental events."

[0067] Thus, the system of the present invention can comprehensively and automatically perform a series of tasks related to internal company events, from collecting and managing employee information to planning, operating, announcing, registering participants, managing the event on the day, measuring its effectiveness, and providing feedback, by utilizing a variety of prompt examples generated by AI as needed.

[0068] (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."

[0069] (Embodiments for carrying out the present invention)

[0070] Embodiments of the present invention will be described in more specific and detail. The automated care support system of the present invention consists of a server and terminals. The server implements a user information management unit, a care event planning and management unit, and an effectiveness analysis unit, while the terminals function as a user interface for use by users, families, and staff. The server may be cloud-based, on-premise, or a hybrid of these configurations, and the terminals consist of a variety of devices such as personal computers, tablets, smartphones, wearable devices, voice recognition terminals, and even digital signage and robot terminals within the facility, which communicate with the server via the facility network or the internet.

[0071] The User Information Management Department manages each user's name, age, gender, health status (care level, medical history, motor function, cognitive function, vital data, medication information, dietary restrictions, sleep patterns, allergy information), interests (music, gymnastics, gardening, calligraphy, cooking, movie watching, Go, karaoke, travel, reading, handicrafts, photography, pets, history, news, sports watching, Shogi, haiku, poetry recitation, dance, puzzles, brain training, games, walks, nature observation, seasonal events, community interaction, etc.), care level, past recreation participation history, satisfaction score, and family information. A wide range of attribute information is recorded and managed in the database, including family structure, contact information, frequency of visits, family wishes, lifestyle habits (wake-up and bedtime, meal times, bathing frequency, elimination patterns), hobbies, daily vital data (blood pressure, pulse, temperature, blood glucose level, oxygen saturation), history of interaction with staff, psychological state (anxiety, depression, loneliness, motivation), rehabilitation history (content and frequency of physical therapy, occupational therapy, speech therapy), social background (work history, educational background, community activity history), religion / beliefs, cultural preferences, whether or not they have pets, history of going out, history of falls, history of infectious diseases, and more. For example, the system comprehensively manages highly individualized information such as: an 80-year-old woman who is interested in "music" and "gardening," has mild dementia, requires care level 2, and whose vital data such as blood pressure and pulse are recorded daily; a 75-year-old man who is interested in "Go" and "exercise," requires care level 4, uses a wheelchair, and has dietary restrictions due to diabetes treatment; an 85-year-old woman whose hobbies are "watching movies" and "reading," requires support level 1, and has an extensive rehabilitation history; a 70-year-old man who actively participates in "dancing" and "karaoke" and is continuing rehabilitation for Parkinson's disease; and a 90-year-old woman who is knowledgeable about "shigin" (traditional Japanese poetry recitation) and "haiku" and looks forward to seasonal events. This information is collected through a variety of means, including direct input from terminals, API linkage with care record systems and electronic medical records, periodic questionnaire forms, automatic acquisition from wearable devices, voice input by staff, information provided by family members, observation records during facility rounds by robots, and real-time questionnaires via digital signage.

[0072] The Care Event Planning and Management Department utilizes generative AI to comprehensively analyze attribute information of all users and skill / qualification information of staff recorded in the User Information Management Department's database. The generative AI combines natural language processing models, large-scale language models, clustering algorithms, recommendation systems, image recognition models, time series analysis models, and personalized recommendation engines to perform tasks such as analyzing user interests and preferences, classifying health status and care levels, extracting patterns from past event participation history, analyzing satisfaction score trends, analyzing interaction trends with family and staff, analyzing time-series changes in vital data and rehabilitation history, detecting fluctuations in psychological state, and linking with seasonal and local events. For example, if a user's interests are diverse, including "music," "gymnastics," "gardening," "watching movies," "Go," "karaoke," "dancing," "haiku," "pets," and "community interaction," the generative AI automatically generates multiple recreation proposals combining these themes (such as music therapy, gymnastics classes, gardening clubs, movie screenings, Go tournaments, karaoke competitions, dance parties, haiku gatherings, pet interaction events, and community interaction events). Furthermore, taking into account the user's health condition and care level, the system automatically generates individually optimized care plans and health promotion programs, such as "chair exercises for users with reduced motor function," "brain training for dementia prevention," "dietary guidance events for users undergoing diabetes treatment," "individualized rehabilitation programs based on rehabilitation history," "rhythmic exercises for Parkinson's disease patients," "swallowing exercises for those with impaired swallowing function," and "relaxation programs for those with sleep disorders." For staff, the system records their name, role (caregiver, nurse, rehabilitation therapist, recreation coordinator, registered dietitian, music therapist, psychologist, volunteer, etc.), qualifications (certified care worker, nurse, occupational therapist, physical therapist, music therapist, registered dietitian, certified psychologist, dementia care specialist, etc.), and areas of expertise and skills (music therapy, exercise instruction, dementia care, health management, rehabilitation instruction, dietary guidance, psychological counseling, recreation planning, community collaboration, etc.), and the generation AI automatically assigns the most suitable staff member to each event or program.For example, music therapy events would be staffed with music therapists and caregivers, exercise classes with physical therapists and rehabilitation specialists, nutrition guidance events with registered dietitians, and psychological care events with certified psychologists, making maximum use of specialized expertise.

[0073] The AI ​​automatically generates various elements of an event, including its specific content, title, description, date and time, potential venues, list of necessary equipment, schedule, assignment of MCs and presenters, participant grouping, online streaming options, recording and archiving settings, participant reminder notifications, family announcements, consent forms for event participation, requests for meal and medication adjustments, infection control instructions, emergency response manuals, participant mobility support plans, and event safety management plans. For example, it can automatically generate diverse patterns based on the user's condition and wishes, the facility's situation, and social circumstances, such as "creating a detailed description for a music therapy event," "creating a schedule for a gymnastics class," "creating a list of necessary equipment for a gardening club," "creating an invitation email for a movie screening," "optimizing participant grouping for a dementia prevention program," "creating an invitation for an online streaming event," "determining whether an event can be held during an infectious disease outbreak," "creating a mobility support plan for event participants," and "automatically generating an emergency evacuation plan." Event announcements are automatically distributed from the server to devices via a variety of means, including internal social networking services, email, chat tools, calendar integration, push notifications, voice notifications, family contact lists, digital signage, and robotic voice guidance. Users and their families can register to participate with a single click from their devices. Registration status is aggregated on the server in real time, and a participant list is automatically generated. For example, if there are many people who wish to participate in the event, the generation AI automatically performs tasks such as lotteries, first-come, first-served, balancing based on health status and care level, limiting the number of participants during infectious disease outbreaks, and optimizing group divisions to create the optimal participant list.

[0074] Event operations on the day of the event are also automated through the coordination of servers and terminals. For example, a generation AI automatically generates QR codes or barcodes for registration, and attendance is confirmed by scanning them with a terminal. The generation AI also automatically optimizes the schedule and timetable, and pushes notifications to terminals. The AI ​​also acts as the facilitator for activities and games during the event, displaying announcements on terminals such as "Next up is music performance time," "It's time for exercise," "Let's start gardening," "The movie screening will begin," "We will announce the matchups for the Go tournament," "We will announce the order for the karaoke competition," "We will announce the start of the dance party," "We will announce the theme for the haiku gathering," "We will provide instructions for the pet interaction event," and "We will assign facilitators for the community exchange meeting." For online events, the generation AI automatically issues URLs for video conferencing tools, distributes them to participants, and automatically handles breakout room assignments and chatbot-based assistance. Furthermore, it is possible to conduct surveys, polls, quizzes, and collect feedback in real time during the event, and compile and display the results on the spot. For example, during an event, questionnaires such as "Please rate your satisfaction with the current program on a scale of 1 to 5," "Please select the next event you would like to participate in," and "Please let us know if you experience any changes in your physical condition" are automatically distributed and the results are compiled immediately.

[0075] After the event concludes, the effectiveness analysis department operates on a server and uses generative AI to collect and analyze diverse data from participants and staff via terminals, including satisfaction questionnaires, free-response comments, participation rates, health indicators (physical fitness test results, cognitive function test results, vital data), event-specific evaluation results, content of comments and chat logs during the event, SNS reactions, changes in internal communication and family interaction frequency after the event, changes in psychological state, rehabilitation effects, presence or absence of falls or accidents, and infection rates. For example, it automatically compiles and visualizes quantitative and qualitative evaluations such as "Satisfaction with the music therapy event was 4.5 points," "Participation rate in the exercise class was 80%," "Many free-response comments included 'It was fun' and 'I want to participate again'," "Walking speed of users improved after the event," "Cognitive decline in participants in the dementia prevention program was suppressed," "Speech frequency increased after participating in the karaoke competition," "Social interaction among gardening club participants became more active," and "There were no health abnormalities among event participants during the infectious disease outbreak." Furthermore, by matching participants' health indicators with their event participation history, the system automatically performs effect analyses such as "vital data showing an improving trend for users who continued to participate in exercise events," "high cognitive function maintenance rates among participants in dementia prevention programs," "reduced risk of falls among rehabilitation program participants," and "improved sleep quality after participating in relaxation events for people with sleep disorders."

[0076] These analysis results are automatically reflected in the planning of future events and programs. For example, the generative AI automatically implements a continuous improvement cycle, such as "increasing the frequency of music therapy events to twice a month due to high satisfaction," "revising the content and timing of exercise classes as participation rates are declining," "further diversifying the dementia prevention program," "adding new events based on requests from families," "increasing online events during infectious disease outbreaks," "increasing the number of mobility support staff for users at high risk of falling," and "adding counseling events for users with unstable psychological states."

[0077] Furthermore, for example, when a new user is admitted, the User Information Management Department automatically collects and integrates information from interviews with family members and the user themselves, referral letters from medical institutions, past living history, hobbies and preferences, and health checkup results. The Care Event Planning and Management Department then uses generative AI to automatically plan welcome events and self-introduction sessions for the new user, interaction programs with existing users, and individual care plans. For example, it automatically generates events such as a "New User Welcome Concert," a "Tea Party with Self-Introductions," a "Pairing Event for New and Existing Users," "Individualized Recreation Tailored to the New User's Hobbies," and a "Family-Participating Online Interaction Session" to support early adaptation to the facility and prevent isolation.

[0078] Furthermore, events tailored to seasons and holidays are automatically planned. The generative AI analyzes calendar information and past event history to automatically suggest a variety of events that reflect the seasons and local characteristics, such as "Spring Cherry Blossom Viewing Event," "Summer Festival," "Autumn Harvest Festival," "Winter Christmas Party," "Respect for the Aged Day Event," "Setsubun Bean Throwing Ceremony," "Tanabata Wish Strip Making," "Joint Disaster Prevention Drill with Local Residents," and "Exchange Meeting with Local Volunteers." It also automatically generates lists of necessary equipment, schedules, invitations for family members, infectious disease control plans, and emergency response plans.

[0079] Thus, the system of the present invention, with its various components centered on a generative AI on a server working in coordination and interacting with users, families, and staff through terminals, comprehensively and automatically performs a series of tasks in the care setting, from collecting and managing user information to planning, operating, announcing, registering participants, managing the event on the day, measuring effectiveness, and providing feedback. The generative AI comprehensively analyzes diverse data such as each user's condition, preferences, health status, past participation history, staff skills and qualifications, family requests, social conditions, and infectious disease outbreaks, and automatically plans, operates, evaluates, and improves optimal recreation and health promotion programs. This simultaneously improves user satisfaction and health maintenance, reduces the workload of staff, enhances the overall service quality of the facility, increases family peace of mind, strengthens cooperation with the community, and improves the ability to respond flexibly during disasters and infectious disease outbreaks.

[0080] (System Configuration)

[0081] The system according to this embodiment comprises a User Information Management Department, a Care Event Planning and Management Department, an Effectiveness Analysis Department, a Staff Management Department, a Family Collaboration Department, a Notification Distribution Department, a Data Collaboration Department, and a Security Management Department. The User Information Management Department records and manages a wide range of attribute information in a database, including each user's name, age, gender, health status, level of care required, medical history, motor function, cognitive function, vital data, medication information, dietary restrictions, sleep patterns, allergy information, interests, hobbies, past recreation participation history, satisfaction score, family information, lifestyle habits, daily vital data, history of interaction with staff, psychological state, rehabilitation history, social background, religion / beliefs, cultural preferences, presence of pets, history of going out, history of falls, and history of infectious diseases. For example, for an 80-year-old woman who is interested in "music" and "gardening"... The system comprehensively manages highly individualized information about each resident, such as: a resident with mild dementia, requiring Level 2 care, whose vital data such as blood pressure and pulse are recorded daily; a 75-year-old male interested in "Go" and "exercise," requiring Level 4 care, using a wheelchair, and undergoing diabetes treatment with dietary restrictions; an 85-year-old female whose hobbies are "watching movies" and "reading," requiring Level 1 support, and who has an extensive rehabilitation history; a 70-year-old male who actively participates in "dancing" and "karaoke" and continues rehabilitation for Parkinson's disease; and a 90-year-old female who is knowledgeable in "shigin" (traditional Japanese poetry recitation) and "haiku" and looks forward to seasonal events. This information is collected through a variety of means, including direct input from terminals, API integration with care record systems and electronic medical records, periodic questionnaire forms, automatic acquisition from wearable devices, voice input by staff, information provided by family members, observation records during facility rounds by robots, and real-time questionnaires via digital signage. Examples of prompts to be fed into the generating AI include: "Create an individual profile based on the user's hobbies, interests, health status, and lifestyle," "Compile past recreation participation history and satisfaction scores," "Analyze the time-series changes in vital data," and "Summarize requests and communications from family members."

[0082] The Care Event Planning and Management Department utilizes generative AI to comprehensively analyze attribute information of all users and skill / qualification information of staff recorded in the User Information Management Department's database. This analysis includes analyzing trends in users' interests and areas of concern, classifying health status and care levels, extracting patterns from past event participation history, analyzing trends in satisfaction scores, analyzing interaction trends with family and staff, analyzing time-series changes in vital data and rehabilitation history, detecting fluctuations in psychological state, and linking with seasonal and local events. For example, if a user's interests are diverse, including "music," "gymnastics," "gardening," "watching movies," "Go," "karaoke," "dance," "haiku," "pets," and "community interaction," the generative AI will automatically generate multiple recreational activity ideas combining these themes (such as music therapy, gymnastics classes, gardening clubs, movie screenings, Go tournaments, karaoke competitions, dance parties, haiku gatherings, pet interaction events, and community interaction events). Furthermore, considering the user's health condition and care level, it will automatically generate individually optimized care plans and health promotion programs such as "chair exercises for users with reduced motor function," "brain training for dementia prevention," "dietary guidance events for users undergoing diabetes treatment," "individualized rehabilitation programs based on rehabilitation history," "rhythmic exercises for Parkinson's disease patients," "swallowing exercises for those with impaired swallowing function," and "relaxation programs for those with sleep disorders." For staff, the system will also record their names, roles, qualifications, areas of expertise, and skills, and the generative AI will automatically assign the most suitable staff member to each event or program. For example, music therapy events will be staffed with music therapists and caregivers, exercise classes with physical therapists and rehabilitation specialists, nutrition guidance events with registered dietitians, and psychological care events with certified psychologists, making maximum use of specialized expertise. The AI ​​will also automatically generate details such as the event's content, title, description, date and time, potential venues, list of necessary equipment, schedule, assignment of facilitators and moderators, participant grouping, whether or not online streaming will be available, recording and archiving settings, participant reminder notifications, family announcements, consent forms for event participation, requests for adjustments to meals and medications, infection control instructions, emergency response manuals, participant mobility support plans, and safety management plans during the event.Examples of prompts to be fed into the AI ​​include: "Generate 10 recreational activity ideas that take into account the user's interests, preferences, and health condition," "Create a schedule for the exercise class," "Create a list of necessary equipment for the gardening club," "Create an invitation email for the movie screening," "Optimize the grouping of participants for the dementia prevention program," "Determine whether or not an event can be held during an infectious disease outbreak," "Create a transportation support plan for event participants," and "Automatically generate an evacuation plan for emergencies."

[0083] The effectiveness analysis department uses generative AI to collect and analyze diverse data from participants and staff via terminals, including satisfaction questionnaires, free-response comments, participation rates, health indicators, evaluation results for each event, content of comments and chat logs during events, reactions on social media, changes in internal communication and frequency of interaction with family after events, fluctuations in psychological state, rehabilitation effects, presence or absence of falls or accidents, and infectious disease outbreaks. For example, it automatically collects and visualizes quantitative and qualitative evaluations such as "Satisfaction level for the music therapy event was 4.5 points," "Participation rate for the exercise class was 80%," "Many free-response comments included 'It was fun' and 'I want to participate again'," "Walking speed of users improved after the event," "Cognitive decline in participants of the dementia prevention program was suppressed," "Speech frequency increased after participating in the karaoke competition," "Social interaction among gardening club participants became more active," and "There were no health abnormalities among event participants during the infectious disease outbreak." Furthermore, by matching participants' health indicators with their event participation history, the system automatically performs effect analyses such as "vital data showing an improving trend for users who continued to participate in exercise events," "high cognitive function maintenance rates among participants in dementia prevention programs," "reduced fall risk among rehabilitation program participants," and "improved sleep quality after participating in relaxation events for people with sleep disorders." Examples of prompts to be fed into the generating AI include: "Please analyze the aggregated results of the event satisfaction survey," "Please extract positive and negative opinions from free-response comments," "Please visualize the changes in the frequency of internal SNS communication after the event," "Please analyze the correlation between event participation history and health indicators," and "Please analyze the changes in fall risk."

[0084] The staff management department manages a wide range of information about each staff member, including their name, role, qualifications, areas of expertise, work shifts, past event assignments, performance evaluations, training history, health status, stress check results, workload, family structure, commuting method, preferred working hours, and emergency contact information. It then automatically optimizes things like a physical therapist's rehabilitation instruction skills, a music therapist's experience with music events, a certified care worker's dementia care experience, a registered dietitian's dietary guidance experience, the level of collaboration among staff, the distribution of staff workloads, shift adjustments, and emergency substitute staff assignments. Examples of prompts that can be fed into the generating AI include: "Optimally assign event staff based on their skills and qualifications," "Create a shift plan to equalize staff workloads," "Compile staff training histories," and "Create an emergency substitute staff assignment plan."

[0085] The Family Collaboration Department manages information such as each user's family structure, contact details, frequency of visits, family wishes and requests, feedback from family, announcements of family-participation events, health status reports to family, scheduling of online meetings with family, distribution of questionnaires to family, reception of emergency contacts from family, family caregiving burden status, and family psychological support history. It then automatically performs tasks such as "automatic creation of health reports for family," "generation of invitations for family-participation events," "reflection of family requests in event planning," "scheduling of online meetings with family," "aggregation of family questionnaire results," and "analysis of family caregiving burden status." Examples of prompts to be fed into the generation AI include "Please create a health report for family," "Please generate an invitation for family-participation events," "Please reflect family requests in event planning," and "Please schedule online meetings with family."

[0086] The notification distribution unit automatically distributes various notifications to users, their families, and staff devices, including event announcements, reminders, registration acceptance, event day instructions, survey requests, analysis report distribution, health status reports to family members, work-related communications to staff, mass notifications in emergencies, warnings during infectious disease outbreaks, and evacuation orders during disasters. Notifications are sent through a variety of means, such as internal social media, email, chat tools, calendar integration, push notifications, voice notifications, digital signage, and voice guidance by robots. Examples of prompts to be fed into the generation AI include: "Please create an event announcement," "Please send a reminder for the registration deadline," "Please generate instructions for the meeting place on the day of the event," "Please create a survey request email," and "Please create a mass notification for emergencies."

[0087] The Data Integration Department integrates via APIs with systems such as the care record system, electronic medical records, attendance management systems, internal social networking services, wearable devices, external calendar services, local medical institutions, government services, and local volunteer groups to automatically acquire and update user information, event information, participation history, health data, work status, internal communication history, and local collaboration information. For example, it provides the AI ​​with prompts such as, "Please acquire the latest user information from the care record system," "Please integrate medical information from the electronic medical record," "Please acquire vital data from wearable devices," and "Please aggregate collaboration information with local medical institutions."

[0088] The Security Management Department securely manages confidential data such as user information, event information, survey results, and analysis reports, and implements access control, data encryption, unauthorized access detection, log monitoring, privacy protection, data backup, disaster recovery, compliance with the Personal Information Protection Act, and control over the disclosure of information to family members and external partners. For example, it provides prompts to the AI ​​such as "Please set access permissions to the user information database," "Please encrypt event participation history data," "Please generate an alert message in case of unauthorized access," "Please create a data backup procedure," and "Please control the scope of information disclosure to external partners."

[0089] Thus, the system according to this embodiment works in conjunction with each other to comprehensively and automatically perform a series of tasks related to the caregiving field, from collecting and managing user information, planning, operating, announcing, registering participants, managing the event on the day, measuring effectiveness and providing feedback, to staff management, family coordination, notification distribution, data linkage, and security management. By utilizing a variety of prompt examples generated by generative AI as needed, it achieves a high level of automation and optimization that surpasses conventional manual and person-dependent operations.

[0090] (Implementation Steps)

[0091] Step 1: Collection and registration of user information The User Information Management Department records and manages a wide range of attribute information for each user in a database, including name, age, gender, health status (care level, medical history, motor function, cognitive function, vital data, medication information, dietary restrictions, sleep patterns, allergy information), interests (music, gymnastics, gardening, calligraphy, cooking, watching movies, Go, karaoke, travel, reading, handicrafts, photography, pets, history, news, watching sports, Shogi, haiku, dancing, puzzles, brain training, games, walks, nature observation, seasonal events, community interaction, etc.), care level, past recreation participation history, satisfaction score, family information, lifestyle habits, daily vital data, history of interaction with staff, psychological state, rehabilitation history, social background, religion / beliefs, cultural preferences, whether or not they have pets, history of going out, history of falls, history of infectious diseases, and more. Information is collected through a variety of means, including direct input from terminals, API integration with care record systems and electronic medical records, periodic questionnaire forms, automatic acquisition from wearable devices, voice input by staff, information provided by family members, observation records from facility robots during rounds, and real-time questionnaires via digital signage. Examples of prompts to be fed into the generating AI include: "Create individual profiles based on the user's hobbies, interests, health status, and lifestyle," "Compile past recreation participation history and satisfaction scores," "Analyze the time-series changes in vital data," and "Summarize requests and communications from family members."

[0092] Step 2: Analysis of user information and extraction of event themes The Care Event Planning and Management Department uses a generation AI to comprehensively analyze attribute information of all users and skill / qualification information of staff accumulated in the User Information Management Department. This analysis includes analyzing trends in users' interests and areas of concern, classifying health status and care level, extracting patterns from past event participation history, analyzing trends in satisfaction scores, analyzing interaction trends with family and staff, analyzing time-series changes in vital data and rehabilitation history, detecting fluctuations in psychological state, and linking with seasonal and local events. Examples of prompts to be fed into the generation AI include: "Generate 10 recreation ideas considering users' interests and health status," "Create a schedule for the exercise class," "Create a list of necessary equipment for the gardening club," "Create an invitation email for the movie screening," "Optimize participant grouping for the dementia prevention program," "Determine whether or not to hold events during infectious disease outbreaks," "Create a mobility support plan for event participants," and "Automatically generate an evacuation guidance plan for emergencies."

[0093] Step 3: Automatic generation and optimization of event proposals The Care Event Planning and Management Department automatically generates multiple event proposals based on themes and trends extracted using a generation AI. For example, it presents specific event proposals such as "music therapy," "exercise classes," "gardening club," "movie screenings," "Go tournaments," "karaoke competitions," "dance parties," "haiku gatherings," "pet interaction events," and "community interaction events." It also automatically suggests rehabilitation exercises, dementia prevention programs, dietary guidance events, individual rehabilitation programs, swallowing exercises, and relaxation programs that take into account the health status and care level of the users. Examples of prompts to be fed into the generation AI include: "Generate 10 event proposals that take into account the interests and health status of the users," "Create a title and description for a welcome event for new users," "Generate event proposals that are aligned with seasonal events," and "Create online event proposals for times of infectious disease outbreaks."

[0094] Step 4: Automatically create event details and prepare announcements. The Care Event Planning and Management Department uses a generation AI to automatically generate specific event details, titles, descriptions, dates and times, potential venues, lists of necessary equipment, schedules, assignments of MCs and facilitators, participant groupings, whether or not online streaming is available, recording and archiving settings, participant reminder notifications, family announcements, consent acquisition for event participation, requests for meal and medication adjustments, infection control instructions, emergency response manuals, participant mobility support plans, and event safety management plans. Examples of prompts to be fed into the generation AI include: "Please create a detailed description for the music therapy event," "Please create a schedule for the exercise class," "Please create a list of necessary equipment for the gardening club," "Please create an invitation email for the movie screening," "Please optimize participant groupings for the dementia prevention program," "Please determine whether or not the event can be held during an infectious disease outbreak," "Please create a mobility support plan for event participants," and "Please automatically generate an emergency evacuation plan."

[0095] Step 5: Event announcement and registration The notification distribution unit automatically distributes event announcements from the server to terminals using a variety of methods, including internal social networking services, email, chat tools, calendar integration, push notifications, voice notifications, family contact lists, digital signage, and robotic voice guidance. Users and their families can register to participate with a single click from their terminals. Registration status is aggregated on the server in real time, and a participant list is automatically generated. Examples of prompts that can be fed into the generation AI include: "Generate an event announcement," "Create a registration acceptance announcement," "Automatically create a participant list," "Create an event announcement for families," and "Create an online event announcement."

[0096] Step 6: Automate event day operations The Care Event Planning and Management Department uses a generation AI to automatically generate QR codes and barcodes for registration, which participants scan with their devices to confirm their attendance. The generation AI also automatically optimizes the event schedule and timetable, sending push notifications to the devices. The AI ​​also acts as the facilitator for activities and games during the event, displaying announcements on the devices such as "Next up is music performance," "It's time for exercise," "Let's start gardening," "The movie screening will begin," "We will now announce the Go tournament pairings," "We will now announce the order for the karaoke competition," "We will now announce the start of the dance party," "We will now announce the theme for the haiku gathering," "We will now provide instructions for the pet interaction event," and "We will now assign facilitators for the community exchange meeting." For online events, the generation AI automatically issues and distributes URLs for video conferencing tools to participants, and also automatically handles breakout room assignments and chatbot-based assistance. Examples of prompts that can be fed into the generation AI include "Please generate a QR code for event registration," "Please optimize the event schedule," "Please create an announcement for the online event," and "Please generate activity announcements for the event."

[0097] Step 7: Post-event effectiveness measurement and analysis The effectiveness analysis department uses a generation AI to collect and analyze diverse data from participants and staff via devices, including satisfaction questionnaires, free-response comments, participation rates, health indicators (physical fitness test results, cognitive function test results, vital data), event-specific evaluation results, content of comments and chat logs during events, SNS reactions, changes in the frequency of internal communication and interaction with family after the event, changes in psychological state, rehabilitation effects, presence or absence of falls and accidents, and infectious disease outbreaks. Examples of prompts to be fed into the generation AI include: "Please analyze the aggregated results of the event satisfaction questionnaire," "Please extract positive and negative opinions from the free-response comments," "Please visualize the changes in the frequency of internal SNS interaction after the event," "Please analyze the correlation between event participation history and health indicators," and "Please analyze the changes in fall risk."

[0098] Step 8: Incorporate feedback into the next event The effectiveness analysis department uses a generation AI to automatically incorporate the analysis results into the planning of future events and programs. For example, the generation AI automatically implements a continuous improvement cycle, such as "increasing the frequency of music therapy events to twice a month because satisfaction levels were high," "revising the content and timing of exercise classes because participation rates are declining," "further diversifying the dementia prevention program," "adding new events based on requests from families," "increasing online events during infectious disease outbreaks," "increasing the number of mobility support staff for users at high risk of falling," and "adding counseling events for users with unstable psychological states." Examples of prompts to be fed into the generation AI include "Please propose event ideas for the next event based on the analysis results of the previous event," "Please optimize the frequency of events with high satisfaction levels," "Please propose improvement plans for events with declining participation rates," and "Please generate new event ideas that reflect requests from families."

[0099] (Specific use cases)

[0100] For example, consider a scenario where a comprehensive database is created in a nursing care facility, containing information such as the diverse health status, interests, lifestyle, family structure, past recreation participation history, satisfaction scores, vital data, rehabilitation history, psychological state, staff skills and qualifications, and requests from families. Based on this information, an AI would automatically plan and operate optimal recreation and health promotion programs. For example, the User Information Management Department centrally manages highly individualized information such as: an 80-year-old woman who is interested in "music" and "gardening," has mild dementia, requires care level 2, and whose vital data such as blood pressure and pulse are recorded daily; a 75-year-old man who is interested in "Go" and "gymnastics," requires care level 4, uses a wheelchair, and is undergoing treatment for diabetes with dietary restrictions; an 85-year-old woman whose hobbies are "watching movies" and "reading," requires support level 1, and has an extensive rehabilitation history; a 70-year-old man who actively participates in "dancing" and "karaoke" and is continuing rehabilitation for Parkinson's disease; and a 90-year-old woman who is knowledgeable about "shigin" (traditional Japanese poetry recitation) and "haiku" and looks forward to seasonal events.

[0101] Based on this information, the Care Event Planning and Management Department uses AI to automatically generate multiple event proposals that comprehensively consider the user's interests, health status, care level, rehabilitation history, seasonal events, family requests, and infectious disease outbreaks. Examples include "music therapy events," "exercise classes," "gardening clubs," "movie screenings," "Go tournaments," "karaoke competitions," "dance parties," "haiku gatherings," "pet interaction events," and "community interaction events." Furthermore, individually optimized health promotion programs are also automatically generated, such as "chair exercises for users with reduced motor function," "brain training for dementia prevention," "dietary guidance events for users undergoing diabetes treatment," "individualized rehabilitation programs based on rehabilitation history," "rhythmic exercises for Parkinson's disease patients," "swallowing exercises for those with impaired swallowing function," and "relaxation programs for those with sleep disorders." Based on staff skills and qualifications, the system also automatically assigns staff to specific events. For example, a music therapist and caregiver are assigned to a music therapy event, a physical therapist and rehabilitation staff to an exercise class, a registered dietitian to a dietary guidance event, and a certified psychologist to a psychological care event.

[0102] The AI ​​also automatically generates event details (title, description, date and time, venue, list of necessary equipment, schedule, assignment of MCs and presenters, participant grouping, whether online streaming is available, recording / archiving settings, reminder notification content, notice to family, consent acquisition, requests for meal and medication adjustments, infection control measures, emergency response manual, mobility support plan, safety management plan, etc.). For example, prompts such as "Create a detailed description for the music therapy event," "Create a schedule for the exercise class," "Create a list of necessary equipment for the gardening club," "Create an invitation email for the movie screening," "Optimize participant grouping for the dementia prevention program," "Determine whether the event can be held during an infectious disease outbreak," "Create a mobility support plan for event participants," and "Automatically generate an emergency evacuation plan" are fed into the AI.

[0103] Event announcements are automatically distributed through a variety of means, including internal social media, email, chat tools, calendar integration, push notifications, voice notifications, family contact lists, digital signage, and voice guidance from robots. Users and their families can register to participate with a single click from their devices. On the day of the event, a generation AI automatically generates QR codes or barcodes for registration, which are scanned by the device to confirm attendance. The event schedule and timetable are also automatically optimized and sent as push notifications to the devices. The AI ​​also acts as the facilitator for activities and games during the event, displaying announcements on the devices such as "Next up is music performance," "It's time for exercise," "Let's start gardening," "We'll start the movie screening," and "We'll now announce the Go tournament pairings." For online events, the generation AI automatically issues and distributes URLs for video conferencing tools to participants, and also automatically handles breakout room assignments and chatbot-based assistance.

[0104] After the event, the effectiveness analysis department uses a generation AI to collect and analyze diverse data from participants and staff via their devices, including satisfaction questionnaires, free-response comments, participation rates, health indicators, evaluation results for each event, comments made during the event and chat logs, reactions on social media, changes in the frequency of internal communication and interaction with family after the event, changes in psychological state, rehabilitation effects, presence or absence of falls and accidents, and infection rates. The AI ​​is then fed prompts such as "Please analyze the aggregated results of the event satisfaction questionnaire," "Please extract positive and negative opinions from the free-response comments," "Please visualize the changes in the frequency of internal social media interaction after the event," "Please analyze the correlation between event participation history and health indicators," and "Please analyze the changes in fall risk." These analysis results are automatically reflected in the planning of future events and programs, and the AI ​​is then fed prompts such as "Please propose an event plan for the next event based on the analysis results of the previous event," "Please optimize the frequency of events with high satisfaction," "Please propose improvement plans for events with low participation rates," and "Please generate a new event plan that reflects the requests of family members."

[0105] In this way, by comprehensively analyzing the diverse conditions and preferences of users, their health status, family requests, staff skills, social conditions, and infectious disease outbreaks, and automatically planning, operating, evaluating, and improving optimal recreation and health promotion programs, it is possible to simultaneously achieve improved user satisfaction and health maintenance, reduced workload for staff, improved overall service quality of the facility, increased peace of mind for families, strengthened cooperation with the community, and improved flexibility in times of disaster or infectious disease outbreaks.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Second Embodiment]

[0112] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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".

[0124] (Example 1)

[0125] Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0126] (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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third Embodiment]

[0132] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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".

[0144] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0145] (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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] [Fourth Embodiment]

[0151] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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".

[0164] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0165] (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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Furthermore, the following additional information is disclosed regarding the above explanation.

[0189] (Claim 1)

[0190] The automated care support system comprises a User Information Management Department, a Care Event Planning and Management Department, and an Effectiveness Analysis Department. The User Information Management Department records and manages a wide range of attribute information for each user in a database, including name, age, gender, health status, care level, medical history, motor function, cognitive function, interests, care level, past recreation participation history, satisfaction score, family information, lifestyle, hobbies, daily vital data, and interaction history with staff. The Care Event Planning and Management Department uses generative AI to analyze the user attribute information and staff skill and qualification information recorded in the User Information Management Department, and then plans and manages the optimal recreation events based on the user's interests, health status, care level, past participation history, and the staff's expertise and qualifications. This system automatically generates health promotion programs and individual care plans, automates event announcements, participant recruitment, staff assignment, on-site operational preparations, progress management, online streaming and remote participation setup, participant grouping, necessary equipment list creation, and reminder notifications. The effectiveness analysis department uses a generative AI to collect and analyze diverse data from participants and staff after the event, including satisfaction questionnaires, free-response comments, participation rates, health indicators, event-specific evaluation results, content of comments and chat logs during the event, and changes in health status and communication frequency after the event. This data is then output as a visualized report, and the analysis results are automatically reflected in future events and care plan planning. (Claim 2)

[0191] An automated care support system that, according to the user's health condition and care level, uses a generative AI to automatically plan and operate rehabilitation exercises, dementia prevention programs, lifestyle-related disease prevention programs, and individual health promotion plans for users with reduced motor function, optimally assigns staff based on their qualifications and expertise, quantitatively evaluates the effectiveness of events and programs by comparing them with health indicators and participation history, and provides functions to support continuous health maintenance and functional improvement. (Claim 3)

[0192] An automated care support system that, based on the user's interests and past participation history, uses a generative AI to automatically plan optimal recreational events and social programs for each user, automatically adjusts the content and progress of the events according to the user's physical and cognitive functions, and analyzes satisfaction levels and free-form comments after the event to automatically reflect these changes in the content, frequency, and participant grouping of future events. [Explanation of symbols]

[0193] 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. An automated in-house event system comprising an employee information management department, an event planning and management department, and an effectiveness analysis department, wherein the employee information management department records attribute information of each employee, including their name, department, areas of interest, date of joining the company, and past event participation history, in a database; the event planning and management department uses generative AI to analyze the employee attribute information recorded in the employee information management department and automatically generates the optimal event theme, target employees, target departments, event content, event title, and description based on the employee's areas of interest, departmental structure, status of new employees, and past event participation trends, and further automates event announcement, participant recruitment, participant registration, on-site preparation, and progress management; and the effectiveness analysis department, after the event, uses generative AI to aggregate and analyze satisfaction scores, participation rates, participation trends by department, and evaluation results for each event collected from participants, outputting them as a visualized report, and automatically reflecting the analysis results in future event planning.

2. An automated in-house event system according to claim 1, which aims to improve the retention rate of new employees, comprising a generation AI that automatically extracts employees within a predetermined period from the date of joining the company, automatically plans and manages welcome parties and social gatherings to promote interaction with existing employees, and analyzes the participation status and satisfaction level of the events to support the prevention of isolation of new employees and early team integration.

3. An automated internal event system according to claim 1, for the purpose of strengthening inter-departmental collaboration and revitalizing communication, comprising a generative AI that analyzes employee information from multiple departments, automatically plans and manages cross-departmental events, optimally selects target departments and participants for events, analyzes participation rates and satisfaction levels for each department after the event, visualizes the state of interaction between departments, and automatically provides feedback that contributes to strengthening collaboration throughout the organization.

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