system

Abstract

The system according to this embodiment aims to efficiently coordinate the schedules of all employees. [Solution] The system according to the embodiment comprises a registration unit, a determination unit, and a proposal unit. The registration unit registers the schedules of all employees in a calendar. The determination unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of their schedule. The proposal unit extracts and proposes the most suitable candidate dates based on the information determined by the determination unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a great deal of time and labor to adjust the schedules of all employees.

[0005] The system according to the embodiment aims to efficiently adjust the schedules of all employees.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, a determination unit, and a proposal unit. The registration unit registers the schedules of all employees in a calendar. The determination unit analyzes the schedules registered by the registration unit and automatically determines the available time and the importance of the schedules of each employee. The proposal unit extracts and proposes an optimal candidate date based on the information determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently coordinate the schedules of all employees. [Brief explanation of the drawing]

[0008] [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]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] 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), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] 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.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific 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.

[0027] 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.

[0028] (Example of form 1) The automated meeting schedule adjustment system according to an embodiment of the present invention is a system that automatically adjusts internal meeting schedules using AI. This automated meeting schedule adjustment system registers the schedules of all employees in a calendar and has the AI ​​read them. Next, the AI ​​analyzes the schedule and automatically determines each employee's available time and the importance of the schedule. Finally, the AI ​​extracts and proposes the most suitable candidate dates. This system significantly reduces the effort required for schedule adjustment, allowing employees to concentrate on their work. For example, the automated meeting schedule adjustment system registers the schedules of all employees in a calendar. At this time, each employee's schedule is entered in detail and has the AI ​​read it. For example, appointments such as meetings, business trips, and vacations are registered in the calendar. This allows the AI ​​to understand each employee's schedule. Next, the AI ​​analyzes the schedule and automatically determines each employee's available time and the importance of the schedule. For example, the AI ​​analyzes each employee's schedule and extracts the most suitable candidate dates, taking into account the importance of the meeting, the positions of the participants, and the history of past meetings. This significantly reduces the effort required for schedule adjustment. Furthermore, the AI ​​extracts and suggests the most suitable candidate dates. For example, the AI ​​considers each employee's available time and suggests the optimal meeting date. This significantly reduces the effort required for scheduling, allowing employees to focus on their work. In this way, the automated meeting scheduling system reduces the effort involved in scheduling by automatically adjusting the schedules of all employees and suggesting the most suitable candidate dates.

[0029] The automated meeting schedule adjustment system according to this embodiment comprises a registration unit, a determination unit, and a proposal unit. The registration unit registers the schedules of all employees in a calendar. For example, the registration unit inputs the schedules of all employees in detail and feeds them into the AI. For example, it registers appointments such as meetings, business trips, and vacations in the calendar. This allows the registration unit to enable the AI ​​to understand each employee's schedule. The determination unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of the schedule. For example, the determination unit uses the AI ​​to analyze each employee's schedule and extracts the optimal candidate date, taking into account the importance of the meeting, the positions of the participants, and the history of past meetings. This allows the determination unit to significantly reduce the effort required for schedule adjustment. The proposal unit extracts and proposes the optimal candidate date based on the information determined by the determination unit. For example, the proposal unit uses the AI ​​to consider each employee's available time and proposes the optimal meeting date. This allows the proposal unit to significantly reduce the effort required for schedule adjustment and secure time to concentrate on work. As a result, the automated meeting schedule adjustment system according to this embodiment can reduce the effort required for scheduling by automatically adjusting the schedules of all employees and suggesting the most suitable candidate dates.

[0030] The registration department registers all employees' schedules in a calendar. Specifically, it inputs each employee's schedule in detail and feeds it into the AI. For example, it registers appointments such as meetings, business trips, and vacations in the calendar. This allows the registration department and the AI ​​to understand each employee's schedule. Furthermore, the registration department can not only allow employees to manually enter their schedules, but can also automatically extract appointments from email, chat tools, project management tools, etc., and reflect them in the calendar. For example, it can detect meeting invitations from email content and automatically add them to the calendar. It can also extract appointments from chat conversations and reflect them in the calendar. This significantly reduces the amount of time employees have to enter their schedules. In addition, the registration department can centrally manage each employee's schedule and update it in real time. For example, if an employee changes their schedule, that information is immediately reflected in the calendar and shared with other employees and the entire system. This allows everyone to always have access to the latest schedule information and improves the accuracy of schedule adjustments.

[0031] The decision unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of each schedule. Specifically, the AI ​​analyzes each employee's schedule, considering factors such as the importance of meetings, the roles of participants, and past meeting history, to extract the most suitable candidate dates. The AI ​​uses natural language processing technology to analyze the content of the schedule and evaluate the importance and urgency of meetings. It also considers the roles and job responsibilities of participants, prioritizing time allocation for important meetings. Furthermore, based on past meeting history, it analyzes how frequently similar meetings are held and sets meetings at appropriate intervals. This significantly reduces the effort required for schedule adjustments by the decision unit. In addition, the decision unit can propose the optimal schedule considering each employee's workload and project progress. For example, if a particular project is urgent, it prioritizes scheduling meetings related to that project to balance them with other tasks. Also, if an employee has a high workload, it reduces the frequency of meetings to allow time for focused work. This enables the decision unit to achieve efficient and effective schedule adjustments, improving employee work efficiency.

[0032] The proposal department extracts and proposes the most suitable candidate dates based on the information determined by the evaluation department. Specifically, the AI ​​considers each employee's available time and proposes the optimal meeting schedule. The AI ​​analyzes each employee's schedule in real time and extracts time slots when everyone can participate. It also considers the importance and urgency of the meeting and prioritizes and proposes the most suitable candidate dates. For example, important meetings are scheduled earlier, and less urgent meetings are postponed. This significantly reduces the effort required for scheduling adjustments for the proposal department, allowing them to focus on their work. Furthermore, the proposal department can continuously improve its proposals based on employee feedback. For example, employees can provide feedback on proposed meeting dates, and this information can be used to make future proposals more appropriate. The proposal department can also present multiple candidate dates, allowing employees to choose the most suitable date. This enables the proposal department to achieve flexible and efficient scheduling adjustments, improving employee satisfaction. In addition, the proposal department can have a function to automatically reflect proposed meeting dates in the calendar. This eliminates the need for employees to manually enter proposed dates, further improving the efficiency of scheduling adjustments.

[0033] The reception desk provides an interface for users to enter their schedules. For example, the reception desk provides a web form or mobile app for users to enter their schedules. This allows the reception desk to provide an interface for users to enter their schedules. Some or all of the above-described processes in the reception desk may be performed using AI or not. For example, the reception desk can input the schedule data entered by the user into an AI, which can then analyze the schedule data and suggest optimal candidate dates.

[0034] The proposal department can use AI to analyze each employee's schedule and extract the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. For example, the proposal department's AI analyzes each employee's schedule and extracts the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. This allows the proposal department to extract the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input each employee's schedule data into a generation AI, which can then analyze the schedule data and extract the optimal candidate date.

[0035] The proposal department can use AI to propose the optimal meeting schedule by considering each employee's available time. For example, the proposal department can have the AI ​​consider each employee's available time and propose the optimal meeting schedule. In this way, the proposal department can propose the optimal meeting schedule by considering each employee's available time. Some or all of the above processing in the proposal department may be performed using a generating AI, or it may be performed without a generating AI. For example, the proposal department can input each employee's available time data into a generating AI, and the generating AI can analyze the available time data and propose the optimal meeting schedule.

[0036] The suggestion unit can present candidate dates for the user to ultimately select. For example, the suggestion unit can present the user with the optimal candidate dates extracted by AI, and then present the candidate date for the user to ultimately select. In this way, the suggestion unit can present candidate dates for the user to ultimately select. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can present candidate date data extracted by AI to the user, and then present the candidate date for the user to ultimately select.

[0037] The proposal unit allows users to provide feedback, which the AI ​​can then use to learn. For example, the proposal unit can improve the system's accuracy by having the AI ​​learn from the user's feedback. In this way, the proposal unit can improve the system's accuracy by having the AI ​​learn from the user's feedback. Some or all of the above-described processes in the proposal unit may be performed using or without AI. For example, the proposal unit can input user-provided feedback data into the AI, which can then learn from the feedback data to improve the system's accuracy.

[0038] The proposed system can implement security measures such as encrypting schedule data and restricting access. For example, the proposed system can improve data security by encrypting schedule data and restricting access. In this way, the proposed system can improve data security by encrypting schedule data and restricting access. Some or all of the above processing in the proposed system may be performed using AI or not. For example, the proposed system can improve data security by using an AI model that encrypts schedule data and restricts access.

[0039] The registration unit can suggest the optimal registration method by referring to the user's past schedule history during registration. For example, the registration unit can automatically display as candidates schedules that the user has frequently entered in the past. The registration unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest schedules to be used during specific time periods based on the user's past schedule history. In this way, the registration unit can suggest the optimal registration method by referring to the user's past schedule history. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past schedule history data into AI, and the AI ​​can analyze the history data to suggest the optimal registration method.

[0040] The registration unit can adjust the level of detail in the schedule based on the user's job title and duties during registration. For example, for managers, the registration unit can provide an option to enter a detailed schedule. For general staff, the registration unit can also provide a simplified schedule entry option. Furthermore, the registration unit can automatically adjust the required level of detail depending on the specific duties. This allows the registration unit to register appropriate schedules by adjusting the level of detail according to the user's job title and duties. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's job title and duties data into the AI, which can then analyze the data and adjust the level of detail in the schedule.

[0041] The registration unit can prioritize registering schedules that are highly relevant to the user, taking into account the user's geographical location information during registration. For example, if the user is in a specific location, the registration unit will prioritize registering schedules related to that location. Furthermore, if the user is on the move, the registration unit can prioritize registering schedules related to their destination. Additionally, if the user is in a specific region, the registration unit can prioritize registering schedules related to that region. In this way, the registration unit can prioritize registering schedules that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location data into an AI, which can then analyze the data and prioritize registering schedules that are highly relevant.

[0042] The registration unit can analyze a user's social media activity during registration and automatically suggest relevant schedules. For example, the registration unit can automatically add events mentioned by the user on social media to the schedule. It can also analyze the content of a user's social media posts and suggest relevant schedules. Furthermore, the registration unit can suggest relevant schedules by referring to the activities of the user's social media friends. In this way, the registration unit can automatically suggest relevant schedules by analyzing the user's social media activity. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's social media activity data into an AI, which can then analyze the data and suggest relevant schedules.

[0043] The decision-making unit can adjust the importance of a schedule by referring to the history of past meetings when making a decision. For example, the decision-making unit can adjust the importance of the current schedule based on the history of meetings that were deemed important in the past. The decision-making unit can also adjust the importance of a schedule by considering the positions of participants in past meetings. Furthermore, the decision-making unit can adjust the importance of a schedule based on the outcomes of past meetings. In this way, the decision-making unit can appropriately adjust the importance of a schedule by referring to the history of past meetings. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input past meeting history data into AI, and the AI ​​can analyze the data and adjust the importance of the schedule.

[0044] The decision-making unit can customize the importance of schedules based on each employee's position and job responsibilities during the decision-making process. For example, in the case of managers, the decision-making unit will prioritize important meetings. It can also prioritize schedules related to daily tasks in the case of general employees. Furthermore, the decision-making unit can customize the importance of schedules according to specific job responsibilities. This allows the decision-making unit to perform appropriate schedule management by customizing the importance of schedules according to each employee's position and job responsibilities. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit can input each employee's position and job responsibilities data into the AI, which can then analyze the data and customize the importance of schedules.

[0045] The judgment unit can determine the importance of schedules by considering their geographical distribution. For example, if the user is in a specific location, the judgment unit will prioritize schedules related to that location. Furthermore, if the user is traveling, the judgment unit can prioritize schedules related to their destination. Additionally, if the user is in a specific region, the judgment unit can prioritize schedules related to that region. This allows the judgment unit to appropriately determine importance by considering the geographical distribution of schedules. Some or all of the above processing in the judgment unit may be performed using AI, or without AI. For example, the judgment unit can input geographical distribution data of schedules into an AI, which can then analyze the data to determine importance.

[0046] The decision-making unit can improve the importance of schedules by referring to relevant literature and data during the decision-making process. For example, the decision-making unit can improve the importance of schedules by referring to relevant research papers. It can also improve the importance of schedules by referring to industry trend data. Furthermore, the decision-making unit can improve the importance of schedules by referring to internal company data. In this way, the decision-making unit can improve the importance of schedules by referring to relevant literature and data. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input relevant literature and data into AI, and the AI ​​can analyze the data to improve the importance of schedules.

[0047] The proposal department can adjust the level of detail in a proposal based on the importance of the meeting and the positions of the participants. For example, the proposal department will provide a detailed proposal for an important meeting. It can also provide a detailed proposal if the participants hold high positions. Furthermore, the proposal department can adjust the level of detail in a proposal according to the importance of the meeting. This allows the proposal department to provide appropriate proposals by adjusting the level of detail according to the importance of the meeting and the positions of the participants. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the importance of the meeting and the positions of the participants into the AI, which can then analyze the data and adjust the level of detail in the proposal.

[0048] The proposal department can extract the optimal candidate date by referring to the history of past meetings when making a proposal. For example, the proposal department can extract the optimal candidate date based on the history of meetings that were considered important in the past. The proposal department can also extract the optimal candidate date by considering the positions of the participants in past meetings. Furthermore, the proposal department can extract the optimal candidate date based on the outcomes of past meetings. In this way, the proposal department can extract the optimal candidate date by referring to the history of past meetings. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input past meeting history data into AI, and the AI ​​can analyze the data to extract the optimal candidate date.

[0049] The proposal department can adjust the order of proposals based on the submission timing of the schedules. For example, the proposal department can prioritize proposals with approaching deadlines. It can also postpone proposals with later deadlines. Furthermore, the proposal department can adjust the order of proposals according to the submission timing. This allows the proposal department to make appropriate proposals by adjusting the order of proposals based on the submission timing of the schedules. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input schedule submission timing data into an AI, which can then analyze the data and adjust the order of proposals.

[0050] The proposal department can suggest optimal candidate dates by referring to relevant market data and trends when making a proposal. For example, the proposal department can suggest optimal candidate dates based on market trend data. It can also suggest optimal candidate dates by referring to industry trends. Furthermore, the proposal department can suggest optimal candidate dates based on the company's internal data. In this way, the proposal department can suggest optimal candidate dates by referring to relevant market data and trends. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input relevant market data and trend data into AI, and the AI ​​can analyze the data and suggest optimal candidate dates.

[0051] The reception desk can suggest the optimal reception method by referring to the user's past schedule history during the reception process. For example, the reception desk can automatically display as suggestions schedules that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest schedules to be used during specific time periods based on the user's past schedule history. In this way, the reception desk can suggest the optimal reception method by referring to the user's past schedule history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past schedule history data into an AI, which can then analyze the data and suggest the optimal reception method.

[0052] The reception desk can prioritize accepting schedules that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk will prioritize accepting schedules related to that location. Furthermore, if the user is on the move, the reception desk can prioritize accepting schedules related to their destination. Additionally, if the user is in a specific region, the reception desk can prioritize accepting schedules related to that region. In this way, the reception desk can prioritize accepting schedules that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then analyze the data and prioritize accepting schedules that are highly relevant.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The automated meeting scheduling system can also include a notification function. This notification function can automatically send notifications to each employee, for example, when potential meeting dates are decided. Notifications can be sent via email, chat apps, calendar apps, etc. Furthermore, the notification function can set meeting reminders and send notifications the day before the meeting. In addition, the notification function can quickly notify employees if meetings are changed or canceled. This allows employees to easily understand their meeting schedules and respond flexibly to schedule changes.

[0055] The automated meeting scheduling system can also include an analytics unit. This unit can, for example, analyze past meeting data to evaluate meeting frequency, participant attendance rates, and meeting outcomes. Based on this data, the analytics unit can make suggestions to improve meeting efficiency. It can also analyze differences in attendance rates based on meeting time and day of the week to suggest optimal meeting times. Furthermore, the analytics unit can provide data for selecting appropriate participants based on meeting content and agenda. In this way, the analytics unit can support more efficient meetings and effective scheduling.

[0056] The automated meeting scheduling system can also include a feedback unit. This unit can, for example, collect feedback from participants after a meeting and use it to improve the system. The feedback unit can collect feedback in the form of a questionnaire and evaluate participant satisfaction and the effectiveness of the meeting. Furthermore, the feedback unit can input the collected feedback into an AI, which then analyzes it to improve the system's accuracy. In addition, the feedback unit can make improvement suggestions that reflect participants' opinions and requests. This allows the feedback unit to continuously improve the system and increase user satisfaction.

[0057] The automated meeting scheduling system can also include a predictive function. This predictive function can forecast future meeting schedules based, for example, on past meeting data and employee scheduling patterns. It can use AI to analyze employee scheduling patterns and suggest optimal meeting dates in advance. Furthermore, it can predict meeting timings by considering factors such as seasons and peak work periods. It can also predict the importance of future meetings based on employee attendance rates and meeting outcomes. This allows the predictive function to support efficient scheduling and effective meeting management.

[0058] The automated meeting scheduling system can also include a customization section. This customization section allows for the customization of scheduling settings according to each employee's individual needs and preferences. For example, it can set preferred meeting times, days of the week, and meeting frequency. It can also prioritize scheduling based on each employee's position and job responsibilities. Furthermore, the customization section can adjust the scheduling algorithm based on employee feedback. This enables flexible scheduling tailored to each employee's needs.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The registration department registers all employees' schedules in the calendar. For example, it inputs detailed information such as meetings, business trips, and vacations, and feeds this information into the AI ​​to understand each employee's schedule. Step 2: The judgment unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of the schedule. For example, the AI ​​analyzes the importance of the meeting, the job titles of the participants, and the history of past meetings. Step 3: The proposal unit extracts and proposes the most suitable candidate dates based on the information determined by the evaluation unit. For example, the AI ​​considers each employee's available time and proposes the optimal meeting schedule.

[0061] (Example of form 2) The automated meeting schedule adjustment system according to an embodiment of the present invention is a system that automatically adjusts internal meeting schedules using AI. This automated meeting schedule adjustment system registers the schedules of all employees in a calendar and has the AI ​​read them. Next, the AI ​​analyzes the schedule and automatically determines each employee's available time and the importance of the schedule. Finally, the AI ​​extracts and proposes the most suitable candidate dates. This system significantly reduces the effort required for schedule adjustment, allowing employees to concentrate on their work. For example, the automated meeting schedule adjustment system registers the schedules of all employees in a calendar. At this time, each employee's schedule is entered in detail and has the AI ​​read it. For example, appointments such as meetings, business trips, and vacations are registered in the calendar. This allows the AI ​​to understand each employee's schedule. Next, the AI ​​analyzes the schedule and automatically determines each employee's available time and the importance of the schedule. For example, the AI ​​analyzes each employee's schedule and extracts the most suitable candidate dates, taking into account the importance of the meeting, the positions of the participants, and the history of past meetings. This significantly reduces the effort required for schedule adjustment. Furthermore, the AI ​​extracts and suggests the most suitable candidate dates. For example, the AI ​​considers each employee's available time and suggests the optimal meeting date. This significantly reduces the effort required for scheduling, allowing employees to focus on their work. In this way, the automated meeting scheduling system reduces the effort involved in scheduling by automatically adjusting the schedules of all employees and suggesting the most suitable candidate dates.

[0062] The automated meeting schedule adjustment system according to this embodiment comprises a registration unit, a determination unit, and a proposal unit. The registration unit registers the schedules of all employees in a calendar. For example, the registration unit inputs the schedules of all employees in detail and feeds them into the AI. For example, it registers appointments such as meetings, business trips, and vacations in the calendar. This allows the registration unit to enable the AI ​​to understand each employee's schedule. The determination unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of the schedule. For example, the determination unit uses the AI ​​to analyze each employee's schedule and extracts the optimal candidate date, taking into account the importance of the meeting, the positions of the participants, and the history of past meetings. This allows the determination unit to significantly reduce the effort required for schedule adjustment. The proposal unit extracts and proposes the optimal candidate date based on the information determined by the determination unit. For example, the proposal unit uses the AI ​​to consider each employee's available time and proposes the optimal meeting date. This allows the proposal unit to significantly reduce the effort required for schedule adjustment and secure time to concentrate on work. As a result, the automated meeting schedule adjustment system according to this embodiment can reduce the effort required for scheduling by automatically adjusting the schedules of all employees and suggesting the most suitable candidate dates.

[0063] The registration department registers all employees' schedules in a calendar. Specifically, it inputs each employee's schedule in detail and feeds it into the AI. For example, it registers appointments such as meetings, business trips, and vacations in the calendar. This allows the registration department and the AI ​​to understand each employee's schedule. Furthermore, the registration department can not only allow employees to manually enter their schedules, but can also automatically extract appointments from email, chat tools, project management tools, etc., and reflect them in the calendar. For example, it can detect meeting invitations from email content and automatically add them to the calendar. It can also extract appointments from chat conversations and reflect them in the calendar. This significantly reduces the amount of time employees have to enter their schedules. In addition, the registration department can centrally manage each employee's schedule and update it in real time. For example, if an employee changes their schedule, that information is immediately reflected in the calendar and shared with other employees and the entire system. This allows everyone to always have access to the latest schedule information and improves the accuracy of schedule adjustments.

[0064] The assessment unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of each schedule. Specifically, the AI ​​analyzes each employee's schedule, considering factors such as the importance of meetings, the roles of participants, and past meeting history, to extract the most suitable candidate dates. The AI ​​uses natural language processing technology to analyze the content of the schedule and evaluate the importance and urgency of meetings. It also considers the roles and job responsibilities of participants, prioritizing time allocation for important meetings. Furthermore, based on past meeting history, it analyzes how frequently similar meetings are held and sets meetings at appropriate intervals. This significantly reduces the effort required for schedule adjustments by the assessment unit. In addition, the assessment unit can propose the optimal schedule considering each employee's workload and project progress. For example, if a particular project is urgent, it prioritizes scheduling meetings related to that project to balance them with other tasks. Also, if an employee has a high workload, it reduces the frequency of meetings to allow time for focused work. This enables the assessment unit to achieve efficient and effective schedule adjustments, improving employee work efficiency.

[0065] The proposal department extracts and proposes the most suitable candidate dates based on the information determined by the evaluation department. Specifically, the AI ​​considers each employee's available time and proposes the optimal meeting schedule. The AI ​​analyzes each employee's schedule in real time and extracts time slots when everyone can participate. It also considers the importance and urgency of the meeting and prioritizes and proposes the most suitable candidate dates. For example, important meetings are scheduled earlier, and less urgent meetings are postponed. This significantly reduces the effort required for scheduling adjustments for the proposal department, allowing them to focus on their work. Furthermore, the proposal department can continuously improve its proposals based on employee feedback. For example, employees can provide feedback on proposed meeting dates, and this information can be used to make future proposals more appropriate. The proposal department can also present multiple candidate dates, allowing employees to choose the most suitable date. This enables the proposal department to achieve flexible and efficient scheduling adjustments, improving employee satisfaction. In addition, the proposal department can have a function to automatically reflect proposed meeting dates in the calendar. This eliminates the need for employees to manually enter proposed dates, further improving the efficiency of scheduling adjustments.

[0066] The reception desk provides an interface for users to enter their schedules. For example, the reception desk provides a web form or mobile app for users to enter their schedules. This allows the reception desk to provide an interface for users to enter their schedules. Some or all of the above-described processes in the reception desk may be performed using AI or not. For example, the reception desk can input the schedule data entered by the user into an AI, which can then analyze the schedule data and suggest optimal candidate dates.

[0067] The proposal department can use AI to analyze each employee's schedule and extract the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. For example, the proposal department's AI analyzes each employee's schedule and extracts the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. This allows the proposal department to extract the optimal candidate date, taking into account factors such as the importance of the meeting, the positions of the participants, and the history of past meetings. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input each employee's schedule data into a generation AI, which can then analyze the schedule data and extract the optimal candidate date.

[0068] The proposal department can use AI to propose the optimal meeting schedule by considering each employee's available time. For example, the proposal department can have the AI ​​consider each employee's available time and propose the optimal meeting schedule. In this way, the proposal department can propose the optimal meeting schedule by considering each employee's available time. Some or all of the above processing in the proposal department may be performed using a generating AI, or it may be performed without a generating AI. For example, the proposal department can input each employee's available time data into a generating AI, and the generating AI can analyze the available time data and propose the optimal meeting schedule.

[0069] The suggestion unit can present candidate dates for the user to ultimately select. For example, the suggestion unit can present the user with the optimal candidate dates extracted by AI, and then present the candidate date for the user to ultimately select. In this way, the suggestion unit can present candidate dates for the user to ultimately select. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can present candidate date data extracted by AI to the user, and then present the candidate date for the user to ultimately select.

[0070] The proposal unit allows users to provide feedback, which the AI ​​can then use to learn. For example, the proposal unit can improve the system's accuracy by having the AI ​​learn from the user's feedback. In this way, the proposal unit can improve the system's accuracy by having the AI ​​learn from the user's feedback. Some or all of the above-described processes in the proposal unit may be performed using or without AI. For example, the proposal unit can input user-provided feedback data into the AI, which can then learn from the feedback data to improve the system's accuracy.

[0071] The proposed system can implement security measures such as encrypting schedule data and restricting access. For example, the proposed system can improve data security by encrypting schedule data and restricting access. In this way, the proposed system can improve data security by encrypting schedule data and restricting access. Some or all of the above processing in the proposed system may be performed using AI or not. For example, the proposed system can improve data security by using an AI model that encrypts schedule data and restricts access.

[0072] The registration unit can estimate the user's emotions and adjust the schedule registration method based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the input steps. If the user is relaxed, the registration unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick schedule registration. In this way, the registration unit can improve user convenience by adjusting the schedule registration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's emotion data into a generative AI, which can analyze the emotion data and adjust the schedule registration method.

[0073] The registration unit can suggest the optimal registration method by referring to the user's past schedule history during registration. For example, the registration unit can automatically display as candidates schedules that the user has frequently entered in the past. The registration unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest schedules to be used during specific time periods based on the user's past schedule history. In this way, the registration unit can suggest the optimal registration method by referring to the user's past schedule history. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the user's past schedule history data into AI, and the AI ​​can analyze the history data to suggest the optimal registration method.

[0074] The registration unit can adjust the level of detail in the schedule based on the user's job title and duties during registration. For example, for managers, the registration unit can provide an option to enter a detailed schedule. For general staff, the registration unit can also provide a simplified schedule entry option. Furthermore, the registration unit can automatically adjust the required level of detail depending on the specific duties. This allows the registration unit to register appropriate schedules by adjusting the level of detail according to the user's job title and duties. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's job title and duties data into the AI, which can then analyze the data and adjust the level of detail in the schedule.

[0075] The registration unit can estimate the user's emotions and determine the order in which to register schedules based on the estimated emotions. For example, if the user is feeling stressed, the registration unit will prioritize registering important schedules. Conversely, if the user is relaxed, the registration unit may postpone the registration of detailed schedules. Furthermore, if the user is in a hurry, the registration unit may register the most important schedules first. In this way, the registration unit can improve user convenience by determining the order in which to register schedules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI, which can analyze the emotion data to determine the order in which to register schedules.

[0076] The registration unit can prioritize registering schedules that are highly relevant to the user, taking into account the user's geographical location information during registration. For example, if the user is in a specific location, the registration unit will prioritize registering schedules related to that location. Furthermore, if the user is on the move, the registration unit can prioritize registering schedules related to their destination. Additionally, if the user is in a specific region, the registration unit can prioritize registering schedules related to that region. In this way, the registration unit can prioritize registering schedules that are highly relevant by considering the user's geographical location information. Some or all of the above processing in the registration unit may be performed using AI, or not. For example, the registration unit can input the user's geographical location data into an AI, which can then analyze the data and prioritize registering schedules that are highly relevant.

[0077] The registration unit can analyze a user's social media activity during registration and automatically suggest relevant schedules. For example, the registration unit can automatically add events mentioned by the user on social media to the schedule. It can also analyze the content of a user's social media posts and suggest relevant schedules. Furthermore, the registration unit can suggest relevant schedules by referring to the activities of the user's social media friends. In this way, the registration unit can automatically suggest relevant schedules by analyzing the user's social media activity. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the user's social media activity data into an AI, which can then analyze the data and suggest relevant schedules.

[0078] The judgment unit can estimate the user's emotions and determine the importance of schedules based on the estimated emotions. For example, if the user is feeling stressed, the judgment unit will prioritize important schedules. It can also postpone the determination of detailed schedules if the user is relaxed. Furthermore, if the user is in a hurry, the judgment unit can prioritize the most important schedules first. This allows for appropriate schedule management by determining the importance of schedules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI, which can then analyze the emotion data to determine the importance of schedules.

[0079] The decision-making unit can adjust the importance of a schedule by referring to the history of past meetings when making a decision. For example, the decision-making unit can adjust the importance of the current schedule based on the history of meetings that were deemed important in the past. The decision-making unit can also adjust the importance of a schedule by considering the positions of participants in past meetings. Furthermore, the decision-making unit can adjust the importance of a schedule based on the outcomes of past meetings. In this way, the decision-making unit can appropriately adjust the importance of a schedule by referring to the history of past meetings. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input past meeting history data into AI, and the AI ​​can analyze the data and adjust the importance of the schedule.

[0080] The decision-making unit can customize the importance of schedules based on each employee's position and job responsibilities during the decision-making process. For example, in the case of managers, the decision-making unit will prioritize important meetings. It can also prioritize schedules related to daily tasks in the case of general employees. Furthermore, the decision-making unit can customize the importance of schedules according to specific job responsibilities. This allows the decision-making unit to perform appropriate schedule management by customizing the importance of schedules according to each employee's position and job responsibilities. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit can input each employee's position and job responsibilities data into the AI, which can then analyze the data and customize the importance of schedules.

[0081] The judgment unit can estimate the user's emotions and adjust the order in which schedule judgment results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit will prioritize displaying important schedules. It can also display detailed schedules later if the user is relaxed. Furthermore, if the user is in a hurry, the judgment unit can display the most important schedules first. This allows the judgment unit to improve user convenience by adjusting the order in which schedule judgment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI, which can analyze the emotion data and adjust the order in which schedule judgment results are displayed.

[0082] The judgment unit can determine the importance of schedules by considering their geographical distribution. For example, if the user is in a specific location, the judgment unit will prioritize schedules related to that location. Furthermore, if the user is traveling, the judgment unit can prioritize schedules related to their destination. Additionally, if the user is in a specific region, the judgment unit can prioritize schedules related to that region. This allows the judgment unit to appropriately determine importance by considering the geographical distribution of schedules. Some or all of the above processing in the judgment unit may be performed using AI, or without AI. For example, the judgment unit can input geographical distribution data of schedules into an AI, which can then analyze the data to determine importance.

[0083] The decision-making unit can improve the importance of schedules by referring to relevant literature and data during the decision-making process. For example, the decision-making unit can improve the importance of schedules by referring to relevant research papers. It can also improve the importance of schedules by referring to industry trend data. Furthermore, the decision-making unit can improve the importance of schedules by referring to internal company data. In this way, the decision-making unit can improve the importance of schedules by referring to relevant literature and data. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input relevant literature and data into AI, and the AI ​​can analyze the data to improve the importance of schedules.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can also provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can provide concise suggestions. In this way, the suggestion unit can improve user convenience by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze the emotion data and adjust the way it presents suggestions.

[0085] The proposal department can adjust the level of detail in a proposal based on the importance of the meeting and the positions of the participants. For example, the proposal department will provide a detailed proposal for an important meeting. It can also provide a detailed proposal if the participants hold high positions. Furthermore, the proposal department can adjust the level of detail in a proposal according to the importance of the meeting. This allows the proposal department to provide appropriate proposals by adjusting the level of detail according to the importance of the meeting and the positions of the participants. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the importance of the meeting and the positions of the participants into the AI, which can then analyze the data and adjust the level of detail in the proposal.

[0086] The proposal department can extract the optimal candidate date by referring to the history of past meetings when making a proposal. For example, the proposal department can extract the optimal candidate date based on the history of meetings that were considered important in the past. The proposal department can also extract the optimal candidate date by considering the positions of the participants in past meetings. Furthermore, the proposal department can extract the optimal candidate date based on the outcomes of past meetings. In this way, the proposal department can extract the optimal candidate date by referring to the history of past meetings. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input past meeting history data into AI, and the AI ​​can analyze the data to extract the optimal candidate date.

[0087] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit may postpone detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit may prioritize the most important suggestions first. In this way, the suggestion unit can improve user convenience by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze the emotion data to determine the priority of suggestions.

[0088] The proposal department can adjust the order of proposals based on the submission timing of the schedules. For example, the proposal department can prioritize proposals with approaching deadlines. It can also postpone proposals with later deadlines. Furthermore, the proposal department can adjust the order of proposals according to the submission timing. This allows the proposal department to make appropriate proposals by adjusting the order of proposals based on the submission timing of the schedules. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input schedule submission timing data into an AI, which can then analyze the data and adjust the order of proposals.

[0089] The proposal department can suggest optimal candidate dates by referring to relevant market data and trends when making a proposal. For example, the proposal department can suggest optimal candidate dates based on market trend data. It can also suggest optimal candidate dates by referring to industry trends. Furthermore, the proposal department can suggest optimal candidate dates based on the company's internal data. In this way, the proposal department can suggest optimal candidate dates by referring to relevant market data and trends. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input relevant market data and trend data into AI, and the AI ​​can analyze the data and suggest optimal candidate dates.

[0090] The reception desk can estimate the user's emotions and adjust the scheduling process based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick scheduling. In this way, the reception desk can improve user convenience by adjusting the scheduling process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze the emotion data and adjust the scheduling process.

[0091] The reception desk can suggest the optimal reception method by referring to the user's past schedule history during the reception process. For example, the reception desk can automatically display as suggestions schedules that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest schedules to be used during specific time periods based on the user's past schedule history. In this way, the reception desk can suggest the optimal reception method by referring to the user's past schedule history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past schedule history data into an AI, which can then analyze the data and suggest the optimal reception method.

[0092] The reception desk can estimate the user's emotions and determine the priority of appointments based on those emotions. For example, if the user is stressed, the reception desk will prioritize important appointments. Conversely, if the user is relaxed, the reception desk may postpone detailed appointments. Furthermore, if the user is in a hurry, the reception desk may prioritize the most important appointments. In this way, the reception desk can improve user convenience by determining the priority of appointments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze the emotion data to determine the priority of appointments.

[0093] The reception desk can prioritize accepting schedules that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific location, the reception desk will prioritize accepting schedules related to that location. Furthermore, if the user is on the move, the reception desk can prioritize accepting schedules related to their destination. Additionally, if the user is in a specific region, the reception desk can prioritize accepting schedules related to that region. In this way, the reception desk can prioritize accepting schedules that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then analyze the data and prioritize accepting schedules that are highly relevant.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The automated meeting scheduling system can also include a notification function. This notification function can automatically send notifications to each employee, for example, when potential meeting dates are decided. Notifications can be sent via email, chat apps, calendar apps, etc. Furthermore, the notification function can set meeting reminders and send notifications the day before the meeting. In addition, the notification function can quickly notify employees if meetings are changed or canceled. This allows employees to easily understand their meeting schedules and respond flexibly to schedule changes.

[0096] The automated meeting scheduling system can also include an analytics unit. This unit can, for example, analyze past meeting data to evaluate meeting frequency, participant attendance rates, and meeting outcomes. Based on this data, the analytics unit can make suggestions to improve meeting efficiency. It can also analyze differences in attendance rates based on meeting time and day of the week to suggest optimal meeting times. Furthermore, the analytics unit can provide data for selecting appropriate participants based on meeting content and agenda. In this way, the analytics unit can support more efficient meetings and effective scheduling.

[0097] The automated meeting scheduling system can also include a feedback unit. This unit can, for example, collect feedback from participants after a meeting and use it to improve the system. The feedback unit can collect feedback in the form of a questionnaire and evaluate participant satisfaction and the effectiveness of the meeting. Furthermore, the feedback unit can input the collected feedback into an AI, which then analyzes it to improve the system's accuracy. In addition, the feedback unit can make improvement suggestions that reflect participants' opinions and requests. This allows the feedback unit to continuously improve the system and increase user satisfaction.

[0098] The automated meeting scheduling system can also include a predictive function. This predictive function can forecast future meeting schedules based, for example, on past meeting data and employee scheduling patterns. It can use AI to analyze employee scheduling patterns and suggest optimal meeting dates in advance. Furthermore, it can predict meeting timings by considering factors such as seasons and peak work periods. It can also predict the importance of future meetings based on employee attendance rates and meeting outcomes. This allows the predictive function to support efficient scheduling and effective meeting management.

[0099] The automated meeting scheduling system can also include a customization section. This customization section allows for the customization of scheduling settings according to each employee's individual needs and preferences. For example, it can set preferred meeting times, days of the week, and meeting frequency. It can also prioritize scheduling based on each employee's position and job responsibilities. Furthermore, the customization section can adjust the scheduling algorithm based on employee feedback. This enables flexible scheduling tailored to each employee's needs.

[0100] The automated meeting scheduling system can further adjust the importance of meetings based on the user's emotions using an emotion estimation function. For example, if the user is feeling stressed, the system can prioritize scheduling important meetings. Conversely, if the user is relaxed, it can postpone scheduling detailed meetings. Furthermore, if the user is in a hurry, it can schedule the most important meetings first. This allows the system to improve user convenience by adjusting the importance of meetings according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The automated meeting scheduling system can further utilize emotion estimation to adjust meeting notification methods based on the user's emotions. For example, the notification unit can provide simple, highly visible notifications when the user is stressed. It can also provide more detailed notifications when the user is relaxed. Furthermore, it can provide concise notifications when the user is in a hurry. This allows the notification unit to improve user convenience by adjusting notification methods according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The automated meeting scheduling system can further use emotion estimation to adjust meeting reminders based on the user's emotions. For example, the notification system can increase the frequency of reminders if the user is stressed, and decrease the frequency if the user is relaxed. Furthermore, if the user is in a hurry, the notification system can prioritize sending reminders for important meetings. In this way, the notification system can improve user convenience by adjusting reminders according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The automated meeting scheduling system can further utilize emotion estimation to adjust meeting feedback based on the user's emotions. For example, if the user is feeling stressed, the feedback system can provide a simple feedback form. If the user is relaxed, it can provide a more detailed feedback form. Furthermore, if the user is in a hurry, it can provide a concise feedback form. This allows the feedback system to improve user convenience by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The automated meeting scheduling system can further utilize emotion estimation to adjust meeting participants based on the user's emotions. For example, if the user is feeling stressed, the system can prioritize inviting important participants. Conversely, if the user is relaxed, it can postpone inviting participants with detailed agenda items. Furthermore, if the user is in a hurry, it can invite the most important participants first. This allows the system to improve user convenience by adjusting meeting participants according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The registration department registers all employees' schedules in the calendar. For example, it inputs detailed information such as meetings, business trips, and vacations, and feeds this information into the AI ​​to understand each employee's schedule. Step 2: The judgment unit analyzes the schedules registered by the registration unit and automatically determines each employee's available time and the importance of the schedule. For example, the AI ​​analyzes the importance of the meeting, the job titles of the participants, and the history of past meetings. Step 3: The proposal unit extracts and proposes the most suitable candidate dates based on the information determined by the evaluation unit. For example, the AI ​​considers each employee's available time and proposes the optimal meeting schedule.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the registration unit, determination unit, proposal unit, and reception unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and registers the schedules of all employees in the calendar. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered schedules to automatically determine each employee's available time and the importance of the schedule. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts and proposes the most suitable candidate dates. The reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0116] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] 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.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing 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 specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device 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 that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 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 such as 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), 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 part described above 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. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 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.

[0126] Each of the multiple elements described above, including the registration unit, determination unit, proposal unit, and reception unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and registers the schedules of all employees in the calendar. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the registered schedules to automatically determine each employee's available time and the importance of the schedule. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts and proposes the most suitable candidate dates. The reception unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an interface for the user to input schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 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.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device 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 that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 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 such as 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), 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 part described above 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. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed 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.

[0142] Each of the multiple elements described above, including the registration unit, determination unit, proposal unit, and reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and registers the schedules of all employees in the calendar. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered schedules to automatically determine each employee's available time and the importance of the schedule. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts and proposes the most suitable candidate dates. The reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] 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.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device 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 that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 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 such as 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 (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), 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 part described above 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. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed 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 performed 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 an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the registration unit, determination unit, proposal unit, and reception unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and registers the schedules of all employees in the calendar. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the registered schedules to automatically determine each employee's available time and the importance of the schedule. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts and proposes the most suitable candidate dates. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and provides an interface for the user to input schedules. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] 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.

[0161] Figure 9 shows the 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.

[0162] 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.

[0163] 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.

[0164] 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, and motorcycles, 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 based, for example, 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.

[0165] 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."

[0166] 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.

[0167] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] 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 other things 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.

[0177] 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.

[0178] (Note 1) A registration section for registering the schedules of all employees in a calendar, A determination unit analyzes the schedules registered by the aforementioned registration unit and automatically determines each employee's available time and the importance of their schedule. The system includes a proposal unit that extracts and proposes the most suitable candidate date based on the information determined by the determination unit. A system characterized by the following features. (Note 2) It features a reception area where users can enter their schedules. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Using AI, the system analyzes each employee's schedule and extracts optimal candidate dates, taking into account factors such as the importance of the meeting, the participants' job titles, and past meeting history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Using AI, we propose the optimal meeting schedule by considering each employee's available time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, The user is presented with a list of possible dates to choose from. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Users provide feedback, and the AI ​​uses that feedback to learn. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Security measures are in place to encrypt schedule data and restrict access. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is It estimates the user's emotions and adjusts the schedule registration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, we refer to the user's past schedule history to suggest the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is During registration, the level of detail in the schedule is adjusted based on the user's job title and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is The system estimates the user's emotions and determines the order in which to register schedules based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is During registration, the system prioritizes registering schedules that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is Upon registration, the system analyzes the user's social media activity and automatically suggests relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, It estimates the user's emotions and determines the importance of the schedule based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a decision, the importance of the schedule is adjusted by referring to the history of past meetings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, During the assessment process, the importance of each employee's schedule is customized based on their job title and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The system estimates the user's emotions and adjusts the order in which schedule decisions are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, When making a decision, the importance level is determined by considering the geographical distribution of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a decision, refer to relevant literature and data to improve the importance of the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meeting and the roles of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, refer to the history of past meetings to extract the most suitable candidate dates. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on the submission schedule. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we will refer to relevant market data and trends to suggest the most suitable candidate dates. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is The system estimates the user's emotions and adjusts the scheduling process based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned reception unit is During registration, the system will refer to the user's past schedule history to suggest the most suitable registration method. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned reception unit is When accepting a request, the system prioritizes scheduling based on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0179] 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. A registration section for registering the schedules of all employees in a calendar, A determination unit analyzes the schedules registered by the aforementioned registration unit and automatically determines each employee's available time and the importance of their schedule. The system includes a proposal unit that extracts and proposes the most suitable candidate date based on the information determined by the determination unit. A system characterized by the following features.

2. It features a reception area where users can enter their schedules. The system according to feature 1.

3. The aforementioned proposal section is, Using AI, the system analyzes each employee's schedule and extracts the optimal candidate date, taking into account factors such as the importance of the meeting, the participants' positions, and past meeting history. The system according to feature 1.

4. The aforementioned proposal section is, Using AI, we propose the optimal meeting schedule by considering each employee's available time. The system according to feature 1.

5. The aforementioned proposal section is, The user is presented with a list of possible dates to choose from. The system according to feature 1.

6. The aforementioned proposal section is, Users provide feedback, and the AI ​​uses that feedback to learn. The system according to feature 1.

7. The aforementioned proposal section is, Security measures are in place to encrypt schedule data and restrict access. The system according to feature 1.

8. The aforementioned registration unit is It estimates the user's emotions and adjusts the schedule registration method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned registration unit is During registration, we refer to the user's past schedule history to suggest the most suitable registration method. The system according to feature 1.

10. The aforementioned registration unit is During registration, the level of detail in the schedule is adjusted based on the user's job title and responsibilities. The system according to feature 1.

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