System

Abstract

An object of a system according to an embodiment is to automatically set an optimal conference date and time based on information on a plurality of participants and matter information.SOLUTION: A system includes a participant information input part, a matter information input part, and a schedule adjustment part. The participant information input unit inputs participant information. The matter information input part inputs matter information. The schedule adjustment unit automatically sets an optimum conference date and time based on the information input by the participant information input unit and the matter information input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is time-consuming to coordinate the schedules of multiple participants to set the optimal meeting date and time, and it is difficult to do this efficiently.

[0005] The system according to the embodiment aims to automatically set the optimal meeting date and time based on information on multiple participants and project information. [Means for solving the problem]

[0006] The system according to the embodiment includes a participant information input unit, a case information input unit, and a schedule adjustment unit. The participant information input unit inputs participant information. The case information input unit inputs case information. The schedule adjustment unit automatically sets an optimal meeting date and time based on the information input by the participant information input unit and the case information input unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically set the optimal meeting date and time based on the information of multiple participants and the matter information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The meeting schedule adjustment system according to an embodiment of the present invention is a system in which a generation AI automatically sets the optimal meeting date and time by inputting participant information and project information. This eliminates the need for secretaries to manually adjust schedules, and enables meetings to be set efficiently.

[0029] A conference schedule adjustment system according to an embodiment includes a participant information input unit, a matter information input unit, and a schedule adjustment unit. The participant information input unit inputs participant information. For example, the participant's name, title, contact information, and schedule can be input. The participant information input unit can also set the importance of the participant. The matter information input unit inputs matter information. For example, the purpose of the conference, the agenda, and related materials can be input. The matter information input unit can also set the importance of the matter. The schedule adjustment unit automatically sets the optimal conference date and time based on the information input by the participant information input unit and the matter information input unit. For example, the schedule adjustment unit selects the optimal conference date and time by taking into account the available time in everyone's schedule. The schedule adjustment unit can also prioritize the available time of participants with high importance. This allows the conference schedule adjustment system to automate conference schedule adjustment and efficiently schedule conferences.

[0030] The participant information input unit can analyze the participant's past meeting attendance history and automatically evaluate the importance based on the attendance rate and speaking frequency. The participant information input unit, for example, retrieves the participant's past meeting attendance history from a database and calculates the attendance rate. For example, the importance is evaluated based on the meeting attendance rate over the past year. The participant information input unit also analyzes the frequency of speaking during the meeting and evaluates participants who speak frequently as having a high importance. For example, it analyzes the meeting minutes and audio data and counts the number of times someone speaks. The participant information input unit also combines the attendance history and speaking frequency to comprehensively evaluate the importance of the participant. For example, participants with a high attendance rate and frequent speaking frequency are considered the most important. In this way, by automatically evaluating the importance of the participants, the optimal meeting date and time can be set.

[0031] The participant information input unit can automatically retrieve participants' fields of expertise and skill sets from a database and recommend participants who are best suited to the content of the meeting. The participant information input unit, for example, automatically retrieves participants' fields of expertise and skill sets from a database and recommends participants who are best suited to the content of the meeting. For example, it would recommend technical experts for technical meetings. The participant information input unit also analyzes participants' resumes and project histories to extract their fields of expertise and skill sets. For example, it evaluates participants based on their roles and achievements in past projects. The participant information input unit also preferentially recommends participants who have fields of expertise and skill sets related to the meeting agenda. For example, it would recommend marketing experts for a planning meeting for a new product. This improves the quality of meetings by recommending participants who are best suited to the content of the meeting.

[0032] The participant information input unit can automatically set the optimal conference location and time zone for the online conference based on the geographical location information of the participants. The participant information input unit, for example, acquires the geographical location information of the participants and automatically sets the optimal conference location. For example, it selects a central conference room that is easily accessible to all. In the case of an online conference, the participant information input unit also takes into account the time zones of the participants and sets a time period that is easy for all to participate. For example, it selects the optimal time when there are participants in different time zones. The participant information input unit also selects the optimal conference location based on the geographical location information, taking into account transportation means and travel time. For example, it selects a location with good access by public transportation. In this way, the optimal conference location and time zone can be set by taking into account the geographical location information of the participants.

[0033] The participant information input unit can analyze participants' social media activity and reflect their interests and concerns related to the conference theme. The participant information input unit, for example, analyzes participants' social media activity and extracts their interests and concerns related to the conference theme. For example, it analyzes posts on Twitter or LinkedIn. The participant information input unit also identifies participants' areas of interest based on social media data and reflects these in the conference agenda. For example, it prioritizes inviting participants who are highly interested in a particular topic. The participant information input unit also monitors participants' social media activity in real time and reflects their latest concerns in the conference theme. For example, it adjusts the agenda based on recent posts. This allows the conference theme to be optimized by reflecting participants' interests and concerns.

[0034] The project information input unit can analyze data from similar past meetings, extract elements of successful meetings, and propose the optimal date and time. The project information input unit, for example, analyzes data from similar past meetings and extracts elements of successful meetings. For example, it evaluates based on attendance rate and progress of agenda items. The project information input unit also proposes the optimal date and time for meetings based on the elements of successful meetings. For example, it refers to the date and time patterns of successful meetings in the past. The project information input unit also continuously collects data from similar meetings and updates the elements of success. For example, it updates the database every time a new success case is added. In this way, the success rate of meetings is improved by proposing the optimal date and time based on the elements of successful meetings in the past.

[0035] The case information input unit can implement an algorithm that automatically adjusts the length and frequency of meetings according to the importance of the case. The case information input unit implements, for example, an algorithm that automatically adjusts the length of meetings based on the importance of the case. For example, longer meeting times are set for cases with high importance. The case information input unit also automatically adjusts the frequency of meetings according to the importance of the case. For example, more frequent meetings are set for cases with high importance. The case information input unit also evaluates the importance of the case in real time and dynamically adjusts the length and frequency of meetings. For example, the meeting settings are changed according to the progress of the case. In this way, the efficiency of meetings is improved by adjusting the length and frequency of meetings according to the importance of the case.

[0036] The project information input unit automatically generates related materials and presentations based on the project information, thereby streamlining meeting preparations. The project information input unit, for example, builds a system that automatically generates related materials and presentations based on project information. For example, it automatically generates data and graphs related to the agenda. The project information input unit also streamlines meeting preparations based on the automatically generated materials and presentations. For example, it automatically provides all the necessary materials together. The project information input unit also analyzes project information in real time and automatically generates the latest materials and presentations. For example, it reflects the latest data just before the meeting. This allows the automatic generation of related materials and presentations to streamline meeting preparations.

[0037] The case information input unit can automatically select a conference format depending on the importance of the case. The case information input unit, for example, builds a system that automatically selects the optimal conference format based on the importance of the case. For example, an offline conference is set for a case with high importance. The case information input unit also selects either online, offline, or hybrid depending on the content of the case and the status of the participants. For example, an online conference is set when there are many participants in remote locations. The case information input unit also dynamically adjusts the conference format based on the progress of the case and participant feedback. For example, the format is changed to reflect the opinions of the participants. In this way, the efficiency of the meeting is improved by selecting the optimal conference format depending on the importance of the case.

[0038] The schedule adjustment unit can learn from past meeting data and automatically generate optimal schedule adjustment patterns. For example, the schedule adjustment unit constructs a system in which a generation AI learns from past meeting data and automatically generates optimal schedule adjustment patterns. For example, it evaluates based on attendance rate and progress of agenda items. The schedule adjustment unit also proposes optimal schedule adjustment patterns based on past meeting data. For example, it refers to the date and time patterns of successful meetings in the past. The schedule adjustment unit also updates the optimal schedule adjustment patterns by having the generation AI continuously learn from past data. For example, it updates the database every time a new success story is added. In this way, optimal schedule adjustment patterns can be generated by learning from past meeting data.

[0039] The schedule adjustment unit updates the individual schedules of participants in real time, making it possible to select the optimal date and time based on the latest information. For example, the schedule adjustment unit builds a system in which the generation AI updates the individual schedules of participants in real time and selects the optimal date and time based on the latest information. For example, it works in conjunction with a calendar app. The schedule adjustment unit also reflects changes in the participants' schedules in real time and dynamically adjusts the optimal meeting date and time. For example, it immediately readjusts the schedule if a change occurs. The schedule adjustment unit also has the generation AI continuously monitor the participants' schedules and select the optimal date and time. For example, it suggests the optimal date and time based on available time in the schedule. In this way, by updating the participants' schedules in real time, the optimal date and time can always be selected based on the latest information.

[0040] The schedule adjustment unit can integrate different meeting schedules and recommend the optimal schedule to avoid overlaps. For example, the schedule adjustment unit builds a system in which the generation AI integrates different meeting schedules and proposes the optimal schedule to avoid overlaps. For example, it centrally manages multiple meeting schedules. The schedule adjustment unit also analyzes different meeting schedules in real time and selects the optimal date and time to avoid overlaps. For example, it automatically reschedules overlapping meetings. The schedule adjustment unit also continuously monitors different meeting schedules with the generation AI and proposes the optimal schedule to avoid overlaps. For example, it immediately reschedules schedules if changes occur. This makes it possible to propose the optimal schedule to avoid overlaps by integrating different meeting schedules.

[0041] The schedule adjustment unit can select the optimal date and time by taking into account the health data of participants. For example, the schedule adjustment unit builds a system in which the generation AI takes into account the health data of participants (e.g., sleep patterns and activity levels) to select the optimal date and time. For example, it works in conjunction with a wearable device. The schedule adjustment unit also identifies the time period in which participants can perform best based on the health data and schedules meetings for that time period. For example, it selects the optimal date and time based on sleep data. The schedule adjustment unit also continuously monitors the health data of participants using the generation AI and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time if their health condition changes. This makes it possible to select the optimal date and time by taking into account the health data of participants.

[0042] The schedule adjustment unit can analyze the participants' past attendance rates for the proposed date and time, and prioritize selecting the date and time with the highest attendance rate. For example, the schedule adjustment unit retrieves the participants' past attendance rates for the date and time proposed by the generation AI from a database, and selects the date and time with the highest attendance rate. For example, it evaluates based on attendance rates over the past year. The schedule adjustment unit also proposes the optimal meeting date and time based on attendance rate data. For example, it prioritizes selecting dates and times with high attendance rates. The schedule adjustment unit also has the generation AI continuously monitor attendance rate data, and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if attendance rates drop. In this way, the date and time with the highest attendance rate can be selected by analyzing the participants' past attendance rates.

[0043] The schedule adjustment unit can collect participant feedback on the proposed date and time in real time and readjust the optimal date and time. The schedule adjustment unit, for example, builds a system that collects participant feedback on the date and time proposed by the generation AI in real time and readjusts the optimal date and time. For example, it uses online surveys and comment functions. The schedule adjustment unit also analyzes participant feedback in real time and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time based on the feedback. The schedule adjustment unit also has the generation AI continuously monitor participant feedback and propose the optimal meeting date and time. For example, it prioritizes selecting dates and times that receive the most feedback. In this way, the optimal date and time can be readjusted by collecting participant feedback in real time.

[0044] The schedule adjustment unit automatically updates the participants' schedules for the proposed date and time, thereby avoiding overlaps with other appointments. The schedule adjustment unit, for example, builds a system that automatically updates the participants' schedules for the proposed date and time, thereby avoiding overlaps with other appointments. For example, it works in conjunction with a calendar app. The schedule adjustment unit also reflects changes in the participants' schedules in real time and selects the optimal date and time to avoid overlaps. For example, it immediately readjusts the schedule if a change occurs. The schedule adjustment unit also continuously monitors the participants' schedules for the proposed date and time, and proposes the optimal date and time to avoid overlaps. For example, it immediately readjusts the schedule if a change occurs. In this way, by automatically updating the participants' schedules, it is possible to avoid overlaps with other appointments.

[0045] The schedule adjustment unit can select the optimal date and time based on the participants' health data for the proposed date and time. The schedule adjustment unit, for example, builds a system that considers the participants' health data (e.g., sleep patterns and activity levels) for the proposed date and time to select the optimal date and time. For example, it works in conjunction with a wearable device. The schedule adjustment unit also identifies a time period in which participants can perform best based on the health data and schedules the meeting for that time period. For example, it selects the optimal date and time based on sleep data. The schedule adjustment unit also continuously monitors the participants' health data for the proposed date and time and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time if their health condition changes. In this way, the optimal date and time can be selected by considering the participants' health data.

[0046] The reminder notification unit can analyze participants' past reminder response data and automatically set the optimal notification timing. For example, the generation AI retrieves participants' past reminder response data from a database and automatically sets the optimal notification timing. For example, it evaluates based on past reminder response times. The reminder notification unit also suggests the optimal notification timing based on the reminder response data. For example, it prioritizes selecting time periods with good responses. The reminder notification unit also continuously monitors reminder response data and dynamically adjusts the optimal notification timing. For example, it readjusts the notification timing if the response is poor. In this way, the optimal notification timing can be automatically set by analyzing participants' past reminder response data.

[0047] The reminder notification unit can implement an algorithm that customizes the frequency and content of notifications according to the importance of the meeting. For example, the generation AI implements an algorithm that customizes the frequency and content of notifications based on the importance of the meeting. For example, notifications are sent more frequently for meetings with high importance. The reminder notification unit also customizes the frequency and content of notifications according to the content of the meeting and the status of the participants. For example, detailed notifications are sent if there is an important agenda item. The generation AI also evaluates the importance of the meeting in real time and dynamically adjusts the frequency and content of notifications. For example, the notification content is readjusted if the importance changes. This makes it possible to customize the frequency and content of notifications according to the importance of the meeting, allowing appropriate notifications to be sent to participants.

[0048] The reminder notification unit can send notifications in the optimal format for the participant's device. For example, the generation AI acquires the participant's device information and builds a system to send notifications in the optimal format. For example, it sends push notifications to smartphones and email notifications to PCs. The reminder notification unit also customizes the content and format of notifications depending on the type of device. For example, it sends a short message to a smartwatch and a detailed notification to a PC. The generation AI also monitors the participant's device usage in real time and sends notifications in the optimal format. For example, it adjusts the notification method depending on how often the device is used. This improves the notification reception rate by sending notifications in the optimal format for the participant's device.

[0049] The reminder notification unit can automatically attach relevant materials and links to reminder notifications according to the content of the meeting. For example, the reminder notification unit constructs a system in which a generation AI analyzes the content of the meeting and automatically attaches relevant materials and links to reminder notifications. For example, materials related to the agenda are automatically attached. The reminder notification unit also customizes the materials and links necessary for reminder notifications according to the content of the meeting. For example, presentation materials and reference links are attached. The reminder notification unit also analyzes the content of the meeting in real time using a generation AI and attaches the latest materials and links to reminder notifications. For example, the latest data is reflected just before the meeting. This allows participants to more efficiently prepare by automatically attaching relevant materials and links according to the content of the meeting.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The participant information input unit can suggest optimal meeting dates and times based on participants' health data. For example, it can analyze participants' sleep patterns and activity levels to identify the time periods when they perform best. The participant information input unit can also monitor participants' health conditions in real time based on data obtained from wearable devices and dynamically adjust the optimal meeting date and time. For example, if a participant's health condition worsens, the meeting date and time can be readjusted. This allows the optimal meeting date and time to be set by taking the participants' health conditions into consideration.

[0052] The participant information input unit can analyze the content of participants' comments in past meetings and automatically evaluate their areas of expertise and skill sets. For example, it can analyze meeting minutes and audio data to identify participants who frequently comment on specific areas of expertise. The participant information input unit can also evaluate participants' skill sets based on the content of their comments and recommend participants best suited to the content of the meeting. For example, it can recommend technical experts for technical topics. This improves the quality of meetings by taking into account participants' areas of expertise and skill sets.

[0053] The participant information input unit can reflect participants' interests and concerns related to the conference theme based on their social media activity. For example, it can analyze participants' Twitter and LinkedIn posts to extract their interests and concerns related to the conference theme. The participant information input unit can also identify participants' areas of interest based on social media data and reflect these in the conference agenda. For example, it can prioritize inviting participants who are highly interested in a particular topic. This allows the conference theme to be optimized by reflecting participants' interests and concerns.

[0054] The participant information input unit can automatically set the optimal meeting location and time zone for an online meeting based on the participants' geographical location information. For example, it can obtain the participants' geographical location information and select a central meeting room that is easily accessible to all. In the case of an online meeting, it can also take into account the participants' time zones and set a time period that is easy for everyone to participate in. For example, it can select the optimal time when there are participants in different time zones. This makes it possible to set the optimal meeting location and time zone by taking into account the participants' geographical location information.

[0055] The participant information input unit can optimize the progress of a meeting based on the content of comments made by participants in past meetings. For example, it can analyze the content of comments made in past meetings and evaluate the progress of the agenda. The participant information input unit can also set an agenda that reflects the opinions of participants based on the content of comments. For example, it can prioritize the opinions of participants who make frequent comments on a particular topic. This allows the progress of a meeting to be optimized by reflecting the opinions of participants.

[0056] The participant information input unit can suggest the optimal meeting date and time based on the participant's past meeting attendance history. For example, it can analyze past meeting attendance rates and select a date and time with a high attendance rate. The participant information input unit can also optimize the participant's schedule based on the attendance history. For example, it can prioritize selecting time periods with a high attendance rate. This makes it possible to set the optimal meeting date and time by taking into account the participant's past meeting attendance history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The participant information input unit inputs participant information. For example, the participant's name, title, contact information, and schedule can be input. The participant information input unit can also set the importance of the participant. Step 2: The matter information input unit inputs matter information. For example, the purpose of the meeting, the agenda, and related materials can be input. The matter information input unit can also set the importance of the matter. Step 3: The schedule adjustment unit automatically sets the optimal meeting date and time based on the information input by the participant information input unit and the matter information input unit. For example, the schedule adjustment unit selects the optimal meeting date and time by taking into consideration the available time in everyone's schedule. The schedule adjustment unit can also prioritize the available time of participants with high importance.

[0059] (Example 2) The meeting schedule adjustment system according to an embodiment of the present invention is a system in which a generation AI automatically sets the optimal meeting date and time by inputting participant information and project information. This eliminates the need for secretaries to manually adjust schedules, and enables meetings to be set efficiently.

[0060] A conference schedule adjustment system according to an embodiment includes a participant information input unit, a matter information input unit, and a schedule adjustment unit. The participant information input unit inputs participant information. For example, the participant's name, title, contact information, and schedule can be input. The participant information input unit can also set the importance of the participant. The matter information input unit inputs matter information. For example, the purpose of the conference, the agenda, and related materials can be input. The matter information input unit can also set the importance of the matter. The schedule adjustment unit automatically sets the optimal conference date and time based on the information input by the participant information input unit and the matter information input unit. For example, the schedule adjustment unit selects the optimal conference date and time by taking into account the available time in everyone's schedule. The schedule adjustment unit can also prioritize the available time of participants with high importance. This allows the conference schedule adjustment system to automate conference schedule adjustment and efficiently schedule conferences.

[0061] The participant information input unit can analyze the participant's past meeting attendance history and automatically evaluate the importance based on the attendance rate and speaking frequency. The participant information input unit, for example, retrieves the participant's past meeting attendance history from a database and calculates the attendance rate. For example, the importance is evaluated based on the meeting attendance rate over the past year. The participant information input unit also analyzes the frequency of speaking during the meeting and evaluates participants who speak frequently as having a high importance. For example, it analyzes the meeting minutes and audio data and counts the number of times someone speaks. The participant information input unit also combines the attendance history and speaking frequency to comprehensively evaluate the importance of the participant. For example, participants with a high attendance rate and frequent speaking frequency are considered the most important. In this way, by automatically evaluating the importance of the participants, the optimal meeting date and time can be set.

[0062] The participant information input unit can automatically retrieve participants' fields of expertise and skill sets from a database and recommend participants who are best suited to the content of the meeting. The participant information input unit, for example, automatically retrieves participants' fields of expertise and skill sets from a database and recommends participants who are best suited to the content of the meeting. For example, it would recommend technical experts for technical meetings. The participant information input unit also analyzes participants' resumes and project histories to extract their fields of expertise and skill sets. For example, it evaluates participants based on their roles and achievements in past projects. The participant information input unit also preferentially recommends participants who have fields of expertise and skill sets related to the meeting agenda. For example, it would recommend marketing experts for a planning meeting for a new product. This improves the quality of meetings by recommending participants who are best suited to the content of the meeting.

[0063] The participant information input unit can use the emotion estimation function to analyze the current emotional state of the participants and prioritize the selection of a date and time that will be less stressful. The participant information input unit, for example, analyzes the current emotional state of the participants in real time and selects a date and time that will be less stressful. For example, it analyzes facial expressions and voice tones to calculate an emotion score. The participant information input unit also identifies a time period when the participants are relaxed based on the emotion estimation data and schedules the meeting for that time period. For example, it prioritizes a time period with a high emotion score. The participant information input unit also uses the emotion estimation function to continuously monitor the emotional state of the participants and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if the emotion score drops. In this way, the meeting can be scheduled for a date and time that will be less stressful by taking the emotional state of the participants into consideration.

[0064] The participant information input unit can automatically set the optimal conference location and time zone for the online conference based on the geographical location information of the participants. The participant information input unit, for example, acquires the geographical location information of the participants and automatically sets the optimal conference location. For example, it selects a central conference room that is easily accessible to all. In the case of an online conference, the participant information input unit also takes into account the time zones of the participants and sets a time period that is easy for all to participate. For example, it selects the optimal time when there are participants in different time zones. The participant information input unit also selects the optimal conference location based on the geographical location information, taking into account transportation means and travel time. For example, it selects a location with good access by public transportation. In this way, the optimal conference location and time zone can be set by taking into account the geographical location information of the participants.

[0065] The participant information input unit can analyze participants' social media activity and reflect their interests and concerns related to the conference theme. The participant information input unit, for example, analyzes participants' social media activity and extracts their interests and concerns related to the conference theme. For example, it analyzes posts on Twitter or LinkedIn. The participant information input unit also identifies participants' areas of interest based on social media data and reflects these in the conference agenda. For example, it prioritizes inviting participants who are highly interested in a particular topic. The participant information input unit also monitors participants' social media activity in real time and reflects their latest concerns in the conference theme. For example, it adjusts the agenda based on recent posts. This allows the conference theme to be optimized by reflecting participants' interests and concerns.

[0066] The participant information input unit can use the emotion estimation function to preferentially identify the time period when participants are most relaxed and schedule a meeting for that time period. The participant information input unit, for example, uses the emotion estimation function to identify the time period when participants are most relaxed. For example, it calculates the degree of relaxation by analyzing facial expressions and voice tones. The participant information input unit also sets the date and time of the meeting based on the time period when participants are relaxed. For example, it preferentially selects time periods with high emotion scores. The participant information input unit also continuously monitors the emotion estimation data and schedules the meeting for a time period when participants are relaxed. For example, it readjusts the date and time if the emotion score drops. In this way, scheduling the meeting for a time period when participants are relaxed improves the quality of the meeting.

[0067] The project information input unit can analyze data from similar past meetings, extract elements of successful meetings, and propose the optimal date and time. The project information input unit, for example, analyzes data from similar past meetings and extracts elements of successful meetings. For example, it evaluates based on attendance rate and progress of agenda items. The project information input unit also proposes the optimal date and time for meetings based on the elements of successful meetings. For example, it refers to the date and time patterns of successful meetings in the past. The project information input unit also continuously collects data from similar meetings and updates the elements of success. For example, it updates the database every time a new success case is added. In this way, the success rate of meetings is improved by proposing the optimal date and time based on the elements of successful meetings in the past.

[0068] The case information input unit can implement an algorithm that automatically adjusts the length and frequency of meetings according to the importance of the case. The case information input unit implements, for example, an algorithm that automatically adjusts the length of meetings based on the importance of the case. For example, longer meeting times are set for cases with high importance. The case information input unit also automatically adjusts the frequency of meetings according to the importance of the case. For example, more frequent meetings are set for cases with high importance. The case information input unit also evaluates the importance of the case in real time and dynamically adjusts the length and frequency of meetings. For example, the meeting settings are changed according to the progress of the case. In this way, the efficiency of meetings is improved by adjusting the length and frequency of meetings according to the importance of the case.

[0069] The case information input unit can use the emotion estimation function to analyze the emotional reactions of participants to the case and prioritize selecting a date and time when a positive reaction can be obtained. The case information input unit, for example, uses the emotion estimation function to analyze the emotional reactions of participants to the case in real time. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The case information input unit also selects a date and time when a positive reaction can be obtained based on the emotional reaction data. For example, it prioritizes selecting a time period when the emotion score is high. The case information input unit also continuously monitors the emotion estimation data and dynamically adjusts the date and time when a positive reaction can be obtained. For example, it readjusts the date and time if the emotion score drops. In this way, by taking the emotional reactions of participants into consideration, it is possible to schedule a meeting for a date and time when a positive reaction can be obtained.

[0070] The project information input unit automatically generates related materials and presentations based on the project information, thereby streamlining meeting preparations. The project information input unit, for example, builds a system that automatically generates related materials and presentations based on project information. For example, it automatically generates data and graphs related to the agenda. The project information input unit also streamlines meeting preparations based on the automatically generated materials and presentations. For example, it automatically provides all the necessary materials together. The project information input unit also analyzes project information in real time and automatically generates the latest materials and presentations. For example, it reflects the latest data just before the meeting. This allows the automatic generation of related materials and presentations to streamline meeting preparations.

[0071] The case information input unit can automatically select a conference format depending on the importance of the case. The case information input unit, for example, builds a system that automatically selects the optimal conference format based on the importance of the case. For example, an offline conference is set for a case with high importance. The case information input unit also selects either online, offline, or hybrid depending on the content of the case and the status of the participants. For example, an online conference is set when there are many participants in remote locations. The case information input unit also dynamically adjusts the conference format based on the progress of the case and participant feedback. For example, the format is changed to reflect the opinions of the participants. In this way, the efficiency of the meeting is improved by selecting the optimal conference format depending on the importance of the case.

[0072] The case information input unit can use the emotion estimation function to monitor participants' emotions regarding a case in real time and adjust the progress of the meeting. The case information input unit, for example, uses the emotion estimation function to build a system that monitors participants' emotions regarding a case in real time. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The case information input unit also dynamically adjusts the progress of the meeting based on participants' emotional response data. For example, it changes the agenda if the emotion score drops. The case information input unit also collects emotion estimation data in real time and develops a system that optimizes the progress of the meeting. For example, it prioritizes agenda items that have strong positive emotions. In this way, the progress of the meeting can be optimized by monitoring participants' emotions in real time.

[0073] The schedule adjustment unit can learn from past meeting data and automatically generate optimal schedule adjustment patterns. For example, the schedule adjustment unit constructs a system in which a generation AI learns from past meeting data and automatically generates optimal schedule adjustment patterns. For example, it evaluates based on attendance rate and progress of agenda items. The schedule adjustment unit also proposes optimal schedule adjustment patterns based on past meeting data. For example, it refers to the date and time patterns of successful meetings in the past. The schedule adjustment unit also updates the optimal schedule adjustment patterns by having the generation AI continuously learn from past data. For example, it updates the database every time a new success story is added. In this way, optimal schedule adjustment patterns can be generated by learning from past meeting data.

[0074] The schedule adjustment unit updates the individual schedules of participants in real time, making it possible to select the optimal date and time based on the latest information. For example, the schedule adjustment unit builds a system in which the generation AI updates the individual schedules of participants in real time and selects the optimal date and time based on the latest information. For example, it works in conjunction with a calendar app. The schedule adjustment unit also reflects changes in the participants' schedules in real time and dynamically adjusts the optimal meeting date and time. For example, it immediately readjusts the schedule if a change occurs. The schedule adjustment unit also has the generation AI continuously monitor the participants' schedules and select the optimal date and time. For example, it suggests the optimal date and time based on available time in the schedule. In this way, by updating the participants' schedules in real time, the optimal date and time can always be selected based on the latest information.

[0075] The schedule adjustment unit can use the emotion estimation function to select a date and time that will cause less stress by taking into account the emotional states of the participants. The schedule adjustment unit, for example, uses the emotion estimation function to analyze the emotional states of the participants in real time and prioritize selecting a date and time that will cause less stress. For example, the schedule adjustment unit calculates an emotion score by analyzing facial expressions and voice tones. The schedule adjustment unit also identifies time periods when the participants are relaxed based on the emotion estimation data and schedules the meeting for those time periods. For example, it prioritizes time periods with high emotion scores. The schedule adjustment unit also uses the emotion estimation function to continuously monitor the emotional states of the participants and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if the emotion score drops. In this way, the schedule adjustment unit can schedule a meeting for a date and time that will cause less stress by taking into account the emotional states of the participants.

[0076] The schedule adjustment unit can integrate different meeting schedules and recommend the optimal schedule to avoid overlaps. For example, the schedule adjustment unit builds a system in which the generation AI integrates different meeting schedules and proposes the optimal schedule to avoid overlaps. For example, it centrally manages multiple meeting schedules. The schedule adjustment unit also analyzes different meeting schedules in real time and selects the optimal date and time to avoid overlaps. For example, it automatically reschedules overlapping meetings. The schedule adjustment unit also continuously monitors different meeting schedules with the generation AI and proposes the optimal schedule to avoid overlaps. For example, it immediately reschedules schedules if changes occur. This makes it possible to propose the optimal schedule to avoid overlaps by integrating different meeting schedules.

[0077] The schedule adjustment unit can select the optimal date and time by taking into account the health data of participants. For example, the schedule adjustment unit builds a system in which the generation AI takes into account the health data of participants (e.g., sleep patterns and activity levels) to select the optimal date and time. For example, it works in conjunction with a wearable device. The schedule adjustment unit also identifies the time period in which participants can perform best based on the health data and schedules meetings for that time period. For example, it selects the optimal date and time based on sleep data. The schedule adjustment unit also continuously monitors the health data of participants using the generation AI and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time if their health condition changes. This makes it possible to select the optimal date and time by taking into account the health data of participants.

[0078] The schedule adjustment unit can use the emotion estimation function to monitor the emotional states of participants in real time and continuously adjust the optimal date and time. The schedule adjustment unit, for example, uses the emotion estimation function to monitor the emotional states of participants in real time and build a system that continuously adjusts the optimal date and time. For example, it analyzes facial expressions and voice tones to calculate an emotion score. The schedule adjustment unit also identifies time periods when participants are relaxed based on the emotion estimation data and schedules meetings for those time periods. For example, it prioritizes time periods with high emotion scores. The schedule adjustment unit also uses the emotion estimation function to continuously monitor the emotional states of participants and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if the emotion score drops. In this way, the optimal date and time can be continuously adjusted by monitoring the emotional states of participants in real time.

[0079] The schedule adjustment unit can analyze the participants' past attendance rates for the proposed date and time, and prioritize selecting the date and time with the highest attendance rate. For example, the schedule adjustment unit retrieves the participants' past attendance rates for the date and time proposed by the generation AI from a database, and selects the date and time with the highest attendance rate. For example, it evaluates based on attendance rates over the past year. The schedule adjustment unit also proposes the optimal meeting date and time based on attendance rate data. For example, it prioritizes selecting dates and times with high attendance rates. The schedule adjustment unit also has the generation AI continuously monitor attendance rate data, and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if attendance rates drop. In this way, the date and time with the highest attendance rate can be selected by analyzing the participants' past attendance rates.

[0080] The schedule adjustment unit can collect participant feedback on the proposed date and time in real time and readjust the optimal date and time. The schedule adjustment unit, for example, builds a system that collects participant feedback on the date and time proposed by the generation AI in real time and readjusts the optimal date and time. For example, it uses online surveys and comment functions. The schedule adjustment unit also analyzes participant feedback in real time and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time based on the feedback. The schedule adjustment unit also has the generation AI continuously monitor participant feedback and propose the optimal meeting date and time. For example, it prioritizes selecting dates and times that receive the most feedback. In this way, the optimal date and time can be readjusted by collecting participant feedback in real time.

[0081] The schedule adjustment unit can use the emotion estimation function to analyze participants' emotional reactions to the proposed date and time, and prioritize selecting a date and time that will elicit a positive reaction. The schedule adjustment unit, for example, uses the emotion estimation function to analyze participants' emotional reactions to the proposed date and time in real time. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The schedule adjustment unit also selects a date and time that will elicit a positive reaction based on the emotional reaction data. For example, it prioritizes selecting a time period with a high emotion score. The schedule adjustment unit also continuously monitors the emotion estimation data and dynamically adjusts a date and time that will elicit a positive reaction. For example, it readjusts the date and time if the emotion score drops. In this way, it is possible to select a date and time that will elicit a positive reaction by analyzing participants' emotional reactions.

[0082] The schedule adjustment unit automatically updates the participants' schedules for the proposed date and time, thereby avoiding overlaps with other appointments. The schedule adjustment unit, for example, builds a system that automatically updates the participants' schedules for the proposed date and time, thereby avoiding overlaps with other appointments. For example, it works in conjunction with a calendar app. The schedule adjustment unit also reflects changes in the participants' schedules in real time and selects the optimal date and time to avoid overlaps. For example, it immediately readjusts the schedule if a change occurs. The schedule adjustment unit also continuously monitors the participants' schedules for the proposed date and time, and proposes the optimal date and time to avoid overlaps. For example, it immediately readjusts the schedule if a change occurs. In this way, by automatically updating the participants' schedules, it is possible to avoid overlaps with other appointments.

[0083] The schedule adjustment unit can select the optimal date and time based on the participants' health data for the proposed date and time. The schedule adjustment unit, for example, builds a system that considers the participants' health data (e.g., sleep patterns and activity levels) for the proposed date and time to select the optimal date and time. For example, it works in conjunction with a wearable device. The schedule adjustment unit also identifies a time period in which participants can perform best based on the health data and schedules the meeting for that time period. For example, it selects the optimal date and time based on sleep data. The schedule adjustment unit also continuously monitors the participants' health data for the proposed date and time and dynamically adjusts the optimal meeting date and time. For example, it readjusts the date and time if their health condition changes. In this way, the optimal date and time can be selected by considering the participants' health data.

[0084] The schedule adjustment unit can use the emotion estimation function to monitor participants' emotions regarding the proposed date and time in real time and continuously adjust the optimal date and time. The schedule adjustment unit, for example, uses the emotion estimation function to monitor participants' emotions regarding the proposed date and time in real time and build a system that continuously adjusts the optimal date and time. For example, the schedule adjustment unit calculates an emotion score by analyzing facial expressions and voice tone. The schedule adjustment unit also identifies time periods when participants are relaxed based on the emotion estimation data and schedules meetings for those time periods. For example, it prioritizes time periods with high emotion scores. The schedule adjustment unit also uses the emotion estimation function to continuously monitor participants' emotional states and dynamically adjust the optimal meeting date and time. For example, it readjusts the date and time if the emotion score drops. In this way, the optimal date and time can be continuously adjusted by monitoring participants' emotions in real time.

[0085] The reminder notification unit can analyze participants' past reminder response data and automatically set the optimal notification timing. For example, the generation AI retrieves participants' past reminder response data from a database and automatically sets the optimal notification timing. For example, it evaluates based on past reminder response times. The reminder notification unit also suggests the optimal notification timing based on the reminder response data. For example, it prioritizes selecting time periods with good responses. The reminder notification unit also continuously monitors reminder response data and dynamically adjusts the optimal notification timing. For example, it readjusts the notification timing if the response is poor. In this way, the optimal notification timing can be automatically set by analyzing participants' past reminder response data.

[0086] The reminder notification unit can implement an algorithm that customizes the frequency and content of notifications according to the importance of the meeting. For example, the generation AI implements an algorithm that customizes the frequency and content of notifications based on the importance of the meeting. For example, notifications are sent more frequently for meetings with high importance. The reminder notification unit also customizes the frequency and content of notifications according to the content of the meeting and the status of the participants. For example, detailed notifications are sent if there is an important agenda item. The generation AI also evaluates the importance of the meeting in real time and dynamically adjusts the frequency and content of notifications. For example, the notification content is readjusted if the importance changes. This makes it possible to customize the frequency and content of notifications according to the importance of the meeting, allowing appropriate notifications to be sent to participants.

[0087] The reminder notification unit can use the emotion estimation function to analyze the emotional reactions of participants at the time of reminder notification and preferentially select a notification method that will elicit a positive reaction. The reminder notification unit, for example, uses the emotion estimation function to analyze the emotional reactions of participants at the time of reminder notification in real time. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The reminder notification unit also selects a notification method that will elicit a positive reaction based on the emotional reaction data. For example, it preferentially selects a notification method with a high emotion score. The reminder notification unit also continuously monitors the emotion estimation data and dynamically adjusts a notification method that will elicit a positive reaction. For example, it readjusts the notification method if the emotion score drops. In this way, it is possible to select a notification method that will elicit a positive reaction by analyzing the emotional reactions of participants.

[0088] The reminder notification unit can send notifications in the optimal format for the participant's device. For example, the generation AI acquires the participant's device information and builds a system to send notifications in the optimal format. For example, it sends push notifications to smartphones and email notifications to PCs. The reminder notification unit also customizes the content and format of notifications depending on the type of device. For example, it sends a short message to a smartwatch and a detailed notification to a PC. The generation AI also monitors the participant's device usage in real time and sends notifications in the optimal format. For example, it adjusts the notification method depending on how often the device is used. This improves the notification reception rate by sending notifications in the optimal format for the participant's device.

[0089] The reminder notification unit can automatically attach relevant materials and links to reminder notifications according to the content of the meeting. For example, the reminder notification unit constructs a system in which a generation AI analyzes the content of the meeting and automatically attaches relevant materials and links to reminder notifications. For example, materials related to the agenda are automatically attached. The reminder notification unit also customizes the materials and links necessary for reminder notifications according to the content of the meeting. For example, presentation materials and reference links are attached. The reminder notification unit also analyzes the content of the meeting in real time using a generation AI and attaches the latest materials and links to reminder notifications. For example, the latest data is reflected just before the meeting. This allows participants to more efficiently prepare by automatically attaching relevant materials and links according to the content of the meeting.

[0090] The reminder notification unit uses the emotion estimation function to monitor the emotions of participants at the time of reminder notification in real time and continuously adjust the optimal notification method. The reminder notification unit, for example, uses the emotion estimation function to build a system that monitors the emotions of participants at the time of reminder notification in real time. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The reminder notification unit also identifies time periods when participants are relaxed based on the emotion estimation data and sends reminder notifications during those time periods. For example, it prioritizes time periods with high emotion scores. The reminder notification unit also uses the emotion estimation function to continuously monitor the emotional state of participants and dynamically adjust the optimal reminder notification method. For example, it readjusts the notification method if the emotion score drops. In this way, the optimal notification method can be continuously adjusted by monitoring the emotions of participants in real time.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The participant information input unit can suggest optimal meeting dates and times based on participants' health data. For example, it can analyze participants' sleep patterns and activity levels to identify the time periods when they perform best. The participant information input unit can also monitor participants' health conditions in real time based on data obtained from wearable devices and dynamically adjust the optimal meeting date and time. For example, if a participant's health condition worsens, the meeting date and time can be readjusted. This allows the optimal meeting date and time to be set by taking the participants' health conditions into consideration.

[0093] The participant information input unit can analyze the content of participants' comments in past meetings and automatically evaluate their areas of expertise and skill sets. For example, it can analyze meeting minutes and audio data to identify participants who frequently comment on specific areas of expertise. The participant information input unit can also evaluate participants' skill sets based on the content of their comments and recommend participants best suited to the content of the meeting. For example, it can recommend technical experts for technical topics. This improves the quality of meetings by taking into account participants' areas of expertise and skill sets.

[0094] The participant information input unit can use the emotion estimation function to adjust the progress of the meeting based on the emotional state of the participants. For example, it can monitor the emotional state of the participants in real time and change the agenda if the emotion score drops. The participant information input unit can also schedule the meeting for a time period when participants are relaxed based on the emotion estimation data. For example, it can prioritize time periods with high emotion scores. This allows the progress of the meeting to be optimized by taking into account the emotional state of the participants.

[0095] The participant information input unit can reflect participants' interests and concerns related to the conference theme based on their social media activity. For example, it can analyze participants' Twitter and LinkedIn posts to extract their interests and concerns related to the conference theme. The participant information input unit can also identify participants' areas of interest based on social media data and reflect these in the conference agenda. For example, it can prioritize inviting participants who are highly interested in a particular topic. This allows the conference theme to be optimized by reflecting participants' interests and concerns.

[0096] The participant information input unit can use the emotion estimation function to select a conference format based on the emotional state of the participants. For example, it can monitor the emotional state of the participants in real time and set up an online conference if the emotional score is high. The participant information input unit can also set up a conference for a time period when participants are relaxed based on the emotion estimation data. For example, it can prioritize selecting a time period with a high emotional score. This allows the optimal conference format to be selected by taking into account the emotional state of the participants.

[0097] The participant information input unit can automatically set the optimal meeting location and time zone for an online meeting based on the participants' geographical location information. For example, it can obtain the participants' geographical location information and select a central meeting room that is easily accessible to all. In the case of an online meeting, it can also take into account the participants' time zones and set a time period that is easy for everyone to participate in. For example, it can select the optimal time when there are participants in different time zones. This makes it possible to set the optimal meeting location and time zone by taking into account the participants' geographical location information.

[0098] The participant information input unit can use the emotion estimation function to adjust the length of a meeting based on the emotional state of the participants. For example, it can monitor the emotional state of the participants in real time, and shorten the length of the meeting if the emotion score drops. The participant information input unit can also schedule the meeting for a time period when participants are relaxed based on the emotion estimation data. For example, it can prioritize selecting a time period with a high emotion score. This allows the length of the meeting to be optimized by taking into account the emotional state of the participants.

[0099] The participant information input unit can optimize the progress of a meeting based on the content of comments made by participants in past meetings. For example, it can analyze the content of comments made in past meetings and evaluate the progress of the agenda. The participant information input unit can also set an agenda that reflects the opinions of participants based on the content of comments. For example, it can prioritize the opinions of participants who make frequent comments on a particular topic. This allows the progress of a meeting to be optimized by reflecting the opinions of participants.

[0100] The participant information input unit can use the emotion estimation function to adjust the frequency of meetings based on the emotional state of the participants. For example, it can monitor the emotional state of the participants in real time and reduce the frequency of meetings if the emotion score drops. The participant information input unit can also schedule meetings for times when participants are relaxed based on the emotion estimation data. For example, it can prioritize selecting times with high emotion scores. This makes it possible to optimize the frequency of meetings by taking into account the emotional state of the participants.

[0101] The participant information input unit can suggest the optimal meeting date and time based on the participant's past meeting attendance history. For example, it can analyze past meeting attendance rates and select a date and time with a high attendance rate. The participant information input unit can also optimize the participant's schedule based on the attendance history. For example, it can prioritize selecting time periods with a high attendance rate. This makes it possible to set the optimal meeting date and time by taking into account the participant's past meeting attendance history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The participant information input unit inputs participant information. For example, the participant's name, title, contact information, and schedule can be input. The participant information input unit can also set the importance of the participant. Step 2: The matter information input unit inputs matter information. For example, the purpose of the meeting, the agenda, and related materials can be input. The matter information input unit can also set the importance of the matter. Step 3: The schedule adjustment unit automatically sets the optimal meeting date and time based on the information input by the participant information input unit and the matter information input unit. For example, the schedule adjustment unit selects the optimal meeting date and time by taking into consideration the available time in everyone's schedule. The schedule adjustment unit can also prioritize the available time of participants with high importance.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a participant information input unit for inputting participant information; a case information input unit for inputting case information; a schedule adjustment unit that automatically sets an optimal conference date and time based on the information input by the participant information input unit and the matter information input unit; A system characterized by:

2. The participant information input unit Automatically retrieve participants' areas of expertise and skill sets from a database and recommend participants best suited to the content of the meeting 2. The system of claim 1.

3. The participant information input unit Automatically set optimal meeting locations and time zones for online meetings based on participants' geographic locations 2. The system of claim 1.

4. The case information input unit Analyze data from similar past meetings, extract elements of successful meetings, and suggest optimal dates and times 2. The system of claim 1.

5. The schedule adjustment unit Learn from past meeting data and automatically generate optimal schedule adjustment patterns 2. The system of claim 1.

6. The participant information input unit Analyze participants' current emotional state and prioritize dates and times that are less stressful 2. The system of claim 1.

7. The case information input unit Analyze participants' emotional reactions to the event and prioritize dates and times that generate positive reactions.

2. The system of claim 1.

8. The schedule adjustment unit Consider the emotional state of participants and choose a date and time that is less stressful.

2. The system of claim 1.

Images