System

Abstract

An object of a system according to an embodiment is to automatically propose an optimal meeting schedule in consideration of the schedules of participants and the availability of conference rooms.SOLUTION: A system according to an embodiment includes a schedule analysis part, a vacancy situation analysis part, a schedule proposal part, and a replacement date proposal part. The schedule analysis part analyzes the schedule of the participant. The availability analysis unit analyzes the availability of the conference room. A schedule proposal part proposes an optimum meeting schedule on the basis of the information analyzed by the schedule analysis part and the availability analysis part. The alternative date proposing unit proposes an alternative candidate date when the schedule conflicts with the schedule of the participant or when the meeting room is unavailable.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 technology has the drawback of being time-consuming and difficult to efficiently plan meetings, as it requires arranging optimal meeting dates that take into account the schedules of participants and the availability of conference rooms.

[0005] The system according to the embodiment aims to automatically propose the optimal meeting date taking into consideration the schedules of the participants and the availability of conference rooms. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule analysis unit, an availability analysis unit, a schedule proposal unit, and an alternative date proposal unit. The schedule analysis unit analyzes the schedules of participants. The availability analysis unit analyzes the availability of conference rooms. The schedule proposal unit proposes an optimal meeting date based on the information analyzed by the schedule analysis unit and the availability analysis unit. The alternative date proposal unit proposes alternative candidate dates when the date conflicts with the schedules of participants or when the conference room is unavailable. [Effects of the Invention]

[0007] The system according to the embodiment can automatically propose the most suitable meeting date, taking into consideration the schedules of the participants and the availability of conference rooms. [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 AI ​​assistant according to an embodiment of the present invention is a system that automatically proposes optimal meeting dates by combining the schedules of all participants and information on the availability of conference rooms. This reduces the communication effort required for arranging meeting dates and supports efficient meeting planning.

[0029] The AI ​​assistant according to the embodiment includes a schedule analysis unit, an availability analysis unit, a schedule proposal unit, and an alternative date proposal unit. The schedule analysis unit analyzes the schedules of the participants. For example, the generation AI collects calendar information from all participants and identifies available time slots. The generation AI can also analyze available time slots based on the participants' schedule information. The generation AI can also identify optimal time slots based on the participants' schedule information. The availability analysis unit analyzes the availability of conference rooms. For example, the generation AI obtains availability information from a conference room reservation system and identifies available time slots. The generation AI can also analyze available time slots based on the conference room availability information. The generation AI can also identify optimal time slots based on the conference room availability information. The schedule proposal unit proposes optimal meeting dates based on the information analyzed by the schedule analysis unit and the availability analysis unit. For example, the generation AI identifies time slots when all participants are free and when the conference room is available, and proposes those time slots. The generation AI can also propose optimal dates based on the participants' schedule information and the conference room availability information. The generation AI can also identify the optimal time slot based on participant schedule information and conference room availability information. The alternative date suggestion unit suggests alternative dates when the date conflicts with a participant's schedule or when a conference room is unavailable. For example, if the proposed date conflicts with a participant's schedule, the generation AI can suggest the next best date. Furthermore, if the proposed date conflicts with a conference room's availability, the generation AI can also suggest the next best date. Furthermore, if the proposed date conflicts with a participant's schedule and a conference room's availability, the generation AI can also suggest the next best date. This allows the AI ​​assistant according to the embodiment to reduce the communication effort required for scheduling meetings and support efficient meeting planning. For example, it is no longer necessary to manually arrange a time slot when all participants are available; the AI ​​can automatically suggest the optimal date, saving time and effort.AI also enables quick and accurate scheduling by proposing optimal dates based on participant schedule information and conference room availability information. AI can also automatically suggest alternative dates, allowing for flexible response to participant schedules and conference room availability.

[0030] The schedule analysis unit can analyze participants' past schedule patterns and predict future schedules. For example, using generation AI, the schedule analysis unit collects participants' schedule data from the past year and identifies frequently repeated patterns. For example, if a regular meeting is held every Monday morning, the schedule analysis unit can predict future schedules based on that pattern. The schedule analysis unit can also use generation AI to analyze participants' past schedule data and predict future schedules. For example, if a participant tends to frequently schedule events on specific days or times, the future schedule can be predicted based on that tendency. The schedule analysis unit can also use generation AI to predict future schedules based on participants' past schedule data. For example, the schedule analysis unit can analyze the frequency and time periods at which participants attend specific events and predict future schedules based on that information. This allows for predicting participants' future schedules, making it possible to suggest more accurate meeting dates.

[0031] The schedule analysis unit can analyze the importance of events included in the participant's schedule and prioritize them based on the importance. The schedule analysis unit can use, for example, a generation AI to analyze the importance of each event included in the participant's schedule. For example, the importance is evaluated based on the number of attendees in a meeting and the purpose of the meeting, and events with high importance are prioritized in the schedule. The schedule analysis unit can also use a generation AI to analyze the importance of events included in the participant's schedule and prioritize them based on the importance. For example, important meetings and project deadlines are prioritized in the schedule. The schedule analysis unit can also use a generation AI to prioritize events included in the participant's schedule based on the importance. For example, events with high importance are prioritized in the schedule, and events with low importance are postponed. In this way, by prioritizing events based on their importance, important events can be prioritized in the schedule.

[0032] The availability analysis unit can analyze the usage history of a conference room and identify time periods when it is used frequently. For example, using generation AI, the availability analysis unit collects the usage history of a conference room over the past year and identifies time periods when it is used frequently. For example, if usage is high every Tuesday afternoon, the availability analysis unit can suggest meetings to be held at times other than that time period. The availability analysis unit can also use generation AI to analyze the usage history of a conference room and identify time periods when it is used frequently. For example, if usage is high on a particular day of the week or time period, the availability analysis unit can suggest meetings to be held at times other than that time period. The availability analysis unit can also use generation AI to identify time periods when it is used frequently based on the usage history of a conference room. For example, if usage is high during a particular time period, the availability analysis unit can suggest meetings to be held at times other than that time period. This makes it possible to suggest meetings to be held at times other than that time period when the conference room is used frequently.

[0033] The availability analysis unit can analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. The availability analysis unit can, for example, use generation AI to analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. For example, for a meeting that requires a projector or whiteboard, it can propose a conference room that is equipped with those facilities. The availability analysis unit can also use generation AI to analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. For example, for a meeting that requires specific equipment or environmental conditions, it can propose a conference room that meets those conditions. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the equipment and environmental conditions of the conference room. For example, for a meeting that requires specific equipment or environmental conditions, it can propose a conference room that meets those conditions. This makes it possible to propose the most suitable conference room according to the purpose of the meeting.

[0034] The availability analysis unit can also analyze information about other offices and remote conference rooms when analyzing the availability of conference rooms. The availability analysis unit can, for example, use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the availability information of other offices and remote conference rooms. For example, if a conference room in another office is available, that conference room will be proposed. This makes it possible to propose conference rooms more flexibly by taking information about other offices and remote conference rooms into consideration.

[0035] The availability analysis unit can also analyze information about other offices and remote conference rooms when analyzing the availability of conference rooms. The availability analysis unit can, for example, use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the availability information of other offices and remote conference rooms. For example, if a conference room in another office is available, that conference room will be proposed. This makes it possible to propose conference rooms more flexibly by taking information about other offices and remote conference rooms into consideration.

[0036] The availability analysis unit updates the availability of conference rooms in real time and can make proposals based on the latest information. The availability analysis unit uses, for example, a generation AI to build a system that updates the availability of conference rooms in real time and makes proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. The availability analysis unit can also use, for example, a generation AI to update the availability of conference rooms in real time and make proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. The availability analysis unit can also use, for example, a generation AI to update the availability of conference rooms in real time and make proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. In this way, by updating the availability of conference rooms in real time, it becomes possible to make proposals based on the latest information.

[0037] The schedule proposal unit can use the generation AI to analyze the success rates of past meetings and propose a time period with a high success rate. The schedule proposal unit can, for example, use the generation AI to analyze the success rates of past meetings and identify a time period with a high success rate. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. The schedule proposal unit can also use the generation AI to analyze the success rates of past meetings and identify a time period with a high success rate. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. The schedule proposal unit can also use the generation AI to identify a time period with a high success rate based on the success rates of past meetings. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. In this way, by proposing a time period with a high success rate based on the success rates of past meetings, it is possible to improve the results of the meeting.

[0038] The schedule proposal unit can propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. The schedule proposal unit can, for example, use a generation AI to propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. The schedule proposal unit can also, using a generation AI, propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. The schedule proposal unit can also, using a generation AI, propose an optimal schedule based on traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. In this way, by taking traffic conditions and weather information into consideration, it is possible to propose a more realistic and feasible meeting date.

[0039] The schedule proposal unit can also take into account the time zones of the remote participants when proposing the optimal meeting date. The schedule proposal unit, for example, uses a generation AI to propose the optimal meeting date taking into account the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. The schedule proposal unit can also use a generation AI to propose the optimal meeting date taking into account the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. The schedule proposal unit can also use a generation AI to propose the optimal meeting date based on the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. In this way, by taking into account the time zones of the remote participants, the meeting can be set at a time that is convenient for all participants to attend.

[0040] The schedule proposal unit can collect participants' feedback on the proposed schedule and reflect it in the next proposal. The schedule proposal unit can, for example, use a generation AI to build a system that collects participants' feedback on the proposed schedule and reflects it in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. The schedule proposal unit can also use a generation AI to collect participants' feedback on the proposed schedule and reflect it in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. The schedule proposal unit can also use a generation AI to reflect participants' feedback on the proposed schedule in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. In this way, by reflecting participants' feedback, the next proposal becomes more appropriate.

[0041] The alternative date proposal unit can use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. The alternative date proposal unit can, for example, use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. The alternative date proposal unit can also use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. The alternative date proposal unit can also use the generation AI to identify the most acceptable alternative date based on the past proposal history of alternative candidate dates. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. In this way, by identifying the most acceptable alternative date based on the past proposal history, the accuracy of alternative date proposals can be improved.

[0042] The alternative date proposal unit can take into account the individual wishes and constraints of participants when proposing alternative candidate dates. The alternative date proposal unit, for example, uses generation AI to build a system that takes into account the individual wishes and constraints of participants when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. The alternative date proposal unit can also use generation AI to take into account the individual wishes and constraints of participants when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. The alternative date proposal unit can also use generation AI to propose the optimal alternative date based on the participants' individual wishes and constraints when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. In this way, by taking into account the participants' individual wishes and constraints, it is possible to propose a more appropriate alternative date.

[0043] The alternative date proposal unit can also include coordination with other meetings and events when proposing alternative candidate dates. The alternative date proposal unit can, for example, use generation AI to build a system that includes coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. The alternative date proposal unit can also, using generation AI, include coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. The alternative date proposal unit can also, using generation AI, propose optimal alternative dates based on coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. In this way, by including coordination with other meetings and events, it is possible to propose more realistic alternative dates.

[0044] The alternative date proposal unit can also take into account participants' travel time and cost when proposing alternative candidate dates. The alternative date proposal unit, for example, uses generation AI to build a system that takes into account participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. The alternative date proposal unit can also use generation AI to take into account participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. The alternative date proposal unit can also use generation AI to propose the optimal alternative date based on participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. This makes it possible to propose more efficient alternative dates by taking participants' travel time and cost into account.

[0045] The system can use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. The system can also use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. The system can also use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. In this way, it is possible to suggest best practices for efficient planning based on past feedback.

[0046] The system can propose the optimal planning method depending on the purpose and content of the meeting. For example, the system uses generative AI to propose the optimal planning method depending on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. The system can also use generative AI to propose the optimal planning method depending on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. The system can also use generative AI to propose the optimal planning method based on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. This makes it possible to propose the optimal planning method depending on the purpose and content of the meeting.

[0047] The system can be linked with other project management tools and calendar apps for efficient meeting planning. The system can, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. The system can also, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. The system can also, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. This enables more efficient meeting planning by linking with other tools and apps.

[0048] The system can take into account the individual skills and roles of participants when planning a meeting. For example, the system uses generative AI to build a system that takes into account the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. The system can also use generative AI to take into account the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. The system can also use generative AI to perform optimal planning based on the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. This enables more appropriate meeting planning by taking into account the skills and roles of participants.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The AI ​​assistant can also analyze participants' health data and suggest optimal meeting times. For example, it can analyze participants' sleep patterns and identify the time of day when they are most focused. It can also take into account participants' exercise habits and suggest a time when they are refreshed after exercise. It can also take into account participants' meal times and suggest a time when they are relaxed after eating. This makes it possible to suggest optimal meeting times that take into account participants' health conditions.

[0051] The AI ​​assistant can also analyze participants' attendance rates in past meetings and suggest time slots with high attendance rates. For example, it can identify the time slots that participants are most likely to attend based on past data. It can also analyze the days and times of the week that participants have high attendance rates and prioritize those time slots. It can also avoid times with low attendance rates when suggesting a time slot. This allows it to suggest the optimal meeting time taking participants' attendance rates into account.

[0052] The AI ​​assistant can analyze the types of events included in participants' schedules and suggest optimal meeting times based on the type. For example, it can suggest a time that requires concentration for a meeting about an important project. It can also suggest a relaxed time for a regular meeting. It can also suggest a time when participants are most relaxed for a creative ideation meeting. This allows it to suggest optimal meeting times based on the type of event.

[0053] The AI ​​assistant can analyze participants' feedback from past meetings and suggest optimal meeting times based on that feedback. For example, it can identify time periods that participants found satisfactory in past meetings. It can also suggest times that avoid dissatisfied participants. Furthermore, it can continuously adjust the optimal meeting time based on participant feedback. This allows it to suggest optimal meeting times that take participant feedback into account.

[0054] The AI ​​assistant can analyze the locations of events included in participants' schedules and suggest optimal meeting times, taking travel time into account. For example, if participants have consecutive meetings in different locations, it can suggest the next meeting time, taking travel time into account. It can also suggest a time slot to shorten travel time if participants are traveling from a long distance. Furthermore, if participants are participating remotely, it can suggest the optimal time slot, taking time zones into account. This makes it possible to suggest optimal meeting times that take travel time into account.

[0055] The AI ​​assistant can analyze the types of events included in participants' schedules and suggest the optimal meeting format based on the type. For example, it can suggest idea generation methods for brainstorming meetings, efficient reporting methods for project progress reports, and a relaxed format for team-building meetings. This allows it to suggest the optimal meeting format based on the type of event.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The schedule analysis unit analyzes the schedules of the participants. For example, the generation AI collects calendar information from all participants and identifies available time slots. The generation AI can also analyze available time slots based on the participants' schedule information and identify the optimal time slot. Step 2: The availability analysis unit analyzes the availability of the conference room. For example, the generation AI obtains availability information from a conference room reservation system and identifies available time slots. The generation AI can also analyze available time slots based on the conference room availability information and identify the optimal time slot. Step 3: The schedule suggestion unit proposes the optimal meeting date based on the information analyzed by the schedule analysis unit and availability analysis unit. For example, the generation AI identifies a time slot when all participants are free and when a conference room is available, and proposes that time slot. The generation AI can also propose the optimal date based on the participants' schedule information and the conference room availability information. Step 4: The alternative date suggestion unit suggests alternative dates if the proposed date conflicts with the participants' schedules or if the conference room is unavailable. For example, if the proposed date conflicts with the participants' schedules, the generation AI suggests the next best date. The generation AI can also suggest the next best date if the proposed date conflicts with the availability of a conference room.

[0058] (Example 2) The AI ​​assistant according to an embodiment of the present invention is a system that automatically proposes optimal meeting dates by combining the schedules of all participants and information on the availability of conference rooms. This reduces the communication effort required for arranging meeting dates and supports efficient meeting planning.

[0059] The AI ​​assistant according to the embodiment includes a schedule analysis unit, an availability analysis unit, a schedule proposal unit, and an alternative date proposal unit. The schedule analysis unit analyzes the schedules of the participants. For example, the generation AI collects calendar information from all participants and identifies available time slots. The generation AI can also analyze available time slots based on the participants' schedule information. The generation AI can also identify optimal time slots based on the participants' schedule information. The availability analysis unit analyzes the availability of conference rooms. For example, the generation AI obtains availability information from a conference room reservation system and identifies available time slots. The generation AI can also analyze available time slots based on the conference room availability information. The generation AI can also identify optimal time slots based on the conference room availability information. The schedule proposal unit proposes optimal meeting dates based on the information analyzed by the schedule analysis unit and the availability analysis unit. For example, the generation AI identifies time slots when all participants are free and when the conference room is available, and proposes those time slots. The generation AI can also propose optimal dates based on the participants' schedule information and the conference room availability information. The generation AI can also identify the optimal time slot based on participant schedule information and conference room availability information. The alternative date suggestion unit suggests alternative dates when the date conflicts with a participant's schedule or when a conference room is unavailable. For example, if the proposed date conflicts with a participant's schedule, the generation AI can suggest the next best date. Furthermore, if the proposed date conflicts with a conference room's availability, the generation AI can also suggest the next best date. Furthermore, if the proposed date conflicts with a participant's schedule and a conference room's availability, the generation AI can also suggest the next best date. This allows the AI ​​assistant according to the embodiment to reduce the communication effort required for scheduling meetings and support efficient meeting planning. For example, it is no longer necessary to manually arrange a time slot when all participants are available; the AI ​​can automatically suggest the optimal date, saving time and effort.AI also enables quick and accurate scheduling by proposing optimal dates based on participant schedule information and conference room availability information. AI can also automatically suggest alternative dates, allowing for flexible response to participant schedules and conference room availability.

[0060] The schedule analysis unit can analyze participants' past schedule patterns and predict future schedules. For example, using generation AI, the schedule analysis unit collects participants' schedule data from the past year and identifies frequently repeated patterns. For example, if a regular meeting is held every Monday morning, the schedule analysis unit can predict future schedules based on that pattern. The schedule analysis unit can also use generation AI to analyze participants' past schedule data and predict future schedules. For example, if a participant tends to frequently schedule events on specific days or times, the future schedule can be predicted based on that tendency. The schedule analysis unit can also use generation AI to predict future schedules based on participants' past schedule data. For example, the schedule analysis unit can analyze the frequency and time periods at which participants attend specific events and predict future schedules based on that information. This allows for predicting participants' future schedules, making it possible to suggest more accurate meeting dates.

[0061] The schedule analysis unit can analyze the importance of events included in the participant's schedule and prioritize them based on the importance. The schedule analysis unit can use, for example, a generation AI to analyze the importance of each event included in the participant's schedule. For example, the importance is evaluated based on the number of attendees in a meeting and the purpose of the meeting, and events with high importance are prioritized in the schedule. The schedule analysis unit can also use a generation AI to analyze the importance of events included in the participant's schedule and prioritize them based on the importance. For example, important meetings and project deadlines are prioritized in the schedule. The schedule analysis unit can also use a generation AI to prioritize events included in the participant's schedule based on the importance. For example, events with high importance are prioritized in the schedule, and events with low importance are postponed. In this way, by prioritizing events based on their importance, important events can be prioritized in the schedule.

[0062] The schedule analysis unit can use the emotion estimation function to analyze participants' emotions regarding the schedule and identify low-stress time periods. The schedule analysis unit can, for example, use the emotion estimation function to analyze participants' emotions regarding the schedule in real time and identify low-stress time periods. For example, the schedule can be prioritized to reflect time periods when participants are relaxed. The schedule analysis unit can also use the emotion estimation function to analyze participants' emotions regarding the schedule and identify low-stress time periods. For example, the schedule can be adjusted to avoid time periods when participants are feeling stressed. The schedule analysis unit can also use the emotion estimation function to identify low-stress time periods based on participants' emotions regarding the schedule. For example, the schedule can be prioritized to reflect time periods when participants are relaxed and meetings can be scheduled for low-stress time periods. In this way, meetings can be scheduled for low-stress time periods by taking participants' emotions into consideration.

[0063] The availability analysis unit can analyze the usage history of a conference room and identify time periods when it is used frequently. For example, using generation AI, the availability analysis unit collects the usage history of a conference room over the past year and identifies time periods when it is used frequently. For example, if usage is high every Tuesday afternoon, the availability analysis unit can suggest meetings to be held at times other than that time period. The availability analysis unit can also use generation AI to analyze the usage history of a conference room and identify time periods when it is used frequently. For example, if usage is high on a particular day of the week or time period, the availability analysis unit can suggest meetings to be held at times other than that time period. The availability analysis unit can also use generation AI to identify time periods when it is used frequently based on the usage history of a conference room. For example, if usage is high during a particular time period, the availability analysis unit can suggest meetings to be held at times other than that time period. This makes it possible to suggest meetings to be held at times other than that time period when the conference room is used frequently.

[0064] The availability analysis unit can analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. The availability analysis unit can, for example, use generation AI to analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. For example, for a meeting that requires a projector or whiteboard, it can propose a conference room that is equipped with those facilities. The availability analysis unit can also use generation AI to analyze the equipment and environmental conditions of a conference room and propose the most suitable conference room. For example, for a meeting that requires specific equipment or environmental conditions, it can propose a conference room that meets those conditions. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the equipment and environmental conditions of the conference room. For example, for a meeting that requires specific equipment or environmental conditions, it can propose a conference room that meets those conditions. This makes it possible to propose the most suitable conference room according to the purpose of the meeting.

[0065] The availability analysis unit can use the emotion estimation function to analyze the emotions of participants in past meetings and preferentially suggest conference rooms in which participants expressed a lot of positive emotions. The availability analysis unit can, for example, use the emotion estimation function to analyze the emotions of participants in past meetings and preferentially suggest conference rooms in which participants expressed a lot of positive emotions. For example, preferentially suggest conference rooms in which participants feel relaxed. The availability analysis unit can also use the emotion estimation function to analyze the emotions of participants in past meetings and preferentially suggest conference rooms in which participants expressed a lot of positive emotions. For example, preferentially suggest conference rooms in which participants feel relaxed. The availability analysis unit can also use the emotion estimation function to preferentially suggest conference rooms in which participants expressed a lot of positive emotions based on the emotions of participants in past meetings. For example, preferentially suggest conference rooms in which participants feel relaxed. This allows the quality of the meeting to be improved by preferentially suggesting conference rooms in which participants feel positive emotions.

[0066] The availability analysis unit can also analyze information about other offices and remote conference rooms when analyzing the availability of conference rooms. The availability analysis unit can, for example, use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the availability information of other offices and remote conference rooms. For example, if a conference room in another office is available, that conference room will be proposed. This makes it possible to propose conference rooms more flexibly by taking information about other offices and remote conference rooms into consideration.

[0067] The availability analysis unit can also analyze information about other offices and remote conference rooms when analyzing the availability of conference rooms. The availability analysis unit can, for example, use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to analyze information about availability of other offices and remote conference rooms and propose the most suitable conference room. For example, if a conference room in another office is available, that conference room will be proposed. The availability analysis unit can also use generation AI to propose the most suitable conference room based on the availability information of other offices and remote conference rooms. For example, if a conference room in another office is available, that conference room will be proposed. This makes it possible to propose conference rooms more flexibly by taking information about other offices and remote conference rooms into consideration.

[0068] The availability analysis unit updates the availability of conference rooms in real time and can make proposals based on the latest information. The availability analysis unit uses, for example, a generation AI to build a system that updates the availability of conference rooms in real time and makes proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. The availability analysis unit can also use, for example, a generation AI to update the availability of conference rooms in real time and make proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. The availability analysis unit can also use, for example, a generation AI to update the availability of conference rooms in real time and make proposals based on the latest information. For example, the information is updated every time the reservation status of a conference room changes. In this way, by updating the availability of conference rooms in real time, it becomes possible to make proposals based on the latest information.

[0069] The availability analysis unit can use the emotion estimation function to identify a conference room in which participants feel most comfortable and preferentially suggest that conference room. The availability analysis unit can, for example, use the emotion estimation function to identify a conference room in which participants feel most comfortable and preferentially suggest that conference room. For example, preferentially suggest a conference room in which participants feel relaxed. The availability analysis unit can also use the emotion estimation function to identify a conference room in which participants feel most comfortable and preferentially suggest that conference room. For example, preferentially suggest a conference room in which participants feel relaxed. The availability analysis unit can also use the emotion estimation function to suggest an optimal conference room based on the conference room in which participants feel most comfortable. For example, preferentially suggest a conference room in which participants feel relaxed. In this way, by suggesting a conference room in which participants feel most comfortable, the quality of the meeting can be improved.

[0070] The schedule proposal unit can use the generation AI to analyze the success rates of past meetings and propose a time period with a high success rate. The schedule proposal unit can, for example, use the generation AI to analyze the success rates of past meetings and identify a time period with a high success rate. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. The schedule proposal unit can also use the generation AI to analyze the success rates of past meetings and identify a time period with a high success rate. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. The schedule proposal unit can also use the generation AI to identify a time period with a high success rate based on the success rates of past meetings. For example, the success rate is evaluated based on the attendance rate of participants and the results of the meetings from past data. In this way, by proposing a time period with a high success rate based on the success rates of past meetings, it is possible to improve the results of the meeting.

[0071] The schedule proposal unit can propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. The schedule proposal unit can, for example, use a generation AI to propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. The schedule proposal unit can also, using a generation AI, propose an optimal schedule by taking into account traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. The schedule proposal unit can also, using a generation AI, propose an optimal schedule based on traffic conditions and weather information in addition to the participants' schedules and the availability of conference rooms. For example, it can propose a time period that avoids traffic congestion and bad weather. In this way, by taking traffic conditions and weather information into consideration, it is possible to propose a more realistic and feasible meeting date.

[0072] The schedule proposal unit can use the emotion estimation function to identify a time period when participants feel the most positive emotions and propose that time period. The schedule proposal unit can, for example, use the emotion estimation function to identify a time period when participants feel the most positive emotions and propose that time period. For example, the schedule proposal unit can set a meeting for a time period when participants are relaxed. The schedule proposal unit can also use the emotion estimation function to identify a time period when participants feel the most positive emotions and propose that time period. For example, the schedule proposal unit can set a meeting for a time period when participants are relaxed. The schedule proposal unit can also use the emotion estimation function to propose an optimal schedule based on the time period when participants feel the most positive emotions. For example, the schedule proposal unit can set a meeting for a time period when participants are relaxed. In this way, by setting a meeting for a time period when participants feel the most positive emotions, the outcome of the meeting can be improved.

[0073] The schedule proposal unit can also take into account the time zones of the remote participants when proposing the optimal meeting date. The schedule proposal unit, for example, uses a generation AI to propose the optimal meeting date taking into account the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. The schedule proposal unit can also use a generation AI to propose the optimal meeting date taking into account the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. The schedule proposal unit can also use a generation AI to propose the optimal meeting date based on the time zones of the remote participants. For example, it proposes a time period that is convenient for all participants in different time zones to attend. In this way, by taking into account the time zones of the remote participants, the meeting can be set at a time that is convenient for all participants to attend.

[0074] The schedule proposal unit can collect participants' feedback on the proposed schedule and reflect it in the next proposal. The schedule proposal unit can, for example, use a generation AI to build a system that collects participants' feedback on the proposed schedule and reflects it in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. The schedule proposal unit can also use a generation AI to collect participants' feedback on the proposed schedule and reflect it in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. The schedule proposal unit can also use a generation AI to reflect participants' feedback on the proposed schedule in the next proposal. For example, it proposes the next meeting taking into consideration the participants' preferred time slot. In this way, by reflecting participants' feedback, the next proposal becomes more appropriate.

[0075] The schedule proposal unit can use the emotion estimation function to monitor participants' emotional reactions to the proposed schedule in real time and continuously adjust the optimal schedule. The schedule proposal unit, for example, uses the emotion estimation function to monitor participants' emotional reactions to the proposed schedule in real time and build a system that continuously adjusts the optimal schedule. For example, it prioritizes proposing time periods when participants have positive emotions. The schedule proposal unit can also use the emotion estimation function to monitor participants' emotional reactions to the proposed schedule in real time and continuously adjust the optimal schedule. For example, it prioritizes proposing time periods when participants have positive emotions. The schedule proposal unit can also use the emotion estimation function to continuously adjust the optimal schedule based on participants' emotional reactions to the proposed schedule. For example, it prioritizes proposing time periods when participants have positive emotions. In this way, by monitoring participants' emotional reactions in real time and continuously adjusting the optimal schedule, participant satisfaction can be increased.

[0076] The alternative date proposal unit can use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. The alternative date proposal unit can, for example, use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. The alternative date proposal unit can also use the generation AI to analyze the past proposal history of alternative candidate dates and identify the most acceptable alternative date. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. The alternative date proposal unit can also use the generation AI to identify the most acceptable alternative date based on the past proposal history of alternative candidate dates. For example, the alternative date proposal unit can propose the next alternative date based on alternative dates accepted by participants from past data. In this way, by identifying the most acceptable alternative date based on the past proposal history, the accuracy of alternative date proposals can be improved.

[0077] The alternative date proposal unit can take into account the individual wishes and constraints of participants when proposing alternative candidate dates. The alternative date proposal unit, for example, uses generation AI to build a system that takes into account the individual wishes and constraints of participants when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. The alternative date proposal unit can also use generation AI to take into account the individual wishes and constraints of participants when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. The alternative date proposal unit can also use generation AI to propose the optimal alternative date based on the participants' individual wishes and constraints when proposing alternative candidate dates. For example, it proposes alternative dates based on the participants' desired time slots and specific constraints. In this way, by taking into account the participants' individual wishes and constraints, it is possible to propose a more appropriate alternative date.

[0078] The alternative date suggestion unit can use the emotion estimation function to analyze participants' emotions regarding the alternative candidate date and suggest an alternative date that elicits the most positive emotions. The alternative date suggestion unit can, for example, use the emotion estimation function to analyze participants' emotions regarding the alternative candidate date and suggest an alternative date that elicits the most positive emotions. For example, it can prioritize proposing a time period when participants are relaxed as an alternative date. The alternative date suggestion unit can also use the emotion estimation function to analyze participants' emotions regarding the alternative candidate date and suggest an alternative date that elicits the most positive emotions. For example, it can prioritize proposing a time period when participants are relaxed as an alternative date. The alternative date suggestion unit can also use the emotion estimation function to suggest an alternative date that elicits the most positive emotions based on participants' emotions regarding the alternative candidate date. For example, it can prioritize proposing a time period when participants are relaxed as an alternative date. In this way, it is possible to suggest an alternative date that elicits the most positive emotions by taking participants' emotions into consideration.

[0079] The alternative date proposal unit can also include coordination with other meetings and events when proposing alternative candidate dates. The alternative date proposal unit can, for example, use generation AI to build a system that includes coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. The alternative date proposal unit can also, using generation AI, include coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. The alternative date proposal unit can also, using generation AI, propose optimal alternative dates based on coordination with other meetings and events when proposing alternative candidate dates. For example, it proposes alternative dates that do not overlap with other meetings or events. In this way, by including coordination with other meetings and events, it is possible to propose more realistic alternative dates.

[0080] The alternative date proposal unit can also take into account participants' travel time and cost when proposing alternative candidate dates. The alternative date proposal unit, for example, uses generation AI to build a system that takes into account participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. The alternative date proposal unit can also use generation AI to take into account participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. The alternative date proposal unit can also use generation AI to propose the optimal alternative date based on participants' travel time and cost when proposing alternative candidate dates. For example, it prioritizes proposing time periods with shortest travel times as alternative dates. This makes it possible to propose more efficient alternative dates by taking participants' travel time and cost into account.

[0081] The alternative date suggestion unit can use the emotion estimation function to monitor participants' emotional responses to alternative candidate dates in real time and continuously adjust the optimal alternative date. The alternative date suggestion unit, for example, uses the emotion estimation function to monitor participants' emotional responses to alternative candidate dates in real time and build a system that continuously adjusts the optimal alternative date. For example, it prioritizes time periods for which participants have positive emotions when proposing alternative dates. The alternative date suggestion unit can also use the emotion estimation function to monitor participants' emotional responses to alternative candidate dates in real time and continuously adjust the optimal alternative date. For example, it prioritizes time periods for which participants have positive emotions when proposing alternative dates. The alternative date suggestion unit can also use the emotion estimation function to continuously adjust the optimal alternative date based on participants' emotional responses to alternative candidate dates. For example, it prioritizes time periods for which participants have positive emotions when proposing alternative dates. In this way, by monitoring participants' emotional responses in real time and continuously adjusting the optimal alternative date, participant satisfaction can be increased.

[0082] The system can use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. The system can also use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. The system can also use generative AI to analyze feedback from past meetings and suggest best practices for efficient planning. For example, the system can suggest an optimal way to proceed with a meeting based on participant feedback. In this way, it is possible to suggest best practices for efficient planning based on past feedback.

[0083] The system can propose the optimal planning method depending on the purpose and content of the meeting. For example, the system uses generative AI to propose the optimal planning method depending on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. The system can also use generative AI to propose the optimal planning method depending on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. The system can also use generative AI to propose the optimal planning method based on the purpose and content of the meeting. For example, it may propose an idea generation method for a brainstorming meeting. This makes it possible to propose the optimal planning method depending on the purpose and content of the meeting.

[0084] The system can use the emotion estimation function to analyze the emotions of participants and propose a planning method that draws out positive emotions. The system, for example, uses the emotion estimation function to analyze the emotions of participants and propose a planning method that draws out positive emotions. For example, the system may schedule a meeting for a time when participants are relaxed. The system can also use the emotion estimation function to analyze the emotions of participants and propose a planning method that draws out positive emotions. For example, the system may schedule a meeting for a time when participants are relaxed. The system can also use the emotion estimation function to propose a planning method that draws out positive emotions based on the emotions of participants. For example, the system may schedule a meeting for a time when participants are relaxed. In this way, the system can propose a planning method that draws out positive emotions by taking the emotions of participants into consideration.

[0085] The system can be linked with other project management tools and calendar apps for efficient meeting planning. The system can, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. The system can also, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. The system can also, for example, use generative AI to link with other project management tools and calendar apps to support efficient meeting planning. For example, the system can suggest meetings based on task information in the project management tool. This enables more efficient meeting planning by linking with other tools and apps.

[0086] The system can take into account the individual skills and roles of participants when planning a meeting. For example, the system uses generative AI to build a system that takes into account the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. The system can also use generative AI to take into account the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. The system can also use generative AI to perform optimal planning based on the individual skills and roles of participants when planning a meeting. For example, if a participant with a specific skill is needed, the system will give priority to inviting that participant. This enables more appropriate meeting planning by taking into account the skills and roles of participants.

[0087] The system can use the emotion estimation function to monitor participants' emotional reactions to meeting planning in real time and continuously adjust the optimal planning method. For example, the system uses the emotion estimation function to build a system that monitors participants' emotional reactions to meeting planning in real time and continuously adjusts the optimal planning method. For example, it prioritizes setting meetings for time periods when participants have positive emotions. The system can also use the emotion estimation function to monitor participants' emotional reactions to meeting planning in real time and continuously adjust the optimal planning method. For example, it prioritizes setting meetings for time periods when participants have positive emotions. The system can also use the emotion estimation function to continuously adjust the optimal planning method based on participants' emotional reactions to meeting planning. For example, it prioritizes setting meetings for time periods when participants have positive emotions. In this way, by monitoring participants' emotional reactions in real time and continuously adjusting the optimal planning method, participant satisfaction can be increased.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The AI ​​assistant can also analyze participants' health data and suggest optimal meeting times. For example, it can analyze participants' sleep patterns and identify the time of day when they are most focused. It can also take into account participants' exercise habits and suggest a time when they are refreshed after exercise. It can also take into account participants' meal times and suggest a time when they are relaxed after eating. This makes it possible to suggest optimal meeting times that take into account participants' health conditions.

[0090] The AI ​​assistant can also analyze participants' attendance rates in past meetings and suggest time slots with high attendance rates. For example, it can identify the time slots that participants are most likely to attend based on past data. It can also analyze the days and times of the week that participants have high attendance rates and prioritize those time slots. It can also avoid times with low attendance rates when suggesting a time slot. This allows it to suggest the optimal meeting time taking participants' attendance rates into account.

[0091] The AI ​​assistant can estimate participants' emotions and suggest meeting formats based on their emotions. For example, if participants are feeling stressed, it can suggest a relaxed, casual meeting format. If participants are feeling positive, it can suggest a brainstorming-style meeting format. Furthermore, if participants are tired, it can suggest a short, efficient meeting format. This allows it to suggest the optimal meeting format taking participants' emotions into consideration.

[0092] The AI ​​assistant can analyze the types of events included in participants' schedules and suggest optimal meeting times based on the type. For example, it can suggest a time that requires concentration for a meeting about an important project. It can also suggest a relaxed time for a regular meeting. It can also suggest a time when participants are most relaxed for a creative ideation meeting. This allows it to suggest optimal meeting times based on the type of event.

[0093] The AI ​​assistant can estimate participants' emotions and adjust the meeting room environment based on their emotions. For example, if participants are feeling stressed, it can suggest playing relaxing music. If participants are feeling positive, it can also suggest brighter lighting. Furthermore, if participants are tired, it can suggest providing comfortable chairs and cushions. This allows it to suggest the optimal meeting room environment taking participants' emotions into consideration.

[0094] The AI ​​assistant can analyze participants' feedback from past meetings and suggest optimal meeting times based on that feedback. For example, it can identify time periods that participants found satisfactory in past meetings. It can also suggest times that avoid dissatisfied participants. Furthermore, it can continuously adjust the optimal meeting time based on participant feedback. This allows it to suggest optimal meeting times that take participant feedback into account.

[0095] The AI ​​assistant can estimate participants' emotions and adjust the meeting agenda based on their feelings. For example, if participants are feeling stressed, it can suggest starting with a relaxing topic. If participants are feeling positive, it can also suggest discussing important topics first. Furthermore, if participants are tired, it can suggest prioritizing topics that can be completed in a short time. This allows it to suggest the optimal meeting agenda that takes participants' emotions into account.

[0096] The AI ​​assistant can analyze the locations of events included in participants' schedules and suggest optimal meeting times, taking travel time into account. For example, if participants have consecutive meetings in different locations, it can suggest the next meeting time, taking travel time into account. It can also suggest a time slot to shorten travel time if participants are traveling from a long distance. Furthermore, if participants are participating remotely, it can suggest the optimal time slot, taking time zones into account. This makes it possible to suggest optimal meeting times that take travel time into account.

[0097] The AI ​​assistant can estimate participants' emotions and suggest ways to proceed with the meeting based on their emotions. For example, if participants are feeling stressed, it can suggest ways to proceed in a relaxed manner. Also, if participants are feeling positive, it can suggest ways to encourage active discussion. Furthermore, if participants are tired, it can suggest ways to proceed in a short and efficient manner. In this way, it can suggest the optimal way to proceed with the meeting taking into account participants' emotions.

[0098] The AI ​​assistant can analyze the types of events included in participants' schedules and suggest the optimal meeting format based on the type. For example, it can suggest idea generation methods for brainstorming meetings, efficient reporting methods for project progress reports, and a relaxed format for team-building meetings. This allows it to suggest the optimal meeting format based on the type of event.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The schedule analysis unit analyzes the schedules of the participants. For example, the generation AI collects calendar information from all participants and identifies available time slots. The generation AI can also analyze available time slots based on the participants' schedule information and identify the optimal time slot. Step 2: The availability analysis unit analyzes the availability of the conference room. For example, the generation AI obtains availability information from a conference room reservation system and identifies available time slots. The generation AI can also analyze available time slots based on the conference room availability information and identify the optimal time slot. Step 3: The schedule suggestion unit proposes the optimal meeting date based on the information analyzed by the schedule analysis unit and availability analysis unit. For example, the generation AI identifies a time slot when all participants are free and when a conference room is available, and proposes that time slot. The generation AI can also propose the optimal date based on the participants' schedule information and the conference room availability information. Step 4: The alternative date suggestion unit suggests alternative dates if the proposed date conflicts with the participants' schedules or if the conference room is unavailable. For example, if the proposed date conflicts with the participants' schedules, the generation AI suggests the next best date. The generation AI can also suggest the next best date if the proposed date conflicts with the availability of a conference room.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 schedule analysis unit that analyzes the schedules of participants; an availability analysis unit that analyzes availability of conference rooms; a schedule suggestion unit that suggests an optimal meeting schedule based on the information analyzed by the schedule analysis unit and the availability analysis unit; An alternative date suggestion unit that suggests alternative dates when the date conflicts with the schedule of a participant or when a conference room is unavailable. A system characterized by:

2. The schedule analysis unit Analyze the participant's past schedule patterns and predict future schedules 2. The system of claim 1.

3. The availability analysis unit Analyze the usage history of the conference room and identify the time periods when it is most frequently used.

2. The system of claim 1.

4. The schedule suggestion unit Using generative AI, we analyze the success rate of past meetings and suggest time slots with high success rates.

2. The system of claim 1.

5. The alternative date proposal unit Using a generation AI, analyze the history of past proposals for the alternative candidate dates and identify the most acceptable alternative date.

2. The system of claim 1.

6. The schedule analysis unit Using an emotion estimation function, the emotions of the participants regarding the schedule are analyzed to identify less stressful times.

2. The system of claim 1.

7. The availability analysis unit Using an emotion estimation function, the emotions of the participants in past meetings are analyzed, and the conference room with the most positive emotions is preferentially suggested.

2. The system of claim 1.

8. The schedule suggestion unit Using an emotion estimation function, identify the time period when the participant has the most positive emotion and suggest the time period.

2. The system of claim 1.

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