System

Abstract

To provide a system capable of finding a date when all members can participate by efficiently adjusting the schedule of a plurality of participants.SOLUTION: This system comprises: a reception unit; an acquisition unit; an analysis unit; an extraction unit; and a presentation unit. The reception unit receives participant information. The acquisition unit acquires a schedule in cooperation with a calendar service on the basis of the information received by the reception unit. The analysis unit analyzes the schedule acquired by the acquisition unit. The extraction unit extracts a date when all members are free or a date where a plurality of participants can participate on the basis of the information analyzed by the analysis unit. The presentation unit presents the date extracted by the extraction unit as a candidate day.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the problem that it is difficult to coordinate the schedules of multiple participants to find a date and time when everyone can attend.

[0005] The system according to the embodiment aims to efficiently coordinate the schedules of multiple participants and find a date and time when all participants can attend. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, an extraction unit, and a presentation unit. The reception unit accepts participant information. The acquisition unit acquires schedules in cooperation with a calendar service based on the information accepted by the reception unit. The analysis unit analyzes the schedules acquired by the acquisition unit. The extraction unit extracts dates and times when all participants are free or dates and times when multiple participants can attend based on the information analyzed by the analysis unit. The presentation unit presents the dates and times extracted by the extraction unit as candidate dates. Effect of the Invention

[0007] The system according to the embodiment can efficiently coordinate the schedules of multiple participants to find a date and time when everyone can attend. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A meeting arrangement system according to an embodiment of the present invention is a system for efficiently arranging a meeting with multiple people using AI. In this meeting arrangement system, a user specifies the participants of the meeting, and the AI ​​cooperates with a calendar service to obtain the schedules of all the participants. Next, the AI ​​analyzes the obtained schedule and extracts the date and time when all the participants are free. If there is no date and time when all the participants are free, the AI ​​extracts a date and time when many participants can participate and presents it as a candidate date. For example, a user specifies participants such as "Mr. A, Mr. B, and Mr. C," and the AI ​​cooperates with a calendar service to obtain the schedule and presents the date and time when all the participants are free or the date and time when many participants can participate as a candidate date, so that the meeting can be arranged smoothly. This allows the meeting arrangement system to allow the user to efficiently arrange a meeting.

[0029] The meeting arrangement system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, an extraction unit, and a presentation unit. The reception unit receives participant information from a user. The user is one of the participants, but may also be an assistant to the participants. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C". This information is input to the AI. The acquisition unit cooperates with a calendar service to acquire the schedules of all the specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The analysis unit analyzes the acquired schedule and identifies the date and time when all the participants are free. For example, if the date and time when all of Mr. A, Mr. B, and Mr. C are free is "14:00 to 15:00", that date and time is extracted. If there is no date and time when all of the participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if Mr. A and Mr. B are free, but Mr. C is not free is "16:00 to 17:00", that date and time is presented as a candidate date. The presentation unit presents the extracted date and time to the user as a candidate date. For example, the user may be allowed to select an optimal date and time, and the meeting arrangement system according to the embodiment may thereby efficiently arrange the schedules of all participants and present the optimal meeting date and time.

[0030] The reception unit accepts participant information from the user. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. Specifically, when the user specifies participants for a meeting, the reception unit receives the information entered through the user interface and stores it in a database. The user interface is provided as a web application or a mobile application, allowing the user to easily enter participant information. The reception unit processes the entered information in real time and checks the integrity of the data before passing it to the AI. For example, it checks whether the specified participant is registered in the system, and if not, notifies the user and prompts them to correct it. In addition, the reception unit encrypts and stores the information entered by the user to ensure the security of the data. This allows the reception unit to accurately and safely accept participant information from the user and pass it on to the next processing step.

[0031] The acquisition unit cooperates with a calendar service to acquire the schedules of all the specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The acquisition unit cooperates with multiple calendar services through APIs to acquire the schedules of each participant in real time. Specifically, the acquisition unit accesses each calendar service using an authentication protocol such as OAuth and acquires the schedule information after obtaining the user's permission. The acquired schedule information includes detailed data such as date and time, location, participants, and type of event. The acquisition unit converts this data into a unified format so that the analysis unit can easily process it. In addition, the acquisition unit temporarily caches the acquired data to reduce the load caused by frequent data access. This allows the acquisition unit to efficiently acquire the schedules of all the specified participants and pass them on to the next analysis step.

[0032] The analysis unit analyzes the acquired schedule and identifies the date and time when all the schedules are free. For example, if the date and time when all of A, B, and C are free is "14:00 to 15:00", that date and time is extracted. The analysis unit uses an AI algorithm to analyze the acquired schedule data and identify the free time of all the participants. Specifically, it organizes the schedule of each participant in a timeline format so that overlaps and free time can be visually grasped. The AI ​​compares these timelines and quickly identifies the time period when all the participants are free. In addition, the analysis unit performs analysis taking into account the priority and conditions specified by the user (for example, specific time periods or days of the week). The analysis result includes not only the time period when all the participants are free, but also the time period when they are partially free. This allows the analysis unit to perform flexible analysis according to the needs of the user and identify the optimal meeting date and time.

[0033] When there is no date and time when all participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if A and B are free but C is not free from the date and time "16:00 to 17:00", that date and time is presented as a candidate date. Based on the data provided by the analysis unit, the extraction unit extracts a date and time when as many participants as possible can participate, even if a date and time when all participants can participate cannot be found. Specifically, the extraction unit scores the free time of each participant and preferentially extracts the time period when the most participants can participate. The extraction unit can present multiple optimal candidate dates taking into account the priority and conditions specified by the user. The extraction unit also provides the user with an option for when partial participation is possible, supporting flexible meeting adjustment. As a result, the extraction unit can efficiently extract optimal candidate dates and present them to the user even when all participants cannot participate.

[0034] The presentation unit presents the extracted dates and times to the user as candidate dates. For example, the user can select the optimal date and time. The presentation unit visually displays the extracted candidate dates through a user interface, allowing the user to easily select the optimal date and time. Specifically, the presentation unit displays the candidate dates in a calendar or list format, and provides detailed information about each candidate date (such as the number of people who can attend, the time period, and the location). After the user selects a candidate date, the presentation unit stores the information in the database again and sends a notification to all participants. In addition, the presentation unit provides additional filtering and sorting functions when the user selects a candidate date, allowing the user to quickly find the optimal date and time. In this way, the presentation unit can provide the user with an efficient and intuitive interface and assist in the process of selecting the optimal meeting date and time.

[0035] The reception unit can receive participant information from a user. The reception unit, for example, provides an interface for a user to specify participants of a meeting. For example, a user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. The reception unit can efficiently receive participant information from a user. For example, the reception unit provides an input form and collects necessary information when a user inputs participant information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can input participant information input by a user to an AI, which analyzes the information and receives the participant information.

[0036] The acquisition unit can acquire the schedules of all participants in cooperation with a calendar service. The acquisition unit, for example, cooperates with a calendar service to acquire the schedules of all specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The acquisition unit can efficiently acquire the schedules of all participants in cooperation with a calendar service. For example, the acquisition unit acquires schedule information of participants using an API of the calendar service. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the schedule information acquired from the calendar service to AI, which analyzes the information to acquire the schedule.

[0037] The analysis unit can analyze the acquired schedule and identify the date and time when everyone is free. The analysis unit, for example, analyzes the acquired schedule and identifies the date and time when everyone is free. For example, if the date and time when A, B, and C are all free is "14:00 to 15:00", that date and time is extracted. The analysis unit can efficiently identify the date and time when everyone is free. For example, the analysis unit executes an algorithm to identify the date and time when everyone is free based on the acquired schedule information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired schedule information to AI, and the AI ​​can analyze the information to identify the date and time when everyone is free.

[0038] The extraction unit can extract a date and time when multiple participants can participate if there is no date and time when all participants are free. For example, when there is no date and time when all participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if A and B are free but C is not free from the date and time "16:00 to 17:00", that date and time is presented as a candidate date. The extraction unit can efficiently extract a date and time when many participants can participate. For example, the extraction unit executes an algorithm that specifies a date and time when many participants can participate based on the acquired schedule information. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the acquired schedule information into AI, and the AI ​​can analyze the information to extract a date and time when many participants can participate.

[0039] The presentation unit can present the extracted date and time to the user as a candidate date. The presentation unit, for example, presents the extracted date and time to the user as a candidate date. For example, the user can select the optimal date and time. The presentation unit can efficiently present the extracted date and time to the user. For example, the presentation unit provides an interface that displays the candidate dates to the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, which analyzes the information and presents the candidate dates to the user.

[0040] The presentation unit can notify the user of the candidate dates. The presentation unit, for example, notifies the user of the candidate dates. For example, the presentation unit enables the user to select the optimal date and time. The presentation unit can efficiently notify the user of the candidate dates. For example, the presentation unit provides an interface for notifying the user of the candidate dates. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, which analyzes the information and notifies the user of the candidate dates.

[0041] The presentation unit can enable the user to select the optimal date and time. The presentation unit, for example, enables the user to select the optimal date and time. For example, the presentation unit provides an interface for the user to select the optimal date and time from candidate dates. The presentation unit enables the user to select the optimal date and time, thereby smoothly arranging the meeting. For example, the presentation unit displays options when the user selects candidate dates, allowing the user to select the optimal date and time. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, and the AI ​​can analyze the information to enable the user to select the optimal date and time.

[0042] The reception unit can refer to past meeting history and automatically suggest frequently participating members. The reception unit, for example, refers to past meeting history and automatically suggests frequently participating members. For example, members with whom the user has frequently met in the past are automatically displayed as candidates. In addition, members with whom the user is frequently involved in a specific project can be preferentially suggested. Furthermore, members who will participate in a specific time period can be predicted and suggested from the user's past meeting history. In this way, by referring to the past meeting history, frequently participating members can be efficiently suggested. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past meeting history into AI, and the AI ​​can analyze the information to suggest frequently participating members.

[0043] The reception unit can automatically evaluate the importance of the meeting and set the priority based on the participants' job titles and fields of expertise. The reception unit can automatically evaluate the importance of the meeting and set the priority based on the participants' job titles and fields of expertise, for example. For example, if the participant has a high job title, the importance of the meeting is set high. Also, if the participant's field of expertise is related to the theme of the meeting, the priority can be set high. Furthermore, the importance of the meeting can be automatically evaluated and the priority can be set based on the participants' job titles and fields of expertise. As a result, important meetings can be prioritized by evaluating the importance of the meeting and setting the priority based on the participants' job titles and fields of expertise. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information on the participants' job titles and fields of expertise into AI, which can analyze the information to evaluate the importance of the meeting and set the priority.

[0044] The reception unit can propose an optimal meeting format in consideration of the geographical location information of the participants. The reception unit, for example, proposes an optimal meeting format (online, offline) in consideration of the geographical location information of the participants. For example, when the participants are in different cities, an online meeting can be proposed. Also, when the participants are in the same office, an offline meeting can be proposed. Furthermore, the optimal meeting format can be proposed based on the geographical location information of the participants. In this way, the optimal meeting format can be proposed by considering the geographical location information of the participants. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the participants to AI, which analyzes the information and proposes the optimal meeting format.

[0045] The reception unit can analyze the social media activity of the participants and automatically add related participants. The reception unit, for example, analyzes the social media activity of the participants and automatically adds related participants. For example, members with whom the participants frequently interact on social media are automatically added. In addition, if the participant posts about a specific project, the member can be automatically added. Furthermore, the social media activity of the participants can be analyzed and related participants can be automatically added. In this way, related participants can be efficiently added by analyzing the social media activity of the participants. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity of the participants to AI, and the AI ​​can analyze the information and add related participants.

[0046] The acquisition unit can improve the acquisition accuracy by referring to the past schedule history of the participant. The acquisition unit can improve the acquisition accuracy by referring to, for example, the past schedule history of the participant. For example, an optimal schedule is acquired based on the past schedule history of the participant. A schedule that avoids congestion can also be acquired from the past schedule history of the participant. Furthermore, the most efficient schedule can be acquired by analyzing the past schedule history of the participant. As a result, the acquisition accuracy can be improved by referring to the past schedule history of the participant. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the past schedule history of the participant to AI, and the AI ​​can analyze the information to acquire the optimal schedule.

[0047] The acquisition unit can acquire appropriate information by taking into account the privacy settings of the participant's calendar. The acquisition unit acquires appropriate information by taking into account, for example, the privacy settings of the participant's calendar. For example, based on the privacy settings of the participant's calendar, only publicly available schedules are acquired. Also, it is possible to acquire only necessary information by taking into account the privacy settings of the participant. Furthermore, it is possible to acquire appropriate information based on the privacy settings of the participant's calendar. In this way, it is possible to acquire appropriate information by taking into account the privacy settings of the participant. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the privacy settings of the participant's calendar to AI, and the AI ​​can analyze the information to acquire appropriate information.

[0048] The acquisition unit can preferentially acquire highly relevant schedules by taking into account the geographical location information of the participants. The acquisition unit, for example, preferentially acquires highly relevant schedules by taking into account the geographical location information of the participants. For example, when the participants are in different cities, the acquisition unit preferentially acquires schedules for online meetings. Also, when the participants are in the same office, the acquisition unit can preferentially acquire schedules for offline meetings. Furthermore, based on the geographical location information of the participants, the acquisition unit can preferentially acquire schedules for offline meetings. As a result, by taking into account the geographical location information of the participants, the acquisition unit can preferentially acquire schedules for high relevance. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to acquire schedules for high relevance.

[0049] The acquisition unit can analyze the social media activity of the participant and acquire the related schedule. The acquisition unit can, for example, analyze the social media activity of the participant and acquire the related schedule. For example, the acquisition unit can acquire schedules with members with whom the participant frequently interacts on social media. In addition, if the participant posts about a specific project, it is also possible to acquire schedules related to the project. Furthermore, it is also possible to analyze the social media activity of the participant and acquire the related schedule. In this way, it is possible to efficiently acquire the related schedule by analyzing the social media activity of the participant. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the social media activity of the participant to AI, and the AI ​​can analyze the information to acquire the related schedule.

[0050] The analysis unit can improve the analysis accuracy by referring to the past meeting data. The analysis unit can improve the analysis accuracy by referring to the past meeting data, for example. For example, the analysis unit selects an optimal analysis method based on the past meeting data. Also, an analysis method that avoids congestion can be selected from the past meeting data. Furthermore, the past meeting data can be analyzed to select the most efficient analysis method. In this way, the analysis accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the past meeting data into AI, which analyzes the information and selects the optimal analysis method.

[0051] The analysis unit can customize the analysis results by considering the position and specialty of the participant. The analysis unit customizes the analysis results by considering, for example, the position and specialty of the participant. For example, if the participant has a high position, important information is analyzed preferentially. In addition, if the participant's specialty is related to the theme of the meeting, a detailed analysis can be performed. Furthermore, the analysis results can be customized based on the participant's position and specialty. As a result, the analysis results can be customized by considering the participant's position and specialty. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the analysis results.

[0052] The analysis unit can optimize the analysis result by taking into account the geographical location information of the participants. The analysis unit optimizes the analysis result by taking into account, for example, the geographical location information of the participants. For example, when the participants are in different cities, the analysis result of the online meeting is optimized. Also, when the participants are in the same office, the analysis result of the offline meeting can be optimized. Furthermore, the analysis result can be optimized based on the geographical location information of the participants. In this way, the analysis result can be optimized by taking into account the geographical location information of the participants. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to optimize the analysis result.

[0053] The analysis unit can improve the analysis accuracy by referring to related literature and data. The analysis unit can improve the analysis accuracy by referring to, for example, related literature and data. For example, the analysis accuracy can be improved by referring to related literature. The analysis accuracy can also be improved by referring to related data. Furthermore, the analysis accuracy can be improved based on related literature and data. As a result, the analysis accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the analysis accuracy.

[0054] The extraction unit can improve the extraction accuracy by referring to the past meeting data. The extraction unit can improve the extraction accuracy by referring to the past meeting data, for example. For example, the optimal date and time is extracted based on the past meeting data. Also, a date and time that avoids crowding can be extracted from the past meeting data. Furthermore, the past meeting data can be analyzed and the most efficient date and time can be extracted. As a result, the extraction accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input the past meeting data into AI, and the AI ​​can analyze the information to extract the optimal date and time.

[0055] The extraction unit can customize the extraction result by considering the position and specialty of the participant. The extraction unit customizes the extraction result by considering, for example, the position and specialty of the participant. For example, if the participant has a high position, important dates and times are preferentially extracted. In addition, if the participant's specialty is related to the theme of the meeting, detailed dates and times can also be extracted. Furthermore, the extraction result can be customized based on the participant's position and specialty. As a result, the extraction result can be customized by considering the participant's position and specialty. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the extraction result.

[0056] The extraction unit can optimize the extraction result by taking into account the geographic location information of the participants. The extraction unit optimizes the extraction result by taking into account, for example, the geographic location information of the participants. For example, when the participants are in different cities, the extraction result of the online meeting is optimized. Also, when the participants are in the same office, the extraction result of the offline meeting can be optimized. Furthermore, the extraction result can be optimized based on the geographic location information of the participants. In this way, the extraction result can be optimized by taking into account the geographic location information of the participants. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input the geographic location information of the participants to AI, and the AI ​​can analyze the information to optimize the extraction result.

[0057] The extraction unit can improve the extraction accuracy by referring to related literature and data. The extraction unit can improve the extraction accuracy by referring to, for example, related literature and data. For example, the extraction accuracy can be improved by referring to related literature. The extraction accuracy can also be improved by referring to related data. Furthermore, the extraction accuracy can be improved based on related literature and data. As a result, the extraction accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the extraction accuracy.

[0058] The presentation unit can improve the presentation accuracy by referring to past meeting data. The presentation unit can improve the presentation accuracy by referring to, for example, past meeting data. For example, the optimal presentation method is selected based on the past meeting data. Also, a presentation method that avoids congestion can be selected from the past meeting data. Furthermore, the past meeting data can be analyzed and the most efficient presentation method can be selected. As a result, the presentation accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI or can be performed without using AI. For example, the presentation unit can input the past meeting data to AI, and the AI ​​can analyze the information to select the optimal presentation method.

[0059] The presentation unit can customize the presentation content taking into consideration the position and specialty of the participant. The presentation unit customizes the presentation content taking into consideration, for example, the position and specialty of the participant. For example, if the participant has a high position, important information is presented preferentially. In addition, if the participant's specialty is related to the theme of the meeting, detailed information can be presented. Furthermore, the presentation content can be customized based on the participant's position and specialty. As a result, the presentation content can be customized by taking into consideration the participant's position and specialty. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the presentation content.

[0060] The presentation unit can select the optimal presentation method by considering the geographical location information of the participants. The presentation unit selects the optimal presentation method by considering, for example, the geographical location information of the participants. For example, when the participants are in different cities, the presentation method of an online meeting is selected. Also, when the participants are in the same office, the presentation method of an offline meeting can be selected. Furthermore, the optimal presentation method can be selected based on the geographical location information of the participants. In this way, the optimal presentation method can be selected by considering the geographical location information of the participants. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to select the optimal presentation method.

[0061] The presentation unit can improve the presentation accuracy by referring to related literature and data. The presentation unit can improve the presentation accuracy by referring to, for example, related literature and data. For example, the presentation accuracy can be improved by referring to related literature. The presentation accuracy can also be improved by referring to related data. Furthermore, the presentation accuracy can be improved based on related literature and data. As a result, the presentation accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI, or can be performed without using AI. For example, the presentation unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the presentation accuracy.

[0062] The presentation unit can notify the user of the candidate dates so that the user can select the optimal date and time. The presentation unit, for example, notifies the user of the candidate dates so that the user can select the optimal date and time. For example, a notification function is provided when the user selects the candidate dates. The presentation unit can also notify the user of the candidate dates so that the user can select the optimal date and time. This allows the user to select the optimal date and time, making it possible to smoothly arrange meetings. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the candidate dates to an AI, which analyzes the information and notifies the user.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The reception unit can provide an interface for the user to input the purpose and agenda of the meeting. For example, the user inputs the purpose of the meeting, such as "checking the progress of a project" or "generating ideas for a new product." This information is shared with the other participants, allowing smooth preparation for the meeting. The reception unit can also automatically suggest related materials and documents based on the agenda entered by the user. This saves the user the trouble of having to prepare the necessary materials in advance. Furthermore, the reception unit can provide useful information by referring to the user's past meeting history and displaying the history of meetings with similar agendas.

[0065] The acquisition unit can acquire not only the calendar information of the participants but also other related information. For example, the acquisition unit can acquire the participants' business trip schedules and vacation information and reflect it in adjusting the meeting date and time. The acquisition unit can also propose the optimal meeting date and time taking into consideration the participants' workload and the progress of the project. This makes it possible to set up meetings that avoid times when the participants are busy. Furthermore, the acquisition unit can acquire the participants' geographic location information and adjust the schedules of participants in different time zones. This makes it possible to efficiently set up meetings even for international teams.

[0066] The presentation unit can provide a feedback function for candidate dates and times so that the user can select the optimal date and time. For example, an interface is provided that allows the user to input feedback such as "this date and time is inconvenient" or "this date and time is good" for the candidate dates and times. This feedback is used as a reference when arranging the next meeting, and more accurate candidate dates and times are presented. The presentation unit can also refer to the user's past feedback history and preferentially suggest dates and times that suit the user's preferences. This allows the user to arrange a meeting with a higher level of satisfaction.

[0067] The reception unit can refer to past meeting history and automatically suggest frequently participating members. For example, members with whom the user has frequently met in the past can be automatically displayed as candidates. In addition, it can also preferentially suggest members with whom the user is frequently involved in specific projects. Furthermore, it can predict and suggest members who will be participating during a specific time period based on the user's past meeting history. In this way, it is possible to efficiently suggest frequently participating members by referring to the past meeting history.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The reception unit receives participant information from the user. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. Step 2: The acquisition unit cooperates with the calendar service to acquire the schedules of all the specified participants. For example, in Person A's calendar, an event such as "Meeting from 10:00 to 11:00" is recorded. Step 3: The analysis unit analyzes the acquired schedule and identifies the date and time when all the people are free. For example, if the date and time when A, B, and C are all free is "14:00 to 15:00," that date and time is extracted. Step 4: If there is no date and time available for all participants, the extraction unit extracts a date and time when the majority of participants can participate. For example, if A and B are available but C is not available from 4pm to 5pm, that date and time is presented as a candidate date. Step 5: The presentation unit presents the extracted dates and times as candidate dates to the user, for example, allowing the user to select the most suitable date and time.

[0070] (Example 2) A meeting arrangement system according to an embodiment of the present invention is a system for efficiently arranging a meeting with multiple people using AI. In this meeting arrangement system, a user specifies the participants of the meeting, and the AI ​​cooperates with a calendar service to obtain the schedules of all the participants. Next, the AI ​​analyzes the obtained schedule and extracts the date and time when all the participants are free. If there is no date and time when all the participants are free, the AI ​​extracts a date and time when many participants can participate and presents it as a candidate date. For example, a user specifies participants such as "Mr. A, Mr. B, and Mr. C," and the AI ​​cooperates with a calendar service to obtain the schedule and presents the date and time when all the participants are free or the date and time when many participants can participate as a candidate date, so that the meeting can be arranged smoothly. This allows the meeting arrangement system to allow the user to efficiently arrange a meeting.

[0071] The meeting arrangement system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, an extraction unit, and a presentation unit. The reception unit receives participant information from a user. The user is one of the participants, but may also be an assistant to the participants. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C". This information is input to the AI. The acquisition unit cooperates with a calendar service to acquire the schedules of all the specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The analysis unit analyzes the acquired schedule and identifies the date and time when all the schedules are free. For example, if the date and time when all of Mr. A, Mr. B, and Mr. C are free is "14:00 to 15:00", that date and time is extracted. If there is no date and time when all of the participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if Mr. A and Mr. B are free, but Mr. C is not free, the date and time is "16:00 to 17:00", that date and time is presented as a candidate date. The presentation unit presents the extracted date and time to the user as a candidate date. For example, the user can be allowed to select the optimal date and time. This allows the meeting arrangement system according to the embodiment to efficiently arrange the schedules of all participants and present the optimal meeting date and time.

[0072] The reception unit accepts participant information from the user. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. Specifically, when the user specifies participants for a meeting, the reception unit receives the information entered through the user interface and stores it in a database. The user interface is provided as a web application or a mobile application, allowing the user to easily enter participant information. The reception unit processes the entered information in real time and checks the integrity of the data before passing it to the AI. For example, it checks whether the specified participant is registered in the system, and if not, notifies the user and prompts them to correct it. In addition, the reception unit encrypts and stores the information entered by the user to ensure the security of the data. This allows the reception unit to accurately and safely accept participant information from the user and pass it on to the next processing step.

[0073] The acquisition unit cooperates with a calendar service to acquire the schedules of all the specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The acquisition unit cooperates with multiple calendar services through APIs to acquire the schedules of each participant in real time. Specifically, the acquisition unit accesses each calendar service using an authentication protocol such as OAuth and acquires the schedule information after obtaining the user's permission. The acquired schedule information includes detailed data such as date and time, location, participants, and type of event. The acquisition unit converts this data into a unified format so that the analysis unit can easily process it. In addition, the acquisition unit temporarily caches the acquired data to reduce the load caused by frequent data access. This allows the acquisition unit to efficiently acquire the schedules of all the specified participants and pass them on to the next analysis step.

[0074] The analysis unit analyzes the acquired schedule and identifies the date and time when all the schedules are free. For example, if the date and time when all of A, B, and C are free is "14:00 to 15:00", that date and time is extracted. The analysis unit uses an AI algorithm to analyze the acquired schedule data and identify the free time of all the participants. Specifically, it organizes the schedule of each participant in a timeline format so that overlaps and free time can be visually grasped. The AI ​​compares these timelines and quickly identifies the time period when all the participants are free. In addition, the analysis unit performs analysis taking into account the priority and conditions specified by the user (for example, specific time periods or days of the week). The analysis result includes not only the time period when all the participants are free, but also the time period when they are partially free. This allows the analysis unit to perform flexible analysis according to the needs of the user and identify the optimal meeting date and time.

[0075] When there is no date and time when all participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if A and B are free but C is not free from the date and time "16:00 to 17:00", that date and time is presented as a candidate date. Based on the data provided by the analysis unit, the extraction unit extracts a date and time when as many participants as possible can participate, even if a date and time when all participants can participate cannot be found. Specifically, the extraction unit scores the free time of each participant and preferentially extracts the time period when the most participants can participate. The extraction unit can present multiple optimal candidate dates taking into account the priority and conditions specified by the user. The extraction unit also provides the user with an option for when partial participation is possible, supporting flexible meeting adjustment. As a result, the extraction unit can efficiently extract optimal candidate dates and present them to the user even when all participants cannot participate.

[0076] The presentation unit presents the extracted dates and times to the user as candidate dates. For example, the user can select the optimal date and time. The presentation unit visually displays the extracted candidate dates through a user interface, allowing the user to easily select the optimal date and time. Specifically, the presentation unit displays the candidate dates in a calendar or list format, and provides detailed information about each candidate date (such as the number of people who can attend, the time period, and the location). After the user selects a candidate date, the presentation unit stores the information in the database again and sends a notification to all participants. In addition, the presentation unit provides additional filtering and sorting functions when the user selects a candidate date, allowing the user to quickly find the optimal date and time. In this way, the presentation unit can provide the user with an efficient and intuitive interface and assist in the process of selecting the optimal meeting date and time.

[0077] The reception unit can receive participant information from a user. The reception unit, for example, provides an interface for a user to specify participants of a meeting. For example, a user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. The reception unit can efficiently receive participant information from a user. For example, the reception unit provides an input form and collects necessary information when a user inputs participant information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can input participant information input by a user to an AI, which analyzes the information and receives the participant information.

[0078] The acquisition unit can acquire the schedules of all participants in cooperation with a calendar service. The acquisition unit, for example, cooperates with a calendar service to acquire the schedules of all specified participants. For example, a schedule such as "Meeting from 10:00 to 11:00" is written on Mr. A's calendar. The acquisition unit can efficiently acquire the schedules of all participants in cooperation with a calendar service. For example, the acquisition unit acquires schedule information of participants using an API of the calendar service. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the schedule information acquired from the calendar service to AI, which analyzes the information to acquire the schedule.

[0079] The analysis unit can analyze the acquired schedule and identify the date and time when everyone is free. The analysis unit, for example, analyzes the acquired schedule and identifies the date and time when everyone is free. For example, if the date and time when A, B, and C are all free is "14:00 to 15:00", that date and time is extracted. The analysis unit can efficiently identify the date and time when everyone is free. For example, the analysis unit executes an algorithm to identify the date and time when everyone is free based on the acquired schedule information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired schedule information to AI, and the AI ​​can analyze the information to identify the date and time when everyone is free.

[0080] The extraction unit can extract a date and time when multiple participants can participate if there is no date and time when all participants are free. For example, when there is no date and time when all participants are free, the extraction unit extracts a date and time when many participants can participate. For example, if A and B are free but C is not free from the date and time "16:00 to 17:00", that date and time is presented as a candidate date. The extraction unit can efficiently extract a date and time when many participants can participate. For example, the extraction unit executes an algorithm that specifies a date and time when many participants can participate based on the acquired schedule information. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the acquired schedule information into AI, and the AI ​​can analyze the information to extract a date and time when many participants can participate.

[0081] The presentation unit can present the extracted date and time to the user as a candidate date. The presentation unit, for example, presents the extracted date and time to the user as a candidate date. For example, the user can select the optimal date and time. The presentation unit can efficiently present the extracted date and time to the user. For example, the presentation unit provides an interface that displays the candidate dates to the user. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, which analyzes the information and presents the candidate dates to the user.

[0082] The presentation unit can notify the user of the candidate dates. The presentation unit, for example, notifies the user of the candidate dates. For example, the presentation unit enables the user to select the optimal date and time. The presentation unit can efficiently notify the user of the candidate dates. For example, the presentation unit provides an interface for notifying the user of the candidate dates. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, which analyzes the information and notifies the user of the candidate dates.

[0083] The presentation unit can enable the user to select the optimal date and time. The presentation unit, for example, enables the user to select the optimal date and time. For example, the presentation unit provides an interface for the user to select the optimal date and time from candidate dates. The presentation unit enables the user to select the optimal date and time, thereby smoothly arranging the meeting. For example, the presentation unit displays options when the user selects candidate dates, allowing the user to select the optimal date and time. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the extracted date and time to AI, and the AI ​​can analyze the information to enable the user to select the optimal date and time.

[0084] The reception unit can estimate the user's emotion and adjust the reception method of the participant information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the reception method of the participant information based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided and the input procedure is minimized. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized and the participant information can be quickly input. This improves the user's convenience by adjusting the reception method of the participant information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the reception method based on the result.

[0085] The reception unit can refer to past meeting history and automatically suggest frequently participating members. The reception unit, for example, refers to past meeting history and automatically suggests frequently participating members. For example, members with whom the user has frequently met in the past are automatically displayed as candidates. In addition, members with whom the user is frequently involved in a specific project can be preferentially suggested. Furthermore, members who will participate in a specific time period can be predicted and suggested from the user's past meeting history. In this way, by referring to the past meeting history, frequently participating members can be efficiently suggested. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past meeting history into AI, and the AI ​​can analyze the information to suggest frequently participating members.

[0086] The reception unit can automatically evaluate the importance of the meeting and set the priority based on the participants' job titles and fields of expertise. The reception unit can automatically evaluate the importance of the meeting and set the priority based on the participants' job titles and fields of expertise, for example. For example, if the participant has a high job title, the importance of the meeting is set high. Also, if the participant's field of expertise is related to the theme of the meeting, the priority can be set high. Furthermore, the importance of the meeting can be automatically evaluated and the priority can be set based on the participants' job titles and fields of expertise. As a result, important meetings can be prioritized by evaluating the importance of the meeting and setting the priority based on the participants' job titles and fields of expertise. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input information on the participants' job titles and fields of expertise into AI, which can analyze the information to evaluate the importance of the meeting and set the priority.

[0087] The reception unit can estimate the user's emotion and adjust the reception order of the participant information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the reception order of the participant information based on the estimated user's emotion. For example, if the user is nervous, important participant information can be input first. Also, if the user is relaxed, detailed participant information can be input later. Furthermore, if the user is in a hurry, the most important participant information can be input first. This improves the user's convenience by adjusting the reception order of the participant information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI, which can estimate the emotion, and adjust the reception order based on the result.

[0088] The reception unit can propose an optimal meeting format in consideration of the geographical location information of the participants. The reception unit, for example, proposes an optimal meeting format (online, offline) in consideration of the geographical location information of the participants. For example, when the participants are in different cities, an online meeting can be proposed. Also, when the participants are in the same office, an offline meeting can be proposed. Furthermore, the optimal meeting format can be proposed based on the geographical location information of the participants. In this way, the optimal meeting format can be proposed by considering the geographical location information of the participants. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the participants to AI, which analyzes the information and proposes the optimal meeting format.

[0089] The reception unit can analyze the social media activity of the participants and automatically add related participants. The reception unit, for example, analyzes the social media activity of the participants and automatically adds related participants. For example, members with whom the participants frequently interact on social media are automatically added. In addition, if the participant posts about a specific project, the member can be automatically added. Furthermore, the social media activity of the participants can be analyzed and related participants can be automatically added. In this way, related participants can be efficiently added by analyzing the social media activity of the participants. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity of the participants to AI, and the AI ​​can analyze the information and add related participants.

[0090] The acquisition unit can estimate the emotion of the user (or each participant) and adjust the timing of acquiring the schedule based on the estimated emotion of the user (or each participant). The acquisition unit, for example, estimates the emotion of the user (or each participant) and adjusts the timing of acquiring the schedule based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) feels stressed, the acquisition of the schedule is performed quickly. In addition, when the user (or each participant) is relaxed, detailed schedule information can also be acquired. Furthermore, when the user (or each participant) is in a hurry, the most important schedule information can also be acquired preferentially. This improves the convenience of the user (or each participant) by adjusting the timing of acquiring the schedule according to the emotion of the user (or each participant). The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input emotion data of a user (or each participant) to the generation AI, which can then estimate the emotion and adjust the acquisition timing based on the result.

[0091] The acquisition unit can improve the acquisition accuracy by referring to the past schedule history of the participant. The acquisition unit can improve the acquisition accuracy by referring to, for example, the past schedule history of the participant. For example, an optimal schedule is acquired based on the past schedule history of the participant. A schedule that avoids congestion can also be acquired from the past schedule history of the participant. Furthermore, the most efficient schedule can be acquired by analyzing the past schedule history of the participant. As a result, the acquisition accuracy can be improved by referring to the past schedule history of the participant. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the past schedule history of the participant to AI, and the AI ​​can analyze the information to acquire the optimal schedule.

[0092] The acquisition unit can acquire appropriate information by taking into account the privacy settings of the participant's calendar. The acquisition unit acquires appropriate information by taking into account, for example, the privacy settings of the participant's calendar. For example, based on the privacy settings of the participant's calendar, only publicly available schedules are acquired. Also, it is possible to acquire only necessary information by taking into account the privacy settings of the participant. Furthermore, it is possible to acquire appropriate information based on the privacy settings of the participant's calendar. In this way, it is possible to acquire appropriate information by taking into account the privacy settings of the participant. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the privacy settings of the participant's calendar to AI, and the AI ​​can analyze the information to acquire appropriate information.

[0093] The acquisition unit can estimate the emotion of the user (or each participant) and determine the priority of the schedule to be acquired based on the estimated emotion of the user (or each participant). The acquisition unit, for example, estimates the emotion of the user (or each participant) and determines the priority of the schedule to be acquired based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) is nervous, important schedules are acquired preferentially. In addition, when the user (or each participant) is relaxed, detailed schedules can also be acquired. Furthermore, when the user (or each participant) is in a hurry, the most important schedule can also be acquired preferentially. In this way, the convenience of the user (or each participant) is improved by determining the priority of the schedule to be acquired according to the emotion of the user (or each participant). The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input emotion data of a user (or each participant) to the generation AI, which can then estimate the emotion and determine the priority of the emotion to be acquired based on the result.

[0094] The acquisition unit can preferentially acquire highly relevant schedules by taking into account the geographical location information of the participants. The acquisition unit, for example, preferentially acquires highly relevant schedules by taking into account the geographical location information of the participants. For example, when the participants are in different cities, the acquisition unit preferentially acquires schedules for online meetings. Also, when the participants are in the same office, the acquisition unit can preferentially acquire schedules for offline meetings. Furthermore, based on the geographical location information of the participants, the acquisition unit can preferentially acquire schedules for offline meetings. As a result, by taking into account the geographical location information of the participants, the acquisition unit can preferentially acquire schedules for high relevance. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to acquire schedules for high relevance.

[0095] The acquisition unit can analyze the social media activity of the participant and acquire the related schedule. The acquisition unit can, for example, analyze the social media activity of the participant and acquire the related schedule. For example, the acquisition unit can acquire schedules with members with whom the participant frequently interacts on social media. In addition, if the participant posts about a specific project, it is also possible to acquire schedules related to the project. Furthermore, it is also possible to analyze the social media activity of the participant and acquire the related schedule. In this way, it is possible to efficiently acquire the related schedule by analyzing the social media activity of the participant. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the social media activity of the participant to AI, and the AI ​​can analyze the information to acquire the related schedule.

[0096] The analysis unit can estimate the emotion of the user (or each participant) and adjust the analysis method based on the estimated emotion of the user (or each participant). The analysis unit, for example, estimates the emotion of the user (or each participant) and adjusts the analysis method based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) is stressed, the analysis is performed quickly. Also, when the user (or each participant) is relaxed, a detailed analysis can be performed. Furthermore, when the user (or each participant) is in a hurry, the most important information can be analyzed preferentially. This improves the convenience of the user (or each participant) by adjusting the analysis method according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's (or each participant's) emotional data into the generation AI, which can then infer the emotions and adjust the analysis method based on the results.

[0097] The analysis unit can improve the analysis accuracy by referring to the past meeting data. The analysis unit can improve the analysis accuracy by referring to the past meeting data, for example. For example, the analysis unit selects an optimal analysis method based on the past meeting data. Also, an analysis method that avoids congestion can be selected from the past meeting data. Furthermore, the past meeting data can be analyzed to select the most efficient analysis method. In this way, the analysis accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input the past meeting data into AI, which analyzes the information and selects the optimal analysis method.

[0098] The analysis unit can customize the analysis results by considering the position and specialty of the participant. The analysis unit customizes the analysis results by considering, for example, the position and specialty of the participant. For example, if the participant has a high position, important information is analyzed preferentially. In addition, if the participant's specialty is related to the theme of the meeting, a detailed analysis can be performed. Furthermore, the analysis results can be customized based on the participant's position and specialty. As a result, the analysis results can be customized by considering the participant's position and specialty. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the analysis results.

[0099] The analysis unit can estimate the emotion of the user (or each participant) and adjust the display method of the analysis result based on the estimated emotion of the user (or each participant). The analysis unit, for example, estimates the emotion of the user (or each participant) and adjusts the display method of the analysis result based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) is nervous, a simple and highly visible display method is provided. Also, when the user (or each participant) is relaxed, a display method including detailed information can be provided. Furthermore, when the user (or each participant) is in a hurry, a display method that focuses on the main points can be provided. This improves the convenience of the user (or each participant) by adjusting the display method of the analysis result according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's (or each participant's) emotional data into the generation AI, which can then infer the emotions and adjust the way the analysis results are displayed based on the inference.

[0100] The analysis unit can optimize the analysis result by taking into account the geographical location information of the participants. The analysis unit optimizes the analysis result by taking into account, for example, the geographical location information of the participants. For example, when the participants are in different cities, the analysis result of the online meeting is optimized. Also, when the participants are in the same office, the analysis result of the offline meeting can be optimized. Furthermore, the analysis result can be optimized based on the geographical location information of the participants. In this way, the analysis result can be optimized by taking into account the geographical location information of the participants. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to optimize the analysis result.

[0101] The analysis unit can improve the analysis accuracy by referring to related literature and data. The analysis unit can improve the analysis accuracy by referring to, for example, related literature and data. For example, the analysis accuracy can be improved by referring to related literature. The analysis accuracy can also be improved by referring to related data. Furthermore, the analysis accuracy can be improved based on related literature and data. As a result, the analysis accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the analysis accuracy.

[0102] The extraction unit can estimate the emotion of the user (or each participant) and determine the priority of the date and time to be extracted based on the estimated emotion of the user (or each participant). The extraction unit, for example, estimates the emotion of the user (or each participant) and determines the priority of the date and time to be extracted based on the estimated emotion of the user (or each participant). For example, if the user (or each participant) is nervous, important dates and times are preferentially extracted. Also, if the user (or each participant) is relaxed, detailed dates and times can be extracted. Furthermore, if the user (or each participant) is in a hurry, the most important dates and times can be preferentially extracted. This improves the convenience of the user (or each participant) by determining the priority of the date and time to be extracted according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input emotion data of a user (or each participant) into the generation AI, which can then estimate the emotion and determine the priority of the extraction dates and times based on the results.

[0103] The extraction unit can improve the extraction accuracy by referring to the past meeting data. The extraction unit can improve the extraction accuracy by referring to the past meeting data, for example. For example, the optimal date and time is extracted based on the past meeting data. Also, a date and time that avoids crowding can be extracted from the past meeting data. Furthermore, the past meeting data can be analyzed and the most efficient date and time can be extracted. As a result, the extraction accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input the past meeting data into AI, and the AI ​​can analyze the information to extract the optimal date and time.

[0104] The extraction unit can customize the extraction result by considering the position and specialty of the participant. The extraction unit customizes the extraction result by considering, for example, the position and specialty of the participant. For example, if the participant has a high position, important dates and times are preferentially extracted. In addition, if the participant's specialty is related to the theme of the meeting, detailed dates and times can also be extracted. Furthermore, the extraction result can be customized based on the participant's position and specialty. As a result, the extraction result can be customized by considering the participant's position and specialty. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the extraction result.

[0105] The extraction unit can estimate the emotion of the user (or each participant) and adjust the display method of the extraction result based on the estimated emotion of the user (or each participant). The extraction unit, for example, estimates the emotion of the user (or each participant) and adjusts the display method of the extraction result based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) is nervous, a display method that is simple and highly visible is provided. Also, when the user (or each participant) is relaxed, a display method including detailed information can be provided. Furthermore, when the user (or each participant) is in a hurry, a display method that focuses on the main points can be provided. This improves the convenience of the user (or each participant) by adjusting the display method of the extraction result according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input emotion data of a user (or each participant) into the generation AI, which can then estimate the emotion and adjust the way in which the extracted results are displayed based on the estimation result.

[0106] The extraction unit can optimize the extraction result by taking into account the geographic location information of the participants. The extraction unit optimizes the extraction result by taking into account, for example, the geographic location information of the participants. For example, when the participants are in different cities, the extraction result of the online meeting is optimized. Also, when the participants are in the same office, the extraction result of the offline meeting can be optimized. Furthermore, the extraction result can be optimized based on the geographic location information of the participants. In this way, the extraction result can be optimized by taking into account the geographic location information of the participants. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, the extraction unit can input the geographic location information of the participants to AI, and the AI ​​can analyze the information to optimize the extraction result.

[0107] The extraction unit can improve the extraction accuracy by referring to related literature and data. The extraction unit can improve the extraction accuracy by referring to, for example, related literature and data. For example, the extraction accuracy can be improved by referring to related literature. The extraction accuracy can also be improved by referring to related data. Furthermore, the extraction accuracy can be improved based on related literature and data. As a result, the extraction accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the extraction unit can be performed, for example, using AI, or can be performed without using AI. For example, the extraction unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the extraction accuracy.

[0108] The presentation unit can estimate the emotion of the user (or each participant) and adjust the presentation method based on the estimated emotion of the user (or each participant). The presentation unit, for example, estimates the emotion of the user (or each participant) and adjusts the presentation method based on the estimated emotion of the user (or each participant). For example, when the user (or each participant) is nervous, a presentation method that is simple and highly visible is provided. Also, when the user (or each participant) is relaxed, a presentation method including detailed information can be provided. Furthermore, when the user (or each participant) is in a hurry, a presentation method that focuses on the main points can be provided. This improves the convenience of the user (or each participant) by adjusting the presentation method according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input emotion data of a user (or each participant) to the generation AI, which can then estimate the emotion and adjust the presentation method based on the results.

[0109] The presentation unit can improve the presentation accuracy by referring to past meeting data. The presentation unit can improve the presentation accuracy by referring to, for example, past meeting data. For example, the optimal presentation method is selected based on the past meeting data. Also, a presentation method that avoids congestion can be selected from the past meeting data. Furthermore, the past meeting data can be analyzed and the most efficient presentation method can be selected. As a result, the presentation accuracy can be improved by referring to the past meeting data. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI or can be performed without using AI. For example, the presentation unit can input the past meeting data to AI, and the AI ​​can analyze the information to select the optimal presentation method.

[0110] The presentation unit can customize the presentation content taking into consideration the position and specialty of the participant. The presentation unit customizes the presentation content taking into consideration, for example, the position and specialty of the participant. For example, if the participant has a high position, important information is presented preferentially. In addition, if the participant's specialty is related to the theme of the meeting, detailed information can be presented. Furthermore, the presentation content can be customized based on the participant's position and specialty. As a result, the presentation content can be customized by taking into consideration the participant's position and specialty. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input information on the participant's position and specialty into AI, and the AI ​​can analyze the information to customize the presentation content.

[0111] The presentation unit can estimate the emotion of the user (or each participant) and adjust the presentation order based on the estimated emotion of the user (or each participant). The presentation unit, for example, estimates the emotion of the user (or each participant) and adjusts the presentation order based on the estimated emotion of the user (or each participant). For example, if the user (or each participant) is nervous, important information is presented first. Also, if the user (or each participant) is relaxed, detailed information can be presented later. Furthermore, if the user (or each participant) is in a hurry, the most important information can be presented first. This improves the convenience of the user (or each participant) by adjusting the presentation order according to the emotion of the user (or each participant). The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input emotion data of a user (or each participant) to the generation AI, which can then estimate the emotion and adjust the presentation order based on the result.

[0112] The presentation unit can select the optimal presentation method by considering the geographical location information of the participants. The presentation unit selects the optimal presentation method by considering, for example, the geographical location information of the participants. For example, when the participants are in different cities, the presentation method of an online meeting is selected. Also, when the participants are in the same office, the presentation method of an offline meeting can be selected. Furthermore, the optimal presentation method can be selected based on the geographical location information of the participants. In this way, the optimal presentation method can be selected by considering the geographical location information of the participants. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the geographical location information of the participants to AI, and the AI ​​can analyze the information to select the optimal presentation method.

[0113] The presentation unit can improve the presentation accuracy by referring to related literature and data. The presentation unit can improve the presentation accuracy by referring to, for example, related literature and data. For example, the presentation accuracy can be improved by referring to related literature. The presentation accuracy can also be improved by referring to related data. Furthermore, the presentation accuracy can be improved based on related literature and data. As a result, the presentation accuracy can be improved by referring to related literature and data. A part or all of the above-mentioned processing in the presentation unit can be performed, for example, using AI, or can be performed without using AI. For example, the presentation unit can input related literature and data into AI, and the AI ​​can analyze the information to improve the presentation accuracy.

[0114] The presentation unit can notify the user of the candidate dates so that the user can select the optimal date and time. The presentation unit, for example, notifies the user of the candidate dates so that the user can select the optimal date and time. For example, a notification function is provided when the user selects the candidate dates. The presentation unit can also notify the user of the candidate dates so that the user can select the optimal date and time. This allows the user to select the optimal date and time, making it possible to smoothly arrange meetings. Some or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI. For example, the presentation unit can input the candidate dates to an AI, which analyzes the information and notifies the user.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The meeting arrangement system may further include a reminder unit. The reminder unit sets a reminder for a meeting date and time selected by the user and notifies the user before the meeting. For example, a notification may be sent one hour or 30 minutes before the meeting to ensure that the user does not forget the meeting. The reminder unit may also automatically add the meeting to the user's calendar. This may save the user the trouble of manually adding the schedule. Furthermore, the reminder unit may estimate the user's emotions and provide a notification method according to the user's situation, such as increasing the frequency of reminders if the user is feeling stressed.

[0117] The reception unit can provide an interface for the user to input the purpose and agenda of the meeting. For example, the user inputs the purpose of the meeting, such as "checking the progress of a project" or "generating ideas for a new product." This information is shared with the other participants, allowing smooth preparation for the meeting. The reception unit can also automatically suggest related materials and documents based on the agenda entered by the user. This saves the user the trouble of having to prepare the necessary materials in advance. Furthermore, the reception unit can provide useful information by referring to the user's past meeting history and displaying the history of meetings with similar agendas.

[0118] The acquisition unit can acquire not only the calendar information of the participants but also other related information. For example, the acquisition unit can acquire the participants' business trip schedules and vacation information and reflect it in adjusting the meeting date and time. The acquisition unit can also propose the optimal meeting date and time taking into consideration the participants' workload and the progress of the project. This makes it possible to set up meetings that avoid times when the participants are busy. Furthermore, the acquisition unit can acquire the participants' geographic location information and adjust the schedules of participants in different time zones. This makes it possible to efficiently set up meetings even for international teams.

[0119] The analysis unit can suggest the optimal time for a meeting based on the acquired schedule information. For example, the analysis unit can suggest a meeting time taking into consideration the time periods when participants are most concentrated and the time periods when they can refresh themselves between work tasks. The analysis unit can also analyze past meeting data to identify time periods with high attendance rates. This makes it possible to set a meeting at a time period when more participants can attend. Furthermore, the analysis unit can estimate the emotions of participants and select a time period when they are least stressed, allowing them to participate in the meeting in a relaxed state.

[0120] If there is no available date and time for all participants, the extraction unit can present multiple candidate dates and times, as well as display the advantages and disadvantages of each candidate date and time. For example, it can provide information that many participants can attend one date and time, but important participants cannot attend another date and time. The extraction unit can also rank the candidate dates and times based on the user's priorities, making it easier for the user to select the optimal date and time. Furthermore, the extraction unit can estimate the user's (or each participant's) emotions and make suggestions according to the user's (or each participant's) situation, such as reducing the number of candidate dates and times to narrow down the options if the user is feeling stressed.

[0121] The presentation unit can provide a visually easy-to-understand interface when presenting the extracted dates and times to the user. For example, the candidate dates and times can be displayed in a calendar format, allowing the user to intuitively select. The presentation unit can also display the attendance status of the participants for each candidate date and time in a different color. This allows the user to determine the optimal date and time at a glance. Furthermore, the presentation unit can provide a display method according to the user's situation, such as estimating the emotions of the user (or each participant) and displaying detailed information when the user is relaxed and displaying only the main points when the user is in a hurry.

[0122] The presentation unit can provide multiple notification methods when notifying the user of the candidate dates. For example, the presentation unit can provide notification in a manner that suits the user's preferences, such as email notification, push notification, or SMS notification. The presentation unit can also enable the user to set the timing of the notification. This allows the user to receive notifications according to his or her schedule. Furthermore, the presentation unit can provide a notification method that suits the user's situation, such as estimating the emotions of the user (or each participant) and reducing the frequency of notifications if the user is feeling stressed.

[0123] The presentation unit can provide a feedback function for candidate dates and times so that the user can select the optimal date and time. For example, an interface is provided that allows the user to input feedback such as "this date and time is inconvenient" or "this date and time is good" for the candidate dates and times. This feedback is used as a reference when arranging the next meeting, and more accurate candidate dates and times are presented. The presentation unit can also refer to the user's past feedback history and preferentially suggest dates and times that suit the user's preferences. This allows the user to arrange a meeting with a higher level of satisfaction.

[0124] The reception unit can estimate the user's emotions and adjust the method of receiving participant information based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input procedures. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input participant information quickly. In this way, by adjusting the method of receiving participant information according to the user's emotions, convenience for the user can be improved.

[0125] The reception unit can refer to past meeting history and automatically suggest frequently participating members. For example, members with whom the user has frequently met in the past can be automatically displayed as candidates. In addition, it can also preferentially suggest members with whom the user is frequently involved in specific projects. Furthermore, it can predict and suggest members who will be participating during a specific time period based on the user's past meeting history. In this way, it is possible to efficiently suggest frequently participating members by referring to the past meeting history.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The reception unit receives participant information from the user. For example, the user specifies participants such as "Mr. A, Mr. B, and Mr. C." This information is input to the AI. Step 2: The acquisition unit cooperates with the calendar service to acquire the schedules of all the specified participants. For example, in Person A's calendar, an event such as "Meeting from 10:00 to 11:00" is recorded. Step 3: The analysis unit analyzes the acquired schedule and identifies the date and time when all the people are free. For example, if the date and time when A, B, and C are all free is "14:00 to 15:00," that date and time is extracted. Step 4: If there is no date and time available for all participants, the extraction unit extracts a date and time when the majority of participants can participate. For example, if A and B are available but C is not available from 4pm to 5pm, that date and time is presented as a candidate date. Step 5: The presentation unit presents the extracted dates and times as candidate dates to the user, for example, allowing the user to select the most suitable date and time.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, extraction unit, and presentation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives participant information from a user. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires the schedules of all participants in cooperation with a calendar service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts the dates and times when all participants are free and the dates and times when a large number of participants can participate. The presentation unit is realized by the control unit 46A of the smart device 14 and presents the extracted dates and times to the user. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] 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 performed in a secure state.

[0139] Fig. 4 shows an example of 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.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0145] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, extraction unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts participant information from a user. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires the schedules of all participants in cooperation with a calendar service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts the date and time when all participants are free and the date and time when a large number of participants can participate. The presentation unit is realized by the control unit 46A of the smart glasses 214 and presents the extracted date and time to the user. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0151] 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.

[0152] 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] 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 performed in a secure state.

[0155] 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.

[0156] 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.

[0157] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, extraction unit, and presentation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives participant information from a user. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires the schedules of all participants in cooperation with a calendar service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts the dates and times when all participants are free and the dates and times when a large number of participants can participate. The presentation unit is realized by the control unit 46A of the headset type terminal 314 and presents the extracted dates and times to the user. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a 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.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0167] 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.

[0168] 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 the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0170] 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 performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0172] 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.

[0173] 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.

[0174] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0176] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] 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 a voice indicating a user input for 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.

[0178] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0180] Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, extraction unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts participant information from a user. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires the schedules of all participants in cooperation with a calendar service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts the date and time when all participants are free and the date and time when a large number of participants can participate. The presentation unit is realized by the control unit 46A of the robot 414 and presents the extracted date and time to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0184] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0188] 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.

[0189] 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 Universal Serial Bus (USB) 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.

[0190] In addition, 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 upon request from the data processing device 12.

[0191] 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.

[0192] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0194] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0195] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0199] (Appendix 1) A reception unit that receives participant information; an acquisition unit that acquires a schedule in cooperation with a calendar service based on the information accepted by the acceptance unit; an analysis unit that analyzes the schedule acquired by the acquisition unit; an extraction unit that extracts a date and time when all participants are free or a date and time when multiple participants can participate based on the information analyzed by the analysis unit; A presentation unit that presents the date and time extracted by the extraction unit as a candidate date. A system characterized by: (Appendix 2) The reception unit is Accept participant information from users 2. The system of claim 1, (Appendix 3) The acquisition unit is Link with a calendar service to get the schedules of all participants 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the events retrieved and identify when everyone is free 2. The system of claim 1, (Appendix 5) The extraction unit is If there are no available dates and times for all participants, extract dates and times when multiple participants can attend 2. The system of claim 1, (Appendix 6) The presentation unit is Present the extracted date and time to the user as candidate dates 2. The system of claim 1, (Appendix 7) The presentation unit is Notify users of proposed dates 2. The system of claim 1, (Appendix 8) The presentation unit is Allow users to choose the best time and date 2. The system of claim 1, (Appendix 9) The reception unit is To estimate a user's emotion and adjust a method of accepting participant information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The reception unit is Automatically suggest frequent attendees by referencing past meeting history 2. The system of claim 1, (Appendix 11) The reception unit is Automatically rate and prioritize meetings based on participants' roles and areas of expertise 2. The system of claim 1, (Appendix 12) The reception unit is To estimate a user's emotion and adjust the order of accepting participant information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The reception unit is Suggest optimal meeting formats based on participants' geographic locations 2. The system of claim 1, (Appendix 14) The reception unit is Analyze attendee social media activity and automatically add relevant attendees 2. The system of claim 1, (Appendix 15) The acquisition unit is To estimate a user's emotion and adjust a schedule acquisition timing based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The acquisition unit is Improve accuracy by referencing past schedule history of participants 2. The system of claim 1, (Appendix 17) The acquisition unit is Obtain the right information by respecting the privacy settings of the participants' calendars 2. The system of claim 1, (Appendix 18) The acquisition unit is Estimate the user's emotions and prioritize the schedule to be acquired based on the estimated user's emotions. 2. The system of claim 1, (Appendix 19) The acquisition unit is Prioritize the most relevant events by taking into account the geographic location of attendees 2. The system of claim 1, (Appendix 20) The acquisition unit is Analyze attendees' social media activity and get relevant events 2. The system of claim 1, (Appendix 21) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The analysis unit is Refer to past meeting data to improve analysis accuracy 2. The system of claim 1, (Appendix 23) The analysis unit is Customize your analysis based on participants' job titles and areas of expertise 2. The system of claim 1, (Appendix 24) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The analysis unit is Optimize your analysis by taking into account participants' geographic locations 2. The system of claim 1, (Appendix 26) The analysis unit is Improve the accuracy of your analysis by referring to relevant literature and data 2. The system of claim 1, (Appendix 27) The extraction unit is A user's emotion is estimated, and a priority order of dates and times to be extracted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The extraction unit is Refer to past meeting data to improve extraction accuracy 2. The system of claim 1, (Appendix 29) The extraction unit is Customize your extraction results by considering participants' job titles and areas of expertise 2. The system of claim 1, (Appendix 30) The extraction unit is Estimate the user's emotion and adjust the display method of the extracted results based on the estimated user's emotion. 2. The system of claim 1, (Appendix 31) The extraction unit is Optimize extraction results by considering participants' geographic location 2. The system of claim 1, (Appendix 32) The extraction unit is Improve extraction accuracy by referring to related literature and data 2. The system of claim 1, (Appendix 33) The presentation unit is Estimate user emotions and adjust presentation methods based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The presentation unit is Refer to past meeting data to improve presentation accuracy 2. The system of claim 1, (Appendix 35) The presentation unit is Tailor your presentation to participants based on their job title and area of ​​expertise 2. The system of claim 1, (Appendix 36) The presentation unit is Estimate the user's emotion and adjust the presentation order based on the estimated user emotion. 2. The system of claim 1, (Appendix 37) The presentation unit is Select the best presentation method based on participants' geographic location 2. The system of claim 1, (Appendix 38) The presentation unit is Refer to related literature and data to improve presentation accuracy 2. The system of claim 1, (Appendix 39) The presentation unit is Present suggested dates so users can choose the best date and time 2. The system of claim 1, [Explanation of symbols]

[0200] 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 reception unit that receives participant information; an acquisition unit that acquires a schedule in cooperation with a calendar service based on the information accepted by the acceptance unit; an analysis unit that analyzes the schedule acquired by the acquisition unit; an extraction unit that extracts a date and time when all participants are free or a date and time when multiple participants can participate based on the information analyzed by the analysis unit; A presentation unit that presents the date and time extracted by the extraction unit as a candidate date. A system characterized by:

2. The reception unit is Accept participant information from users 2. The system of claim 1 .

3. The reception unit is Automatically suggest frequent attendees by referencing past meeting history 2. The system of claim 1 .

4. The reception unit is Analyze attendee social media activity and automatically add relevant attendees 2. The system of claim 1 .

5. The presentation unit is Tailor your presentation to participants based on their job title and area of ​​expertise 2. The system of claim 1 .

6. The presentation unit is Estimate the emotions of participants and adjust the presentation method based on the estimated emotions of participants 2. The system of claim 1 .

7. The extraction unit is Estimate the emotions of participants and prioritize the dates and times to be extracted based on the estimated emotions of participants.

2. The system of claim 1 .

8. The presentation unit is Present suggested dates so users can choose the best date and time 2. The system of claim 1 .

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