system

Abstract

The system according to the embodiment aims to efficiently adjust the schedules of participants and propose the optimal date and time for a meeting. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a scheduled conference period and participants. The acquisition unit acquires the schedules of the participants based on the information input by the reception unit. The analysis unit analyzes the schedules acquired by the acquisition unit. The proposal unit proposes candidate dates and times based on the information analyzed by the analysis unit. The provision unit provides the candidate dates and times proposed by the proposal unit to a user.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to efficiently coordinate the schedules of participants when setting up a conference.

[0005] The system according to the embodiment aims to efficiently adjust the schedules of participants and propose the optimal date and time for a meeting. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a scheduled conference period and participants. The acquisition unit acquires the schedules of the participants based on the information input by the reception unit. The analysis unit analyzes the schedules acquired by the acquisition unit. The proposal unit proposes candidate dates and times based on the information analyzed by the analysis unit. The provision unit provides the candidate dates and times proposed by the proposal unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently adjust the schedules of participants and propose the optimal date and time for the meeting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A conference scheduling system according to an embodiment of the present invention is a system that can efficiently schedule a conference by suggesting candidate dates and times simply by inputting the scheduled conference period and participants. Even if a conference is scheduled in the participants' schedules, this system selects candidates along with constraints by taking into account factors such as the response status to the scheduled conference, the importance of the conference, and past attendance. This allows the conference scheduling system to select a date and time that is convenient for all participants. For example, in the conference scheduling system, a user inputs the scheduled conference period and participants. The conference scheduling system then obtains the participants' schedules and checks each participant's scheduled conference. At this time, the system considers factors such as the response status to the scheduled conference (responded / unresponded), the importance of the conference (required / optional), and past attendance. The conference scheduling system then proposes candidate dates and times for the conference based on this information. For example, even if a conference is already scheduled in the participants' schedules, if the conference is optional or unresponded to, it may be proposed as a candidate date and time. Furthermore, by taking into account past attendance, a date and time that is convenient for the participants to attend is selected. Furthermore, the conference scheduling system also considers constraints. For example, it is possible to set conditions such as avoiding certain time periods or requiring certain participants to be present. This allows the system to propose more appropriate candidate dates and times, which makes it possible for the system to efficiently schedule meetings and select dates and times that are convenient for all participants to attend.

[0029] A conference scheduling system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a scheduled conference period and participants. The acquisition unit acquires the participants' schedules based on the information input by the reception unit. The analysis unit analyzes the schedules acquired by the acquisition unit. The proposal unit proposes candidate dates and times based on the information analyzed by the analysis unit. The provision unit provides the user with the candidate dates and times proposed by the proposal unit. For example, the reception unit provides an interface through which the user inputs the scheduled conference period and participants. The acquisition unit acquires the participants' calendar information and checks each participant's schedule. The analysis unit analyzes the acquired schedule and considers factors such as the response status (responded / unresponded) to the scheduled conference, the importance of the conference (required / optional), and past attendance status. The proposal unit proposes candidate dates and times for the conference based on this information. The provision unit displays the proposed candidate dates and times to the user and prompts the user to select one. As a result, the conference scheduling system according to an embodiment can propose candidate dates and times and efficiently schedule a conference simply by inputting the scheduled conference period and participants.

[0030] The conference arrangement system includes a confirmation unit that confirms the status of responses to a conference schedule. The confirmation unit confirms the status of responses to a conference schedule. For example, the confirmation unit confirms whether a participant has responded to a conference request or not. The confirmation unit can also confirm whether a participant is available to attend a conference. The confirmation unit can also confirm whether a participant has responded to a conference request within a deadline. In this way, the conference arrangement system can propose more appropriate candidate dates and times by confirming the status of responses to a conference request.

[0031] The conference arrangement system includes a determination unit that determines the importance of a conference. The determination unit determines the importance of a conference. For example, the determination unit determines the importance based on the purpose of the conference. The determination unit can also determine the importance based on the positions of the participants. The determination unit can also determine the importance based on the content of the conference. In this way, the conference arrangement system can prioritize important conferences by determining the importance of the conferences.

[0032] The conference arrangement system includes an evaluation unit that evaluates attendance status based on past attendance status. The evaluation unit evaluates attendance status based on past attendance status. For example, the evaluation unit evaluates the past attendance rate of participants. The evaluation unit can also evaluate attendance status based on the importance of past meetings. The evaluation unit can also evaluate attendance status based on the frequency of attendance at past meetings. In this way, the conference arrangement system can select a date and time that is easy for participants to attend by taking past attendance status into consideration.

[0033] The conference scheduling system includes a setting unit that sets constraints. The setting unit sets the constraints. For example, the setting unit sets a constraint that avoids a specific time period. The setting unit can also set a constraint that requires specific participants to be present. The setting unit can also set a constraint regarding the location of the conference. In this way, the conference scheduling system can set constraints and propose candidate dates and times based on specific conditions.

[0034] The conference arrangement system includes an update unit that updates the schedules of participants in real time. The update unit updates the schedules of participants in real time. For example, the update unit obtains calendar information of participants in real time and updates the schedules. The update unit can also immediately reflect any changes to the schedules of participants. The update unit can also periodically check the schedules of participants and update them based on the latest information. In this way, the conference arrangement system can propose candidate dates and times based on the latest information by updating the schedules of participants in real time.

[0035] The reception unit can analyze the user's past conference setting history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates conference periods that the user has frequently set in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest conference periods to be set for specific time periods based on the user's past conference setting history. In this way, by analyzing the past conference setting history, it is possible to suggest the optimal input method for the user.

[0036] The reception unit can perform filtering based on the user's current project and areas of interest when inputting the meeting schedule period. For example, the reception unit preferentially displays meeting periods related to the user's ongoing project. The reception unit can also suggest related meeting periods based on the user's areas of interest. The reception unit can also suggest the optimal meeting period depending on the progress of the user's project. In this way, by filtering based on the user's current project and areas of interest, it is possible to suggest highly relevant meeting schedule periods.

[0037] When inputting the scheduled conference period, the reception unit can select an appropriate input means according to the user's input method. For example, when the user inputs the scheduled conference period by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the scheduled conference period by text, the reception unit can also support text input preferentially. Furthermore, when the user inputs the scheduled conference period by image, the reception unit can also support the input using image recognition technology. This allows the scheduled conference period to be input efficiently by selecting the optimal input means according to the user's input method.

[0038] When inputting a scheduled conference period, the reception unit can prioritize inputting a highly relevant period in consideration of the user's geographical location information. For example, the reception unit prioritizes inputting a scheduled conference period in a location close to the user's current location. The reception unit can also propose an optimal scheduled conference period based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also input the scheduled conference period in consideration of the geographical location information of the destination. In this way, a highly relevant scheduled conference period can be proposed by taking the user's geographical location information into consideration.

[0039] When inputting the meeting schedule period, the reception unit can analyze the user's social media activity and input a relevant period. For example, the reception unit can suggest a meeting schedule period related to a location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest a relevant meeting schedule period. The reception unit can also suggest a relevant meeting schedule period by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a highly relevant meeting schedule period.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting the conference setting period. The reception unit can propose the optimal input method, for example, based on feedback provided by the user in the past. The reception unit can also optimize the input procedure based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's feedback. In this way, the optimal input method can be proposed by reflecting the user's past feedback.

[0041] The acquisition unit can analyze the user's past schedule acquisition history and select an appropriate acquisition method. For example, the acquisition unit automatically acquires schedules that the user frequently acquired in the past. The acquisition unit can also preferentially select an acquisition method (voice, text, etc.) that the user has used in the past. The acquisition unit can also predict and select a schedule to be acquired during a specific time period from the user's past schedule acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past schedule acquisition history.

[0042] When acquiring a schedule, the acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the acquisition unit preferentially acquires schedules related to a project currently in progress by the user. The acquisition unit can also acquire related schedules based on the user's area of ​​interest. The acquisition unit can also acquire an optimal schedule depending on the progress of the user's project. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to acquire highly relevant schedules.

[0043] When acquiring a schedule, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, when the user acquires the schedule by voice, the acquisition unit supports acquisition using voice recognition technology. Furthermore, when the user acquires the schedule by text, the acquisition unit can also support text input preferentially. Furthermore, when the user acquires the schedule by image, the acquisition unit can also support acquisition using image recognition technology. This allows for efficient schedule acquisition by selecting the optimal acquisition means according to the user's input method.

[0044] When acquiring a schedule, the acquisition unit can prioritize acquiring highly relevant schedules by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring schedules for locations close to the user's current location. The acquisition unit can also propose an optimal schedule based on the user's geographical location information. Furthermore, when the user is traveling, the acquisition unit can also acquire a schedule by taking into account the geographical location information of the destination. In this way, a highly relevant schedule can be acquired by taking into account the user's geographical location information.

[0045] When acquiring a schedule, the acquisition unit can analyze the user's social media activity and acquire a related schedule. For example, the acquisition unit can suggest a schedule related to a place where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and suggest a related schedule. The acquisition unit can also suggest a related schedule by referring to the activities of the user's friends on social media. In this way, a highly relevant schedule can be acquired by analyzing the user's social media activity.

[0046] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a schedule. The acquisition unit can propose an optimal acquisition method based on, for example, feedback provided by the user in the past. The acquisition unit can also optimize the acquisition procedure based on the user's past feedback. The acquisition unit can also customize the acquisition interface by reflecting the user's feedback. This makes it possible to propose an optimal acquisition method by reflecting the user's past feedback.

[0047] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the schedule. For example, the analysis unit performs a detailed analysis on a schedule with a high level of importance. The analysis unit can also perform a simplified analysis on a schedule with a low level of importance. The analysis unit can also determine the priority of the analysis based on the importance of the schedule. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the schedule.

[0048] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the schedule. For example, the analysis unit can apply a business analysis algorithm to a schedule for a business meeting. The analysis unit can also apply a private analysis algorithm to a schedule for a private event. The analysis unit can also select and apply the optimal analysis algorithm for each category. This allows for efficient analysis by applying the optimal analysis algorithm depending on the schedule category.

[0049] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also reflect feedback from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also improve the analysis procedure by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0050] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. For example, the analysis unit prioritizes analysis of schedules with an upcoming submission deadline. The analysis unit can also postpone analysis of schedules with a distant submission deadline. The analysis unit can also dynamically adjust the priority of analysis depending on the submission time. In this way, by determining the priority of analysis based on the submission time of the schedule, analysis can be performed efficiently.

[0051] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the schedules. For example, the analysis unit prioritizes analysis of highly relevant schedules. The analysis unit can also postpone analysis of less relevant schedules. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the schedules. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the schedules.

[0052] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can provide an analysis that uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can provide an analysis that avoids technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for efficient analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0053] The suggestion unit can adjust the level of detail of the proposal based on the importance of the candidate date and time when making a proposal. For example, the suggestion unit makes a detailed proposal for a candidate date and time with high importance. The suggestion unit can also make a simplified proposal for a candidate date and time with low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the candidate date and time. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the candidate date and time.

[0054] The suggestion unit can apply an appropriate suggestion algorithm depending on the category of the candidate date and time when making a suggestion. For example, the suggestion unit can apply a business-oriented suggestion algorithm to a candidate date and time for a business meeting. The suggestion unit can also apply a private-oriented suggestion algorithm to a candidate date and time for a private event. The suggestion unit can also select and apply an optimal suggestion algorithm for each category. This allows for efficient suggestions by applying the optimal suggestion algorithm depending on the category of the candidate date and time.

[0055] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also reflect feedback from the user's past proposal results to improve the accuracy of the proposal. The suggestion unit can also improve the proposal procedure by referring to the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results.

[0056] The suggestion unit can determine the priority of the proposals based on the submission dates of the candidate dates and times when making the proposals. For example, the suggestion unit can prioritize proposals for candidate dates and times with a submission deadline that is close. The suggestion unit can also postpone proposals for candidate dates and times with a submission deadline that is far away. The suggestion unit can also dynamically adjust the priority of the proposals depending on the submission dates. This allows efficient proposals to be made by determining the priority of the proposals based on the submission dates of the candidate dates and times.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the candidate dates and times when making suggestions. For example, the suggestion unit can prioritize suggestions for candidate dates and times with high relevance. The suggestion unit can also postpone suggestions for candidate dates and times with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the candidate dates and times. This allows suggestions to be made efficiently by adjusting the order of suggestions based on the relevance of the candidate dates and times.

[0058] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, if the user's level of expertise is high, the suggestion unit can provide a proposal that uses a lot of technical terms. Also, if the user's level of expertise is low, the suggestion unit can provide a proposal that avoids technical terms. Also, the suggestion unit can dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, suggestions can be made efficiently.

[0059] The providing unit can adjust the level of detail to be provided based on the importance of the candidate date and time when providing the information. For example, the providing unit provides detailed information for a candidate date and time with a high level of importance. The providing unit can also provide simplified information for a candidate date and time with a low level of importance. The providing unit can also dynamically adjust the level of detail to be provided according to the importance of the candidate date and time. This allows information to be provided efficiently by adjusting the level of detail to be provided based on the importance of the candidate date and time.

[0060] The providing unit can apply different providing algorithms depending on the category of the candidate date and time when providing information. For example, the providing unit can apply a business-oriented providing algorithm to candidate dates and times for a business meeting. The providing unit can also apply a private-oriented providing algorithm to candidate dates and times for a private event. The providing unit can also select and apply the optimal providing algorithm for each category. This allows information to be provided efficiently by applying the optimal providing algorithm depending on the category of the candidate date and time.

[0061] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit optimizes the provision algorithm based on the user's past provision results. The providing unit can also reflect feedback to improve the accuracy of provision from the user's past provision results. The providing unit can also improve the provision procedure by referring to the user's past provision results. In this way, the accuracy of provision can be improved by referring to the user's past provision results.

[0062] The providing unit can determine the priority of provision based on the submission time of the candidate date and time at the time of provision. For example, the providing unit provides preferentially the candidate date and time with a submission deadline approaching. The providing unit can also postpone the provision of the candidate date and time with a submission deadline further away. The providing unit can also dynamically adjust the priority of provision depending on the submission time. In this way, by determining the priority of provision based on the submission time of the candidate date and time, information can be provided efficiently.

[0063] The providing unit can adjust the order of provision based on the relevance of the candidate dates and times when providing information. For example, the providing unit provides highly relevant candidate dates and times with priority. The providing unit can also provide less relevant candidate dates and times at a later date. The providing unit can also dynamically adjust the order of provision according to the relevance of the candidate dates and times. In this way, by adjusting the order of provision based on the relevance of the candidate dates and times, information can be provided efficiently.

[0064] The providing unit can adjust the use of technical terms in the provided information according to the user's level of expertise when providing the information. For example, if the user's level of expertise is high, the providing unit can provide information that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can also provide information that avoids technical terms. Furthermore, the providing unit can dynamically adjust the use of technical terms in the provided information according to the user's level of expertise. This allows information to be provided efficiently by adjusting the use of technical terms in the provided information according to the user's level of expertise.

[0065] The confirmation unit can adjust the level of detail of the confirmation based on the importance of the answer status during confirmation. For example, the confirmation unit performs detailed confirmation for an answer status with a high level of importance. The confirmation unit can also perform simplified confirmation for an answer status with a low level of importance. The confirmation unit can also dynamically adjust the level of detail of the confirmation according to the importance of the answer status. This allows for efficient confirmation by adjusting the level of detail of the confirmation based on the importance of the answer status.

[0066] The confirmation unit can apply different confirmation algorithms depending on the category of the response status during confirmation. For example, the confirmation unit applies a business confirmation algorithm to the response status of a business meeting. The confirmation unit can also apply a private confirmation algorithm to the response status of a private event. The confirmation unit can also select and apply the optimal confirmation algorithm for each category. This allows efficient confirmation by applying the optimal confirmation algorithm depending on the category of the response status.

[0067] The confirmation unit can determine the confirmation priority based on the submission time of the answer status when confirming. For example, the confirmation unit prioritizes confirmation of answer statuses whose submission deadline is approaching. The confirmation unit can also postpone confirmation of answer statuses whose submission deadline is far away. The confirmation unit can also dynamically adjust the confirmation priority according to the submission time. In this way, by determining the confirmation priority based on the submission time of the answer status, confirmation can be performed efficiently.

[0068] The confirmation unit can adjust the confirmation order based on the relevance of the answer status during confirmation. For example, the confirmation unit prioritizes confirmation of highly relevant answer statuses. The confirmation unit can also postpone confirmation of less relevant answer statuses. The confirmation unit can also dynamically adjust the confirmation order according to the relevance of the answer statuses. Thus, by adjusting the confirmation order based on the relevance of the answer statuses, confirmation can be performed efficiently.

[0069] The determination unit can adjust the level of detail of the determination based on the importance level at the time of determination. For example, the determination unit performs a detailed determination for a high importance level. The determination unit can also perform a simplified determination for a low importance level. The determination unit can also dynamically adjust the level of detail of the determination according to the importance level. This allows for efficient determination by adjusting the level of detail of the determination based on the importance level.

[0070] The determination unit can apply different determination algorithms depending on the importance category when making a determination. For example, the determination unit can apply a business-oriented determination algorithm to the importance of a business meeting. The determination unit can also apply a private-oriented determination algorithm to the importance of a private event. The determination unit can also select and apply an optimal determination algorithm for each category. This allows for efficient determination by applying the optimal determination algorithm depending on the importance category.

[0071] The determination unit can determine the priority of the determination based on the submission time of the importance level at the time of determination. For example, the determination unit prioritizes the determination for the importance level with an upcoming submission deadline. The determination unit can also postpone the determination for the importance level with a distant submission deadline. The determination unit can also dynamically adjust the priority of the determination based on the submission time. In this way, by determining the priority of the determination based on the submission time of the importance level, the determination can be made efficiently.

[0072] The determination unit can adjust the order of determination based on the relevance of the importance levels when making a determination. For example, the determination unit prioritizes determination for highly relevant importance levels. The determination unit can also postpone determination for less relevant importance levels. The determination unit can also dynamically adjust the order of determination according to the relevance of the importance levels. This allows for efficient determination by adjusting the order of determination based on the relevance of the importance levels.

[0073] The consideration unit can adjust the level of detail of consideration based on the importance of past attendance status during consideration. For example, the consideration unit performs detailed consideration for past attendance status with high importance. The consideration unit can also perform simplified consideration for past attendance status with low importance. The consideration unit can also dynamically adjust the level of detail of consideration according to the importance of past attendance status. In this way, by adjusting the level of detail of consideration based on the importance of past attendance status, consideration can be performed efficiently.

[0074] The consideration unit can apply an appropriate consideration algorithm depending on the category of the past attendance status during consideration. For example, the consideration unit applies a business-oriented consideration algorithm to the past attendance status of a business meeting. The consideration unit can also apply a private-oriented consideration algorithm to the past attendance status of a private event. The consideration unit can also select and apply an optimal consideration algorithm for each category. This allows for efficient consideration by applying an optimal consideration algorithm depending on the category of the past attendance status.

[0075] The consideration unit can determine the priority of consideration based on the submission time of past attendance records during consideration. For example, the consideration unit prioritizes consideration of past attendance records with an upcoming submission deadline. The consideration unit can also postpone consideration of past attendance records with a distant submission deadline. The consideration unit can also dynamically adjust the priority of consideration depending on the submission time. This allows for efficient consideration by determining the priority of consideration based on the submission time of past attendance records.

[0076] The consideration unit can adjust the order of consideration based on the relevance of past attendance records during consideration. For example, the consideration unit prioritizes consideration of past attendance records with high relevance. The consideration unit can also postpone consideration of past attendance records with low relevance. The consideration unit can also dynamically adjust the order of consideration according to the relevance of past attendance records. As a result, by adjusting the order of consideration based on the relevance of past attendance records, consideration can be performed efficiently.

[0077] The setting unit can adjust the level of detail of the setting based on the importance of the constraint condition during setting. For example, the setting unit performs detailed setting for a constraint condition with high importance. The setting unit can also perform simplified setting for a constraint condition with low importance. The setting unit can also dynamically adjust the level of detail of the setting according to the importance of the constraint condition. This allows for efficient setting by adjusting the level of detail of the setting based on the importance of the constraint condition.

[0078] During the setting process, the setting unit can apply an appropriate setting algorithm depending on the category of the constraint. For example, the setting unit can apply a business setting algorithm to the constraints of a business meeting. The setting unit can also apply a private setting algorithm to the constraints of a private event. The setting unit can also select and apply the optimal setting algorithm for each category. This allows for efficient setting by applying the optimal setting algorithm depending on the category of the constraint.

[0079] The setting unit can determine the priority of the settings based on the submission time of the constraints when setting them. For example, the setting unit prioritizes the setting of constraints whose submission deadline is approaching. The setting unit can also postpone the setting of constraints whose submission deadline is far away. The setting unit can also dynamically adjust the priority of the settings according to the submission time. This allows for efficient setting by determining the priority of the settings based on the submission time of the constraints.

[0080] The setting unit can adjust the order of setting based on the relevance of the constraint conditions during setting. For example, the setting unit prioritizes setting for highly relevant constraint conditions. The setting unit can also postpone setting for less relevant constraint conditions. The setting unit can also dynamically adjust the order of setting according to the relevance of the constraint conditions. This allows for efficient setting by adjusting the order of setting based on the relevance of the constraint conditions.

[0081] The update unit can adjust the level of detail of the update based on the importance of the schedule during the update. For example, the update unit performs a detailed update for a schedule with a high level of importance. The update unit can also perform a simplified update for a schedule with a low level of importance. The update unit can also dynamically adjust the level of detail of the update according to the importance of the schedule. This allows for efficient update by adjusting the level of detail of the update based on the importance of the schedule.

[0082] The update unit can apply an appropriate update algorithm depending on the category of the schedule when updating. For example, the update unit can apply a business update algorithm to a schedule for a business meeting. The update unit can also apply a private update algorithm to a schedule for a private event. The update unit can also select and apply the optimal update algorithm for each category. This allows for efficient updating by applying the optimal update algorithm depending on the schedule category.

[0083] The update unit can determine the priority of updates based on the submission time of the schedule when updating. For example, the update unit prioritizes updating of schedules with an upcoming submission deadline. The update unit can also postpone updating of schedules with a distant submission deadline. The update unit can also dynamically adjust the priority of updates according to the submission time. In this way, by determining the priority of updates based on the submission time of the schedule, updates can be performed efficiently.

[0084] The update unit can adjust the order of updates based on the relevance of the schedules when updating. For example, the update unit prioritizes updating of highly relevant schedules. The update unit can also postpone updating of less relevant schedules. The update unit can also dynamically adjust the order of updates according to the relevance of the schedules. In this way, by adjusting the order of updates based on the relevance of the schedules, updates can be performed efficiently.

[0085] The update unit can propose an appropriate update method by reflecting the user's past feedback during an update. The update unit can propose an optimal update method based on, for example, feedback provided by the user in the past. The update unit can also optimize the update procedure based on the user's past feedback. The update unit can also customize the update interface by reflecting the user's past feedback. This allows the optimal update method to be proposed by reflecting the user's past feedback.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The conference scheduling system can also include a health management unit that takes into account the health status of participants. For example, the health management unit acquires health data of participants and adjusts the proposed date and time of the conference if the participant is not feeling well. The health management unit can also shorten the conference time if a participant is overworked. The health management unit can also recommend remote participation based on the participant's health status. This makes it possible to schedule a conference that takes into account the participant's health status.

[0088] The conference scheduling system can also include a travel time management unit that takes into account participants' travel time. The travel time management unit calculates travel time based on, for example, the participants' current locations and the conference location, and suggests candidate dates and times with the shortest travel time. The travel time management unit can also suggest the optimal means of transportation taking traffic conditions into account. The travel time management unit can also suggest candidate dates and times for remote conferences that participants can join while traveling. This makes it possible to schedule conferences that minimize participants' travel time.

[0089] The conference scheduling system may also include a work efficiency management unit that takes into account the individual work efficiency of the participants. The work efficiency management unit, for example, analyzes the work patterns of the participants and schedules the conference to avoid the time periods when work progresses most efficiently. The work efficiency management unit may also adjust the time of the conference, taking into account the time periods when participants are most focused. The work efficiency management unit may also adjust the frequency of the conference to evenly distribute the workload of the participants. This makes it possible to schedule a conference while maximizing the work efficiency of the participants.

[0090] The conference scheduling system may also include an agenda generation unit that automatically generates a conference agenda based on the schedules of the participants. The agenda generation unit, for example, analyzes the schedules of the participants and selects the most appropriate agenda items. The agenda generation unit may also prioritize agenda items based on the positions and expertise of the participants. The agenda generation unit may also suggest highly relevant agenda items by referring to the contents of past meetings. This makes it possible to generate an efficient agenda based on the schedules of the participants.

[0091] The processing flow of the first embodiment will be briefly explained below.

[0092] Step 1: The reception unit inputs the conference period and participants, and provides an interface for the user to input the conference period and participants. Step 2: The acquisition unit acquires the schedules of the participants based on the information input by the reception unit, acquires the calendar information of the participants, and checks the schedule of each participant. Step 3: The analysis unit analyzes the schedule acquired by the acquisition unit, taking into account the response status to the meeting schedule (responded / unresponded), the importance of the meeting (required / optional), past attendance status, etc. Step 4: The suggestion unit proposes candidate dates and times based on the information analyzed by the analysis unit. Based on this information, candidate dates and times for the meeting are proposed. Step 5: The providing unit provides the candidate dates and times proposed by the proposing unit to the user, and displays the proposed candidate dates and times to the user, prompting the user to make a selection.

[0093] (Example 2) A conference scheduling system according to an embodiment of the present invention is a system that can efficiently schedule a conference by suggesting candidate dates and times simply by inputting the scheduled conference period and participants. Even if a conference is scheduled in the participants' schedules, this system selects candidates along with constraints by taking into account factors such as the response status to the scheduled conference, the importance of the conference, and past attendance. This allows the conference scheduling system to select a date and time that is convenient for all participants. For example, in the conference scheduling system, a user inputs the scheduled conference period and participants. The conference scheduling system then obtains the participants' schedules and checks each participant's scheduled conference. At this time, the system considers factors such as the response status to the scheduled conference (responded / unresponded), the importance of the conference (required / optional), and past attendance. The conference scheduling system then proposes candidate dates and times for the conference based on this information. For example, even if a conference is already scheduled in the participants' schedules, if the conference is optional or unresponded to, it may be proposed as a candidate date and time. Furthermore, by taking into account past attendance, a date and time that is convenient for the participants to attend is selected. Furthermore, the conference scheduling system also considers constraints. For example, it is possible to set conditions such as avoiding certain time periods or requiring certain participants to be present. This allows the system to propose more appropriate candidate dates and times, which makes it possible for the system to efficiently schedule meetings and select dates and times that are convenient for all participants to attend.

[0094] A conference scheduling system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs a scheduled conference period and participants. The acquisition unit acquires the participants' schedules based on the information input by the reception unit. The analysis unit analyzes the schedules acquired by the acquisition unit. The proposal unit proposes candidate dates and times based on the information analyzed by the analysis unit. The provision unit provides the user with the candidate dates and times proposed by the proposal unit. For example, the reception unit provides an interface through which the user inputs the scheduled conference period and participants. The acquisition unit acquires the participants' calendar information and checks each participant's schedule. The analysis unit analyzes the acquired schedule and considers factors such as the response status (responded / unresponded) to the scheduled conference, the importance of the conference (required / optional), and past attendance status. The proposal unit proposes candidate dates and times for the conference based on this information. The provision unit displays the proposed candidate dates and times to the user and prompts the user to select one. As a result, the conference scheduling system according to an embodiment can propose candidate dates and times and efficiently schedule a conference simply by inputting the scheduled conference period and participants.

[0095] The conference arrangement system includes a confirmation unit that confirms the status of responses to a conference schedule. The confirmation unit confirms the status of responses to a conference schedule. For example, the confirmation unit confirms whether a participant has responded to a conference request or not. The confirmation unit can also confirm whether a participant is available to attend a conference. The confirmation unit can also confirm whether a participant has responded to a conference request within a deadline. In this way, the conference arrangement system can propose more appropriate candidate dates and times by confirming the status of responses to a conference request.

[0096] The conference arrangement system includes a determination unit that determines the importance of a conference. The determination unit determines the importance of a conference. For example, the determination unit determines the importance based on the purpose of the conference. The determination unit can also determine the importance based on the positions of the participants. The determination unit can also determine the importance based on the content of the conference. In this way, the conference arrangement system can prioritize important conferences by determining the importance of the conferences.

[0097] The conference arrangement system includes an evaluation unit that evaluates attendance status based on past attendance status. The evaluation unit evaluates attendance status based on past attendance status. For example, the evaluation unit evaluates the past attendance rate of participants. The evaluation unit can also evaluate attendance status based on the importance of past meetings. The evaluation unit can also evaluate attendance status based on the frequency of attendance at past meetings. In this way, the conference arrangement system can select a date and time that is easy for participants to attend by taking past attendance status into consideration.

[0098] The conference scheduling system includes a setting unit that sets constraints. The setting unit sets the constraints. For example, the setting unit sets a constraint that avoids a specific time period. The setting unit can also set a constraint that requires specific participants to be present. The setting unit can also set a constraint regarding the location of the conference. In this way, the conference scheduling system can set constraints and propose candidate dates and times based on specific conditions.

[0099] The conference arrangement system includes an update unit that updates the schedules of participants in real time. The update unit updates the schedules of participants in real time. For example, the update unit obtains calendar information of participants in real time and updates the schedules. The update unit can also immediately reflect any changes to the schedules of participants. The update unit can also periodically check the schedules of participants and update them based on the latest information. In this way, the conference arrangement system can propose candidate dates and times based on the latest information by updating the schedules of participants in real time.

[0100] The reception unit can estimate the user's emotions and change the input method for the scheduled meeting period based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the scheduled meeting period. In this way, adjusting the input method according to the user's emotions reduces the user's stress and enables the user to efficiently input the scheduled meeting period.

[0101] The reception unit can analyze the user's past conference setting history and suggest an appropriate input method. For example, the reception unit automatically displays as candidates conference periods that the user has frequently set in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest conference periods to be set for specific time periods based on the user's past conference setting history. In this way, by analyzing the past conference setting history, it is possible to suggest the optimal input method for the user.

[0102] The reception unit can perform filtering based on the user's current project and areas of interest when inputting the meeting schedule period. For example, the reception unit preferentially displays meeting periods related to the user's ongoing project. The reception unit can also suggest related meeting periods based on the user's areas of interest. The reception unit can also suggest the optimal meeting period depending on the progress of the user's project. In this way, by filtering based on the user's current project and areas of interest, it is possible to suggest highly relevant meeting schedule periods.

[0103] When inputting the scheduled conference period, the reception unit can select an appropriate input means according to the user's input method. For example, when the user inputs the scheduled conference period by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the scheduled conference period by text, the reception unit can also support text input preferentially. Furthermore, when the user inputs the scheduled conference period by image, the reception unit can also support the input using image recognition technology. This allows the scheduled conference period to be input efficiently by selecting the optimal input means according to the user's input method.

[0104] The reception unit can estimate the user's emotions and change the priority of the conference setting periods to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can input a conference setting period with a high level of importance with priority. Furthermore, if the user is relaxed, the reception unit can input a detailed conference setting period with priority. Furthermore, if the user is in a hurry, the reception unit can input a conference setting period that can be input quickly with priority. In this way, by determining the priority of the conference setting periods according to the user's emotions, important meetings can be set with priority.

[0105] When inputting a scheduled conference period, the reception unit can prioritize inputting a highly relevant period in consideration of the user's geographical location information. For example, the reception unit prioritizes inputting a scheduled conference period in a location close to the user's current location. The reception unit can also propose an optimal scheduled conference period based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also input the scheduled conference period in consideration of the geographical location information of the destination. In this way, a highly relevant scheduled conference period can be proposed by taking the user's geographical location information into consideration.

[0106] When inputting the meeting schedule period, the reception unit can analyze the user's social media activity and input a relevant period. For example, the reception unit can suggest a meeting schedule period related to a location where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest a relevant meeting schedule period. The reception unit can also suggest a relevant meeting schedule period by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to suggest a highly relevant meeting schedule period.

[0107] The reception unit can customize the input method by reflecting the user's past feedback when inputting the conference setting period. The reception unit can propose the optimal input method, for example, based on feedback provided by the user in the past. The reception unit can also optimize the input procedure based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's feedback. In this way, the optimal input method can be proposed by reflecting the user's past feedback.

[0108] The acquisition unit can estimate the user's emotions and change the timing of schedule acquisition based on the estimated user's emotions. For example, the acquisition unit can delay the timing of schedule acquisition when the user is feeling stressed. Furthermore, the acquisition unit can also advance the timing of schedule acquisition when the user is relaxed. Furthermore, the acquisition unit can quickly acquire the schedule when the user is in a hurry. In this way, the schedule can be acquired efficiently by adjusting the timing of schedule acquisition according to the user's emotions.

[0109] The acquisition unit can analyze the user's past schedule acquisition history and select an appropriate acquisition method. For example, the acquisition unit automatically acquires schedules that the user frequently acquired in the past. The acquisition unit can also preferentially select an acquisition method (voice, text, etc.) that the user has used in the past. The acquisition unit can also predict and select a schedule to be acquired during a specific time period from the user's past schedule acquisition history. In this way, the optimal acquisition method can be selected by analyzing the user's past schedule acquisition history.

[0110] When acquiring a schedule, the acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the acquisition unit preferentially acquires schedules related to a project currently in progress by the user. The acquisition unit can also acquire related schedules based on the user's area of ​​interest. The acquisition unit can also acquire an optimal schedule depending on the progress of the user's project. In this way, by filtering based on the user's current project or area of ​​interest, it is possible to acquire highly relevant schedules.

[0111] When acquiring a schedule, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, when the user acquires the schedule by voice, the acquisition unit supports acquisition using voice recognition technology. Furthermore, when the user acquires the schedule by text, the acquisition unit can also support text input preferentially. Furthermore, when the user acquires the schedule by image, the acquisition unit can also support acquisition using image recognition technology. This allows for efficient schedule acquisition by selecting the optimal acquisition means according to the user's input method.

[0112] The acquisition unit can estimate the user's emotions and determine the priority of schedules to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring schedules with high importance. Furthermore, when the user is relaxed, the acquisition unit can also acquire detailed schedules. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring schedules that can be acquired quickly. In this way, by determining the priority of schedules according to the user's emotions, important schedules can be acquired with priority.

[0113] When acquiring a schedule, the acquisition unit can prioritize acquiring highly relevant schedules by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring schedules for locations close to the user's current location. The acquisition unit can also propose an optimal schedule based on the user's geographical location information. Furthermore, when the user is traveling, the acquisition unit can also acquire a schedule by taking into account the geographical location information of the destination. In this way, a highly relevant schedule can be acquired by taking into account the user's geographical location information.

[0114] When acquiring a schedule, the acquisition unit can analyze the user's social media activity and acquire a related schedule. For example, the acquisition unit can suggest a schedule related to a place where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media and suggest a related schedule. The acquisition unit can also suggest a related schedule by referring to the activities of the user's friends on social media. In this way, a highly relevant schedule can be acquired by analyzing the user's social media activity.

[0115] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring a schedule. The acquisition unit can propose an optimal acquisition method based on, for example, feedback provided by the user in the past. The acquisition unit can also optimize the acquisition procedure based on the user's past feedback. The acquisition unit can also customize the acquisition interface by reflecting the user's feedback. This makes it possible to propose an optimal acquisition method by reflecting the user's past feedback.

[0116] The analysis unit can estimate the user's emotions and change the schedule analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple analysis method to minimize the analysis steps. Alternatively, if the user is relaxed, the analysis unit can provide detailed analysis options and suggest a customizable analysis method. Alternatively, if the user is in a hurry, the analysis unit can prioritize a method that can analyze quickly. In this way, the schedule can be analyzed efficiently by adjusting the analysis method according to the user's emotions.

[0117] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the schedule. For example, the analysis unit performs a detailed analysis on a schedule with a high level of importance. The analysis unit can also perform a simplified analysis on a schedule with a low level of importance. The analysis unit can also determine the priority of the analysis based on the importance of the schedule. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the schedule.

[0118] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the schedule. For example, the analysis unit can apply a business analysis algorithm to a schedule for a business meeting. The analysis unit can also apply a private analysis algorithm to a schedule for a private event. The analysis unit can also select and apply the optimal analysis algorithm for each category. This allows for efficient analysis by applying the optimal analysis algorithm depending on the schedule category.

[0119] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also reflect feedback from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also improve the analysis procedure by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0120] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can also prioritize a method that can perform the analysis quickly. This allows for efficient analysis by adjusting the length of the analysis according to the user's emotions.

[0121] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. For example, the analysis unit prioritizes analysis of schedules with an upcoming submission deadline. The analysis unit can also postpone analysis of schedules with a distant submission deadline. The analysis unit can also dynamically adjust the priority of analysis depending on the submission time. In this way, by determining the priority of analysis based on the submission time of the schedule, analysis can be performed efficiently.

[0122] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the schedules. For example, the analysis unit prioritizes analysis of highly relevant schedules. The analysis unit can also postpone analysis of less relevant schedules. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the schedules. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the schedules.

[0123] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can provide an analysis that uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can provide an analysis that avoids technical terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for efficient analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0124] The suggestion unit can estimate the user's emotions and change the way suggestions are expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. When the user is relaxed, the suggestion unit can also provide detailed suggestions. When the user is in a hurry, the suggestion unit can also provide suggestions that can be quickly understood. In this way, suggestions can be made efficiently by adjusting the way suggestions are expressed according to the user's emotions.

[0125] The suggestion unit can adjust the level of detail of the proposal based on the importance of the candidate date and time when making a proposal. For example, the suggestion unit makes a detailed proposal for a candidate date and time with high importance. The suggestion unit can also make a simplified proposal for a candidate date and time with low importance. The suggestion unit can also dynamically adjust the level of detail of the proposal according to the importance of the candidate date and time. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the candidate date and time.

[0126] The suggestion unit can apply an appropriate suggestion algorithm depending on the category of the candidate date and time when making a suggestion. For example, the suggestion unit can apply a business-oriented suggestion algorithm to a candidate date and time for a business meeting. The suggestion unit can also apply a private-oriented suggestion algorithm to a candidate date and time for a private event. The suggestion unit can also select and apply an optimal suggestion algorithm for each category. This allows for efficient suggestions by applying the optimal suggestion algorithm depending on the category of the candidate date and time.

[0127] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also reflect feedback from the user's past proposal results to improve the accuracy of the proposal. The suggestion unit can also improve the proposal procedure by referring to the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results.

[0128] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is stressed, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can also provide a detailed suggestion. If the user is in a hurry, the suggestion unit can also provide a quickly understandable suggestion. In this way, by adjusting the length of the suggestion according to the user's emotion, suggestions can be made efficiently.

[0129] The suggestion unit can determine the priority of the proposals based on the submission dates of the candidate dates and times when making the proposals. For example, the suggestion unit can prioritize proposals for candidate dates and times with a submission deadline that is close. The suggestion unit can also postpone proposals for candidate dates and times with a submission deadline that is far away. The suggestion unit can also dynamically adjust the priority of the proposals depending on the submission dates. This allows efficient proposals to be made by determining the priority of the proposals based on the submission dates of the candidate dates and times.

[0130] The suggestion unit can adjust the order of suggestions based on the relevance of the candidate dates and times when making suggestions. For example, the suggestion unit can prioritize suggestions for candidate dates and times with high relevance. The suggestion unit can also postpone suggestions for candidate dates and times with low relevance. The suggestion unit can also dynamically adjust the order of suggestions according to the relevance of the candidate dates and times. This allows suggestions to be made efficiently by adjusting the order of suggestions based on the relevance of the candidate dates and times.

[0131] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, if the user's level of expertise is high, the suggestion unit can provide a proposal that uses a lot of technical terms. Also, if the user's level of expertise is low, the suggestion unit can provide a proposal that avoids technical terms. Also, the suggestion unit can dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terms in the proposal according to the user's level of expertise, suggestions can be made efficiently.

[0132] The providing unit can estimate the user's emotions and change the method of providing information based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit can provide a simple, highly visible method of providing information. When the user is relaxed, the providing unit can also provide a method of providing information that includes detailed information. When the user is in a hurry, the providing unit can also provide a method of providing information that can be quickly understood. In this way, by adjusting the method of providing information according to the user's emotions, it is possible to provide information efficiently.

[0133] The providing unit can adjust the level of detail to be provided based on the importance of the candidate date and time when providing the information. For example, the providing unit provides detailed information for a candidate date and time with a high level of importance. The providing unit can also provide simplified information for a candidate date and time with a low level of importance. The providing unit can also dynamically adjust the level of detail to be provided according to the importance of the candidate date and time. This allows information to be provided efficiently by adjusting the level of detail to be provided based on the importance of the candidate date and time.

[0134] The providing unit can apply different providing algorithms depending on the category of the candidate date and time when providing information. For example, the providing unit can apply a business-oriented providing algorithm to candidate dates and times for a business meeting. The providing unit can also apply a private-oriented providing algorithm to candidate dates and times for a private event. The providing unit can also select and apply the optimal providing algorithm for each category. This allows information to be provided efficiently by applying the optimal providing algorithm depending on the category of the candidate date and time.

[0135] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the information. For example, the providing unit optimizes the provision algorithm based on the user's past provision results. The providing unit can also reflect feedback to improve the accuracy of provision from the user's past provision results. The providing unit can also improve the provision procedure by referring to the user's past provision results. In this way, the accuracy of provision can be improved by referring to the user's past provision results.

[0136] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can also provide detailed information. If the user is in a hurry, the providing unit can also provide information that can be quickly understood. In this way, by adjusting the length of the information provided according to the user's emotions, it is possible to provide information efficiently.

[0137] The providing unit can determine the priority of provision based on the submission time of the candidate date and time at the time of provision. For example, the providing unit provides preferentially the candidate date and time with a submission deadline approaching. The providing unit can also postpone the provision of the candidate date and time with a submission deadline further away. The providing unit can also dynamically adjust the priority of provision depending on the submission time. In this way, by determining the priority of provision based on the submission time of the candidate date and time, information can be provided efficiently.

[0138] The providing unit can adjust the order of provision based on the relevance of the candidate dates and times when providing information. For example, the providing unit provides highly relevant candidate dates and times with priority. The providing unit can also provide less relevant candidate dates and times at a later date. The providing unit can also dynamically adjust the order of provision according to the relevance of the candidate dates and times. In this way, by adjusting the order of provision based on the relevance of the candidate dates and times, information can be provided efficiently.

[0139] The providing unit can adjust the use of technical terms in the provided information according to the user's level of expertise when providing the information. For example, if the user's level of expertise is high, the providing unit can provide information that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can also provide information that avoids technical terms. Furthermore, the providing unit can dynamically adjust the use of technical terms in the provided information according to the user's level of expertise. This allows information to be provided efficiently by adjusting the use of technical terms in the provided information according to the user's level of expertise.

[0140] The confirmation unit can estimate the user's emotions and change the method for checking the answer status based on the estimated user emotions. For example, when the user is feeling stressed, the confirmation unit can provide a simple and highly visible confirmation method. When the user is relaxed, the confirmation unit can also provide a detailed confirmation method. When the user is in a hurry, the confirmation unit can also provide a quickly understandable confirmation method. In this way, by adjusting the method for checking the answer status according to the user's emotions, confirmation can be performed efficiently.

[0141] The confirmation unit can adjust the level of detail of the confirmation based on the importance of the answer status during confirmation. For example, the confirmation unit performs detailed confirmation for an answer status with a high level of importance. The confirmation unit can also perform simplified confirmation for an answer status with a low level of importance. The confirmation unit can also dynamically adjust the level of detail of the confirmation according to the importance of the answer status. This allows for efficient confirmation by adjusting the level of detail of the confirmation based on the importance of the answer status.

[0142] The confirmation unit can apply different confirmation algorithms depending on the category of the response status during confirmation. For example, the confirmation unit applies a business confirmation algorithm to the response status of a business meeting. The confirmation unit can also apply a private confirmation algorithm to the response status of a private event. The confirmation unit can also select and apply the optimal confirmation algorithm for each category. This allows efficient confirmation by applying the optimal confirmation algorithm depending on the category of the response status.

[0143] The confirmation unit can determine the confirmation priority based on the submission time of the answer status when confirming. For example, the confirmation unit prioritizes confirmation of answer statuses whose submission deadline is approaching. The confirmation unit can also postpone confirmation of answer statuses whose submission deadline is far away. The confirmation unit can also dynamically adjust the confirmation priority according to the submission time. In this way, by determining the confirmation priority based on the submission time of the answer status, confirmation can be performed efficiently.

[0144] The confirmation unit can adjust the confirmation order based on the relevance of the answer status during confirmation. For example, the confirmation unit prioritizes confirmation of highly relevant answer statuses. The confirmation unit can also postpone confirmation of less relevant answer statuses. The confirmation unit can also dynamically adjust the confirmation order according to the relevance of the answer statuses. Thus, by adjusting the confirmation order based on the relevance of the answer statuses, confirmation can be performed efficiently.

[0145] The determination unit can estimate the user's emotion and change the importance determination method based on the estimated user's emotion. For example, if the user is feeling stressed, the determination unit can provide a simple and highly visible determination method. Furthermore, if the user is relaxed, the determination unit can also provide a detailed determination method. Furthermore, if the user is in a hurry, the determination unit can also provide a quickly understandable determination method. In this way, by adjusting the importance determination method according to the user's emotion, it is possible to make an efficient determination.

[0146] The determination unit can adjust the level of detail of the determination based on the importance level at the time of determination. For example, the determination unit performs a detailed determination for a high importance level. The determination unit can also perform a simplified determination for a low importance level. The determination unit can also dynamically adjust the level of detail of the determination according to the importance level. This allows for efficient determination by adjusting the level of detail of the determination based on the importance level.

[0147] The determination unit can apply different determination algorithms depending on the importance category when making a determination. For example, the determination unit can apply a business-oriented determination algorithm to the importance of a business meeting. The determination unit can also apply a private-oriented determination algorithm to the importance of a private event. The determination unit can also select and apply an optimal determination algorithm for each category. This allows for efficient determination by applying the optimal determination algorithm depending on the importance category.

[0148] The determination unit can determine the priority of the determination based on the submission time of the importance level at the time of determination. For example, the determination unit prioritizes the determination for the importance level with an upcoming submission deadline. The determination unit can also postpone the determination for the importance level with a distant submission deadline. The determination unit can also dynamically adjust the priority of the determination based on the submission time. In this way, by determining the priority of the determination based on the submission time of the importance level, the determination can be made efficiently.

[0149] The determination unit can adjust the order of determination based on the relevance of the importance levels when making a determination. For example, the determination unit prioritizes determination for highly relevant importance levels. The determination unit can also postpone determination for less relevant importance levels. The determination unit can also dynamically adjust the order of determination according to the relevance of the importance levels. This allows for efficient determination by adjusting the order of determination based on the relevance of the importance levels.

[0150] The consideration unit can estimate the user's emotions and change the method of considering the past attendance status based on the estimated user's emotions. For example, if the user is feeling stressed, the consideration unit can provide a simple and highly visible consideration method. Furthermore, if the user is relaxed, the consideration unit can also provide a detailed consideration method. Furthermore, if the user is in a hurry, the consideration unit can also provide a quickly understandable consideration method. In this way, by adjusting the method of considering the past attendance status according to the user's emotions, efficient consideration can be achieved.

[0151] The consideration unit can adjust the level of detail of consideration based on the importance of past attendance status during consideration. For example, the consideration unit performs detailed consideration for past attendance status with high importance. The consideration unit can also perform simplified consideration for past attendance status with low importance. The consideration unit can also dynamically adjust the level of detail of consideration according to the importance of past attendance status. In this way, by adjusting the level of detail of consideration based on the importance of past attendance status, consideration can be performed efficiently.

[0152] The consideration unit can apply an appropriate consideration algorithm depending on the category of the past attendance status during consideration. For example, the consideration unit applies a business-oriented consideration algorithm to the past attendance status of a business meeting. The consideration unit can also apply a private-oriented consideration algorithm to the past attendance status of a private event. The consideration unit can also select and apply an optimal consideration algorithm for each category. This allows for efficient consideration by applying an optimal consideration algorithm depending on the category of the past attendance status.

[0153] The consideration unit can determine the priority of consideration based on the submission time of past attendance records during consideration. For example, the consideration unit prioritizes consideration of past attendance records with an upcoming submission deadline. The consideration unit can also postpone consideration of past attendance records with a distant submission deadline. The consideration unit can also dynamically adjust the priority of consideration depending on the submission time. This allows for efficient consideration by determining the priority of consideration based on the submission time of past attendance records.

[0154] The consideration unit can adjust the order of consideration based on the relevance of past attendance records during consideration. For example, the consideration unit prioritizes consideration of past attendance records with high relevance. The consideration unit can also postpone consideration of past attendance records with low relevance. The consideration unit can also dynamically adjust the order of consideration according to the relevance of past attendance records. As a result, by adjusting the order of consideration based on the relevance of past attendance records, consideration can be performed efficiently.

[0155] The setting unit can estimate the user's emotions and change the constraint condition setting method based on the estimated user's emotions. For example, when the user is feeling stressed, the setting unit can provide a simple and highly visible setting method. Furthermore, when the user is relaxed, the setting unit can provide a detailed setting method. Furthermore, when the user is in a hurry, the setting unit can provide a setting method that can be quickly understood. In this way, by adjusting the constraint condition setting method according to the user's emotions, it is possible to perform settings efficiently.

[0156] The setting unit can adjust the level of detail of the setting based on the importance of the constraint condition during setting. For example, the setting unit performs detailed setting for a constraint condition with high importance. The setting unit can also perform simplified setting for a constraint condition with low importance. The setting unit can also dynamically adjust the level of detail of the setting according to the importance of the constraint condition. This allows for efficient setting by adjusting the level of detail of the setting based on the importance of the constraint condition.

[0157] During the setting process, the setting unit can apply an appropriate setting algorithm depending on the category of the constraint. For example, the setting unit can apply a business setting algorithm to the constraints of a business meeting. The setting unit can also apply a private setting algorithm to the constraints of a private event. The setting unit can also select and apply the optimal setting algorithm for each category. This allows for efficient setting by applying the optimal setting algorithm depending on the category of the constraint.

[0158] The setting unit can determine the priority of the settings based on the submission time of the constraints when setting them. For example, the setting unit prioritizes the setting of constraints whose submission deadline is approaching. The setting unit can also postpone the setting of constraints whose submission deadline is far away. The setting unit can also dynamically adjust the priority of the settings according to the submission time. This allows for efficient setting by determining the priority of the settings based on the submission time of the constraints.

[0159] The setting unit can adjust the order of setting based on the relevance of the constraint conditions during setting. For example, the setting unit prioritizes setting for highly relevant constraint conditions. The setting unit can also postpone setting for less relevant constraint conditions. The setting unit can also dynamically adjust the order of setting according to the relevance of the constraint conditions. This allows for efficient setting by adjusting the order of setting based on the relevance of the constraint conditions.

[0160] The update unit can estimate the user's emotions and change the schedule update method based on the estimated user's emotions. For example, when the user is feeling stressed, the update unit can provide a simple and highly visible update method. When the user is relaxed, the update unit can also provide a detailed update method. When the user is in a hurry, the update unit can also provide a quickly understandable update method. This allows for efficient schedule updating by adjusting the schedule update method according to the user's emotions.

[0161] The update unit can adjust the level of detail of the update based on the importance of the schedule during the update. For example, the update unit performs a detailed update for a schedule with a high level of importance. The update unit can also perform a simplified update for a schedule with a low level of importance. The update unit can also dynamically adjust the level of detail of the update according to the importance of the schedule. This allows for efficient update by adjusting the level of detail of the update based on the importance of the schedule.

[0162] The update unit can apply an appropriate update algorithm depending on the category of the schedule when updating. For example, the update unit can apply a business update algorithm to a schedule for a business meeting. The update unit can also apply a private update algorithm to a schedule for a private event. The update unit can also select and apply the optimal update algorithm for each category. This allows for efficient updating by applying the optimal update algorithm depending on the schedule category.

[0163] The update unit can determine the priority of updates based on the submission time of the schedule when updating. For example, the update unit prioritizes updating of schedules with an upcoming submission deadline. The update unit can also postpone updating of schedules with a distant submission deadline. The update unit can also dynamically adjust the priority of updates according to the submission time. In this way, by determining the priority of updates based on the submission time of the schedule, updates can be performed efficiently.

[0164] The update unit can adjust the order of updates based on the relevance of the schedules when updating. For example, the update unit prioritizes updating of highly relevant schedules. The update unit can also postpone updating of less relevant schedules. The update unit can also dynamically adjust the order of updates according to the relevance of the schedules. In this way, by adjusting the order of updates based on the relevance of the schedules, updates can be performed efficiently.

[0165] The update unit can propose an appropriate update method by reflecting the user's past feedback during an update. The update unit can propose an optimal update method based on, for example, feedback provided by the user in the past. The update unit can also optimize the update procedure based on the user's past feedback. The update unit can also customize the update interface by reflecting the user's past feedback. This allows the optimal update method to be proposed by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, proposal unit, provision unit, confirmation unit, determination unit, evaluation unit, setting unit, and update 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 provides an interface for the user to input the scheduled meeting period and participants. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information of participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes candidate dates and times based on the analyzed information. The provision unit is realized by the control unit 46A of the smart device 14 and displays the proposed candidate dates and times to the user. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the status of responses to the scheduled meeting. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the importance of the meeting. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the attendance status based on past attendance status. The setting unit is realized by the control unit 46A of the smart device 14 and sets constraint conditions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the schedules of participants in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, proposal unit, provision unit, confirmation unit, determination unit, evaluation unit, setting unit, and update 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 provides an interface for the user to input the scheduled meeting period and participants. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information of participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes candidate dates and times based on the analyzed information. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays the proposed candidate dates and times to the user. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the status of responses to the scheduled meeting. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the importance of the meeting. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the attendance status based on past attendance status. The setting unit is realized by the control unit 46A of the smart glasses 214 and sets constraint conditions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the schedules of participants in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, proposal unit, provision unit, confirmation unit, determination unit, evaluation unit, setting unit, and update 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 provides an interface for the user to input the scheduled conference period and participants. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information of the participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes candidate dates and times based on the analyzed information. The provision unit is realized by the control unit 46A of the headset type terminal 314 and displays the proposed candidate dates and times to the user. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the status of responses to the conference schedule. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the importance of the conference. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the attendance status based on past attendance status. The setting unit is realized by the control unit 46A of the headset type terminal 314 and sets constraint conditions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the schedules of participants in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, proposal unit, provision unit, confirmation unit, determination unit, evaluation unit, setting unit, and update 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 provides an interface for a user to input the scheduled meeting period and participants. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires calendar information of participants. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired schedule. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes candidate dates and times based on the analyzed information. The provision unit is realized by the control unit 46A of the robot 414 and displays the proposed candidate dates and times to the user. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the status of responses to the scheduled meeting. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the importance of the meeting. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the attendance status based on past attendance status. The setting unit is realized by the control unit 46A of the robot 414 and sets constraint conditions. The update unit is realized by the specific processing unit 290 of the data processing device 12 and updates the schedules of participants in real time.

[0166] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0167] The conference scheduling system can also include a health management unit that takes into account the health status of participants. For example, the health management unit acquires health data of participants and adjusts the proposed date and time of the conference if the participant is not feeling well. The health management unit can also shorten the conference time if a participant is overworked. The health management unit can also recommend remote participation based on the participant's health status. This makes it possible to schedule a conference that takes into account the participant's health status.

[0168] The conference scheduling system can also include a travel time management unit that takes into account participants' travel time. The travel time management unit calculates travel time based on, for example, the participants' current locations and the conference location, and suggests candidate dates and times with the shortest travel time. The travel time management unit can also suggest the optimal means of transportation taking traffic conditions into account. The travel time management unit can also suggest candidate dates and times for remote conferences that participants can join while traveling. This makes it possible to schedule conferences that minimize participants' travel time.

[0169] The conference scheduling system may also include a work efficiency management unit that takes into account the individual work efficiency of the participants. The work efficiency management unit, for example, analyzes the work patterns of the participants and schedules the conference to avoid the time periods when work progresses most efficiently. The work efficiency management unit may also adjust the time of the conference, taking into account the time periods when participants are most focused. The work efficiency management unit may also adjust the frequency of the conference to evenly distribute the workload of the participants. This makes it possible to schedule a conference while maximizing the work efficiency of the participants.

[0170] The conference arrangement system may also include a progress management unit that estimates the emotions of participants and adjusts the way the conference proceeds based on the estimated emotions. For example, if a participant is feeling stressed, the progress management unit may summarize the agenda briefly to ensure the conference proceeds smoothly. The progress management unit may also promote detailed discussions if participants are relaxed. The progress management unit may also suggest a way to proceed so that a conclusion can be reached quickly if participants are in a hurry. This allows the conference to proceed flexibly according to the emotions of the participants.

[0171] The conference scheduling system may also include a content management unit that estimates the emotions of participants and adjusts the content of the conference based on the estimated emotions. For example, if a participant is feeling stressed, the content management unit may prioritize important topics. If a participant is feeling relaxed, the content management unit may also provide detailed information. If a participant is in a hurry, the content management unit may also select topics that will allow a quick conclusion. This makes it possible to provide optimal conference content according to the emotions of the participants.

[0172] The conference arrangement system may also include a location management unit that estimates the emotions of participants and selects a location for the conference based on the estimated emotions. For example, if a participant is feeling stressed, the location management unit may provide a relaxing environment. If a participant is relaxed, the location management unit may also provide an environment where the participant can concentrate. If a participant is in a hurry, the location management unit may also select a location that is easily accessible. This makes it possible to provide the optimal conference location according to the emotions of the participants.

[0173] The conference arrangement system may also include a notification management unit that estimates the emotions of participants and adjusts the conference notification method based on the estimated emotions. For example, if a participant is feeling stressed, the notification management unit may provide a simple, highly visible notification. If a participant is feeling relaxed, the notification management unit may also provide a detailed notification. If a participant is in a hurry, the notification management unit may also provide a quickly understandable notification. This makes it possible to provide the optimal notification method according to the emotions of participants.

[0174] The conference arrangement system may also include a follow-up management unit that estimates the emotions of participants and adjusts a conference follow-up method based on the estimated emotions. For example, the follow-up management unit may provide a brief follow-up if a participant is feeling stressed. The follow-up management unit may also provide a detailed follow-up if a participant is relaxed. The follow-up management unit may also provide a quick and understandable follow-up if a participant is in a hurry. This makes it possible to provide an optimal follow-up method according to the emotions of participants.

[0175] The conference arrangement system may also include a feedback management unit that estimates the emotions of participants and adjusts the conference feedback method based on the estimated emotions. For example, the feedback management unit may provide brief feedback when a participant is stressed. The feedback management unit may also provide detailed feedback when a participant is relaxed. The feedback management unit may also provide quick, understandable feedback when a participant is in a hurry. This makes it possible to provide an optimal feedback method according to the emotions of participants.

[0176] The conference scheduling system may also include an agenda generation unit that automatically generates a conference agenda based on the schedules of the participants. The agenda generation unit, for example, analyzes the schedules of the participants and selects the most appropriate agenda items. The agenda generation unit may also prioritize agenda items based on the positions and expertise of the participants. The agenda generation unit may also suggest highly relevant agenda items by referring to the contents of past meetings. This makes it possible to generate an efficient agenda based on the schedules of the participants.

[0177] The processing flow of the second embodiment will be briefly explained below.

[0178] Step 1: The reception unit inputs the conference period and participants, and provides an interface for the user to input the conference period and participants. Step 2: The acquisition unit acquires the schedules of the participants based on the information input by the reception unit, acquires the calendar information of the participants, and checks the schedule of each participant. Step 3: The analysis unit analyzes the schedule acquired by the acquisition unit, taking into account the response status to the meeting schedule (responded / unresponded), the importance of the meeting (required / optional), past attendance status, etc. Step 4: The suggestion unit proposes candidate dates and times based on the information analyzed by the analysis unit. Based on this information, candidate dates and times for the meeting are proposed. Step 5: The providing unit provides the candidate dates and times proposed by the proposing unit to the user, and displays the proposed candidate dates and times to the user, prompting the user to make a selection.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0184] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] 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.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] 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.

[0192] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0200] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] 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.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] 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.

[0207] 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.

[0208] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0209] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0210] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0211] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0213] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0214] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0215] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0216] 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.

[0217] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0218] 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.

[0219] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0221] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0222] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0223] 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.

[0224] 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.

[0225] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0226] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0227] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0228] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0229] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0230] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0231] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0232] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0233] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0234] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0235] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0236] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0237] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0238] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0239] 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.

[0240] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0241] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0242] 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.

[0243] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0244] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0245] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0246] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0247] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0248] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0249] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0250] [Explanation of symbols]

[0251] 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 for inputting a conference period and participants; an acquisition unit that acquires schedules of participants based on the information input by the reception unit; an analysis unit that analyzes the schedule acquired by the acquisition unit; a suggestion unit that proposes candidate dates and times based on the information analyzed by the analysis unit; a providing unit that provides the user with the candidate dates and times proposed by the proposing unit; Equipped with A system characterized by:

2. Equipped with a confirmation section to check the status of responses to meeting schedules 2. The system of claim 1.

3. Equipped with a determination unit that determines the importance of the meeting 2. The system of claim 1.

4. Equipped with an evaluation section that evaluates attendance based on past attendance records 2. The system of claim 1.

5. Equipped with a setting unit for setting constraint conditions 2. The system of claim 1.

6. Equipped with an update section that updates participants' schedules in real time 2. The system of claim 1.

7. The reception unit The user's feelings are estimated, and a method for inputting a meeting duration is changed based on the estimated user's feelings.

2. The system of claim 1.

8. The reception unit Analyzes the user's past conference setting history and suggests appropriate input methods 2. The system of claim 1.

9. The reception unit Filter based on your current projects and interests when entering meeting dates 2. The system of claim 1.

10. The reception unit When inputting the conference setting period, select an appropriate input method according to the input method of the user.

2. The system of claim 1.

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