System

Abstract

An object of a system according to an embodiment is to automatically grasp a schedule of a target member and propose an optimal meeting date.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects schedule data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a meeting date based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies require manual adjustment of the schedules of the target members, making it difficult to propose efficient meeting dates.

[0005] The system according to the embodiment aims to automatically grasp the schedules of target members and propose optimal meeting dates. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects schedule data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a meeting schedule based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically grasp the schedules of target members and propose optimal meeting dates. [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 schedule adjustment system according to an embodiment of the present invention utilizes a generation AI to reduce the effort and man-hours required for schedule adjustment. The schedule adjustment system automatically grasps the schedules of target members, and the generation AI analyzes each member's schedule and proposes optimal meeting dates. This mechanism significantly reduces the effort and man-hours required for schedule adjustment. For example, the schedule adjustment system collects each member's calendar information and schedules and inputs them into the generation AI. For example, data can be obtained from each member's calendar app or schedule management tool. This allows for centralized management of each member's schedule. Next, the schedule adjustment system analyzes the collected schedule data using the generation AI. The generation AI analyzes each member's schedule and identifies free time and overlapping appointments. For example, the generation AI can analyze each member's calendar and identify time periods when everyone is free. This allows for efficient extraction of candidate meeting dates. Furthermore, the schedule adjustment system uses the generation AI to propose optimal meeting dates. The generation AI considers each member's schedule and proposes the most suitable meeting date. For example, it can not only propose time periods when everyone is available, but also select the optimal date based on the importance and priority of the meeting. This allows the effort required for schedule adjustment to be significantly reduced. As a result, the schedule adjustment system can significantly reduce the effort and man-hours required for schedule adjustment. As a result, the schedule adjustment system can significantly reduce the time required for schedule adjustment and hold meetings more efficiently. For example, in the past, it was necessary to manually check and adjust each member's schedule, but by utilizing generative AI, it is possible to automatically propose the optimal meeting date. In addition, the schedule adjustment system can record the history of schedule adjustments and use this information for future schedule adjustments. For example, it can record past meeting dates and participant attendance status and reflect this in the next schedule adjustment. This allows for more accurate schedule adjustments.

[0029] A schedule adjustment system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects schedule data. The schedule data includes, but is not limited to, meeting schedules, task deadlines, and event dates and times. The collection unit acquires data from, for example, a calendar app or a schedule management tool. For example, the collection unit can acquire data from Google® Calendar or Outlook® Calendar. The collection unit can also acquire data from schedule management tools such as Trello and Asana. The collection unit can also acquire data using an API. For example, the collection unit acquires data using the API of a calendar app. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, a data analysis method or an algorithm used, but is not limited to, examples. For example, the analysis unit analyzes the collected data to identify free time and overlapping appointments. The analysis unit identifies free time based on, for example, the smallest consecutive unit of free time or a specific time period. The analysis unit can also identify overlapping appointments when multiple appointments occur in the same time period. The proposal unit proposes a meeting schedule based on the analysis results obtained by the analysis unit. The proposal is made, for example, based on selection criteria for the proposed meeting schedule and the proposal format, but is not limited to these examples. For example, the proposal unit proposes a meeting schedule based on the importance and priority of the meeting. The proposal unit evaluates the importance of the meeting based on, for example, the positions of the participants and the purpose of the meeting. The proposal unit can also evaluate the priority of the meeting based on the urgency and relevance to other appointments. This allows the schedule adjustment system according to the embodiment to efficiently collect and analyze schedule data and propose optimal meeting dates.

[0030] The collection unit can acquire data from a calendar app or a schedule management tool. The collection unit can acquire data from, for example, Google Calendar or Outlook Calendar. The collection unit can also acquire data from schedule management tools such as Trello or Asana. For example, the collection unit acquires data using the API of Google Calendar. The collection unit can also acquire data using the API of Outlook Calendar. The collection unit can also acquire data using the API of Trello or Asana. This makes it possible to efficiently collect schedule data by acquiring data from calendar apps and schedule management tools.

[0031] The analysis unit can analyze the collected data and identify free time or overlapping appointments. The analysis unit, for example, analyzes the collected data and identifies free time. For example, the analysis unit identifies free time based on the smallest continuous unit of free time or a specific time period. The analysis unit can also analyze the collected data and identify overlapping appointments. For example, the analysis unit identifies overlapping appointments when there are multiple appointments in the same time period. Furthermore, the analysis unit can analyze the collected data and identify both free time and overlapping appointments. In this way, by identifying free time and overlapping appointments, candidate dates for meetings can be efficiently extracted.

[0032] The proposal unit can propose a meeting date based on the importance or priority of the meeting. The proposal unit, for example, proposes a meeting date based on the importance of the meeting. For example, the proposal unit evaluates the importance of the meeting based on the positions of the participants and the purpose of the meeting. The proposal unit can also propose a meeting date based on the priority of the meeting. For example, the proposal unit evaluates the priority of the meeting based on the urgency and the relevance to other appointments. Furthermore, the proposal unit can propose a meeting date based on both the importance and priority of the meeting. This allows the optimal meeting date to be proposed based on the importance and priority of the meeting, significantly reducing the effort required for scheduling.

[0033] The schedule adjustment system includes a recording unit that records the history of schedule adjustments. The recording unit records the history of schedule adjustments. The history includes, for example, the date and time of adjustments and the details of adjustments, but is not limited to these examples. The recording unit records, for example, past meeting dates and the attendance status of participants. For example, the recording unit records the date and time of meetings and the names of participants. The recording unit can also record whether attendees attended or not. Furthermore, the recording unit can reflect the adjustment history in the next schedule adjustment. For example, the recording unit adjusts the next schedule based on the past meeting dates and the attendance status of participants. In this way, recording the history of schedule adjustments can be useful for future schedule adjustments.

[0034] The recording unit can record past meeting dates and participant attendance status and reflect this in the next schedule adjustment. The recording unit, for example, records past meeting dates. For example, the recording unit records the date and time of the meeting and the names of the participants. The recording unit can also record the attendance status of the participants. For example, the recording unit records whether or not the attendees were present. Furthermore, the recording unit can also adjust the next schedule based on the past meeting dates and participant attendance status. For example, the recording unit adjusts the next schedule based on the past meeting dates and participant attendance status. In this way, by recording past meeting dates and participant attendance status, the accuracy of the next schedule adjustment is improved.

[0035] The collection unit can analyze the user's past schedule history and select a data acquisition method. The collection unit, for example, analyzes the user's past schedule history and selects the optimal data acquisition method. For example, the collection unit prioritizes acquiring data from a calendar app that the user has frequently used in the past. The collection unit can also select the most efficient data acquisition method based on the user's past schedule history. Furthermore, the collection unit can also prioritize acquiring data from a schedule management tool that the user has used in the past. In this way, the optimal data acquisition method can be selected by analyzing the user's past schedule history.

[0036] The collection unit can perform filtering based on the user's current project and areas of interest when acquiring schedule data. For example, the collection unit performs filtering based on the user's current project and areas of interest when acquiring schedule data. For example, the collection unit preferentially acquires schedule data related to a project currently in progress by the user. The collection unit can also filter and acquire related schedule data based on the user's areas of interest. Furthermore, the collection unit can also preferentially acquire schedule data related to projects in which the user has shown interest in the past. In this way, highly relevant data can be acquired by filtering based on the user's current project and areas of interest.

[0037] The collection unit can select an acquisition means according to the user's input method when acquiring schedule data. For example, when acquiring schedule data, the collection unit selects an acquisition means according to the user's input method. For example, if the user uses voice input, the collection unit analyzes the voice data to acquire schedule data. Also, if the user uses text input, the collection unit can analyze the text data to acquire schedule data. Furthermore, if the user uses image input, the collection unit can analyze image data to acquire schedule data. This improves the efficiency of data acquisition by selecting the optimal acquisition means according to the user's input method.

[0038] When acquiring schedule data, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when acquiring schedule data, the collection unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes acquiring schedule data related to the user's current location. The collection unit can also acquire schedule data of nearby events or meetings based on the user's geographical location information. Furthermore, the collection unit can also prioritize acquiring schedule data related to places the user has visited in the past. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and acquire related data when acquiring schedule data. For example, the collection unit analyzes the user's social media activities and acquires related data when acquiring schedule data. For example, the collection unit acquires schedule data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and acquire schedule data of related events and meetings. Furthermore, the collection unit can also acquire related schedule data by referring to the activities of the user's friends on social media. In this way, related data can be acquired by analyzing the user's social media activities.

[0040] The collection unit can customize the acquisition method by reflecting the user's past feedback when acquiring schedule data. For example, the collection unit customizes the acquisition method by reflecting the user's past feedback when acquiring schedule data. For example, the collection unit selects the optimal schedule data acquisition method based on feedback provided by the user in the past. The collection unit can also adjust the type of schedule data to be acquired by reflecting the user's past feedback. Furthermore, the collection unit can adjust the acquisition timing based on feedback provided by the user in the past. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the schedule during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the schedule. For example, the analysis unit performs a detailed analysis of a schedule with high importance. The analysis unit can also perform a concise analysis of a schedule with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the schedule.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the schedule during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the schedule during analysis. For example, the analysis unit applies a business analysis algorithm to a schedule for a business meeting. The analysis unit can also apply a private analysis algorithm to a private appointment. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the schedule.

[0043] 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, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. The analysis unit determines the priority of analysis based on, for example, the submission time of the schedule. For example, the analysis unit prioritizes analysis of schedules with upcoming submission deadlines. The analysis unit can also postpone schedules with distant submission deadlines. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the submission time. In this way, efficient analysis is possible by determining the priority of analysis based on the submission time of the schedule.

[0045] The analysis unit can adjust the order of analysis based on the relevance of schedules during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of schedules. For example, the analysis unit prioritizes analysis of highly relevant schedules. The analysis unit can also postpone analysis of less relevant schedules. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance. In this way, adjusting the order of analysis based on the relevance of schedules enables efficient analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the meeting when making the proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the meeting. For example, the proposal unit makes a detailed proposal for a meeting with high importance. The proposal unit can also make a concise proposal for a meeting with low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal depending on the importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the meeting.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the meeting when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the meeting when making a suggestion. For example, the suggestion unit applies a business-oriented suggestion algorithm to a business meeting. The suggestion unit can also apply a private-oriented suggestion algorithm to a private meeting. Furthermore, the suggestion unit can select and apply the optimal suggestion algorithm for each category. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the meeting.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, by referring to the user's past suggestion results. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and identify areas for improvement in the suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0050] The proposal unit can determine the priority of proposals based on the submission time of the meetings when making proposals. The proposal unit determines the priority of proposals based on, for example, the submission time of the meetings. For example, the proposal unit gives priority to proposing meetings with an upcoming submission deadline. The proposal unit can also postpone meetings with a distant submission deadline. Furthermore, the proposal unit can gradually adjust the priority of proposals depending on the submission time. This enables efficient proposals by determining the priority of proposals based on the submission time of the meetings.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the meetings when making proposals. The proposal unit adjusts the order of proposals based on, for example, the relevance of the meetings. For example, the proposal unit prioritizes proposing highly relevant meetings. The proposal unit can also postpone less relevant meetings. Furthermore, the proposal unit can gradually adjust the order of proposals according to the relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the meetings.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate suggestions can be provided.

[0053] The recording unit can optimize the recording algorithm by referring to previously recorded data when recording. For example, the recording unit optimizes the recording algorithm by referring to previously recorded data when recording. For example, the recording unit optimizes the recording algorithm based on previously recorded data. The recording unit can also improve recording accuracy by referring to previously recorded data. Furthermore, the recording unit can analyze previously recorded data and identify areas for improvement in recording. In this way, the recording algorithm can be optimized by referring to previously recorded data.

[0054] The recording unit can update the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data based on user feedback. The recording unit can also optimize the recording algorithm by reflecting user feedback. Furthermore, the recording unit can analyze user feedback and identify areas for improvement in recording. In this way, the recorded data can be updated by reflecting user feedback.

[0055] The recording unit can weight the recorded data based on the submission time of the schedule when recording. The recording unit, for example, weights the recorded data based on the submission time of the schedule when recording. For example, the recording unit gives priority to recording schedule data with an upcoming submission deadline. The recording unit can also postpone recording schedule data with a distant submission deadline. Furthermore, the recording unit can gradually adjust the weighting of the recorded data depending on the submission time. As a result, weighting the recorded data based on the submission time of the schedule enables efficient recording.

[0056] The recording unit can integrate information from different data sources to enrich the recorded data during recording. For example, the recording unit integrates and records data from a calendar app or a schedule management tool. The recording unit can also integrate social media activity and geographic location information to enrich the recorded data. Furthermore, the recording unit can integrate user feedback and past recorded data to enrich the recorded data. In this way, the recorded data can be enriched by integrating information from different data sources.

[0057] The recording unit can analyze the user's past behavioral patterns and optimize the recorded data when recording. For example, the recording unit analyzes the user's past behavioral patterns and optimizes the recorded data when recording. For example, the recording unit optimizes the recorded data based on the user's past behavioral patterns. The recording unit can also analyze the user's past behavioral patterns and improve the accuracy of recording. Furthermore, the recording unit can identify areas for improvement in the recorded data by referring to the user's past behavioral patterns. In this way, the recorded data can be optimized by analyzing the user's past behavioral patterns.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The schedule adjustment system may further include a notification unit. The notification unit sends a notification to each member when a meeting date is decided. For example, the notification unit may notify the meeting date using email or push notification. The notification unit may also set a meeting reminder and send a notification the day before the meeting. Furthermore, the notification unit may also quickly send a notification when a meeting is changed or canceled. This allows information regarding the decision or change of the meeting date to be shared quickly, facilitating smooth communication between members.

[0060] The schedule adjustment system can further include a priority setting unit. The priority setting unit sets priorities for each member's schedule. For example, the priority setting unit can set a high priority for important meetings or tasks. The priority setting unit can also set priorities based on the member's job title or the importance of the project. Furthermore, the priority setting unit can also set priorities for tasks or meetings with high urgency. This allows important schedules to be prioritized, enabling efficient schedule management.

[0061] The schedule adjustment system can further include a feedback collection unit. The feedback collection unit collects feedback from each member after the meeting. For example, the feedback collection unit can send a questionnaire about the content and progress of the meeting to collect the opinions of the members. The feedback collection unit can also collect feedback about the time and location of the meeting. Furthermore, the feedback collection unit can analyze the collected feedback and identify areas for improvement for the next meeting. This can improve the quality of the meeting and increase member satisfaction.

[0062] The schedule adjustment system can further include a prediction unit. The prediction unit predicts future schedules based on past schedule data. For example, the prediction unit can analyze the frequency and patterns of past meetings to predict the optimal timing for the next meeting. The prediction unit can also predict the attendance rate for the next meeting based on the past attendance status of members. Furthermore, the prediction unit can predict future task deadlines taking into account the progress of the project. This makes it possible to efficiently plan future schedules and prevent unexpected problems.

[0063] The schedule adjustment system can further include a resource management unit. The resource management unit manages the resources required for meetings and tasks. For example, the resource management unit can manage the reservation status of conference rooms and suggest the most suitable conference room. The resource management unit can also manage the usage status of equipment required for meetings, such as projectors and whiteboards. Furthermore, the resource management unit can check the preparation status of materials and documents required for meetings and notify members. This makes it possible to efficiently manage the resources required for meetings and tasks and support smooth progress.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects schedule data. Schedule data includes meeting schedules, task deadlines, event dates and times, etc. The collection unit obtains data from calendar apps and schedule management tools. For example, data can be obtained from tools such as Google Calendar, Outlook Calendar, Trello, and Asana. It is also possible to obtain data from calendar apps using APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the data analysis method and algorithm used. For example, the analysis unit analyzes the collected data to identify free time and overlapping appointments. Free time can be identified based on the smallest continuous unit of free time or a specific time period, and overlapping appointments can be identified when there are multiple appointments in the same time period. Step 3: The proposal unit proposes meeting dates based on the analysis results obtained by the analysis unit. Proposals are made based on the selection criteria for the proposed meeting dates and the proposal format. For example, the proposal unit proposes meeting dates based on the importance and priority of the meeting. The proposal unit can evaluate the importance of the meeting based on the participants' positions and the purpose of the meeting, and the priority of the meeting based on the urgency and relevance to other schedules.

[0066] (Example 2) A schedule adjustment system according to an embodiment of the present invention utilizes a generation AI to reduce the effort and man-hours required for schedule adjustment. The schedule adjustment system automatically grasps the schedules of target members, and the generation AI analyzes each member's schedule and proposes optimal meeting dates. This mechanism significantly reduces the effort and man-hours required for schedule adjustment. For example, the schedule adjustment system collects each member's calendar information and schedules and inputs them into the generation AI. For example, data can be obtained from each member's calendar app or schedule management tool. This allows for centralized management of each member's schedule. Next, the schedule adjustment system analyzes the collected schedule data using the generation AI. The generation AI analyzes each member's schedule and identifies free time and overlapping appointments. For example, the generation AI can analyze each member's calendar and identify time periods when everyone is free. This allows for efficient extraction of candidate meeting dates. Furthermore, the schedule adjustment system uses the generation AI to propose optimal meeting dates. The generation AI considers each member's schedule and proposes the most suitable meeting date. For example, it can not only propose time periods when everyone is available, but also select the optimal date based on the importance and priority of the meeting. This allows the effort required for schedule adjustment to be significantly reduced. As a result, the schedule adjustment system can significantly reduce the effort and man-hours required for schedule adjustment. As a result, the schedule adjustment system can significantly reduce the time required for schedule adjustment and hold meetings more efficiently. For example, in the past, it was necessary to manually check and adjust each member's schedule, but by utilizing generative AI, it is possible to automatically propose the optimal meeting date. In addition, the schedule adjustment system can record the history of schedule adjustments and use this information for future schedule adjustments. For example, it can record past meeting dates and participant attendance status and reflect this in the next schedule adjustment. This allows for more accurate schedule adjustments.

[0067] A schedule adjustment system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects schedule data. The schedule data includes, but is not limited to, meeting schedules, task deadlines, and event dates and times. The collection unit acquires data from, for example, a calendar app or a schedule management tool. For example, the collection unit can acquire data from Google Calendar or Outlook Calendar. The collection unit can also acquire data from schedule management tools such as Trello and Asana. The collection unit can also acquire data using an API. For example, the collection unit acquires data using the API of a calendar app. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, a data analysis method or an algorithm used, but is not limited to, examples. For example, the analysis unit analyzes the collected data to identify free time and overlapping appointments. The analysis unit identifies free time based on, for example, the smallest consecutive unit of free time or a specific time period. The analysis unit can also identify overlapping appointments when multiple appointments occur in the same time period. The proposal unit proposes a meeting date based on the analysis results obtained by the analysis unit. The proposal is made based on, for example, selection criteria for the proposed meeting date and the format of the proposal, but is not limited to such examples. For example, the proposal unit proposes a meeting date based on the importance and priority of the meeting. The proposal unit evaluates the importance of the meeting based on, for example, the positions of the participants and the purpose of the meeting. The proposal unit can also evaluate the priority of the meeting based on the urgency and relevance to other schedules. As a result, the schedule adjustment system according to the embodiment can efficiently collect and analyze schedule data and propose optimal meeting dates.

[0068] The collection unit can acquire data from a calendar app or a schedule management tool. The collection unit can acquire data from, for example, Google Calendar or Outlook Calendar. The collection unit can also acquire data from schedule management tools such as Trello or Asana. For example, the collection unit acquires data using the API of Google Calendar. The collection unit can also acquire data using the API of Outlook Calendar. The collection unit can also acquire data using the API of Trello or Asana. This makes it possible to efficiently collect schedule data by acquiring data from calendar apps and schedule management tools.

[0069] The analysis unit can analyze the collected data and identify free time or overlapping appointments. The analysis unit, for example, analyzes the collected data and identifies free time. For example, the analysis unit identifies free time based on the smallest continuous unit of free time or a specific time period. The analysis unit can also analyze the collected data and identify overlapping appointments. For example, the analysis unit identifies overlapping appointments when there are multiple appointments in the same time period. Furthermore, the analysis unit can analyze the collected data and identify both free time and overlapping appointments. In this way, by identifying free time and overlapping appointments, candidate dates for meetings can be efficiently extracted.

[0070] The proposal unit can propose a meeting date based on the importance or priority of the meeting. The proposal unit, for example, proposes a meeting date based on the importance of the meeting. For example, the proposal unit evaluates the importance of the meeting based on the positions of the participants and the purpose of the meeting. The proposal unit can also propose a meeting date based on the priority of the meeting. For example, the proposal unit evaluates the priority of the meeting based on the urgency and the relevance to other appointments. Furthermore, the proposal unit can propose a meeting date based on both the importance and priority of the meeting. This allows the optimal meeting date to be proposed based on the importance and priority of the meeting, significantly reducing the effort required for scheduling.

[0071] The schedule adjustment system includes a recording unit that records the history of schedule adjustments. The recording unit records the history of schedule adjustments. The history includes, for example, the date and time of adjustments and the details of adjustments, but is not limited to these examples. The recording unit records, for example, past meeting dates and the attendance status of participants. For example, the recording unit records the date and time of meetings and the names of participants. The recording unit can also record whether attendees attended or not. Furthermore, the recording unit can reflect the adjustment history in the next schedule adjustment. For example, the recording unit adjusts the next schedule based on the past meeting dates and the attendance status of participants. In this way, recording the history of schedule adjustments can be useful for future schedule adjustments.

[0072] The recording unit can record past meeting dates and participant attendance status and reflect this in the next schedule adjustment. The recording unit, for example, records past meeting dates. For example, the recording unit records the date and time of the meeting and the names of the participants. The recording unit can also record the attendance status of the participants. For example, the recording unit records whether or not the attendees were present. Furthermore, the recording unit can also adjust the next schedule based on the past meeting dates and participant attendance status. For example, the recording unit adjusts the next schedule based on the past meeting dates and participant attendance status. In this way, by recording past meeting dates and participant attendance status, the accuracy of the next schedule adjustment is improved.

[0073] The collection unit can estimate the user's emotions and adjust the timing of schedule data acquisition based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of schedule data acquisition. For example, if the user is feeling stressed, the collection unit delays schedule data acquisition and acquires it when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately acquire schedule data and quickly proceed with processing. Furthermore, if the user is in a hurry, the collection unit can prioritize acquisition of schedule data and quickly start analysis. This allows the timing of schedule data acquisition to be adjusted according to the user's emotions, thereby acquiring data at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can analyze the user's past schedule history and select a data acquisition method. The collection unit, for example, analyzes the user's past schedule history and selects the optimal data acquisition method. For example, the collection unit prioritizes acquiring data from a calendar app that the user has frequently used in the past. The collection unit can also select the most efficient data acquisition method based on the user's past schedule history. Furthermore, the collection unit can also prioritize acquiring data from a schedule management tool that the user has used in the past. In this way, the optimal data acquisition method can be selected by analyzing the user's past schedule history.

[0075] The collection unit can perform filtering based on the user's current project and areas of interest when acquiring schedule data. For example, the collection unit performs filtering based on the user's current project and areas of interest when acquiring schedule data. For example, the collection unit preferentially acquires schedule data related to a project currently in progress by the user. The collection unit can also filter and acquire related schedule data based on the user's areas of interest. Furthermore, the collection unit can also preferentially acquire schedule data related to projects in which the user has shown interest in the past. In this way, highly relevant data can be acquired by filtering based on the user's current project and areas of interest.

[0076] The collection unit can select an acquisition means according to the user's input method when acquiring schedule data. For example, when acquiring schedule data, the collection unit selects an acquisition means according to the user's input method. For example, if the user uses voice input, the collection unit analyzes the voice data to acquire schedule data. Also, if the user uses text input, the collection unit can analyze the text data to acquire schedule data. Furthermore, if the user uses image input, the collection unit can analyze image data to acquire schedule data. This improves the efficiency of data acquisition by selecting the optimal acquisition means according to the user's input method.

[0077] The collection unit can estimate the user's emotions and determine the priority of schedule data to be acquired based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of schedule data. For example, when the user is feeling stressed, the collection unit postpones schedule data of low importance. The collection unit can also acquire all schedule data equally when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can also prioritize acquiring schedule data of high importance. In this way, by determining the priority of schedule data according to the user's emotions, important data can be acquired preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When acquiring schedule data, the collection unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when acquiring schedule data, the collection unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes acquiring schedule data related to the user's current location. The collection unit can also acquire schedule data of nearby events or meetings based on the user's geographical location information. Furthermore, the collection unit can also prioritize acquiring schedule data related to places the user has visited in the past. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0079] The collection unit can analyze the user's social media activities and acquire related data when acquiring schedule data. For example, the collection unit analyzes the user's social media activities and acquires related data when acquiring schedule data. For example, the collection unit acquires schedule data related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and acquire schedule data of related events and meetings. Furthermore, the collection unit can also acquire related schedule data by referring to the activities of the user's friends on social media. In this way, related data can be acquired by analyzing the user's social media activities.

[0080] The collection unit can customize the acquisition method by reflecting the user's past feedback when acquiring schedule data. For example, the collection unit customizes the acquisition method by reflecting the user's past feedback when acquiring schedule data. For example, the collection unit selects the optimal schedule data acquisition method based on feedback provided by the user in the past. The collection unit can also adjust the type of schedule data to be acquired by reflecting the user's past feedback. Furthermore, the collection unit can adjust the acquisition timing based on feedback provided by the user in the past. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the schedule during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the schedule. For example, the analysis unit performs a detailed analysis of a schedule with high importance. The analysis unit can also perform a concise analysis of a schedule with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the schedule.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the schedule during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the schedule during analysis. For example, the analysis unit applies a business analysis algorithm to a schedule for a business meeting. The analysis unit can also apply a private analysis algorithm to a private appointment. Furthermore, the analysis unit can select and apply the optimal analysis algorithm for each category. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the schedule.

[0084] 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, improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide a concise analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the schedule. The analysis unit determines the priority of analysis based on, for example, the submission time of the schedule. For example, the analysis unit prioritizes analysis of schedules with upcoming submission deadlines. The analysis unit can also postpone schedules with distant submission deadlines. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the submission time. In this way, efficient analysis is possible by determining the priority of analysis based on the submission time of the schedule.

[0087] The analysis unit can adjust the order of analysis based on the relevance of schedules during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of schedules. For example, the analysis unit prioritizes analysis of highly relevant schedules. The analysis unit can also postpone analysis of less relevant schedules. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance. In this way, adjusting the order of analysis based on the relevance of schedules enables efficient analysis.

[0088] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0089] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way in which suggestions are expressed. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. This allows for adjusting the way in which suggestions are expressed according to the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The proposal unit can adjust the level of detail of the proposal based on the importance of the meeting when making the proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the meeting. For example, the proposal unit makes a detailed proposal for a meeting with high importance. The proposal unit can also make a concise proposal for a meeting with low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal depending on the importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the meeting.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of the meeting when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the meeting when making a suggestion. For example, the suggestion unit applies a business-oriented suggestion algorithm to a business meeting. The suggestion unit can also apply a private-oriented suggestion algorithm to a private meeting. Furthermore, the suggestion unit can select and apply the optimal suggestion algorithm for each category. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the meeting.

[0092] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, by referring to the user's past suggestion results. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can analyze the user's past suggestion results and identify areas for improvement in the suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results.

[0093] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion. For example, if the user is nervous, the suggestion unit can make a short and to-the-point suggestion. Also, if the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can make a concise suggestion. In this way, by adjusting the length of the suggestion according to the user's emotion, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The proposal unit can determine the priority of proposals based on the submission time of the meetings when making proposals. The proposal unit determines the priority of proposals based on, for example, the submission time of the meetings. For example, the proposal unit gives priority to proposing meetings with an upcoming submission deadline. The proposal unit can also postpone meetings with a distant submission deadline. Furthermore, the proposal unit can gradually adjust the priority of proposals depending on the submission time. This enables efficient proposals by determining the priority of proposals based on the submission time of the meetings.

[0095] The proposal unit can adjust the order of proposals based on the relevance of the meetings when making proposals. The proposal unit adjusts the order of proposals based on, for example, the relevance of the meetings. For example, the proposal unit prioritizes proposing highly relevant meetings. The proposal unit can also postpone less relevant meetings. Furthermore, the proposal unit can gradually adjust the order of proposals according to the relevance. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the meetings.

[0096] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical knowledge, the suggestion unit can make a concise and easy-to-understand proposal. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate suggestions can be provided.

[0097] The recording unit can estimate the user's emotions and select record data based on the estimated user emotions. The recording unit, for example, estimates the user's emotions and selects record data. For example, if the user is feeling stressed, the recording unit postpones record data of low importance. The recording unit can also record all record data evenly when the user is relaxed. Furthermore, if the user is in a hurry, the recording unit can also prioritize recording record data of high importance. In this way, by selecting record data according to the user's emotions, important data can be prioritized and recorded. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The recording unit can optimize the recording algorithm by referring to previously recorded data when recording. For example, the recording unit optimizes the recording algorithm by referring to previously recorded data when recording. For example, the recording unit optimizes the recording algorithm based on previously recorded data. The recording unit can also improve recording accuracy by referring to previously recorded data. Furthermore, the recording unit can analyze previously recorded data and identify areas for improvement in recording. In this way, the recording algorithm can be optimized by referring to previously recorded data.

[0099] The recording unit can update the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data by reflecting user feedback during recording. For example, the recording unit updates the recorded data based on user feedback. The recording unit can also optimize the recording algorithm by reflecting user feedback. Furthermore, the recording unit can analyze user feedback and identify areas for improvement in recording. In this way, the recorded data can be updated by reflecting user feedback.

[0100] The recording unit can estimate the user's emotions and adjust the frequency of recording based on the estimated user emotions. The recording unit, for example, estimates the user's emotions and adjusts the frequency of recording. For example, the recording unit can reduce the frequency of recording when the user is stressed. The recording unit can also increase the frequency of recording when the user is relaxed. Furthermore, the recording unit can prioritize recording only important data when the user is in a hurry. In this way, by adjusting the frequency of recording according to the user's emotions, recording can be performed at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The recording unit can weight the recorded data based on the submission time of the schedule when recording. The recording unit, for example, weights the recorded data based on the submission time of the schedule when recording. For example, the recording unit gives priority to recording schedule data with an upcoming submission deadline. The recording unit can also postpone recording schedule data with a distant submission deadline. Furthermore, the recording unit can gradually adjust the weighting of the recorded data depending on the submission time. As a result, weighting the recorded data based on the submission time of the schedule enables efficient recording.

[0102] The recording unit can integrate information from different data sources to enrich the recorded data during recording. For example, the recording unit integrates and records data from a calendar app or a schedule management tool. The recording unit can also integrate social media activity and geographic location information to enrich the recorded data. Furthermore, the recording unit can integrate user feedback and past recorded data to enrich the recorded data. In this way, the recorded data can be enriched by integrating information from different data sources.

[0103] The recording unit can analyze the user's past behavioral patterns and optimize the recorded data when recording. For example, the recording unit analyzes the user's past behavioral patterns and optimizes the recorded data when recording. For example, the recording unit optimizes the recorded data based on the user's past behavioral patterns. The recording unit can also analyze the user's past behavioral patterns and improve the accuracy of recording. Furthermore, the recording unit can identify areas for improvement in the recorded data by referring to the user's past behavioral patterns. In this way, the recorded data can be optimized by analyzing the user's past behavioral patterns. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and recording unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit acquires data from a calendar app or a schedule management tool of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal meeting date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the history of schedule adjustments. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and recording unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit acquires data from a calendar app or a schedule management tool of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal meeting date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the history of schedule adjustments. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and recording unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit acquires data from a calendar app or a schedule management tool of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the history of schedule adjustments. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and recording unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit acquires data from a calendar app or a schedule management tool of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the history of schedule adjustments.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The schedule adjustment system may further include a notification unit. The notification unit sends a notification to each member when a meeting date is decided. For example, the notification unit may notify the meeting date using email or push notification. The notification unit may also set a meeting reminder and send a notification the day before the meeting. Furthermore, the notification unit may also quickly send a notification when a meeting is changed or canceled. This allows information regarding the decision or change of the meeting date to be shared quickly, facilitating smooth communication between members.

[0106] The schedule adjustment system can further include a priority setting unit. The priority setting unit sets priorities for each member's schedule. For example, the priority setting unit can set a high priority for important meetings or tasks. The priority setting unit can also set priorities based on the member's job title or the importance of the project. Furthermore, the priority setting unit can also set priorities for tasks or meetings with high urgency. This allows important schedules to be prioritized, enabling efficient schedule management.

[0107] The schedule adjustment system can further include a feedback collection unit. The feedback collection unit collects feedback from each member after the meeting. For example, the feedback collection unit can send a questionnaire about the content and progress of the meeting to collect the opinions of the members. The feedback collection unit can also collect feedback about the time and location of the meeting. Furthermore, the feedback collection unit can analyze the collected feedback and identify areas for improvement for the next meeting. This can improve the quality of the meeting and increase member satisfaction.

[0108] The schedule adjustment system can further include a prediction unit. The prediction unit predicts future schedules based on past schedule data. For example, the prediction unit can analyze the frequency and patterns of past meetings to predict the optimal timing for the next meeting. The prediction unit can also predict the attendance rate for the next meeting based on the past attendance status of members. Furthermore, the prediction unit can predict future task deadlines taking into account the progress of the project. This makes it possible to efficiently plan future schedules and prevent unexpected problems.

[0109] The schedule adjustment system can further include a resource management unit. The resource management unit manages the resources required for meetings and tasks. For example, the resource management unit can manage the reservation status of conference rooms and suggest the most suitable conference room. The resource management unit can also manage the usage status of equipment required for meetings, such as projectors and whiteboards. Furthermore, the resource management unit can check the preparation status of materials and documents required for meetings and notify members. This makes it possible to efficiently manage the resources required for meetings and tasks and support smooth progress.

[0110] The schedule adjustment system can further include an emotion analysis unit. The emotion analysis unit analyzes the emotions of members during a meeting in real time. For example, the emotion analysis unit can analyze the facial expressions and tone of voice of members to estimate their emotions. The emotion analysis unit can also analyze the content of members' comments and detect changes in their emotions. Furthermore, the emotion analysis unit can provide feedback on members' emotions in real time according to the progress of the meeting. This makes it possible to understand the emotions of members during a meeting and take appropriate measures to improve the quality of the meeting.

[0111] The schedule adjustment system can further include a stress management unit. The stress management unit monitors the stress levels of members and suggests appropriate measures. For example, the stress management unit can measure the members' heart rates and respiratory rates to estimate their stress levels. The stress management unit can also analyze members' schedules and identify factors that cause stress. Furthermore, the stress management unit can suggest relaxation methods and breaks to reduce stress. This can reduce members' stress and support healthy working styles.

[0112] The schedule adjustment system can further include a motivation management unit. The motivation management unit monitors the motivation of members and proposes measures to improve it. For example, the motivation management unit can analyze members' comments and actions to detect changes in motivation. The motivation management unit can also monitor members' goal achievement status and provide feedback according to the level of achievement. Furthermore, the motivation management unit can propose incentives and rewards to improve members' motivation. This makes it possible to maintain members' motivation and improve team performance.

[0113] The schedule adjustment system can further include a communication support unit. The communication support unit provides support to facilitate smooth communication between members. For example, the communication support unit can transcribe what is said during a meeting in real time and share it with members. The communication support unit can also automatically generate meeting minutes and distribute them to members after the meeting. Furthermore, the communication support unit can provide discussion tools to promote the exchange of opinions between members. This facilitates smooth communication between members and improves the efficiency of meetings.

[0114] The schedule adjustment system may further include an emotion feedback unit. The emotion feedback unit provides feedback on the emotions of members after a meeting and reflects them in the next meeting. For example, the emotion feedback unit may analyze the emotions of members during a meeting and provide feedback on their emotions regarding the progress and content of the meeting. The emotion feedback unit may also suggest improvements for the next meeting based on the emotions of members. Furthermore, the emotion feedback unit may adjust the progress and content of the meeting taking into account the emotions of members. This makes it possible to improve meetings by reflecting the emotions of members, thereby improving the quality of meetings.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects schedule data. Schedule data includes meeting schedules, task deadlines, event dates and times, etc. The collection unit obtains data from calendar apps and schedule management tools. For example, data can be obtained from tools such as Google Calendar, Outlook Calendar, Trello, and Asana. It is also possible to obtain data from calendar apps using APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the data analysis method and algorithm used. For example, the analysis unit analyzes the collected data to identify free time and overlapping appointments. Free time can be identified based on the smallest continuous unit of free time or a specific time period, and overlapping appointments can be identified when there are multiple appointments in the same time period. Step 3: The proposal unit proposes meeting dates based on the analysis results obtained by the analysis unit. Proposals are made based on the selection criteria for the proposed meeting dates and the proposal format. For example, the proposal unit proposes meeting dates based on the importance and priority of the meeting. The proposal unit can evaluate the importance of the meeting based on the participants' positions and the purpose of the meeting, and the priority of the meeting based on the urgency and relevance to other schedules.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 collection unit that collects schedule data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes a meeting schedule based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Get data from a calendar app or scheduling tool 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify free time or overlapping appointments 2. The system of claim 1.

4. The proposal unit Suggest meeting dates based on importance or priority of the meeting 2. The system of claim 1.

5. Equipped with a recording unit that records the history of schedule adjustments 2. The system of claim 1.

6. Equipped with a recording section that records past meeting dates and participant attendance status and reflects them in the next schedule adjustment 2. The system of claim 1.

7. The collecting unit To estimate a user's emotion and adjust the timing of acquiring schedule data based on the estimated user's emotion.

2. The system of claim 1.

8. The collecting unit Analyze the user's past schedule history and select the data acquisition method 2. The system of claim 1.

9. The collecting unit Filter schedule data based on the user's current projects and interests when retrieving it 2. The system of claim 1.

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