System

Abstract

To provide a system capable of efficiently adjusting the schedules of a plurality of participants and proposing the optimum meeting schedule.SOLUTION: A system according to an embodiment includes an acquisition unit, an analysis unit, a proposal unit, and a response collection unit. The acquisition unit acquires calendar information of a participant. The analysis unit analyzes the calendar information acquired by the acquisition unit, and detects an idle time. The proposal unit proposes an efficient meeting schedule on the basis of the idle time detected by the analysis unit. The response collection unit collects participant responses for the schedule proposed by the proposal 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 a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that coordinating the schedules of multiple participants is time-consuming and makes it difficult to set an efficient meeting schedule.

[0005] An object of the system according to the embodiment is to efficiently adjust the schedules of multiple participants and propose an optimal conference date. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a proposal unit, and a response collection unit. The acquisition unit acquires calendar information of participants. The analysis unit analyzes the calendar information acquired by the acquisition unit and detects free time. The proposal unit proposes an efficient meeting schedule based on the free time detected by the analysis unit. The response collection unit collects responses from participants to the schedule proposed by the proposal unit. Effect of the Invention

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

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The conference schedule tool according to the embodiment of the present invention is a system that automatically adjusts the schedules of participants by inputting multiple email addresses, detects free time in the schedule, and proposes a date. This system contributes to saving time and realizing a smooth conference by streamlining cumbersome adjustment work and avoiding schedule conflicts among participants. First, a user inputs the email addresses of participants who plan to attend the conference. Next, the system acquires calendar information of each participant and analyzes the schedule of each participant. This analysis uses AI to detect free time in the schedule of each participant. For example, if participant A is free from 10:00 to 11:00 a.m., that time period is detected as free time. Next, the system proposes an optimal conference date that all participants can participate in based on the detected free time. For example, if participants A, B, and C are all free from 2:00 to 3:00 p.m., that time period is proposed as the optimal conference date. This proposal is notified to the user, and the user can confirm the proposed date. Furthermore, the system collects participants' responses to the proposed date and finalizes the final conference date. For example, if participant A responds to the proposed date by saying "I can participate" and participant B responds by saying "I cannot participate," the system will again detect available time and propose a new date. In this way, the conference schedule tool of the present invention automatically adjusts the schedules of participants and proposes the optimal conference date, thereby making the cumbersome coordination work more efficient and avoiding conflicts in participants' schedules. This contributes to saving time and realizing smooth conferences. In this way, the conference schedule tool automatically adjusts the schedules of participants and proposes the optimal conference date, thereby making the cumbersome coordination work more efficient and avoiding conflicts in participants' schedules.

[0029] The conference schedule tool according to the embodiment includes an acquisition unit, an analysis unit, a proposal unit, and a response collection unit. The acquisition unit acquires calendar information of the participants. The calendar information of the participants includes, for example, a type of plan, a time period, a location, and the like, but is not limited to such examples. The acquisition unit acquires, for example, the calendar information of the participants through an API. The acquisition unit can also manually input the calendar information of the participants. Furthermore, the acquisition unit can also acquire the calendar information of the participants from a cloud service. For example, the acquisition unit acquires the calendar information of the participants through an API of the cloud service and imports it into the system. The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. The free time is detected based on, for example, a length of continuous time or a specific time period, but is not limited to such examples. For example, the analysis unit uses AI to analyze the calendar information and detect free time. The analysis unit can also detect free time using a rule-based algorithm. Furthermore, the analysis unit can detect free time using a machine learning algorithm. For example, the analysis unit analyzes the calendar information using AI to detect free time. The suggestion unit proposes an optimal meeting schedule based on the free time detected by the analysis unit. The optimal meeting schedule is proposed based on, for example, a time period when all participants' free times overlap or the shortest travel time, but is not limited to such examples. For example, the suggestion unit proposes an optimal meeting schedule using AI. The suggestion unit can also propose an optimal meeting schedule using a rule-based algorithm. Furthermore, the suggestion unit can also propose an optimal meeting schedule using a machine learning algorithm. For example, the suggestion unit proposes an optimal meeting schedule using AI. The response collection unit collects responses of participants to the schedule proposed by the suggestion unit. The response collection unit collects, for example, the responses of participants through email or a messaging app. The response collection unit can also collect the responses of participants through a web form. Furthermore, the response collection unit can collect the responses of participants through voice input. For example, the response collection unit collects the responses of participants through email or a messaging app.As a result, the conference schedule tool according to the embodiment can automatically adjust the schedules of the participants and propose optimal conference dates. Some or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit may collect and analyze responses of participants using an AI model.

[0030] The acquisition unit acquires the calendar information of the participants. The calendar information of the participants includes, for example, the type of schedule, time period, and location, but is not limited to such examples. The acquisition unit acquires, for example, the calendar information of the participants through an API. Specifically, the acquisition unit accesses the API of the calendar service used by each participant and acquires the calendar information using the authentication information. As a result, the acquisition unit can acquire the latest schedule information of the participants in real time. The acquisition unit can also manually input the calendar information of the participants. For example, an interface is provided for the participants to manually input their schedules, and the acquisition unit collects the information. Furthermore, the acquisition unit can also acquire the calendar information of the participants from a cloud service. For example, the acquisition unit acquires the calendar information through the API of the cloud service and imports it into the system. As a result, the acquisition unit can integrate information from multiple calendar services and manage it in a centralized manner. The acquisition unit is required to securely manage this information and take appropriate measures to protect privacy. For example, the acquisition unit performs data encryption and access control to prevent unauthorized access to the information of the participants. As a result, the acquisition unit can efficiently and safely acquire the calendar information of the participants, thereby improving the reliability of the entire system.

[0031] The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. Free time is detected, for example, based on the length of continuous time or a specific time period, but is not limited to such examples. Specifically, the analysis unit uses AI to analyze the calendar information to detect free time. The AI ​​can understand the contents of the calendar schedule using natural language processing technology and evaluate the importance and priority of the schedule. For example, the AI ​​can distinguish between important schedules such as conferences and meetings and low-priority schedules such as personal tasks and reminders, and detect free time more accurately. The analysis unit can also detect free time using a rule-based algorithm. For example, a rule for detecting free time based on a specific time period or day of the week is set, and analysis is performed according to the rule. Furthermore, the analysis unit can detect free time using a machine learning algorithm. For example, by learning past calendar information and grasping the behavior patterns and schedule trends of participants, it is possible to detect free time with higher accuracy. As a result, the analysis unit can quickly and accurately analyze the acquired calendar information and efficiently detect free time for all participants. Furthermore, the analysis unit can visualize the detected free time and provide it to the participants. For example, free time can be displayed in graph or calendar format so that participants can easily check it. This allows the analysis unit to not only detect free time but also support participants in making effective use of the information.

[0032] The suggestion unit proposes an optimal meeting schedule based on the free time detected by the analysis unit. The optimal meeting schedule is proposed based on, for example, the time period when all participants' free times overlap or the shortest travel time, but is not limited to such examples. Specifically, the suggestion unit proposes the optimal meeting schedule using AI. The AI ​​can calculate the optimal schedule based on the participants' calendar information and past meeting history. For example, the AI ​​considers the participants' past meeting attendance rates and the importance of the meetings to propose the most efficient schedule. The suggestion unit can also propose the optimal meeting schedule using a rule-based algorithm. For example, a rule is set to set a meeting at a specific time period or day of the week, and the optimal schedule is proposed according to the rule. Furthermore, the suggestion unit can also propose the optimal meeting schedule using a machine learning algorithm. For example, by learning past meeting data and understanding the participants' behavior patterns and meeting trends, it is possible to propose a schedule with higher accuracy. As a result, the suggestion unit can efficiently propose the optimal meeting schedule by considering the free time of all participants. Furthermore, the suggestion unit can notify the participants of the proposed schedule and ask for their confirmation. For example, the proposed schedule is notified through email or a messaging app, and an interface is provided for the participants to confirm or modify it. This allows the proposal unit to not only propose optimal meeting dates, but also support their confirmation and adjustment.

[0033] The response collection unit collects responses of the participants to the schedule proposed by the proposal unit. The response collection unit, for example, collects the responses of the participants through email or a messaging app. Specifically, the response collection unit collects the attendance confirmations and comments of the participants to the proposed schedule through email or a messaging app, and incorporates them into the system. The response collection unit can also collect the responses of the participants through a web form. For example, a dedicated web form can be provided, and the participants can input their responses through the form. Furthermore, the response collection unit can also collect the responses of the participants through voice input. For example, the voice responses of the participants are converted into text using voice recognition technology and incorporated into the system. This allows the response collection unit to collect the responses of the participants by various means and efficiently manage them. Furthermore, the response collection unit can also analyze the collected responses and grasp the opinions and requests of the participants. For example, the responses collected using AI are analyzed, the opinions and requests of the participants are classified, and the responses are reflected in the adjustment of the optimal meeting schedule. This allows the response collection unit to efficiently collect the responses of the participants and adjust the optimal meeting schedule based on the information. Furthermore, the response collection unit can update the collected responses in real time and make adjustments based on the latest information. For example, if a participant's response changes, it is immediately reflected in the system to respond to the latest situation. This allows the response collection unit to support highly accurate adjustments of meeting schedules based on the latest information at all times.

[0034] The proposal unit can detect free time again and propose a new schedule. The proposal unit can detect free time again, for example, based on the participant's response to the proposed schedule. For example, the proposal unit can analyze the participant's response and detect new free time. The proposal unit can also re-propose an optimal meeting schedule based on the participant's response. For example, the proposal unit can detect free time again and propose a new schedule based on the participant's response. This allows the proposal unit to determine an optimal meeting schedule. A part or all of the above-mentioned processing in the proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the proposal unit can analyze the participant's response using an AI model and propose a new schedule.

[0035] The acquisition unit can analyze the past conference participation history of the participant and select an efficient method of acquiring calendar information. The acquisition unit adjusts the timing of acquiring calendar information based on, for example, the time period of the conference that the participant frequently participated in in the past. The acquisition unit can also analyze the type of conference that the participant participated in in the past and preferentially acquire related calendar information. Furthermore, the acquisition unit can select calendar information to be acquired on a specific day of the week or time period from the participant's past conference participation history. For example, the acquisition unit adjusts the timing of acquiring calendar information based on the time period that the participant frequently participated in in the past. In this way, the acquisition unit can select an optimal method of acquiring calendar information by analyzing the past conference participation history and realize efficient information acquisition. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the participant's past conference participation history to the generation AI and cause the generation AI to select an optimal method of acquiring calendar information.

[0036] The acquisition unit may perform filtering based on the participant's current project or field of interest when acquiring the calendar information. For example, the acquisition unit may preferentially acquire calendar information related to a project currently in progress by the participant. The acquisition unit may also filter and acquire related calendar information based on the participant's field of interest. Furthermore, when the participant is concentrating on a specific project, the acquisition unit may also acquire only calendar information related to the project. For example, the acquisition unit preferentially acquires calendar information related to a project currently in progress by the participant. As a result, the acquisition unit can preferentially acquire highly relevant information by filtering the calendar information based on the current project or field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on the participant's current project or field of interest to the generation AI, and cause the generation AI to execute filtering of the calendar information.

[0037] When acquiring the calendar information, the acquisition unit can preferentially acquire information with high relevance by considering the geographical location information of the participant. For example, when the participant is in a specific area, the acquisition unit preferentially acquires calendar information related to the area. In addition, when the participant is on a business trip, the acquisition unit can also preferentially acquire calendar information related to the business trip destination. In addition, when the participant is at home, the acquisition unit can preferentially acquire calendar information around the home. For example, when the participant is in a specific area, the acquisition unit preferentially acquires calendar information related to the area. As a result, the acquisition unit can preferentially acquire calendar information with high relevance by considering the geographical location information, thereby realizing efficient information acquisition. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the participant to the generation AI and cause the generation AI to execute acquisition of calendar information with high relevance.

[0038] The acquisition unit can analyze the social media activity of the participant and acquire related information when acquiring the calendar information. The acquisition unit can acquire related calendar information based on, for example, event information shared by the participant on social media. The acquisition unit can also identify events or meetings of interest from the participant's social media activity and acquire calendar information. The acquisition unit can also acquire calendar information based on event information of accounts followed by the participant on social media. For example, the acquisition unit acquires related calendar information based on event information shared by the participant on social media. As a result, the acquisition unit can acquire related calendar information by analyzing social media activity, thereby achieving efficient information acquisition. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the social media activity data of the participant to the generation AI and cause the generation AI to acquire related calendar information.

[0039] During the analysis, the analysis unit can improve the detection accuracy of free time based on the past conference participation patterns of the participants. The analysis unit improves the detection accuracy of free time based on, for example, the time period in which the participants frequently participated in the past. The analysis unit can also analyze the past conference participation patterns of the participants and detect the optimal free time. Furthermore, the analysis unit can improve the detection accuracy of free time by considering the tendency of the participants to participate in specific days of the week or time periods. For example, the analysis unit improves the detection accuracy of free time based on the time period in which the participants frequently participated in the past. As a result, the analysis unit can improve the detection accuracy of free time by considering the past conference participation patterns, and realize the proposal of an efficient conference schedule. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the past conference participation pattern data of the participants to the generation AI and cause the generation AI to execute the improvement of the detection accuracy of free time.

[0040] The analysis unit can apply different analysis algorithms depending on the job or position of the participant during analysis. For example, if the participant is a manager, the analysis unit applies an algorithm that detects free time by prioritizing meetings with high importance. In addition, if the participant is a general employee, the analysis unit can also apply an algorithm that detects free time by considering all meetings equally. In addition, if the participant is involved in a specific project, the analysis unit can also apply an algorithm that detects free time by prioritizing meetings related to the project. For example, if the participant is a manager, the analysis unit applies an algorithm that detects free time by prioritizing meetings with high importance. In this way, the analysis unit can realize efficient detection of free time by applying an analysis algorithm according to the job or position. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the job and position data of the participant to the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] During the analysis, the analysis unit can determine the priority of free time based on the submission time of the participant. For example, the analysis unit detects free time by giving priority to a schedule submitted early by the participant. The analysis unit can also detect free time by taking into account a schedule submitted immediately before by the participant. Furthermore, the analysis unit can preferentially display the optimal free time based on the submission time of the participant. For example, the analysis unit detects free time by giving priority to a schedule submitted early by the participant. In this way, the analysis unit can realize an efficient proposal of a meeting schedule by determining the priority of free time based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the participant to the generation AI and cause the generation AI to execute the determination of the priority of free time.

[0042] During the analysis, the analysis unit can improve the detection accuracy of free time based on the participant's related literature. The analysis unit detects the optimal free time based on the participant's related literature, for example. The analysis unit can also improve the detection accuracy of free time by referring to the participant's related literature. Furthermore, if the participant refers to a specific literature, the analysis unit can detect the free time based on the literature. For example, the analysis unit detects the optimal free time based on the participant's related literature. As a result, the analysis unit can improve the detection accuracy of free time by referring to the related literature, and realize efficient meeting schedule proposals. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the participant's related literature data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy of free time.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of the meeting at the time of proposal. For example, in the case of a meeting with high importance, the proposal unit makes a detailed proposal. In addition, in the case of a meeting with low importance, the proposal unit can also make a concise proposal. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the importance of the meeting. For example, in the case of a meeting with high importance, the proposal unit makes a detailed proposal. As a result, the proposal unit can realize an efficient proposal of a meeting schedule by adjusting the level of detail of the proposal based on the importance of the meeting. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the importance data of the meeting to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0044] The suggestion unit can apply different proposal algorithms depending on the category of the meeting at the time of proposal. For example, in the case of a business meeting, the suggestion unit applies an efficient proposal algorithm. In addition, in the case of a casual meeting, the suggestion unit can also apply a flexible proposal algorithm. Furthermore, the suggestion unit can apply an optimal proposal algorithm depending on the category of the meeting. For example, in the case of a business meeting, the suggestion unit applies an efficient proposal algorithm. As a result, the suggestion unit can realize an efficient proposal of a meeting schedule by applying a proposal algorithm according to the category of the meeting. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the category data of the meeting to the generation AI and cause the generation AI to apply different proposal algorithms.

[0045] The proposal unit can determine the priority of the proposal based on the submission time of the meeting at the time of proposal. For example, the proposal unit gives priority to a meeting submitted early. The proposal unit can also make a proposal taking into consideration a meeting submitted immediately before. Furthermore, the proposal unit can give priority to an optimal proposal based on the submission time. For example, the proposal unit gives priority to a meeting submitted early. In this way, the proposal unit can realize an efficient proposal of a meeting schedule by determining the priority of the proposal based on the submission time. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the submission time data of the meeting to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the meetings at the time of proposal. For example, the proposal unit gives priority to proposing highly relevant meetings. The proposal unit can also put off proposing less relevant meetings. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the meetings. For example, the proposal unit gives priority to proposing highly relevant meetings. As a result, the proposal unit can realize efficient proposal of meeting schedules by adjusting the order of proposals based on the relevance of the meetings. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the relevance data of the meetings to the generation AI and cause the generation AI to adjust the order of proposals.

[0047] The response collection unit can select the optimal collection method when collecting responses by analyzing the past response history of the participants. For example, the response collection unit prioritizes collection of a method in which the participant responded quickly in the past. The response collection unit can also select the optimal collection method by analyzing the past response history of the participants. Furthermore, the response collection unit can select the optimal collection method by taking into consideration the tendency of the participants to respond in a specific way. For example, the response collection unit prioritizes collection of a method in which the participant responded quickly in the past. In this way, the response collection unit can select the optimal collection method by analyzing the past response history, thereby realizing efficient response collection. A part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the response collection unit can input the past response history data of the participants to the generation AI and cause the generation AI to select the optimal collection method.

[0048] The response collection unit can customize the collection means based on the current situation of the participant when collecting the response. For example, if the participant is in a meeting, the response collection unit collects the response after the meeting ends. In addition, if the participant is on the move, the response collection unit can also collect the response using a mobile device. In addition, if the participant is on vacation, the response collection unit can collect the response after the vacation ends. For example, if the participant is in a meeting, the response collection unit collects the response after the meeting ends. In this way, the response collection unit can realize efficient response collection by customizing the collection means based on the current situation. Part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit can input the current situation data of the participant to the generation AI and cause the generation AI to execute customization of the collection means.

[0049] The response collection unit can select the optimal collection method in consideration of the geographical location information of the participant when collecting the response. For example, when the participant is in a specific area, the response collection unit selects a collection method related to that area. In addition, when the participant is on a business trip, the response collection unit can also select a collection method related to the business trip destination. In addition, when the participant is at home, the response collection unit can also select a collection method around the home. For example, when the participant is in a specific area, the response collection unit selects a collection method related to that area. In this way, the response collection unit can select the optimal collection method by considering the geographical location information and realize efficient response collection. Part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit can input the geographical location information data of the participant to the generation AI and cause the generation AI to select the optimal collection method.

[0050] The response collection unit can analyze the social media activities of the participants and suggest collection means when collecting responses. The response collection unit can, for example, suggest the optimal collection means based on the information shared by the participants on social media. The response collection unit can also identify and suggest collection means of interest from the participants' social media activities. Furthermore, the response collection unit can also suggest collection means based on information on accounts the participants follow on social media. For example, the response collection unit can suggest the optimal collection means based on the information shared by the participants on social media. As a result, the response collection unit can propose the optimal collection means by analyzing social media activities, thereby realizing efficient response collection. Part or all of the above-mentioned processing in the response collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the response collection unit can input the participants' social media activity data to the generation AI and cause the generation AI to execute the suggestion of the collection means.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When acquiring calendar information of a participant, the acquisition unit can adjust the acquisition timing in consideration of the participant's health condition. For example, if a participant is in poor health, the acquisition unit can delay acquiring the calendar information and wait until the participant recovers. Also, if a participant is in good health, the acquisition unit can immediately acquire the calendar information and start processing promptly. Furthermore, if a participant is in a specific health condition, the acquisition unit can acquire the calendar information at the optimal timing according to that condition. This allows the acquisition unit to adjust the acquisition timing of the calendar information according to the participant's health condition.

[0053] The suggestion unit can determine the priority order of the proposed meeting schedules based on the importance of the meeting. For example, for a highly important meeting, the suggestion unit preferentially suggests the earliest available time. Also, for a less important meeting, the suggestion unit can suggest it later. Furthermore, the suggestion unit can adjust the level of detail of the proposed schedule depending on the importance of the meeting. This allows the suggestion unit to suggest the optimal meeting schedule based on the importance of the meeting.

[0054] The acquisition unit can analyze the participant's past conference participation history and select an efficient method for acquiring calendar information. For example, the timing for acquiring calendar information can be adjusted based on the time periods of conferences that the participant frequently attended in the past. The acquisition unit can also analyze the types of conferences that the participant attended in the past and prioritize acquiring related calendar information. Furthermore, the acquisition unit can select calendar information to be acquired on a specific day of the week or time period from the participant's past conference participation history. In this way, the acquisition unit can select the optimal method for acquiring calendar information by analyzing the past conference participation history, and achieve efficient information acquisition.

[0055] When acquiring the calendar information, the acquisition unit can perform filtering based on the participant's current project or field of interest. For example, calendar information related to a project currently being carried out by the participant is preferentially acquired. Also, related calendar information can be filtered and acquired based on the participant's field of interest. Furthermore, if a participant is concentrating on a specific project, only calendar information related to that project can be acquired. In this way, the acquisition unit can preferentially acquire highly relevant information by filtering the calendar information based on the current project or field of interest.

[0056] When acquiring calendar information, the acquisition unit can prioritize acquiring highly relevant information by taking into consideration the geographical location information of the participant. For example, if a participant is in a specific area, calendar information related to that area can be acquired preferentially. Also, if a participant is on a business trip, calendar information related to the business trip destination can be acquired preferentially. Furthermore, if a participant is at home, calendar information around the home can be acquired preferentially. In this way, the acquisition unit can prioritize acquiring highly relevant calendar information by taking into consideration the geographical location information, thereby realizing efficient information acquisition.

[0057] When acquiring the calendar information, the acquisition unit can analyze the social media activity of the participant and acquire related information. For example, related calendar information can be acquired based on event information shared by the participant on social media. Also, events and meetings of interest can be identified from the participant's social media activity and calendar information can be acquired. Furthermore, calendar information can be acquired based on event information of accounts that the participant follows on social media. In this way, the acquisition unit can acquire related calendar information by analyzing social media activity, thereby achieving efficient information acquisition.

[0058] During the analysis, the analysis unit can improve the accuracy of detecting free time based on the past conference participation patterns of the participants. For example, the accuracy of detecting free time can be improved based on the time periods in which the participants frequently participated in the past. The analysis unit can also analyze the past conference participation patterns of the participants to detect optimal free time. Furthermore, the accuracy of detecting free time can be improved by taking into account the tendency of participants to participate on specific days of the week or at specific times of the day. In this way, the analysis unit can improve the accuracy of detecting free time by taking into account the past conference participation patterns, and realize efficient meeting schedule suggestions.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The acquisition unit acquires the calendar information of the participant. The calendar information of the participant includes the type of event, time period, location, etc. The acquisition unit can acquire the calendar information through an API, or can also input the calendar information manually. The acquisition unit can also acquire the calendar information from a cloud service. Step 2: The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. Free time is detected based on the length of continuous time or a specific time period. The analysis unit analyzes the calendar information using AI, a rule-based algorithm, or a machine learning algorithm to detect free time. Step 3: The suggestion unit suggests the optimal meeting date based on the free time detected by the analysis unit. The optimal meeting date is suggested based on the overlapping free time of all participants and the shortest travel time. The suggestion unit suggests the optimal meeting date using AI, rule-based algorithms, or machine learning algorithms. Step 4: The response collection unit collects participant responses to the schedule proposed by the proposal unit. The response collection unit can collect participant responses through email, messaging apps, web forms, and voice input. The response collection unit can also collect and analyze participant responses using an AI model.

[0061] (Example 2) The conference schedule tool according to the embodiment of the present invention is a system that automatically adjusts the schedules of participants by inputting multiple email addresses, detects free time in the schedule, and proposes a date. This system contributes to saving time and realizing a smooth conference by streamlining cumbersome adjustment work and avoiding schedule conflicts among participants. First, a user inputs the email addresses of participants who plan to attend the conference. Next, the system acquires calendar information of each participant and analyzes the schedule of each participant. This analysis uses AI to detect free time in the schedule of each participant. For example, if participant A is free from 10:00 to 11:00 a.m., that time period is detected as free time. Next, the system proposes an optimal conference date that all participants can participate in based on the detected free time. For example, if participants A, B, and C are all free from 2:00 to 3:00 p.m., that time period is proposed as the optimal conference date. This proposal is notified to the user, and the user can confirm the proposed date. Furthermore, the system collects participants' responses to the proposed date and finalizes the final conference date. For example, if participant A responds to the proposed date by saying "I can participate" and participant B responds by saying "I cannot participate," the system will again detect available time and propose a new date. In this way, the conference schedule tool of the present invention automatically adjusts the schedules of participants and proposes the optimal conference date, thereby making the cumbersome coordination work more efficient and avoiding conflicts in participants' schedules. This contributes to saving time and realizing smooth conferences. In this way, the conference schedule tool automatically adjusts the schedules of participants and proposes the optimal conference date, thereby making the cumbersome coordination work more efficient and avoiding conflicts in participants' schedules.

[0062] The conference schedule tool according to the embodiment includes an acquisition unit, an analysis unit, a proposal unit, and a response collection unit. The acquisition unit acquires calendar information of the participants. The calendar information of the participants includes, for example, a type of plan, a time period, a location, and the like, but is not limited to such examples. The acquisition unit acquires, for example, the calendar information of the participants through an API. The acquisition unit can also manually input the calendar information of the participants. Furthermore, the acquisition unit can also acquire the calendar information of the participants from a cloud service. For example, the acquisition unit acquires the calendar information of the participants through an API of the cloud service and imports it into the system. The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. The free time is detected based on, for example, a length of continuous time or a specific time period, but is not limited to such examples. For example, the analysis unit uses AI to analyze the calendar information and detect free time. The analysis unit can also detect free time using a rule-based algorithm. Furthermore, the analysis unit can detect free time using a machine learning algorithm. For example, the analysis unit analyzes the calendar information using AI to detect free time. The suggestion unit proposes an optimal meeting schedule based on the free time detected by the analysis unit. The optimal meeting schedule is proposed based on, for example, a time period when all participants' free times overlap or the shortest travel time, but is not limited to such examples. For example, the suggestion unit proposes an optimal meeting schedule using AI. The suggestion unit can also propose an optimal meeting schedule using a rule-based algorithm. Furthermore, the suggestion unit can also propose an optimal meeting schedule using a machine learning algorithm. For example, the suggestion unit proposes an optimal meeting schedule using AI. The response collection unit collects responses of participants to the schedule proposed by the suggestion unit. The response collection unit collects, for example, the responses of participants through email or a messaging app. The response collection unit can also collect the responses of participants through a web form. Furthermore, the response collection unit can collect the responses of participants through voice input. For example, the response collection unit collects the responses of participants through email or a messaging app.As a result, the conference schedule tool according to the embodiment can automatically adjust the schedules of the participants and propose optimal conference dates. Some or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit may collect and analyze responses of participants using an AI model.

[0063] The acquisition unit acquires the calendar information of the participants. The calendar information of the participants includes, for example, the type of schedule, time period, and location, but is not limited to such examples. The acquisition unit acquires, for example, the calendar information of the participants through an API. Specifically, the acquisition unit accesses the API of the calendar service used by each participant and acquires the calendar information using the authentication information. As a result, the acquisition unit can acquire the latest schedule information of the participants in real time. The acquisition unit can also manually input the calendar information of the participants. For example, an interface is provided for the participants to manually input their schedules, and the acquisition unit collects the information. Furthermore, the acquisition unit can also acquire the calendar information of the participants from a cloud service. For example, the acquisition unit acquires the calendar information through the API of the cloud service and imports it into the system. As a result, the acquisition unit can integrate information from multiple calendar services and manage it in a centralized manner. The acquisition unit is required to securely manage this information and take appropriate measures to protect privacy. For example, the acquisition unit performs data encryption and access control to prevent unauthorized access to the information of the participants. As a result, the acquisition unit can efficiently and safely acquire the calendar information of the participants, thereby improving the reliability of the entire system.

[0064] The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. Free time is detected, for example, based on the length of continuous time or a specific time period, but is not limited to such examples. Specifically, the analysis unit uses AI to analyze the calendar information to detect free time. The AI ​​can understand the contents of the calendar schedule using natural language processing technology and evaluate the importance and priority of the schedule. For example, the AI ​​can distinguish between important schedules such as conferences and meetings and low-priority schedules such as personal tasks and reminders, and detect free time more accurately. The analysis unit can also detect free time using a rule-based algorithm. For example, a rule for detecting free time based on a specific time period or day of the week is set, and analysis is performed according to the rule. Furthermore, the analysis unit can detect free time using a machine learning algorithm. For example, by learning past calendar information and grasping the behavior patterns and schedule trends of participants, it is possible to detect free time with higher accuracy. As a result, the analysis unit can quickly and accurately analyze the acquired calendar information and efficiently detect free time for all participants. Furthermore, the analysis unit can visualize the detected free time and provide it to the participants. For example, free time can be displayed in graph or calendar format so that participants can easily check it. This allows the analysis unit to not only detect free time but also support participants in making effective use of the information.

[0065] The suggestion unit proposes an optimal meeting schedule based on the free time detected by the analysis unit. The optimal meeting schedule is proposed based on, for example, the time period when all participants' free times overlap or the shortest travel time, but is not limited to such examples. Specifically, the suggestion unit proposes the optimal meeting schedule using AI. The AI ​​can calculate the optimal schedule based on the participants' calendar information and past meeting history. For example, the AI ​​considers the participants' past meeting attendance rates and the importance of the meetings to propose the most efficient schedule. The suggestion unit can also propose the optimal meeting schedule using a rule-based algorithm. For example, a rule is set to set a meeting at a specific time period or day of the week, and the optimal schedule is proposed according to the rule. Furthermore, the suggestion unit can also propose the optimal meeting schedule using a machine learning algorithm. For example, by learning past meeting data and understanding the participants' behavior patterns and meeting trends, it is possible to propose a schedule with higher accuracy. As a result, the suggestion unit can efficiently propose the optimal meeting schedule by considering the free time of all participants. Furthermore, the suggestion unit can notify the participants of the proposed schedule and ask for their confirmation. For example, the proposed schedule is notified through email or a messaging app, and an interface is provided for the participants to confirm or modify it. This allows the proposal unit to not only propose optimal meeting dates, but also support their confirmation and adjustment.

[0066] The response collection unit collects responses of the participants to the schedule proposed by the proposal unit. The response collection unit, for example, collects the responses of the participants through email or a messaging app. Specifically, the response collection unit collects the attendance confirmations and comments of the participants to the proposed schedule through email or a messaging app, and incorporates them into the system. The response collection unit can also collect the responses of the participants through a web form. For example, a dedicated web form can be provided, and the participants can input their responses through the form. Furthermore, the response collection unit can also collect the responses of the participants through voice input. For example, the voice responses of the participants are converted into text using voice recognition technology and incorporated into the system. This allows the response collection unit to collect the responses of the participants by various means and efficiently manage them. Furthermore, the response collection unit can also analyze the collected responses and grasp the opinions and requests of the participants. For example, the responses collected using AI are analyzed, the opinions and requests of the participants are classified, and the responses are reflected in the adjustment of the optimal meeting schedule. This allows the response collection unit to efficiently collect the responses of the participants and adjust the optimal meeting schedule based on the information. Furthermore, the response collection unit can update the collected responses in real time and make adjustments based on the latest information. For example, if a participant's response changes, it is immediately reflected in the system to respond to the latest situation. This allows the response collection unit to support highly accurate adjustments of meeting schedules based on the latest information at all times.

[0067] The proposal unit can detect free time again and propose a new schedule. The proposal unit can detect free time again, for example, based on the participant's response to the proposed schedule. For example, the proposal unit can analyze the participant's response and detect new free time. The proposal unit can also re-propose an optimal meeting schedule based on the participant's response. For example, the proposal unit can detect free time again and propose a new schedule based on the participant's response. This allows the proposal unit to determine an optimal meeting schedule. A part or all of the above-mentioned processing in the proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the proposal unit can analyze the participant's response using an AI model and propose a new schedule.

[0068] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the calendar information based on the estimated emotion of the user. For example, when the user is feeling stressed, the acquisition unit delays the acquisition of the calendar information and waits until the user is relaxed. Furthermore, when the user is relaxed, the acquisition unit can also immediately acquire the calendar information and quickly start processing. Furthermore, when the user is in a hurry, the acquisition unit can preferentially acquire the calendar information and quickly perform analysis. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on a change in facial expression. Furthermore, the acquisition unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the acquisition unit can collect the user's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on a change in heart rate. This allows the acquisition unit to adjust the timing of acquiring the calendar information according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI or may be performed without using an AI. For example, the acquisition unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0069] The acquisition unit can analyze the past conference participation history of the participant and select an efficient method of acquiring calendar information. The acquisition unit adjusts the timing of acquiring calendar information based on, for example, the time period of the conference that the participant frequently participated in in the past. The acquisition unit can also analyze the type of conference that the participant participated in in the past and preferentially acquire related calendar information. Furthermore, the acquisition unit can select calendar information to be acquired on a specific day of the week or time period from the participant's past conference participation history. For example, the acquisition unit adjusts the timing of acquiring calendar information based on the time period that the participant frequently participated in in the past. In this way, the acquisition unit can select an optimal method of acquiring calendar information by analyzing the past conference participation history and realize efficient information acquisition. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the participant's past conference participation history to the generation AI and cause the generation AI to select an optimal method of acquiring calendar information.

[0070] The acquisition unit may perform filtering based on the participant's current project or field of interest when acquiring the calendar information. For example, the acquisition unit may preferentially acquire calendar information related to a project currently in progress by the participant. The acquisition unit may also filter and acquire related calendar information based on the participant's field of interest. Furthermore, when the participant is concentrating on a specific project, the acquisition unit may also acquire only calendar information related to the project. For example, the acquisition unit preferentially acquires calendar information related to a project currently in progress by the participant. As a result, the acquisition unit can preferentially acquire highly relevant information by filtering the calendar information based on the current project or field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on the participant's current project or field of interest to the generation AI, and cause the generation AI to execute filtering of the calendar information.

[0071] The acquisition unit can estimate the user's emotion and determine the priority of the calendar information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit postpones the acquisition of calendar information with low importance and prioritizes the acquisition of information with high importance. Furthermore, when the user is relaxed, the acquisition unit can acquire all the calendar information evenly. Furthermore, when the user is in a hurry, the acquisition unit can prioritize the acquisition of the most important calendar information and perform processing quickly. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on the change in the facial expression. Furthermore, the acquisition unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the acquisition unit can collect the user's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on the fluctuation of the heart rate. This allows the acquisition unit to determine the priority of the calendar information according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI or may be performed without using an AI. For example, the acquisition unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0072] When acquiring the calendar information, the acquisition unit can preferentially acquire information with high relevance by considering the geographical location information of the participant. For example, when the participant is in a specific area, the acquisition unit preferentially acquires calendar information related to the area. In addition, when the participant is on a business trip, the acquisition unit can also preferentially acquire calendar information related to the business trip destination. In addition, when the participant is at home, the acquisition unit can preferentially acquire calendar information around the home. For example, when the participant is in a specific area, the acquisition unit preferentially acquires calendar information related to the area. As a result, the acquisition unit can preferentially acquire calendar information with high relevance by considering the geographical location information, thereby realizing efficient information acquisition. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location information of the participant to the generation AI and cause the generation AI to execute acquisition of calendar information with high relevance.

[0073] The acquisition unit can analyze the social media activity of the participant and acquire related information when acquiring the calendar information. The acquisition unit can acquire related calendar information based on, for example, event information shared by the participant on social media. The acquisition unit can also identify events or meetings of interest from the participant's social media activity and acquire calendar information. The acquisition unit can also acquire calendar information based on event information of accounts followed by the participant on social media. For example, the acquisition unit acquires related calendar information based on event information shared by the participant on social media. As a result, the acquisition unit can acquire related calendar information by analyzing social media activity, thereby achieving efficient information acquisition. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the social media activity data of the participant to the generation AI and cause the generation AI to acquire related calendar information.

[0074] The analysis unit can estimate the user's emotion and adjust the detection method of free time based on the estimated emotion of the user. For example, when the user is stressed, the analysis unit uses a simple algorithm to quickly detect free time. Also, when the user is relaxed, the analysis unit can perform detailed analysis and detect free time with high accuracy. Furthermore, when the user is in a hurry, the analysis unit can detect free time in the simplest way and quickly suggest it. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. Also, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the detection method of free time according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0075] During the analysis, the analysis unit can improve the detection accuracy of free time based on the past conference participation patterns of the participants. The analysis unit improves the detection accuracy of free time based on, for example, the time period in which the participants frequently participated in the past. The analysis unit can also analyze the past conference participation patterns of the participants and detect the optimal free time. Furthermore, the analysis unit can improve the detection accuracy of free time by considering the tendency of the participants to participate in specific days of the week or time periods. For example, the analysis unit improves the detection accuracy of free time based on the time period in which the participants frequently participated in the past. As a result, the analysis unit can improve the detection accuracy of free time by considering the past conference participation patterns, and realize the proposal of an efficient conference schedule. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the past conference participation pattern data of the participants to the generation AI and cause the generation AI to execute the improvement of the detection accuracy of free time.

[0076] The analysis unit can apply different analysis algorithms depending on the job or position of the participant during analysis. For example, if the participant is a manager, the analysis unit applies an algorithm that detects free time by prioritizing meetings with high importance. In addition, if the participant is a general employee, the analysis unit can also apply an algorithm that detects free time by considering all meetings equally. In addition, if the participant is involved in a specific project, the analysis unit can also apply an algorithm that detects free time by prioritizing meetings related to the project. For example, if the participant is a manager, the analysis unit applies an algorithm that detects free time by prioritizing meetings with high importance. In this way, the analysis unit can realize efficient detection of free time by applying an analysis algorithm according to the job or position. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the job and position data of the participant to the generation AI and cause the generation AI to apply different analysis algorithms.

[0077] The analysis unit can estimate the user's emotion and adjust the display method of the free time based on the estimated emotion of the user. For example, when the user is stressed, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression. Furthermore, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate. This allows the analysis unit to adjust the display method of the free time according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0078] During the analysis, the analysis unit can determine the priority of free time based on the submission time of the participant. For example, the analysis unit detects free time by giving priority to a schedule submitted early by the participant. The analysis unit can also detect free time by taking into account a schedule submitted immediately before by the participant. Furthermore, the analysis unit can preferentially display the optimal free time based on the submission time of the participant. For example, the analysis unit detects free time by giving priority to a schedule submitted early by the participant. In this way, the analysis unit can realize an efficient proposal of a meeting schedule by determining the priority of free time based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the participant to the generation AI and cause the generation AI to execute the determination of the priority of free time.

[0079] During the analysis, the analysis unit can improve the detection accuracy of free time based on the participant's related literature. The analysis unit detects the optimal free time based on the participant's related literature, for example. The analysis unit can also improve the detection accuracy of free time by referring to the participant's related literature. Furthermore, if the participant refers to a specific literature, the analysis unit can detect the free time based on the literature. For example, the analysis unit detects the optimal free time based on the participant's related literature. As a result, the analysis unit can improve the detection accuracy of free time by referring to the related literature, and realize efficient meeting schedule proposals. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the participant's related literature data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy of free time.

[0080] The suggestion unit can estimate the user's emotion and adjust the meeting schedule proposal method based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit provides a simple and highly visible proposal method. Furthermore, when the user is relaxed, the suggestion unit can also provide a proposal method including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also provide a proposal method that focuses on the main points. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression. Furthermore, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on fluctuations in heart rate. This allows the suggestion unit to adjust the meeting schedule proposal method according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using an AI or may be performed without using an AI. For example, the suggestion unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0081] The proposal unit can adjust the level of detail of the proposal based on the importance of the meeting at the time of proposal. For example, in the case of a meeting with high importance, the proposal unit makes a detailed proposal. In addition, in the case of a meeting with low importance, the proposal unit can also make a concise proposal. Furthermore, the proposal unit can adjust the level of detail of the proposal according to the importance of the meeting. For example, in the case of a meeting with high importance, the proposal unit makes a detailed proposal. As a result, the proposal unit can realize an efficient proposal of a meeting schedule by adjusting the level of detail of the proposal based on the importance of the meeting. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the importance data of the meeting to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] The suggestion unit can apply different proposal algorithms depending on the category of the meeting when making a proposal. For example, in the case of a business meeting, the suggestion unit applies an efficient proposal algorithm. In addition, in the case of a casual meeting, the suggestion unit can also apply a flexible proposal algorithm. Furthermore, the suggestion unit can apply an optimal proposal algorithm depending on the category of the meeting. For example, in the case of a business meeting, the suggestion unit applies an efficient proposal algorithm. As a result, the suggestion unit can realize an efficient proposal of a meeting schedule by applying a proposal algorithm according to the category of the meeting. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the category data of the meeting to the generation AI and cause the generation AI to apply different proposal algorithms.

[0083] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit makes a short and to the point suggestion. Also, when the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can make a quick suggestion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in the facial expression. Also, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the suggestion unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0084] The proposal unit can determine the priority of the proposal based on the submission time of the meeting at the time of proposal. For example, the proposal unit gives priority to a meeting submitted early. The proposal unit can also make a proposal taking into consideration a meeting submitted immediately before. Furthermore, the proposal unit can give priority to an optimal proposal based on the submission time. For example, the proposal unit gives priority to a meeting submitted early. In this way, the proposal unit can realize an efficient proposal of a meeting schedule by determining the priority of the proposal based on the submission time. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the submission time data of the meeting to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0085] The proposal unit can adjust the order of proposals based on the relevance of the meetings at the time of proposal. For example, the proposal unit gives priority to proposing highly relevant meetings. The proposal unit can also put off proposing less relevant meetings. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the meetings. For example, the proposal unit gives priority to proposing highly relevant meetings. As a result, the proposal unit can realize efficient proposal of meeting schedules by adjusting the order of proposals based on the relevance of the meetings. A part or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the relevance data of the meetings to the generation AI and cause the generation AI to adjust the order of proposals.

[0086] The response collection unit can estimate the user's emotion and adjust the response collection method based on the estimated user's emotion. For example, the response collection unit provides a simple and quick response collection method when the user is stressed. Also, the response collection unit can provide a detailed response collection method when the user is relaxed. Furthermore, the response collection unit can collect responses in the simplest way when the user is in a hurry. For example, the response collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the response collection unit calculates an emotion score based on the change in the facial expression. Also, the response collection unit can record the user's voice and estimate the emotion using a voice analysis technique. For example, the response collection unit analyzes the tone and speed of the voice and calculates the emotion score. Also, the response collection unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the response collection unit calculates an emotion score based on the fluctuation of the heart rate. This allows the response collection unit to adjust the response collection method according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the response collection unit may be performed, for example, using an AI or may be performed without using an AI. For example, the response collection unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0087] The response collection unit can select the optimal collection method when collecting responses by analyzing the past response history of the participants. For example, the response collection unit prioritizes collection of a method in which the participant responded quickly in the past. The response collection unit can also select the optimal collection method by analyzing the past response history of the participants. Furthermore, the response collection unit can select the optimal collection method by taking into consideration the tendency of the participants to respond in a specific way. For example, the response collection unit prioritizes collection of a method in which the participant responded quickly in the past. In this way, the response collection unit can select the optimal collection method by analyzing the past response history, thereby realizing efficient response collection. A part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the response collection unit can input the past response history data of the participants to the generation AI and cause the generation AI to select the optimal collection method.

[0088] The response collection unit can customize the collection means based on the current situation of the participant when collecting the response. For example, if the participant is in a meeting, the response collection unit collects the response after the meeting ends. In addition, if the participant is on the move, the response collection unit can also collect the response using a mobile device. In addition, if the participant is on vacation, the response collection unit can collect the response after the vacation ends. For example, if the participant is in a meeting, the response collection unit collects the response after the meeting ends. In this way, the response collection unit can realize efficient response collection by customizing the collection means based on the current situation. Part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit can input the current situation data of the participant to the generation AI and cause the generation AI to execute customization of the collection means.

[0089] The response collection unit can estimate the user's emotion and determine the priority of response collection based on the estimated user's emotion. For example, when the user is stressed, the response collection unit prioritizes collecting responses with high importance. In addition, when the user is relaxed, the response collection unit can also collect all responses evenly. In addition, when the user is in a hurry, the response collection unit can also prioritize collecting the most important responses. For example, the response collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the response collection unit calculates an emotion score based on the change in facial expression. In addition, the response collection unit can record the user's voice and estimate the emotion using a voice analysis technology. For example, the response collection unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the response collection unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the response collection unit calculates an emotion score based on the fluctuation of the heart rate. In this way, the response collection unit can determine the priority of response collection according to the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the response collection unit may be performed, for example, using an AI or may be performed without using an AI. For example, the response collection unit may input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0090] The response collection unit can select the optimal collection method in consideration of the geographical location information of the participant when collecting the response. For example, when the participant is in a specific area, the response collection unit selects a collection method related to that area. In addition, when the participant is on a business trip, the response collection unit can also select a collection method related to the business trip destination. In addition, when the participant is at home, the response collection unit can also select a collection method around the home. For example, when the participant is in a specific area, the response collection unit selects a collection method related to that area. In this way, the response collection unit can select the optimal collection method by considering the geographical location information and realize efficient response collection. Part or all of the above-mentioned processing in the response collection unit may be performed, for example, using AI or may be performed without using AI. For example, the response collection unit can input the geographical location information data of the participant to the generation AI and cause the generation AI to select the optimal collection method.

[0091] The response collection unit can analyze the social media activities of the participants and suggest collection means when collecting responses. The response collection unit can, for example, suggest the optimal collection means based on the information shared by the participants on social media. The response collection unit can also identify and suggest collection means of interest from the participants' social media activities. Furthermore, the response collection unit can also suggest collection means based on information on accounts the participants follow on social media. For example, the response collection unit can suggest the optimal collection means based on the information shared by the participants on social media. As a result, the response collection unit can propose the optimal collection means by analyzing social media activities, thereby realizing efficient response collection. Part or all of the above-mentioned processing in the response collection unit can be performed, for example, using AI or without using AI. For example, the response collection unit can input the participants' social media activity data to the generation AI and cause the generation AI to execute the suggestion of the collection means.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When acquiring calendar information of a participant, the acquisition unit can adjust the acquisition timing in consideration of the participant's health condition. For example, if a participant is in poor health, the acquisition unit can delay acquiring the calendar information and wait until the participant recovers. Also, if a participant is in good health, the acquisition unit can immediately acquire the calendar information and start processing promptly. Furthermore, if a participant is in a specific health condition, the acquisition unit can acquire the calendar information at the optimal timing according to that condition. This allows the acquisition unit to adjust the acquisition timing of the calendar information according to the participant's health condition.

[0094] The suggestion unit can determine the priority order of the proposed meeting schedules based on the importance of the meeting. For example, for a highly important meeting, the suggestion unit preferentially suggests the earliest available time. Also, for a less important meeting, the suggestion unit can suggest it later. Furthermore, the suggestion unit can adjust the level of detail of the proposed schedule depending on the importance of the meeting. This allows the suggestion unit to suggest the optimal meeting schedule based on the importance of the meeting.

[0095] The acquisition unit can estimate the user's emotion and adjust the method of acquiring calendar information based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit simplifies the acquisition of calendar information to reduce the burden on the user. When the user is relaxed, detailed calendar information can be acquired. Furthermore, when the user is in a hurry, the most important calendar information can be acquired with priority. This allows the acquisition unit to adjust the method of acquiring calendar information according to the user's emotion.

[0096] The acquisition unit can analyze the participant's past conference participation history and select an efficient method for acquiring calendar information. For example, the timing for acquiring calendar information can be adjusted based on the time periods of conferences that the participant frequently attended in the past. The acquisition unit can also analyze the types of conferences that the participant attended in the past and prioritize acquiring related calendar information. Furthermore, the acquisition unit can select calendar information to be acquired on a specific day of the week or time period from the participant's past conference participation history. In this way, the acquisition unit can select the optimal method for acquiring calendar information by analyzing the past conference participation history, and achieve efficient information acquisition.

[0097] When acquiring the calendar information, the acquisition unit can perform filtering based on the participant's current project or field of interest. For example, calendar information related to a project currently being carried out by the participant is preferentially acquired. Also, related calendar information can be filtered and acquired based on the participant's field of interest. Furthermore, if a participant is concentrating on a specific project, only calendar information related to that project can be acquired. In this way, the acquisition unit can preferentially acquire highly relevant information by filtering the calendar information based on the current project or field of interest.

[0098] The acquisition unit can estimate the user's emotions and determine the priority order of calendar information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, calendar information of low importance is postponed and information of high importance is acquired preferentially. Also, when the user is relaxed, all calendar information can be acquired equally. Furthermore, when the user is in a hurry, the most important calendar information can be acquired preferentially and processed quickly. In this way, the acquisition unit can determine the priority order of calendar information according to the user's emotions.

[0099] When acquiring calendar information, the acquisition unit can prioritize acquiring highly relevant information by taking into consideration the geographical location information of the participant. For example, if a participant is in a specific area, calendar information related to that area can be acquired preferentially. Also, if a participant is on a business trip, calendar information related to the business trip destination can be acquired preferentially. Furthermore, if a participant is at home, calendar information around the home can be acquired preferentially. In this way, the acquisition unit can prioritize acquiring highly relevant calendar information by taking into consideration the geographical location information, thereby realizing efficient information acquisition.

[0100] When acquiring the calendar information, the acquisition unit can analyze the social media activity of the participant and acquire related information. For example, related calendar information can be acquired based on event information shared by the participant on social media. Also, events and meetings of interest can be identified from the participant's social media activity and calendar information can be acquired. Furthermore, calendar information can be acquired based on event information of accounts that the participant follows on social media. In this way, the acquisition unit can acquire related calendar information by analyzing social media activity, thereby achieving efficient information acquisition.

[0101] The analysis unit can estimate the user's emotions and adjust the method of detecting free time based on the estimated user's emotions. For example, if the user is stressed, a simple algorithm can be used to quickly detect free time. Also, if the user is relaxed, a detailed analysis can be performed to detect free time with high accuracy. Furthermore, if the user is in a hurry, free time can be detected in the simplest way and quickly suggested. This allows the analysis unit to adjust the method of detecting free time according to the user's emotions.

[0102] During the analysis, the analysis unit can improve the accuracy of detecting free time based on the past conference participation patterns of the participants. For example, the accuracy of detecting free time can be improved based on the time periods in which the participants frequently participated in the past. The analysis unit can also analyze the past conference participation patterns of the participants to detect optimal free time. Furthermore, the accuracy of detecting free time can be improved by taking into account the tendency of participants to participate on specific days of the week or at specific times of the day. In this way, the analysis unit can improve the accuracy of detecting free time by taking into account the past conference participation patterns, and realize efficient meeting schedule suggestions.

[0103] The process flow of the second embodiment will be briefly described below.

[0104] Step 1: The acquisition unit acquires the calendar information of the participant. The calendar information of the participant includes the type of event, time period, location, etc. The acquisition unit can acquire the calendar information through an API, or can also input the calendar information manually. The acquisition unit can also acquire the calendar information from a cloud service. Step 2: The analysis unit analyzes the calendar information acquired by the acquisition unit to detect free time. Free time is detected based on the length of continuous time or a specific time period. The analysis unit analyzes the calendar information using AI, a rule-based algorithm, or a machine learning algorithm to detect free time. Step 3: The suggestion unit suggests the optimal meeting date based on the free time detected by the analysis unit. The optimal meeting date is suggested based on the overlapping free time of all participants and the shortest travel time. The suggestion unit suggests the optimal meeting date using AI, rule-based algorithms, or machine learning algorithms. Step 4: The response collection unit collects participant responses to the schedule proposed by the proposal unit. The response collection unit can collect participant responses through email, messaging apps, web forms, and voice input. The response collection unit can also collect and analyze participant responses using an AI model.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0107] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, proposal unit, and response collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition 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, and acquires calendar information of the participants. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired calendar information to detect free time. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting schedule based on the detected free time. The response collection unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and collects responses of participants to the proposed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0116] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, proposal unit, and response collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition 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, and acquires calendar information of the participants. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired calendar information to detect free time. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting schedule based on the detected free time. The response collection unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and collects responses of participants to the proposed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, proposal unit, and response collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition 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, and acquires calendar information of the participants. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired calendar information to detect free time. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting schedule based on the detected free time. The response collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and collects responses of participants to the proposed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, proposal unit, and response collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and acquires calendar information of the participants. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the acquired calendar information to detect free time. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal meeting schedule based on the detected free time. The response collection unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and collects responses of the participants to the proposed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0159] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0163] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0168] 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.

[0169] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0170] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0174] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0176] (Appendix 1) An acquisition unit for acquiring calendar information of participants; an analysis unit that analyzes the calendar information acquired by the acquisition unit and detects free time; a proposal unit that proposes an efficient meeting schedule based on the free time detected by the analysis unit; A response collection unit that collects responses from participants to the schedule proposed by the proposal unit. A system characterized by: (Appendix 2) The suggestion unit, Re-detect free times and suggest new dates 2. The system of claim 1, (Appendix 3) The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring calendar information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 4) The acquisition unit is Analyze participants' past meeting participation history and select an efficient way to obtain calendar information 2. The system of claim 1, (Appendix 5) The acquisition unit is Filtering calendar information based on participants' current projects or interests when retrieving it 2. The system of claim 1, (Appendix 6) The acquisition unit is To estimate a user's emotion and determine a priority of calendar information to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The acquisition unit is When retrieving calendar information, the geographic location of participants is taken into account to prioritize retrieving the most relevant information. 2. The system of claim 1, (Appendix 8) The acquisition unit is When retrieving calendar information, analyze attendees' social media activity to retrieve relevant information 2. The system of claim 1, (Appendix 9) The analysis unit is To estimate a user's emotion and adjust a method for detecting free time based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The analysis unit is During analysis, improve availability detection based on participants' past meeting joining patterns 2. The system of claim 1, (Appendix 11) The analysis unit is During analysis, apply different analysis algorithms depending on the participant's job or role 2. The system of claim 1, (Appendix 12) The analysis unit is Inferring user sentiment and adjusting how availability is displayed based on the estimated user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, prioritize availability based on when participants submitted 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, improve the accuracy of free time detection based on participants' related literature 2. The system of claim 1, (Appendix 15) The suggestion unit, To estimate a user's emotion and adjust a method of proposing a meeting schedule based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The suggestion unit, When proposing, adjust the level of detail in the proposal based on the importance of the meeting 2. The system of claim 1, (Appendix 17) The suggestion unit, During suggestions, apply different suggestion algorithms depending on the meeting category 2. The system of claim 1, (Appendix 18) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 19) The suggestion unit, When proposing, prioritize proposals based on when they were submitted for a meeting 2. The system of claim 1, (Appendix 20) The suggestion unit, When proposing, adjust the order of suggestions based on meeting relevance 2. The system of claim 1, (Appendix 21) The response collection unit: To estimate a user's emotion and adjust a response collection method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The response collection unit: When collecting responses, the most appropriate collection method is selected by analyzing the participant's past response history. 2. The system of claim 1, (Appendix 23) The response collection unit: Customize responses based on the participant's current situation when collecting them 2. The system of claim 1, (Appendix 24) The response collection unit: To estimate a user's emotion and prioritize response collection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 25) The response collection unit: When collecting responses, choose the most appropriate collection method based on participants' geographic location 2. The system of claim 1, (Appendix 26) The response collection unit: When collecting responses, we will analyze participants' social media activity and suggest collection methods. 2. The system of claim 1, [Explanation of symbols]

[0177] 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. An acquisition unit for acquiring calendar information of participants; an analysis unit that analyzes the calendar information acquired by the acquisition unit and detects free time; a proposal unit that proposes an efficient meeting schedule based on the free time detected by the analysis unit; A response collection unit that collects responses from participants to the schedule proposed by the proposal unit. A system characterized by:

2. The suggestion unit, Re-detect free times and suggest new dates 2. The system of claim 1 .

3. The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring calendar information based on the estimated user's emotion.

2. The system of claim 1 .

4. The acquisition unit is Analyze participants' past meeting participation history and select an efficient way to obtain calendar information 2. The system of claim 1 .

5. The acquisition unit is Filtering calendar information based on participants' current projects or interests when retrieving it 2. The system of claim 1 .

6. The acquisition unit is To estimate a user's emotion and determine a priority of calendar information to be acquired based on the estimated user's emotion.

2. The system of claim 1 .

7. The acquisition unit is When retrieving calendar information, the geographic location of participants is taken into account to prioritize retrieving the most relevant information.

2. The system of claim 1 .

8. The acquisition unit is When retrieving calendar information, analyze attendees' social media activity to retrieve relevant information 2. The system of claim 1 .

9. The analysis unit is To estimate a user's emotion and adjust a method for detecting free time based on the estimated user's emotion.

2. The system of claim 1 .

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