system

Abstract

The system according to this embodiment aims to streamline the scheduling of meetings. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a proposal unit, and a transmission unit. The acquisition unit acquires data from a calendar system. The analysis unit analyzes the data acquired by the acquisition unit. The proposal unit proposes candidate dates based on the analysis results obtained by the analysis unit. The transmission unit sends emails or direct messages when a negotiation button is pressed.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it takes a lot of time to schedule meetings, and there is room for improving work efficiency.

[0005] The system according to the embodiment aims to improve the efficiency of meeting scheduling.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a proposal unit, and a transmission unit. The acquisition unit acquires data of a calendar system. The analysis unit analyzes the data acquired by the acquisition unit. The proposal unit proposes candidate dates based on the analysis result obtained by the analysis unit. The transmission unit sends an email or a DM by pressing a negotiation button.

Effects of the Invention

[0007] The system according to this embodiment can streamline the scheduling of meetings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The meeting scheduling system according to an embodiment of the present invention is a system that uses a generating AI to acquire data from a calendar system and automates the scheduling of meetings. This meeting scheduling system uses a generating AI to acquire data from a calendar system and proposes several candidate dates that are compatible with the schedules of all meeting participants. If a similar group of members has held a meeting in the past, the system also considers the time of the previous meeting and prioritizes the proposal. If everyone is busy, the system may suggest alternative dates that are closer to the present, such as within 3 days or 1 week, to minimize the number of absentees. Participants can be prioritized, and members with high priority can be prevented from being absent. The system also collects various past meeting information of all participants, such as "a meeting was scheduled, but the participation status was 'no,' 'undecided,' or 'no response'," and if the schedules do not match, it provides information such as "based on past experience, they might be able to make time," and the system implements a function that allows the user to press a "negotiate button" to send a negotiation email (or DM in a communication tool, etc.) to the target person. By using this system, the time previously spent on scheduling can be used more effectively. For example, the generating AI retrieves data from a calendar system. It obtains the schedules of all meeting participants and identifies each participant's availability. For instance, if participant A is free from 10:00 to 11:00 on Monday, that time slot is suggested as a possibility. Next, based on the retrieved data, the generating AI proposes several possible dates that suit all meeting participants. For example, if participants A, B, and C are all free from 14:00 to 15:00 on Tuesday, that time slot is suggested as a possibility. Furthermore, if a similar group has held a meeting in the past, the AI ​​considers the time of that previous meeting and prioritizes suggesting it. For example, if a meeting was previously held on Tuesday from 14:00 to 15:00, that time slot is prioritized. If everyone is busy and the earliest date everyone can attend is far in the future, the AI ​​also suggests alternative dates within the next three days or a week to minimize absences.For example, if the earliest date everyone can attend is two weeks away, the system will suggest an alternative date within three days if participant A is unavailable but other participants are available. It can also prioritize participants, ensuring that high-priority members are not absent. For instance, if the project leader is a high priority, the system will prioritize suggesting dates the leader can attend. Furthermore, it collects various past meeting information from all participants, including whether a meeting was scheduled but the attendance status was "no," "undecided," or "no response." If scheduling conflicts arise, it provides information such as, "Based on past experience, they might be available at this time." For example, if participant B previously attended a meeting during a time slot they had marked as "undecided," that time slot will be suggested. Finally, the system will implement a feature where users can press a "negotiate button" to send a negotiation email (or direct message via a communication tool, etc.) to the relevant person. For example, an email might be sent to participant C asking, "We'd like to hold a meeting during this time slot; would you be available?" This allows the meeting scheduling system to effectively utilize the time previously spent on scheduling. For example, scheduling a meeting that previously took an hour can now be completed in just a few minutes. This allows the meeting scheduling system to make more efficient use of the time previously spent on scheduling.

[0029] The meeting scheduling system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, and a transmission unit. The acquisition unit acquires data from a calendar system. The acquisition unit can acquire data, for example, by using the API of the calendar system. Alternatively, the acquisition unit can acquire data by directly accessing the database of the calendar system. Furthermore, the acquisition unit can be configured to acquire data from the calendar system periodically. For example, the acquisition unit can be configured to acquire data from the calendar system at 9:00 AM every day. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data, for example, by using a generation AI. The generation AI analyzes the data from the calendar system and proposes several candidate dates that match the schedules of all meeting participants. For example, if all participants A, B, and C are free from 2:00 PM to 3:00 PM on Tuesday, the analysis unit proposes that time slot as a candidate. The proposal unit proposes candidate dates based on the analysis results obtained by the analysis unit. The proposal unit can propose candidate dates, for example, by using a generation AI. If a meeting has been held with similar members in the past, the generation AI also considers the time of the previous meeting and prioritizes it when proposing a date. For example, if the proposal department has previously held a meeting between 2 PM and 3 PM on a Tuesday, it will prioritize proposing that time slot. The sending department sends an email or direct message (DM) when the negotiation button is pressed. For example, when a user presses the "negotiate button," the sending department sends a negotiation email (or DM via a communication tool, etc.) to the target person. For example, the sending department sends an email to participant C saying, "We would like to hold a meeting during this time slot, would you be able to participate?" In this way, the meeting scheduling system according to the embodiment can automate meeting scheduling by acquiring and analyzing data from a calendar system, proposing candidate dates, and sending emails or DMs.

[0030] The data acquisition unit retrieves data from the calendar system. For example, the data acquisition unit can retrieve data using the calendar system's API. Specifically, the acquisition unit sends a request to the calendar system's API endpoint, parses the data returned as a response, and extracts the necessary information. Alternatively, the acquisition unit can directly access the calendar system's database to retrieve data. In this case, the acquisition unit executes database queries to retrieve the required data. Furthermore, the acquisition unit can be configured to retrieve data from the calendar system periodically. For example, the acquisition unit can be configured to retrieve data from the calendar system at 9 AM every day. This periodic data retrieval ensures the system always maintains the latest information, enabling accurate scheduling. The data acquisition unit can flexibly configure the frequency and timing of data retrieval, allowing for customization to meet user needs. For example, the frequency of data retrieval can be increased to match specific projects or events. Additionally, the acquisition unit can centrally manage the retrieved data and integrate it with other departments and systems. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data using, for example, a generative AI. The generative AI analyzes the data from the calendar system and suggests several possible meeting times that suit all participants. Specifically, the generative AI compares each participant's schedule and identifies common free time slots. For example, if participants A, B, and C are all free from 2 PM to 3 PM on Tuesday, the analysis unit will suggest that time slot as a candidate. The generative AI can also use natural language processing technology to extract useful information from calendar notes and comments and incorporate it into the analysis. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and statistical information. For example, based on the history of past meetings, it can identify trends in which meetings are frequently held on specific days or times and incorporate that information into the analysis. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The proposal department proposes candidate dates based on the analysis results obtained by the analysis department. The proposal department can, for example, use a generation AI to propose candidate dates. If the generation AI has held a meeting with similar members in the past, it will also consider the time of the previous meeting and prioritize that suggestion. Specifically, the proposal department analyzes past meeting data and, if participants frequently hold meetings at a particular time, it will prioritize suggesting that time slot. For example, if the proposal department has held a meeting on Tuesday from 2 PM to 3 PM in the past, it will prioritize suggesting that time slot. Furthermore, the proposal department can propose the optimal candidate date by considering the individual preferences and constraints of the participants. For example, if a particular participant has another important appointment on a specific day or time, it will adjust the candidate date to take that information into account. The proposal department can also provide multiple candidate dates and allow participants to choose, enabling flexible scheduling. This allows the proposal department to propose the optimal candidate date considering the schedules of all participants and efficiently schedule meetings.

[0033] The sending unit sends emails or direct messages (DMs) when a user presses the "Negotiate" button. For example, when a user presses the "Negotiate" button, the sending unit sends a negotiation email (or DM via a communication tool, etc.) to the target person. Specifically, the sending unit sends an email to participant C saying, "I would like to have a meeting at this time; would you be able to participate?" The sending unit can send messages quickly and efficiently by pre-setting email and DM templates. Furthermore, the sending unit can track the status of sent messages, automatically analyze replies from participants, and reflect them in the system. For example, if a participant replies "I can participate," that information is automatically registered in the system, and the meeting date is confirmed. The sending unit can also reliably transmit information using multiple communication methods. For example, in addition to sending emails, it can use voice calls, SMS, chat apps, etc. in combination to reliably deliver important information. As a result, the sending unit can provide users with quick and reliable instructions and efficiently schedule meetings.

[0034] The data collection unit can collect past meeting information. For example, it can collect past meeting information from a calendar system database. The data collection unit can collect information such as the date and time of past meetings, participants, and agenda. For example, the data collection unit can collect meeting information for the past year. The data collection unit can also analyze past meeting information to understand meeting trends. For example, the data collection unit can analyze the frequency and time of past meetings to understand meeting trends. As a result, by collecting past meeting information, the data collection unit can suggest more appropriate candidate dates.

[0035] The priority setting unit can assign priorities to participants. For example, it can set priorities based on the participant's position or importance. For instance, if the project leader has high priority, the priority setting unit will prioritize suggesting dates when the leader is available. The priority setting unit can also use generative AI to set participant priorities. For example, the generative AI can analyze the participant's position and past meeting attendance history to set priorities. This allows the priority setting unit to adjust the schedule to ensure that important members are not absent by assigning priorities to participants.

[0036] The alternative proposal team can propose alternative dates that minimize absences, such as within three days or a week, if the date when everyone can attend is far in the future. For example, if the date when everyone can attend is two weeks away, the alternative proposal team will propose an alternative date within three days where participant A is absent but other participants can attend. The alternative proposal team can also use generative AI to make alternative proposals. For example, the generative AI can analyze participants' schedules and propose alternative dates that minimize absences. This allows the alternative proposal team to propose alternative dates that minimize absences, even when the date when everyone can attend is far in the future.

[0037] The data collection unit can collect past meeting information for all participants. For example, it can collect past meeting information for all participants from the calendar system's database. The data collection unit can collect information such as the date and time of past meetings, participants, and agenda. For example, the data collection unit can collect meeting information for the past year. The data collection unit can also analyze past meeting information to understand meeting trends. For example, the data collection unit can analyze the frequency and time of past meetings to understand meeting trends. As a result, by collecting past meeting information for all participants, the data collection unit can suggest more appropriate candidate dates.

[0038] The priority setting unit can assign priorities to participants. For example, it can set priorities based on the participant's position or importance. For instance, if the project leader has high priority, the priority setting unit will prioritize suggesting dates when the leader is available. The priority setting unit can also use generative AI to set participant priorities. For example, the generative AI can analyze the participant's position and past meeting attendance history to set priorities. This allows the priority setting unit to adjust the schedule to ensure that important members are not absent by assigning priorities to participants.

[0039] The alternative proposal team can propose alternative dates that minimize absences, such as within three days or a week, if the date when everyone can attend is far in the future. For example, if the date when everyone can attend is two weeks away, the alternative proposal team will propose an alternative date within three days where participant A is absent but other participants can attend. The alternative proposal team can also use generative AI to make alternative proposals. For example, the generative AI can analyze participants' schedules and propose alternative dates that minimize absences. This allows the alternative proposal team to propose alternative dates that minimize absences, even when the date when everyone can attend is far in the future.

[0040] The sending unit allows users to send negotiation emails (or direct messages, etc., via communication tools) to the target person by pressing the "Negotiate button." For example, when a user presses the "Negotiate button," the sending unit sends a negotiation email (or direct message, etc., via communication tools) to the target person. For example, the sending unit sends an email to participant C saying, "I would like to have a meeting at this time; would you be able to participate?" In this way, the sending unit allows users to send negotiation emails or direct messages to the target person by pressing the "Negotiate button."

[0041] The data acquisition unit can analyze the user's past calendar usage history and select the optimal data acquisition method. For example, the data acquisition unit can use a generating AI to acquire calendar data based on the time slots the user frequently used in the past. The data acquisition unit can also use a generating AI to select the optimal data acquisition method by referring to the settings of the calendar app the user has used in the past. The data acquisition unit can also analyze the user's past calendar usage history and have the generating AI suggest the optimal data acquisition timing. In this way, the data acquisition unit can select the optimal data acquisition method by analyzing the user's past calendar usage history.

[0042] The data acquisition unit can filter calendar data based on the user's current projects and areas of interest. For example, the unit can prioritize acquiring meeting information related to the user's current projects. The unit can also prioritize acquiring event information related to the user's areas of interest. The generation AI can also filter calendar data based on the project priorities set by the user. This allows the data acquisition unit to acquire highly relevant data by filtering based on the user's current projects and areas of interest.

[0043] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring calendar data. For example, the acquisition unit can prioritize the acquisition of meeting information in locations close to the user's current location. If the user is on a business trip, the acquisition unit can also prioritize the acquisition of event information related to the region the user is visiting. If the user is interested in a particular region, the acquisition unit can also prioritize the acquisition of data related to that region. In this way, the acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location.

[0044] The data acquisition unit can analyze the user's social media activity when acquiring calendar data and obtain relevant data. For example, the data acquisition unit can prioritize acquiring event information that the user has mentioned on social media. The data acquisition unit can also prioritize acquiring event information from accounts that the user follows. The data acquisition unit can also acquire event information that the user plans to attend, as mentioned on social media. In this way, the data acquisition unit can acquire relevant data by analyzing the user's social media activity.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit's generating AI can perform a detailed analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data using the generating AI. The analysis unit can also dynamically adjust the level of detail of the analysis using the generating AI according to the importance of the data. This allows the analysis unit to perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the generation AI in the analysis unit applies a specific analysis algorithm to meeting data. The generation AI in the analysis unit can also apply a different analysis algorithm to event data. The generation AI in the analysis unit can also select the optimal analysis algorithm depending on the data category. As a result, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the data category.

[0047] The analysis unit can determine the priority of analysis based on the data submission date. For example, the generation AI will prioritize the analysis of data with an approaching submission deadline. The analysis unit can also postpone the analysis of data with a distant submission deadline. The generation AI can dynamically adjust the analysis priority according to the data submission date. As a result, the analysis unit can prioritize the analysis of data with an approaching submission deadline by determining the priority of analysis based on the data submission date.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the generation AI will prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The generation AI can dynamically adjust the order of analysis according to the relevance of the data. As a result, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0049] The proposal function can adjust the level of detail in its proposals based on the importance of the candidate dates. For example, the AI ​​can generate detailed proposals for high-importance candidate dates. For low-importance candidate dates, the AI ​​can generate simplified proposals. The AI ​​can also dynamically adjust the level of detail in the proposals according to the importance of the candidate dates. This allows the proposal function to provide detailed proposals for important candidate dates by adjusting the level of detail based on the importance of each date.

[0050] The proposal function can apply different proposal algorithms depending on the category of the candidate date during the proposal process. For example, the proposal function's generating AI can apply a specific proposal algorithm to candidate meeting dates. The proposal function can also apply a different proposal algorithm to candidate event dates. The proposal function can also have the generating AI select the optimal proposal algorithm depending on the category of the candidate date. This allows the proposal function to provide optimal proposals by applying different proposal algorithms depending on the category of the candidate date.

[0051] The proposal department can determine the priority of proposals based on the submission timing of candidate dates. For example, the proposal department's generating AI will prioritize proposals for candidate dates with approaching deadlines. The proposal department can also have the generating AI postpone proposals for candidate dates with distant deadlines. The proposal department can also have the generating AI dynamically adjust the priority of proposals according to the submission timing of candidate dates. This allows the proposal department to prioritize proposals for candidate dates with approaching deadlines by determining the priority of proposals based on the submission timing of candidate dates.

[0052] The proposal unit can adjust the order of proposals based on the relevance of candidate dates. For example, the generation AI will prioritize proposals for highly relevant candidate dates. The proposal unit can also postpone proposals for less relevant candidate dates. The generation AI can dynamically adjust the order of proposals according to the relevance of the candidate dates. This allows the proposal unit to prioritize proposals for highly relevant candidate dates by adjusting the order of proposals based on their relevance.

[0053] The sending unit can select the optimal sending method by referring to past sending history when sending a message. For example, if the user has preferred using email in the past, the generating AI will send the message via email. If the user has preferred using a communication tool in the past, the generating AI can also send the message using that tool. The sending unit can also analyze the user's past sending history, and the generating AI will select the optimal sending method. In this way, the sending unit can select the optimal sending method by referring to past sending history.

[0054] The transmission unit can select the optimal transmission method by considering the user's device information during transmission. For example, if the user is using a smartphone, the generating AI will send a message optimized for smartphones. If the user is using a tablet, the generating AI can also send a message optimized for tablets. If the user is using a desktop, the generating AI can also send a message optimized for desktops. In this way, the transmission unit can select the optimal transmission method by considering the user's device information.

[0055] The data collection unit can improve the accuracy of data collection by referring to past meeting information during the collection process. For example, the data collection unit uses a generating AI to collect optimal meeting information based on the user's past meeting history. The data collection unit can also analyze the user's past meeting history, and the generating AI can further improve the accuracy of data collection. The data collection unit can also refer to past meeting information, and the generating AI can select the optimal collection method. In this way, the data collection unit can improve the accuracy of data collection by referring to past meeting information.

[0056] The data collection unit can improve the accuracy of its collection by referring to relevant meeting literature during the collection process. For example, the data collection unit uses relevant meeting literature to enable the generative AI to collect the most relevant meeting information. The data collection unit can also use relevant literature to enable the generative AI to improve the accuracy of its collection. The data collection unit can also analyze relevant meeting literature and enable the generative AI to select the most suitable collection method. As a result, the data collection unit can improve the accuracy of its collection by referring to relevant meeting literature.

[0057] The priority setting unit can select the optimal setting method by referring to past priority setting history when setting priorities. For example, the priority setting unit can have the generating AI propose the optimal priority setting method based on priorities previously set by the user. The priority setting unit can also analyze the user's past priority setting history and have the generating AI select the optimal setting method. The priority setting unit can also have the generating AI provide the optimal priority setting method by referring to past priority setting history. In this way, the priority setting unit can select the optimal setting method by referring to past priority setting history.

[0058] The priority setting unit can select the optimal setting method by considering the user's device information when setting priorities. For example, if the user is using a smartphone, the generating AI can provide a priority setting method optimized for smartphones. If the user is using a tablet, the generating AI can also provide a priority setting method optimized for tablets. If the user is using a desktop, the generating AI can also provide a priority setting method optimized for desktops. In this way, the priority setting unit can select the optimal setting method by considering the user's device information.

[0059] The alternative proposal unit can select the optimal proposal method by referring to past proposal history when making an alternative proposal. For example, the alternative proposal unit can use the generation AI to make the optimal alternative proposal based on proposals previously accepted by the user. The alternative proposal unit can also analyze the user's past proposal history and have the generation AI select the optimal proposal method. The alternative proposal unit can also refer to past proposal history and have the generation AI provide the optimal alternative proposal. In this way, the alternative proposal unit can select the optimal proposal method by referring to past proposal history.

[0060] The alternative suggestion unit can select the optimal suggestion method by considering the user's device information when making alternative suggestions. For example, if the user is using a smartphone, the generating AI will provide alternative suggestions optimized for smartphones. If the user is using a tablet, the generating AI can also provide alternative suggestions optimized for tablets. If the user is using a desktop, the generating AI can also provide alternative suggestions optimized for desktops. In this way, the alternative suggestion unit can select the optimal suggestion method by considering the user's device information.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data acquisition unit can filter calendar data based on the user's current projects and areas of interest. For example, it can prioritize acquiring meeting information related to the user's current projects. It can also prioritize acquiring event information related to the user's areas of interest. The generating AI can also filter calendar data based on the project priorities set by the user. As a result, the data acquisition unit can acquire highly relevant data by filtering based on the user's current projects and areas of interest.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the generating AI can perform a detailed analysis on highly important data. For less important data, the generating AI can perform a simplified analysis. The generating AI can also dynamically adjust the level of detail of the analysis according to the importance of the data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0064] The proposal function can adjust the level of detail in its proposals based on the importance of the candidate dates. For example, the generating AI can provide detailed proposals for highly important candidate dates, and simplified proposals for less important dates. The generating AI can also dynamically adjust the level of detail in its proposals according to the importance of the candidate dates. This allows the proposal function to provide detailed proposals for important candidate dates by adjusting the level of detail based on the importance of each date.

[0065] The sending unit can select the optimal sending method by referring to past sending history when sending a message. For example, if the user previously preferred using email, the generating AI will send the message via email. If the user previously preferred using a communication tool, the generating AI can also send the message using that tool. The generating AI can also analyze the user's past sending history and select the optimal sending method. In this way, the sending unit can select the optimal sending method by referring to past sending history.

[0066] The alternative proposal unit can select the optimal proposal method by referring to past proposal history when making an alternative proposal. For example, the generation AI can make the optimal alternative proposal based on proposals previously accepted by the user. The generation AI can also select the optimal proposal method by analyzing the user's past proposal history. The generation AI can also provide the optimal alternative proposal by referring to past proposal history. In this way, the alternative proposal unit can select the optimal proposal method by referring to past proposal history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The retrieval unit retrieves data from the calendar system. The retrieval unit can retrieve data using, for example, the calendar system's API. Alternatively, the retrieval unit can directly access the calendar system's database to retrieve data. Furthermore, the retrieval unit can be configured to retrieve data from the calendar system periodically. For example, the retrieval unit can be configured to retrieve data from the calendar system every day at 9:00 AM. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data using, for example, a generative AI. The generative AI analyzes the data from the calendar system and suggests several possible times that would suit all meeting participants. For example, if participants A, B, and C are all free from 2 PM to 3 PM on Tuesday, the analysis unit will suggest that time slot as a candidate. Step 3: The proposal team proposes candidate dates based on the analysis results obtained by the analysis team. The proposal team can, for example, use a generation AI to propose candidate dates. If the generation AI has held a meeting with similar members in the past, it will also consider the time of the previous meeting and prioritize that time slot when proposing a date. For example, if the proposal team has held a meeting on a Tuesday from 2 PM to 3 PM in the past, it will prioritize proposing that time slot. Step 4: The sending unit sends emails or direct messages when the user presses the "Negotiate" button. For example, when a user presses the "Negotiate" button, the sending unit sends a negotiation email (or direct message via a communication tool, etc.) to the target person. For example, the sending unit sends an email to participant C saying, "We would like to have a meeting at this time. Are you available to participate?"

[0069] (Example of form 2) The meeting scheduling system according to an embodiment of the present invention is a system that uses a generating AI to acquire data from a calendar system and automates the scheduling of meetings. This meeting scheduling system uses a generating AI to acquire data from a calendar system and proposes several candidate dates that are compatible with the schedules of all meeting participants. If a similar group of members has held a meeting in the past, the system also considers the time of the previous meeting and prioritizes the proposal. If everyone is busy, the system may suggest alternative dates that are closer to the present, such as within 3 days or 1 week, to minimize the number of absentees. Participants can be prioritized, and members with high priority can be prevented from being absent. The system also collects various past meeting information of all participants, such as "a meeting was scheduled, but the participation status was 'no,' 'undecided,' or 'no response'," and if the schedules do not match, it provides information such as "based on past experience, they might be able to make time," and the system implements a function that allows the user to press a "negotiate button" to send a negotiation email (or DM in a communication tool, etc.) to the target person. By using this system, the time previously spent on scheduling can be used more effectively. For example, the generating AI retrieves data from a calendar system. It obtains the schedules of all meeting participants and identifies each participant's availability. For instance, if participant A is free from 10:00 to 11:00 on Monday, that time slot is suggested as a possibility. Next, based on the retrieved data, the generating AI proposes several possible dates that suit all meeting participants. For example, if participants A, B, and C are all free from 14:00 to 15:00 on Tuesday, that time slot is suggested as a possibility. Furthermore, if a similar group has held a meeting in the past, the AI ​​considers the time of that previous meeting and prioritizes suggesting it. For example, if a meeting was previously held on Tuesday from 14:00 to 15:00, that time slot is prioritized. If everyone is busy and the earliest date everyone can attend is far in the future, the AI ​​also suggests alternative dates within the next three days or a week to minimize absences.For example, if the earliest date everyone can attend is two weeks away, the system will suggest an alternative date within three days if participant A is unavailable but other participants are available. It can also prioritize participants, ensuring that high-priority members are not absent. For instance, if the project leader is a high priority, the system will prioritize suggesting dates the leader can attend. Furthermore, it collects various past meeting information from all participants, including whether a meeting was scheduled but the attendance status was "no," "undecided," or "no response." If scheduling conflicts arise, it provides information such as, "Based on past experience, they might be available at this time." For example, if participant B previously attended a meeting during a time slot they had marked as "undecided," that time slot will be suggested. Finally, the system will implement a feature where users can press a "negotiate button" to send a negotiation email (or direct message via a communication tool, etc.) to the relevant person. For example, an email might be sent to participant C asking, "We'd like to hold a meeting during this time slot; would you be available?" This allows the meeting scheduling system to effectively utilize the time previously spent on scheduling. For example, scheduling a meeting that previously took an hour can now be completed in just a few minutes. This allows the meeting scheduling system to make more efficient use of the time previously spent on scheduling.

[0070] The meeting scheduling system according to this embodiment comprises an acquisition unit, an analysis unit, a proposal unit, and a transmission unit. The acquisition unit acquires data from a calendar system. The acquisition unit can acquire data, for example, by using the API of the calendar system. Alternatively, the acquisition unit can acquire data by directly accessing the database of the calendar system. Furthermore, the acquisition unit can be configured to acquire data from the calendar system periodically. For example, the acquisition unit can be configured to acquire data from the calendar system at 9:00 AM every day. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data, for example, by using a generation AI. The generation AI analyzes the data from the calendar system and proposes several candidate dates that match the schedules of all meeting participants. For example, if all participants A, B, and C are free from 2:00 PM to 3:00 PM on Tuesday, the analysis unit proposes that time slot as a candidate. The proposal unit proposes candidate dates based on the analysis results obtained by the analysis unit. The proposal unit can propose candidate dates, for example, by using a generation AI. If a meeting has been held with similar members in the past, the generation AI also considers the time of the previous meeting and prioritizes it when proposing a date. For example, if the proposal department has previously held a meeting between 2 PM and 3 PM on a Tuesday, it will prioritize proposing that time slot. The sending department sends an email or direct message (DM) when the negotiation button is pressed. For example, when a user presses the "negotiate button," the sending department sends a negotiation email (or DM via a communication tool, etc.) to the target person. For example, the sending department sends an email to participant C saying, "We would like to hold a meeting during this time slot, would you be able to participate?" In this way, the meeting scheduling system according to the embodiment can automate meeting scheduling by acquiring and analyzing data from a calendar system, proposing candidate dates, and sending emails or DMs.

[0071] The data acquisition unit retrieves data from the calendar system. For example, the data acquisition unit can retrieve data using the calendar system's API. Specifically, the acquisition unit sends a request to the calendar system's API endpoint, parses the data returned as a response, and extracts the necessary information. Alternatively, the acquisition unit can directly access the calendar system's database to retrieve data. In this case, the acquisition unit executes database queries to retrieve the required data. Furthermore, the acquisition unit can be configured to retrieve data from the calendar system periodically. For example, the acquisition unit can be configured to retrieve data from the calendar system at 9 AM every day. This periodic data retrieval ensures the system always maintains the latest information, enabling accurate scheduling. The data acquisition unit can flexibly configure the frequency and timing of data retrieval, allowing for customization to meet user needs. For example, the frequency of data retrieval can be increased to match specific projects or events. Additionally, the acquisition unit can centrally manage the retrieved data and integrate it with other departments and systems. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data using, for example, a generative AI. The generative AI analyzes the data from the calendar system and suggests several possible meeting times that suit all participants. Specifically, the generative AI compares each participant's schedule and identifies common free time slots. For example, if participants A, B, and C are all free from 2 PM to 3 PM on Tuesday, the analysis unit will suggest that time slot as a candidate. The generative AI can also use natural language processing technology to extract useful information from calendar notes and comments and incorporate it into the analysis. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and statistical information. For example, based on the history of past meetings, it can identify trends in which meetings are frequently held on specific days or times and incorporate that information into the analysis. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0073] The proposal department proposes candidate dates based on the analysis results obtained by the analysis department. The proposal department can, for example, use a generation AI to propose candidate dates. If the generation AI has held a meeting with similar members in the past, it will also consider the time of the previous meeting and prioritize that suggestion. Specifically, the proposal department analyzes past meeting data and, if participants frequently hold meetings at a particular time, it will prioritize suggesting that time slot. For example, if the proposal department has held a meeting on Tuesday from 2 PM to 3 PM in the past, it will prioritize suggesting that time slot. Furthermore, the proposal department can propose the optimal candidate date by considering the individual preferences and constraints of the participants. For example, if a particular participant has another important appointment on a specific day or time, it will adjust the candidate date to take that information into account. The proposal department can also provide multiple candidate dates and allow participants to choose, enabling flexible scheduling. This allows the proposal department to propose the optimal candidate date considering the schedules of all participants and efficiently schedule meetings.

[0074] The sending unit sends emails or direct messages (DMs) when a user presses the "Negotiate" button. For example, when a user presses the "Negotiate" button, the sending unit sends a negotiation email (or DM via a communication tool, etc.) to the target person. Specifically, the sending unit sends an email to participant C saying, "I would like to have a meeting at this time; would you be able to participate?" The sending unit can send messages quickly and efficiently by pre-setting email and DM templates. Furthermore, the sending unit can track the status of sent messages, automatically analyze replies from participants, and reflect them in the system. For example, if a participant replies "I can participate," that information is automatically registered in the system, and the meeting date is confirmed. The sending unit can also reliably transmit information using multiple communication methods. For example, in addition to sending emails, it can use voice calls, SMS, chat apps, etc. in combination to reliably deliver important information. As a result, the sending unit can provide users with quick and reliable instructions and efficiently schedule meetings.

[0075] The data collection unit can collect past meeting information. For example, it can collect past meeting information from a calendar system database. The data collection unit can collect information such as the date and time of past meetings, participants, and agenda. For example, the data collection unit can collect meeting information for the past year. The data collection unit can also analyze past meeting information to understand meeting trends. For example, the data collection unit can analyze the frequency and time of past meetings to understand meeting trends. As a result, by collecting past meeting information, the data collection unit can suggest more appropriate candidate dates.

[0076] The priority setting unit can assign priorities to participants. For example, it can set priorities based on the participant's position or importance. For instance, if the project leader has high priority, the priority setting unit will prioritize suggesting dates when the leader is available. The priority setting unit can also use generative AI to set participant priorities. For example, the generative AI can analyze the participant's position and past meeting attendance history to set priorities. This allows the priority setting unit to adjust the schedule to ensure that important members are not absent by assigning priorities to participants.

[0077] The alternative proposal team can propose alternative dates that minimize absences, such as within three days or a week, if the date when everyone can attend is far in the future. For example, if the date when everyone can attend is two weeks away, the alternative proposal team will propose an alternative date within three days where participant A is absent but other participants can attend. The alternative proposal team can also use generative AI to make alternative proposals. For example, the generative AI can analyze participants' schedules and propose alternative dates that minimize absences. This allows the alternative proposal team to propose alternative dates that minimize absences, even when the date when everyone can attend is far in the future.

[0078] The data collection unit can collect past meeting information for all participants. For example, it can collect past meeting information for all participants from the calendar system's database. The data collection unit can collect information such as the date and time of past meetings, participants, and agenda. For example, the data collection unit can collect meeting information for the past year. The data collection unit can also analyze past meeting information to understand meeting trends. For example, the data collection unit can analyze the frequency and time of past meetings to understand meeting trends. As a result, by collecting past meeting information for all participants, the data collection unit can suggest more appropriate candidate dates.

[0079] The priority setting unit can assign priorities to participants. For example, it can set priorities based on the participant's position or importance. For instance, if the project leader has high priority, the priority setting unit will prioritize suggesting dates when the leader is available. The priority setting unit can also use generative AI to set participant priorities. For example, the generative AI can analyze the participant's position and past meeting attendance history to set priorities. This allows the priority setting unit to adjust the schedule to ensure that important members are not absent by assigning priorities to participants.

[0080] The alternative proposal team can propose alternative dates that minimize absences, such as within three days or a week, if the date when everyone can attend is far in the future. For example, if the date when everyone can attend is two weeks away, the alternative proposal team will propose an alternative date within three days where participant A is absent but other participants can attend. The alternative proposal team can also use generative AI to make alternative proposals. For example, the generative AI can analyze participants' schedules and propose alternative dates that minimize absences. This allows the alternative proposal team to propose alternative dates that minimize absences, even when the date when everyone can attend is far in the future.

[0081] The sending unit allows users to send negotiation emails (or direct messages, etc., via communication tools) to the target person by pressing the "Negotiate button." For example, when a user presses the "Negotiate button," the sending unit sends a negotiation email (or direct message, etc., via communication tools) to the target person. For example, the sending unit sends an email to participant C saying, "I would like to have a meeting at this time; would you be able to participate?" In this way, the sending unit allows users to send negotiation emails or direct messages to the target person by pressing the "Negotiate button."

[0082] The data acquisition unit can estimate the user's emotions and adjust the timing of calendar data acquisition based on the estimated emotions. For example, if the user is stressed, the generation AI can delay calendar data acquisition and acquire it when the user is relaxed. If the user is relaxed, the generation AI can acquire calendar data immediately and make suggestions quickly. If the user is in a hurry, the generation AI can acquire calendar data quickly and make suggestions immediately. In this way, the data acquisition unit can reduce user stress by adjusting the timing of calendar data acquisition based on the user's emotions.

[0083] The data acquisition unit can analyze the user's past calendar usage history and select the optimal data acquisition method. For example, the data acquisition unit can use a generating AI to acquire calendar data based on the time slots the user frequently used in the past. The data acquisition unit can also use a generating AI to select the optimal data acquisition method by referring to the settings of the calendar app the user has used in the past. The data acquisition unit can also analyze the user's past calendar usage history and have the generating AI suggest the optimal data acquisition timing. In this way, the data acquisition unit can select the optimal data acquisition method by analyzing the user's past calendar usage history.

[0084] The data acquisition unit can filter calendar data based on the user's current projects and areas of interest. For example, the unit can prioritize acquiring meeting information related to the user's current projects. The unit can also prioritize acquiring event information related to the user's areas of interest. The generation AI can also filter calendar data based on the project priorities set by the user. This allows the data acquisition unit to acquire highly relevant data by filtering based on the user's current projects and areas of interest.

[0085] The data acquisition unit can estimate the user's emotions and determine the priority of calendar data to acquire based on the estimated emotions. For example, if the user is stressed, the generating AI will prioritize acquiring high-priority data and postpone less important data. If the user is relaxed, the generating AI can acquire all data equally. If the user is in a hurry, the generating AI can prioritize acquiring the most important data. In this way, the data acquisition unit can prioritize acquiring important data by determining the priority of calendar data based on the user's emotions.

[0086] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring calendar data. For example, the acquisition unit can prioritize the acquisition of meeting information in locations close to the user's current location. If the user is on a business trip, the acquisition unit can also prioritize the acquisition of event information related to the region the user is visiting. If the user is interested in a particular region, the acquisition unit can also prioritize the acquisition of data related to that region. In this way, the acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location.

[0087] The data acquisition unit can analyze the user's social media activity when acquiring calendar data and obtain relevant data. For example, the data acquisition unit can prioritize acquiring event information that the user has mentioned on social media. The data acquisition unit can also prioritize acquiring event information from accounts that the user follows. The data acquisition unit can also acquire event information that the user plans to attend, as mentioned on social media. In this way, the data acquisition unit can acquire relevant data by analyzing the user's social media activity.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on those emotions. For example, if the user is stressed, the generating AI will present the analysis results in a simple way. If the user is relaxed, the generating AI can also provide detailed analysis results. If the user is in a hurry, the generating AI can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the way the analysis is presented based on the user's emotions.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit's generating AI can perform a detailed analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data using the generating AI. The analysis unit can also dynamically adjust the level of detail of the analysis using the generating AI according to the importance of the data. This allows the analysis unit to perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the generation AI in the analysis unit applies a specific analysis algorithm to meeting data. The generation AI in the analysis unit can also apply a different analysis algorithm to event data. The generation AI in the analysis unit can also select the optimal analysis algorithm depending on the data category. As a result, the analysis unit can provide optimal analysis results by applying different analysis algorithms depending on the data category.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the generating AI will summarize the analysis results concisely. If the user is relaxed, the generating AI can also provide detailed analysis results. If the user is in a hurry, the generating AI can provide a short, to-the-point analysis result. In this way, the analysis unit can provide analysis results of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions.

[0092] The analysis unit can determine the priority of analysis based on the data submission date. For example, the generation AI will prioritize the analysis of data with an approaching submission deadline. The analysis unit can also postpone the analysis of data with a distant submission deadline. The generation AI can dynamically adjust the analysis priority according to the data submission date. As a result, the analysis unit can prioritize the analysis of data with an approaching submission deadline by determining the priority of analysis based on the data submission date.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the generation AI will prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The generation AI can dynamically adjust the order of analysis according to the relevance of the data. As a result, the analysis unit can prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0094] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function's generating AI will offer simple suggestions. If the user is relaxed, the suggestion function's generating AI can offer more detailed suggestions. If the user is in a hurry, the suggestion function's generating AI can offer concise suggestions. In this way, the suggestion function can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions based on the user's emotions.

[0095] The proposal function can adjust the level of detail in its proposals based on the importance of the candidate dates. For example, the AI ​​can generate detailed proposals for high-importance candidate dates. For low-importance candidate dates, the AI ​​can generate simplified proposals. The AI ​​can also dynamically adjust the level of detail in the proposals according to the importance of the candidate dates. This allows the proposal function to provide detailed proposals for important candidate dates by adjusting the level of detail based on the importance of each date.

[0096] The proposal function can apply different proposal algorithms depending on the category of the candidate date during the proposal process. For example, the proposal function's generating AI can apply a specific proposal algorithm to candidate meeting dates. The proposal function can also apply a different proposal algorithm to candidate event dates. The proposal function can also have the generating AI select the optimal proposal algorithm depending on the category of the candidate date. This allows the proposal function to provide optimal proposals by applying different proposal algorithms depending on the category of the candidate date.

[0097] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function's generating AI will make the suggestions shorter. If the user is relaxed, the suggestion function's generating AI can also make more detailed suggestions. If the user is in a hurry, the suggestion function's generating AI can also make concise suggestions that get straight to the point. In this way, the suggestion function can provide suggestions of an appropriate length for the user by adjusting the length based on the user's emotions.

[0098] The proposal department can determine the priority of proposals based on the submission timing of candidate dates. For example, the proposal department's generating AI will prioritize proposals for candidate dates with approaching deadlines. The proposal department can also have the generating AI postpone proposals for candidate dates with distant deadlines. The proposal department can also have the generating AI dynamically adjust the priority of proposals according to the submission timing of candidate dates. This allows the proposal department to prioritize proposals for candidate dates with approaching deadlines by determining the priority of proposals based on the submission timing of candidate dates.

[0099] The proposal unit can adjust the order of proposals based on the relevance of candidate dates. For example, the generation AI will prioritize proposals for highly relevant candidate dates. The proposal unit can also postpone proposals for less relevant candidate dates. The generation AI can dynamically adjust the order of proposals according to the relevance of the candidate dates. This allows the proposal unit to prioritize proposals for highly relevant candidate dates by adjusting the order of proposals based on their relevance.

[0100] The sending unit can estimate the user's emotions and adjust the content of the message based on those emotions. For example, if the user is stressed, the generating AI will send a simple message. If the user is relaxed, the generating AI can send a more detailed message. If the user is in a hurry, the generating AI can send a concise message. In this way, the sending unit can send messages that are appropriate for the user by adjusting the content of the message based on the user's emotions.

[0101] The sending unit can select the optimal sending method by referring to past sending history when sending a message. For example, if the user has preferred using email in the past, the generating AI will send the message via email. If the user has preferred using a communication tool in the past, the generating AI can also send the message using that tool. The sending unit can also analyze the user's past sending history, and the generating AI will select the optimal sending method. In this way, the sending unit can select the optimal sending method by referring to past sending history.

[0102] The sending unit can estimate the user's emotions and determine the priority of messages based on those emotions. For example, if the user is stressed, the generating AI will prioritize sending high-priority messages. If the user is relaxed, the generating AI can also send all messages equally. If the user is in a hurry, the generating AI can also prioritize sending the most important messages. In this way, the sending unit can prioritize sending important messages by determining the priority of messages based on the user's emotions.

[0103] The transmission unit can select the optimal transmission method by considering the user's device information during transmission. For example, if the user is using a smartphone, the generating AI will send a message optimized for smartphones. If the user is using a tablet, the generating AI can also send a message optimized for tablets. If the user is using a desktop, the generating AI can also send a message optimized for desktops. In this way, the transmission unit can select the optimal transmission method by considering the user's device information.

[0104] The data collection unit can estimate the user's emotions and prioritize the meeting information to collect based on those emotions. For example, if the user is stressed, the generating AI will prioritize collecting high-priority meeting information. If the user is relaxed, the generating AI can collect all meeting information equally. If the user is in a hurry, the generating AI can prioritize collecting the most important meeting information. In this way, the data collection unit can prioritize collecting important meeting information by prioritizing the meeting information to collect based on the user's emotions.

[0105] The data collection unit can improve the accuracy of data collection by referring to past meeting information during the collection process. For example, the data collection unit uses a generating AI to collect optimal meeting information based on the user's past meeting history. The data collection unit can also analyze the user's past meeting history, and the generating AI can further improve the accuracy of data collection. The data collection unit can also refer to past meeting information, and the generating AI can select the optimal collection method. In this way, the data collection unit can improve the accuracy of data collection by referring to past meeting information.

[0106] The data collection unit can estimate the user's emotions and adjust how the collected meeting information is displayed based on those emotions. For example, if the user is stressed, the generating AI can provide a simpler display. If the user is relaxed, the generating AI can provide a more detailed display. If the user is in a hurry, the generating AI can provide a more concise display. In this way, the data collection unit can provide a user-friendly display by adjusting how the collected meeting information is displayed based on the user's emotions.

[0107] The data collection unit can improve the accuracy of its collection by referring to relevant meeting literature during the collection process. For example, the data collection unit uses relevant meeting literature to enable the generative AI to collect the most relevant meeting information. The data collection unit can also use relevant literature to enable the generative AI to improve the accuracy of its collection. The data collection unit can also analyze relevant meeting literature and enable the generative AI to select the most suitable collection method. As a result, the data collection unit can improve the accuracy of its collection by referring to relevant meeting literature.

[0108] The priority setting unit can estimate the user's emotions and adjust the priority setting method based on those emotions. For example, if the user is stressed, the generating AI in the priority setting unit can provide a simple priority setting method. If the user is relaxed, the generating AI in the priority setting unit can also provide a more detailed priority setting method. If the user is in a hurry, the generating AI in the priority setting unit can also provide a concise priority setting method. In this way, the priority setting unit can provide the user with an appropriate priority setting method by adjusting the priority setting method based on the user's emotions.

[0109] The priority setting unit can select the optimal setting method by referring to past priority setting history when setting priorities. For example, the priority setting unit can have the generating AI propose the optimal priority setting method based on priorities previously set by the user. The priority setting unit can also analyze the user's past priority setting history and have the generating AI select the optimal setting method. The priority setting unit can also have the generating AI provide the optimal priority setting method by referring to past priority setting history. In this way, the priority setting unit can select the optimal setting method by referring to past priority setting history.

[0110] The priority setting unit can estimate the user's emotions and adjust the display method of priorities based on those emotions. For example, if the user is stressed, the generating AI in the priority setting unit provides a simple display method. If the user is relaxed, the generating AI can also provide a more detailed display method. If the user is in a hurry, the generating AI can also provide a concise display method. In this way, the priority setting unit can provide a display method that is easy for the user to understand by adjusting the display method of priorities based on the user's emotions.

[0111] The priority setting unit can select the optimal setting method by considering the user's device information when setting priorities. For example, if the user is using a smartphone, the generating AI can provide a priority setting method optimized for smartphones. If the user is using a tablet, the generating AI can also provide a priority setting method optimized for tablets. If the user is using a desktop, the generating AI can also provide a priority setting method optimized for desktops. In this way, the priority setting unit can select the optimal setting method by considering the user's device information.

[0112] The alternative suggestion unit can estimate the user's emotions and adjust the content of the alternative suggestions based on those emotions. For example, if the user is feeling stressed, the generating AI will provide a simple alternative suggestion. If the user is relaxed, the generating AI can also provide a more detailed alternative suggestion. If the user is in a hurry, the generating AI can provide a concise alternative suggestion. In this way, the alternative suggestion unit can provide appropriate alternative suggestions to the user by adjusting the content of the suggestions based on the user's emotions.

[0113] The alternative proposal unit can select the optimal proposal method by referring to past proposal history when making an alternative proposal. For example, the alternative proposal unit can use the generation AI to make the optimal alternative proposal based on proposals previously accepted by the user. The alternative proposal unit can also analyze the user's past proposal history and have the generation AI select the optimal proposal method. The alternative proposal unit can also refer to past proposal history and have the generation AI provide the optimal alternative proposal. In this way, the alternative proposal unit can select the optimal proposal method by referring to past proposal history.

[0114] The alternative suggestion unit can estimate the user's emotions and prioritize alternative suggestions based on those emotions. For example, if the user is stressed, the generating AI will prioritize high-priority alternative suggestions. If the user is relaxed, the generating AI can also distribute all alternative suggestions equally. If the user is in a hurry, the generating AI can also prioritize the most important alternative suggestions. In this way, the alternative suggestion unit can prioritize important alternative suggestions by determining their priority based on the user's emotions.

[0115] The alternative suggestion unit can select the optimal suggestion method by considering the user's device information when making alternative suggestions. For example, if the user is using a smartphone, the generating AI will provide alternative suggestions optimized for smartphones. If the user is using a tablet, the generating AI can also provide alternative suggestions optimized for tablets. If the user is using a desktop, the generating AI can also provide alternative suggestions optimized for desktops. In this way, the alternative suggestion unit can select the optimal suggestion method by considering the user's device information.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the generating AI will prioritize analyzing the most important data. If the user is relaxed, the generating AI can analyze all data equally. If the user is in a hurry, the generating AI can prioritize analyzing the most important data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the user's emotions.

[0118] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the generative AI will offer simple suggestions. If the user is relaxed, the generative AI can offer more detailed suggestions. If the user is in a hurry, the generative AI can offer concise suggestions. In this way, the suggestion function can provide suggestions that are easy for the user to understand by adjusting the way suggestions are presented based on the user's emotions.

[0119] The sending unit can estimate the user's emotions and adjust the content of the message based on those emotions. For example, if the user is stressed, the generating AI can send a simple message. If the user is relaxed, the generating AI can send a more detailed message. If the user is in a hurry, the generating AI can send a concise message. In this way, the sending unit can send a message that is appropriate for the user by adjusting the content of the message based on the user's emotions.

[0120] The data collection unit can estimate the user's emotions and determine the priority of meeting information to collect based on those emotions. For example, if the user is stressed, the generating AI will prioritize collecting high-priority meeting information. If the user is relaxed, the generating AI can collect all meeting information equally. If the user is in a hurry, the generating AI can prioritize collecting the most important meeting information. In this way, the data collection unit can prioritize collecting important meeting information by determining the priority of meeting information to collect based on the user's emotions.

[0121] The priority setting unit can estimate the user's emotions and adjust the priority setting method based on those emotions. For example, if the user is stressed, the generating AI can provide a simple priority setting method. If the user is relaxed, the generating AI can provide a more detailed priority setting method. If the user is in a hurry, the generating AI can provide a concise priority setting method. In this way, the priority setting unit can provide the user with an appropriate priority setting method by adjusting the method based on the user's emotions.

[0122] The data acquisition unit can filter calendar data based on the user's current projects and areas of interest. For example, it can prioritize acquiring meeting information related to the user's current projects. It can also prioritize acquiring event information related to the user's areas of interest. The generating AI can also filter calendar data based on the project priorities set by the user. As a result, the data acquisition unit can acquire highly relevant data by filtering based on the user's current projects and areas of interest.

[0123] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the generating AI can perform a detailed analysis on highly important data. For less important data, the generating AI can perform a simplified analysis. The generating AI can also dynamically adjust the level of detail of the analysis according to the importance of the data. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0124] The proposal function can adjust the level of detail in its proposals based on the importance of the candidate dates. For example, the generating AI can provide detailed proposals for highly important candidate dates, and simplified proposals for less important dates. The generating AI can also dynamically adjust the level of detail in its proposals according to the importance of the candidate dates. This allows the proposal function to provide detailed proposals for important candidate dates by adjusting the level of detail based on the importance of each date.

[0125] The sending unit can select the optimal sending method by referring to past sending history when sending a message. For example, if the user previously preferred using email, the generating AI will send the message via email. If the user previously preferred using a communication tool, the generating AI can also send the message using that tool. The generating AI can also analyze the user's past sending history and select the optimal sending method. In this way, the sending unit can select the optimal sending method by referring to past sending history.

[0126] The alternative proposal unit can select the optimal proposal method by referring to past proposal history when making an alternative proposal. For example, the generation AI can make the optimal alternative proposal based on proposals previously accepted by the user. The generation AI can also select the optimal proposal method by analyzing the user's past proposal history. The generation AI can also provide the optimal alternative proposal by referring to past proposal history. In this way, the alternative proposal unit can select the optimal proposal method by referring to past proposal history.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The retrieval unit retrieves data from the calendar system. The retrieval unit can retrieve data using, for example, the calendar system's API. Alternatively, the retrieval unit can directly access the calendar system's database to retrieve data. Furthermore, the retrieval unit can be configured to retrieve data from the calendar system periodically. For example, the retrieval unit can be configured to retrieve data from the calendar system every day at 9:00 AM. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit can analyze the data using, for example, a generative AI. The generative AI analyzes the data from the calendar system and suggests several possible times that would suit all meeting participants. For example, if participants A, B, and C are all free from 2 PM to 3 PM on Tuesday, the analysis unit will suggest that time slot as a candidate. Step 3: The proposal team proposes candidate dates based on the analysis results obtained by the analysis team. The proposal team can, for example, use a generation AI to propose candidate dates. If the generation AI has held a meeting with similar members in the past, it will also consider the time of the previous meeting and prioritize that time slot when proposing a date. For example, if the proposal team has held a meeting on a Tuesday from 2 PM to 3 PM in the past, it will prioritize proposing that time slot. Step 4: The sending unit sends emails or direct messages when the user presses the "Negotiate" button. For example, when a user presses the "Negotiate" button, the sending unit sends a negotiation email (or direct message via a communication tool, etc.) to the target person. For example, the sending unit sends an email to participant C saying, "We would like to have a meeting at this time. Are you available to participate?"

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, transmission unit, collection unit, priority setting unit, and alternative proposal unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The transmission unit is implemented by the control unit 46A of the smart device 14. The collection unit is implemented by the specific processing unit 290 of the data processing device 12. The priority setting unit is implemented by the specific processing unit 290 of the data processing device 12. The alternative proposal unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, transmission unit, collection unit, priority setting unit, and alternative proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The transmission unit is implemented, for example, by the control unit 46A of the smart glasses 214. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The priority setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The alternative proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, transmission unit, collection unit, priority setting unit, and alternative proposal unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The transmission unit is implemented by the control unit 46A of the headset terminal 314. The collection unit is implemented by the specific processing unit 290 of the data processing device 12. The priority setting unit is implemented by the specific processing unit 290 of the data processing device 12. The alternative proposal unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the acquisition unit, analysis unit, proposal unit, transmission unit, collection unit, priority setting unit, and alternative proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The transmission unit is implemented, for example, by the control unit 46A of the robot 414. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The priority setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The alternative proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A data acquisition unit that acquires data from the calendar system, An analysis unit analyzes the data acquired by the acquisition unit, A proposal unit proposes candidate dates based on the analysis results obtained by the analysis unit, It includes a sending unit that sends emails and direct messages when a negotiation button is pressed. A system characterized by the following features. (Note 2) It includes a collection unit that collects past meeting information. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a priority setting unit for setting priorities. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an alternative proposal department that makes alternative suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect past meeting information from all participants. The system described in Appendix 2, characterized by the features described herein. (Note 6) The priority setting unit is, Set priorities for participants The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned alternative proposal unit is, If the earliest date when everyone can attend is still far off, propose an alternative date within the next few days or a week to minimize the number of absentees. The system described in Appendix 4, characterized by the features described herein. (Note 8) The aforementioned transmitting unit When a user presses the "Negotiate button," a negotiation email (or direct message via a communication tool, etc.) is sent to the target person. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of calendar data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, Analyze the user's past calendar usage history and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving calendar data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, It estimates the user's emotions and determines the priority of calendar data to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When retrieving calendar data, the system prioritizes retrieving highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, When retrieving calendar data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed dates. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the candidate date. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the submission date of the proposed dates. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on the relevance of the candidate dates. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmitting unit It estimates the user's emotions and adjusts the content of outgoing messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmitting unit When sending, the system refers to past sending history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmitting unit It estimates the user's emotions and determines the priority of messages based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmitting unit When sending data, the system selects the optimal transmission method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned collection unit is It estimates the user's emotions and prioritizes the meeting information to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned collection unit is During data collection, past meeting information is referenced to improve the accuracy of the collection. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is We estimate the user's emotions and adjust how the collected meeting information is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned collection unit is During data collection, we refer to relevant literature from the meeting to improve the accuracy of the collection. The system described in Appendix 2, characterized by the features described herein. (Note 35) The priority setting unit is, It estimates the user's emotions and adjusts the priority setting method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The priority setting unit is, When setting priorities, the system refers to past priority setting history to select the optimal setting method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The priority setting unit is, It estimates the user's emotions and adjusts how priorities are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The priority setting unit is, When setting priorities, the optimal setting method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned alternative proposal unit is, It estimates the user's emotions and adjusts the content of alternative suggestions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned alternative proposal unit is, When making alternative proposals, refer to past proposal history to select the most suitable proposal method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned alternative proposal unit is, It estimates the user's emotions and prioritizes alternative suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned alternative proposal unit is, When proposing alternatives, the optimal proposal method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data acquisition unit that acquires data from the calendar system, An analysis unit analyzes the data acquired by the acquisition unit, A proposal unit proposes candidate dates based on the analysis results obtained by the analysis unit, It includes a sending unit that sends emails and direct messages when a negotiation button is pressed. A system characterized by the following features.

2. It includes a collection unit that collects past meeting information. The system according to feature 1.

3. It includes a priority setting unit for setting priorities. The system according to feature 1.

4. It has an alternative proposal department that makes alternative suggestions. The system according to feature 1.

5. The aforementioned collection unit is Collect past meeting information from all participants. The system according to feature 2.

6. The priority setting unit is, Set priorities for participants The system according to claim 3.

7. The aforementioned alternative proposal unit is, If the earliest date when everyone can attend is still far off, propose an alternative date within the next few days or a week to minimize the number of absentees. The system according to feature 4.

8. The aforementioned transmitting unit When the user presses the negotiate button, an email is sent to the target person for negotiation. The system according to feature 1.

9. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of calendar data acquisition based on those estimated emotions. The system according to feature 1.

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