system

Abstract

The system according to this embodiment aims to automate meeting progress management, meeting minute creation, and notification of the next action. [Solution] The system according to the embodiment comprises a reception unit, a planning unit, an intervention unit, a minutes generation unit, an action transmission unit, and a report material generation unit. The reception unit receives input of the purpose and time of the meeting. The planning unit sets up the meeting plan based on the information received by the reception unit. The intervention unit intervenes if the topic deviates from the main subject during the meeting, based on the plan set by the planning unit. The minutes generation unit records the content of statements and decisions made during the meeting and generates minutes. The action transmission unit sends emails to each participant with the next action based on the minutes generated by the minutes generation unit. The report material generation unit generates report materials based on the information collected during the meeting.

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, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, there is a problem that the progress management of meetings, the creation of minutes, the notification of next actions, etc. are performed manually, making it difficult to efficiently operate meetings.

[0005] The system according to the embodiment aims to automate the progress management of meetings, the creation of minutes, and the notification of next actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a planning unit, an intervention unit, a minutes generation unit, an action transmission unit, and a report material generation unit. The reception unit receives input of the purpose and time of the meeting. The planning unit sets up the meeting plan based on the information received by the reception unit. The intervention unit intervenes during the meeting if the topic deviates from the main subject, based on the plan set by the planning unit. The minutes generation unit records the content of statements and decisions made during the meeting and generates minutes. The action transmission unit sends emails to each participant with the next action based on the minutes generated by the minutes generation unit. The report material generation unit generates report materials based on the information collected during the meeting. [Effects of the Invention]

[0007] The system according to this embodiment can automate meeting progress management, meeting minute creation, and notification of the next action. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 support system according to an embodiment of the present invention is a mechanism for improving meeting efficiency by having an agent participate in the meeting. This meeting support system allows the purpose and time of the meeting to be initially set in natural language, and the agent automatically sets up the meeting procedure. If the topic strays from the subject during the meeting, the agent subtly brings it back to the subject at an appropriate time. It also automatically generates meeting minutes and automatically sends Next Actions via email to each participant. Furthermore, it automatically generates meeting report materials in addition to meeting minutes. For example, the purpose and time of the meeting are initially set in natural language. In this case, the user only needs to input the purpose and time of the meeting. For example, information such as "Hold a 1-hour meeting to check the progress of the project" is entered. This information is entered into the agent. Next, the agent analyzes the entered information and automatically sets up the meeting procedure. The agent sets the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress check meeting, the agenda would include progress reports, sharing of problems, and discussion of solutions. If the topic strays from the subject during the meeting, the agent subtly brings it back to the subject at an appropriate time. The agent monitors the meeting's progress in real time and intervenes at the appropriate time if the topic strays from the main subject. For example, if the discussion goes in circles, the agent will prompt, "Let's move on to the next agenda item." The agent also automatically generates meeting minutes and sends Next Actions to each participant via email. It records what was said and decided during the meeting and generates meeting minutes after the meeting ends. Furthermore, it extracts each participant's Next Action and sends it automatically via email. For example, it might notify participants via email that "Person A will prepare the materials for the next meeting." In addition to meeting minutes, it also automatically generates meeting reports. The agent creates reports based on the information collected during the meeting. For example, it automatically generates reports summarizing project progress, problems, and solutions. This improves meeting efficiency and eliminates wasted time. Users can effectively conduct meetings with the agent's support. The automatic generation of meeting minutes and reports also reduces the workload after the meeting. For example, meetings will proceed more smoothly, and discussions will no longer go in circles.Furthermore, automating the creation of meeting minutes and reports reduces the workload after meetings. This means that meeting support systems improve meeting efficiency and eliminate wasted time.

[0029] The meeting support system according to this embodiment comprises a reception unit, a scheduling unit, an intervention unit, a meeting minutes generation unit, a Next Action transmission unit, and a report material generation unit. The reception unit receives input of the purpose and time of the meeting. The reception unit can, for example, analyze the purpose and time of the meeting entered by the user in natural language. For example, if the user enters "Hold a 1-hour meeting to check the progress of the project," the reception unit can analyze this information and recognize the purpose and time of the meeting. The scheduling unit sets the meeting schedule based on the information received by the reception unit. The scheduling unit can, for example, set the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress check meeting, the scheduling unit can set agenda items such as progress reporting, sharing of problems, and discussion of solutions. The intervention unit intervenes when the topic deviates from the subject during the meeting, based on the schedule set by the scheduling unit. The intervention unit can, for example, monitor the progress of the meeting in real time and intervene at an appropriate time when the topic deviates from the subject. For example, if a discussion goes in circles, the intervention unit can prompt participants to "move on to the next topic." The minutes generation unit records the content of discussions and decisions made during the meeting and generates minutes. The minutes generation unit can, for example, record the content of discussions and decisions made during the meeting and generate minutes after the meeting ends. The Next Action sending unit sends emails to each participant with the next action to be taken, based on the minutes generated by the minutes generation unit. The Next Action sending unit can, for example, extract each participant's Next Action and automatically send it via email. For example, it can notify participants via email with content such as "Mr. / Ms. A will prepare the materials by the next meeting." The report generation unit generates reports based on the information collected during the meeting. The report generation unit can, for example, automatically generate reports summarizing the project's progress, problems, and solutions based on the information collected during the meeting. As a result, the meeting support system according to this embodiment automates the input of the meeting's purpose and time, setting up the arrangements, intervention, minutes generation, Next Action sending, and report generation, improving the efficiency of meetings.

[0030] The reception desk accepts input for the purpose and duration of meetings. For example, the reception desk can analyze the purpose and duration of meetings entered by users in natural language. Specifically, if a user enters "Hold a one-hour meeting to check on the project's progress," the reception desk can analyze this information and recognize the purpose and duration of the meeting. The reception desk uses natural language processing technology to analyze user input and accurately extract the purpose and duration of the meeting. For example, if a user enters "I want to set up a two-hour brainstorming meeting next Monday," the reception desk will extract the information "next Monday," "two hours," and "brainstorming meeting," recognizing the purpose and duration of the meeting. Furthermore, the reception desk can supplement detailed meeting information based on the information entered by the user. For example, it can automatically suggest additional information such as the meeting location and participant list, making it easy for users to set up meetings. The reception desk can also refer to past meeting data and suggest similar meeting settings. This allows users to set up meetings efficiently.

[0031] The planning unit sets up the meeting schedule based on the information received by the reception unit. For example, the planning unit can set the agenda and order of proceedings based on the purpose of the meeting. Specifically, in the case of a project progress review meeting, the planning unit can set agenda items such as progress reports, problem sharing, and discussion of solutions. The planning unit uses AI to automatically generate the optimal agenda and order of proceedings for the purpose of the meeting. For example, the AI ​​learns from past meeting data and proposes the optimal agenda and order of proceedings by referring to effective procedures in similar meetings. The planning unit also provides flexible settings that can be customized by the user. Users can review the proposed agenda and order of proceedings and make modifications or additions as needed. Furthermore, the planning unit can assign speakers and responsible persons for each agenda item, taking into account the roles and expertise of the meeting participants. This ensures that the meeting proceeds smoothly and allows for efficient discussion.

[0032] The intervention unit intervenes during a meeting if the topic deviates from the main subject, based on the procedures set by the planning unit. For example, the intervention unit can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. Specifically, if the discussion goes in circles, the intervention unit can prompt participants to "move on to the next agenda item." The intervention unit uses AI to analyze the progress of the meeting in real time and detect deviations from the topic. For example, the AI ​​analyzes the content of the statements made during the meeting and issues a warning if the topics do not match the set agenda. The intervention unit can also make suggestions to facilitate the smooth progress of the meeting. For example, if the discussion reaches an impasse, it may suggest "let's reconsider this issue later" to support the progress of the meeting. Furthermore, the intervention unit provides a function to visualize the progress of the meeting, making it easier for participants to understand the current progress. In this way, the intervention unit can improve the efficiency of the meeting and promote effective discussion.

[0033] The minutes generation unit records the content of discussions and decisions made during meetings and generates meeting minutes. For example, the minutes generation unit can record discussions and decisions made during a meeting and generate minutes after the meeting ends. Specifically, the minutes generation unit uses speech recognition technology to transcribe discussions in real time and organizes them by speaker. Furthermore, it uses AI to analyze the content of discussions and extract important points and decisions. For example, the AI ​​extracts specific action items from the discussion content, such as "Prepare materials for the next meeting," and reflects them in the minutes. In addition, the minutes generation unit automatically updates the minutes according to the progress of the meeting and provides the completed minutes at the end of the meeting. Furthermore, the minutes generation unit has a function to share the generated minutes with participants, so participants can check the minutes immediately after the meeting ends. As a result, the minutes generation unit can streamline the meeting recording process and provide accurate minutes quickly.

[0034] The Next Action Sending Unit sends emails to each participant regarding their next action, based on the meeting minutes generated by the Meeting Minutes Generation Unit. For example, the Next Action Sending Unit can extract each participant's Next Action and automatically send it via email. Specifically, it creates individual emails for each participant based on the action items extracted by the Meeting Minutes Generation Unit, notifying them of their next action. For instance, it can send an email stating, "Person A should prepare the materials before the next meeting." The Next Action Sending Unit uses AI to consider each participant's role and responsibilities, assigning appropriate action items. Furthermore, the Next Action Sending Unit adjusts the timing of email delivery to ensure participants confirm their next actions at the appropriate time. In addition, the Next Action Sending Unit tracks the status of emails received and sends reminders for unread emails. This allows the Next Action Sending Unit to streamline post-meeting follow-up and support participants in ensuring they take their next actions.

[0035] The report generation unit generates reports based on information collected during meetings. For example, it can automatically generate reports summarizing project progress, problems, and solutions based on information collected during meetings. Specifically, the report generation unit automatically creates the structure of the report based on the statements and decisions recorded by the meeting minutes generation unit. Furthermore, it uses AI to analyze the statements, extract important points and data, and reflect them in the report. For example, it automatically generates graphs showing project progress, lists of problems, and proposed solutions. The report generation unit also provides flexible editing functions that users can customize, allowing them to modify or add to the report content as needed. In addition, the report generation unit has a function to share the generated report with participants, allowing them to review the report immediately after the meeting. This enables the report generation unit to effectively summarize and quickly share the results of meetings.

[0036] The reception desk can analyze the purpose and duration of meetings entered in natural language. For example, if a user enters "We will hold a one-hour meeting to check the project's progress," the reception desk can use natural language processing technology to analyze this information and recognize the purpose and duration of the meeting. For example, the reception desk can use natural language processing technology to extract the purpose and duration of a meeting from the entered text. The reception desk can also analyze the purpose and duration of meetings using voice input. For example, if a user says "We will hold a one-hour meeting to check the project's progress" aloud, the reception desk can use speech recognition technology to convert this information into text and then use natural language processing technology to recognize the purpose and duration of the meeting. This simplifies user input by analyzing the purpose and duration of meetings entered in natural language.

[0037] The planning unit can set the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress review meeting, the planning unit can set agenda items such as progress reports, sharing of problems, and discussion of solutions. The planning unit can automatically set the types and order of agenda items based on the purpose of the meeting. The planning unit can also set the order of proceedings of the meeting. For example, the planning unit can set the order of speakers and the time allocation for each agenda item. By setting the agenda and order of proceedings based on the purpose of the meeting, the meeting proceeds more smoothly.

[0038] The intervention unit can continuously monitor the progress of the meeting and intervene if the topic deviates from the main subject. For example, the intervention unit can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. For example, if the discussion goes in circles, the intervention unit can prompt participants to "move on to the next agenda item." The intervention unit can also continuously monitor the progress of the meeting and set criteria for intervening when the topic deviates from the main subject. For example, the intervention unit can set criteria for determining when a topic has deviated from the main subject and the timing of intervention. This improves the efficiency of the meeting by monitoring the progress of the meeting in real time and intervening when the topic deviates from the main subject.

[0039] The minutes generation unit can record the content of discussions and decisions made during a meeting and generate meeting minutes. For example, the minutes generation unit can record the content of discussions and decisions made during a meeting and generate minutes after the meeting has ended. The minutes generation unit can record the content of discussions in real time during a meeting and generate minutes. For example, the minutes generation unit can use speech recognition technology to convert the content of discussions into text and generate minutes. In addition, the minutes generation unit can record the decisions made during a meeting and reflect them in the minutes. For example, the minutes generation unit can automatically record the matters decided during a meeting and reflect them in the minutes. In this way, by recording the content of discussions and decisions made during a meeting and generating minutes, the content of the meeting can be accurately recorded.

[0040] The Next Action Sending Unit can send Next Actions to each participant via email based on the meeting minutes. For example, it can extract each participant's Next Action and automatically send it via email. For instance, it can send an email notification stating, "Person A will prepare materials before the next meeting." Based on the meeting minutes, the Next Action Sending Unit can configure the type of next action and the details of the information to be sent. Furthermore, the Next Action Sending Unit can configure the email format. For example, it can set the subject and body format of the email, sending Next Actions in an appropriate format for each participant. This clarifies post-meeting actions by sending Next Actions to each participant via email based on the meeting minutes.

[0041] The report generation unit can generate reports based on information collected during meetings. For example, it can automatically generate reports summarizing project progress, problems, and solutions based on information collected during meetings. The report generation unit can analyze information collected during meetings and reflect it in the reports. For example, it can create reports based on statements and decisions made during meetings. Furthermore, the report generation unit can generate reports in a visually easy-to-understand format based on information collected during meetings. For example, it can visually represent data collected during meetings using graphs and charts. This streamlines the reporting process after meetings by generating reports based on information collected during meetings.

[0042] The reception desk can refer to past meeting data and automatically suggest similar meeting purposes and times. For example, it can automatically suggest similar meetings based on the purpose and duration of meetings the user has previously attended. Furthermore, the reception desk can learn the patterns of meetings the user frequently attends and suggest the most suitable meeting purposes and times. In addition, the reception desk can predict and suggest meetings to be held on specific days of the week and time slots based on past meeting data. This simplifies user input by allowing referencing past meeting data.

[0043] The reception desk can provide an appropriate input format based on the user's job title or position when entering the purpose and time of a meeting. For example, if the user is in a management position, the reception desk can provide a detailed input format for the purpose and time of the meeting. If the user is in a general position, the reception desk can provide a simpler input format for the purpose and time of the meeting. Furthermore, the reception desk can provide a customized input format according to the user's job duties. This streamlines the input process by providing an input format tailored to the user's job title or position.

[0044] The reception desk can provide input assistance when users enter the purpose and time of a meeting, based on their past meeting attendance history. For example, the reception desk can provide optimal input assistance based on the purpose and time of meetings the user has previously attended. Furthermore, the reception desk can suggest the purpose and time of frequently held meetings based on the user's past meeting attendance history. In addition, the reception desk can analyze the user's past meeting attendance history and provide the most suitable input format. This streamlines the input process by providing input assistance based on the user's past meeting attendance history.

[0045] The reception desk can suggest the optimal meeting time by referring to the user's calendar information when the purpose and time of the meeting are entered. For example, the reception desk can refer to appointments registered in the user's calendar and suggest the optimal meeting time. Furthermore, the reception desk can suggest meeting times related to specific events based on the user's calendar information. In addition, the reception desk can suggest the optimal meeting time tailored to the user's schedule based on their calendar information. Thus, by referring to the user's calendar information, the reception desk can suggest the optimal meeting time.

[0046] The planning unit can automatically generate the optimal agenda and sequence of proceedings by referring to past meeting data. For example, it can automatically generate the optimal agenda based on past meeting data. Furthermore, it can automatically generate an efficient sequence of proceedings from past meeting data. In addition, it can analyze past meeting data and propose the optimal agenda and sequence of proceedings. Thus, by referring to past meeting data, the optimal agenda and sequence of proceedings can be automatically generated.

[0047] The planning section can apply different meeting templates based on the purpose of the meeting. For example, in a project progress review meeting, the planning section can apply templates for progress reporting, problem sharing, and solution discussion. In a new product planning meeting, the planning section can apply templates for idea generation, feedback, and next step decision-making. Furthermore, in a regular meeting, the planning section can apply templates for reviewing the previous meeting minutes, progress reporting, and confirming the next meeting schedule. By applying different meeting templates based on the purpose of the meeting, the meeting proceeds more smoothly.

[0048] The planning unit can automatically collect relevant materials and information based on the purpose of the meeting and incorporate them into the plan. For example, in the case of a project progress review meeting, the planning unit can automatically collect progress reports and problem lists and incorporate them into the plan. In the case of a new product planning meeting, the planning unit can automatically collect materials and feedback for idea generation and incorporate them into the plan. Furthermore, in the case of a regular meeting, the planning unit can automatically collect minutes from the previous meeting and progress reports and incorporate them into the plan. As a result, by automatically collecting relevant materials and information based on the purpose of the meeting and incorporating them into the plan, the meeting proceeds more smoothly.

[0049] The planning unit can automatically assign roles to participants based on the purpose of the meeting. For example, in a project progress review meeting, the planning unit can automatically assign roles such as progress reporter, problem sharer, and solution proposer. Similarly, in a new product planning meeting, the planning unit can automatically assign roles such as idea generation leader, feedback provider, and next step decision-maker. Furthermore, in a regular meeting, the planning unit can automatically assign roles such as minute taker, progress reporter, and next meeting schedule facilitator. By automatically assigning roles to participants based on the purpose of the meeting, the meeting proceeds more smoothly.

[0050] The intervention unit can analyze the progress of the meeting in real time and select the most appropriate intervention method. For example, the intervention unit can analyze the progress of the meeting in real time and intervene if the topic deviates from the main subject. It can also analyze the progress of the meeting in real time and intervene if the discussion becomes repetitive. Furthermore, the intervention unit can analyze the progress of the meeting in real time and intervene at the appropriate time to move on to the next agenda item. This allows for smoother meeting progress by analyzing the meeting's progress in real time and selecting the most appropriate intervention method.

[0051] The intervention team can apply different intervention methods based on the progress of the meeting. For example, based on the progress of the meeting, the intervention team can apply a method to subtly bring the topic back to the main subject if it strays from the subject. Also, based on the progress of the meeting, the intervention team can apply a method to move on to the next agenda item if the discussion becomes circular. Furthermore, based on the progress of the meeting, the intervention team can apply a method to mediate when participants' opinions clash. In this way, by applying different intervention methods based on the progress of the meeting, the meeting can proceed more smoothly.

[0052] The intervention team can intervene by presenting relevant materials and information based on the progress of the meeting. For example, the intervention team can intervene by presenting relevant materials if the topic strays from the main subject, based on the progress of the meeting. Furthermore, the intervention team can intervene by presenting relevant information if the discussion becomes repetitive, based on the progress of the meeting. In addition, the intervention team can intervene by presenting relevant materials at the appropriate time to move on to the next agenda item, based on the progress of the meeting. This allows for smoother meeting progress by intervening by presenting relevant materials and information based on the meeting's progress.

[0053] The intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback. For example, the intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the topic strays from the main subject. Furthermore, the intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback when it is time to move on to the next agenda item. This ensures that the meeting proceeds smoothly by analyzing participants' comments based on the progress of the meeting and providing appropriate feedback.

[0054] The meeting minutes generation unit can analyze the content of discussions during a meeting in real time and automatically highlight important points. For example, it can analyze the content of discussions during a meeting in real time and automatically highlight important points. Furthermore, it can analyze the content of discussions during a meeting in real time and automatically highlight decisions made. In addition, it can analyze the content of discussions during a meeting in real time and automatically highlight the next actions. This real-time analysis of discussions and automatic highlighting of important points deepens the understanding of the meeting minutes.

[0055] The minutes generation unit can apply different minutes formats based on the progress of the meeting. For example, in a project progress review meeting, it can apply formats such as progress reports, problem sharing, and solution discussion. In a new product planning meeting, it can apply formats such as idea generation, feedback, and decision-making for the next steps. Furthermore, in a regular meeting, it can apply formats such as reviewing the previous minutes, progress reports, and confirming the next meeting's schedule. By applying different minutes formats based on the progress of the meeting, the understanding of the minutes is enhanced.

[0056] The meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related documents and information. For example, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related documents. Furthermore, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related data. This allows for a deeper understanding of the meeting minutes by analyzing the content of discussions and automatically linking related documents and information.

[0057] The meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract important decisions. For example, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract important decisions. Furthermore, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract the next actions. In addition, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract key points. This allows for a deeper understanding of the meeting minutes by analyzing the content of discussions and automatically extracting important decisions.

[0058] The Next Action sending unit can analyze the content of discussions during a meeting and automatically extract the Next Action for each participant. For example, the Next Action sending unit can analyze the content of discussions during a meeting and automatically extract the Next Action for each participant. Furthermore, the Next Action sending unit can analyze the content of discussions during a meeting and automatically extract important points. This allows for a deeper understanding of the Next Action by analyzing the content of discussions during a meeting and automatically extracting the Next Action for each participant.

[0059] The Next Action transmission unit can analyze the content of discussions during meetings and link relevant documents and information to Next Action. For example, the Next Action transmission unit can analyze the content of discussions during meetings and link relevant documents to Next Action. Furthermore, the Next Action transmission unit can analyze the content of discussions during meetings and link relevant data to Next Action. This allows for a deeper understanding of Next Action by analyzing the content of discussions during meetings and linking relevant documents and information to Next Action.

[0060] The report generation unit can analyze information collected during a meeting and automatically highlight key points. For example, it can analyze information collected during a meeting and automatically highlight key points. Furthermore, it can analyze information collected during a meeting and automatically highlight decisions made. In addition, it can analyze information collected during a meeting and automatically highlight the next steps to take. This analysis of information collected during a meeting and the automatic highlighting of key points deepens the understanding of the report.

[0061] The report generation unit can apply different report formats based on the progress of the meeting. For example, in a project progress review meeting, it can apply formats such as progress report, problem sharing, and solution discussion. In a new product planning meeting, it can apply formats such as idea generation, feedback, and next step decision. Furthermore, in a regular meeting, it can apply formats such as review of previous meeting minutes, progress report, and confirmation of the next meeting schedule. By applying different report formats based on the progress of the meeting, the understanding of the report is enhanced.

[0062] The report generation unit can analyze information collected during meetings and automatically link related documents and information. For example, the report generation unit can analyze information collected during meetings and automatically link related documents. Furthermore, the report generation unit can analyze information collected during meetings and automatically link related data. This allows for a deeper understanding of the report by analyzing information collected during meetings and automatically linking related documents and information.

[0063] The report generation unit can analyze information collected during meetings and automatically extract important decisions. For example, it can analyze information collected during meetings and automatically extract important decisions. Furthermore, it can analyze information collected during meetings and automatically extract the next actions. In addition, it can analyze information collected during meetings and automatically extract key points. This allows for a deeper understanding of the report by analyzing information collected during meetings and automatically extracting important decisions.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The planning unit can automatically collect relevant materials and information based on the purpose of the meeting and incorporate them into the planning. For example, in the case of a project progress review meeting, it can automatically collect progress reports and problem lists and incorporate them into the planning. In the case of a new product planning meeting, it can automatically collect materials and feedback for idea generation and incorporate them into the planning. Furthermore, in the case of a regular meeting, it can automatically collect the minutes of the previous meeting and progress reports and incorporate them into the planning. As a result, by automatically collecting relevant materials and information based on the purpose of the meeting and incorporating them into the planning, the meeting proceeds more smoothly.

[0066] The meeting minutes generation unit can analyze the content of discussions during a meeting in real time and automatically highlight important points. For example, it can analyze the content of discussions during a meeting in real time and automatically highlight important points. It can also analyze the content of discussions during a meeting in real time and automatically highlight decisions made. Furthermore, it can analyze the content of discussions during a meeting in real time and automatically highlight the next actions. As a result, by analyzing the content of discussions during a meeting in real time and automatically highlighting important points, the understanding of the meeting minutes is deepened.

[0067] The report generation unit can analyze information collected during a meeting and automatically highlight key points. For example, it can analyze information collected during a meeting and automatically highlight important points. It can also analyze information collected during a meeting and automatically highlight decisions made. Furthermore, it can analyze information collected during a meeting and automatically highlight the next actions to be taken. As a result, by analyzing information collected during a meeting and automatically highlighting key points, the report becomes easier to understand.

[0068] The planning unit can automatically assign roles to participants based on the purpose of the meeting. For example, in a project progress review meeting, it can automatically assign roles such as progress reporter, problem sharer, and solution proposer. In a new product planning meeting, it can automatically assign roles such as idea generation leader, feedback provider, and next step decision-maker. Furthermore, in a regular meeting, it can automatically assign roles such as minute taker, progress reporter, and next meeting schedule facilitator. By automatically assigning roles to participants based on the purpose of the meeting, the meeting proceeds more smoothly.

[0069] The intervention unit can analyze participants' comments based on the progress of the meeting and provide appropriate feedback. For example, it can analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the topic strays from the subject. It can also analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the discussion becomes repetitive. Furthermore, it can analyze participants' comments based on the progress of the meeting and provide appropriate feedback when it is time to move on to the next agenda item. In this way, by analyzing participants' comments based on the progress of the meeting and providing appropriate feedback, the meeting proceeds more smoothly.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk accepts input regarding the purpose and duration of the meeting. For example, it can analyze and recognize the purpose and duration of a meeting entered by the user in natural language. For instance, if a user enters "Hold a 1-hour meeting to check on the project's progress," the reception desk will analyze this information and recognize the purpose and duration of the meeting. Step 2: The scheduling department sets up the meeting schedule based on the information received by the reception department. For example, they can set the agenda and order of proceedings based on the purpose of the meeting. In the case of a project progress confirmation meeting, the scheduling department sets the agenda items such as progress reports, sharing of problems, and discussion of solutions. Step 3: The intervention unit intervenes during the meeting if the topic deviates from the main subject, based on the procedures set by the scheduling unit. For example, it can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. If the discussion goes in circles, the intervention unit can prompt by saying, "Let's move on to the next topic." Step 4: The minutes generation unit records the content of discussions and decisions made during the meeting and generates the minutes. For example, it can record the content of discussions and decisions made during the meeting and generate the minutes after the meeting has ended. Step 5: The action sending unit sends emails to each participant with the next action based on the meeting minutes generated by the meeting minutes generation unit. For example, it can extract each participant's Next Action and automatically send it via email. It can notify participants via email with content such as, "Person A will prepare materials for the next meeting." Step 6: The report generation unit generates reports based on the information collected during the meeting. For example, it can automatically generate reports summarizing the project's progress, problems, and solutions based on the information collected during the meeting.

[0072] (Example of form 2) The meeting support system according to an embodiment of the present invention is a mechanism for improving meeting efficiency by having an agent participate in the meeting. This meeting support system allows the purpose and time of the meeting to be initially set in natural language, and the agent automatically sets up the meeting procedure. If the topic strays from the subject during the meeting, the agent subtly brings it back to the subject at an appropriate time. It also automatically generates meeting minutes and automatically sends Next Actions via email to each participant. Furthermore, it automatically generates meeting report materials in addition to meeting minutes. For example, the purpose and time of the meeting are initially set in natural language. In this case, the user only needs to input the purpose and time of the meeting. For example, information such as "Hold a 1-hour meeting to check the progress of the project" is entered. This information is entered into the agent. Next, the agent analyzes the entered information and automatically sets up the meeting procedure. The agent sets the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress check meeting, the agenda would include progress reports, sharing of problems, and discussion of solutions. If the topic strays from the subject during the meeting, the agent subtly brings it back to the subject at an appropriate time. The agent monitors the meeting's progress in real time and intervenes at the appropriate time if the topic strays from the main subject. For example, if the discussion goes in circles, the agent will prompt, "Let's move on to the next agenda item." The agent also automatically generates meeting minutes and sends Next Actions to each participant via email. It records what was said and decided during the meeting and generates meeting minutes after the meeting ends. Furthermore, it extracts each participant's Next Action and sends it automatically via email. For example, it might notify participants via email that "Person A will prepare the materials for the next meeting." In addition to meeting minutes, it also automatically generates meeting reports. The agent creates reports based on the information collected during the meeting. For example, it automatically generates reports summarizing project progress, problems, and solutions. This improves meeting efficiency and eliminates wasted time. Users can effectively conduct meetings with the agent's support. The automatic generation of meeting minutes and reports also reduces the workload after the meeting. For example, meetings will proceed more smoothly, and discussions will no longer go in circles.Furthermore, automating the creation of meeting minutes and reports reduces the workload after meetings. This means that meeting support systems improve meeting efficiency and eliminate wasted time.

[0073] The meeting support system according to this embodiment comprises a reception unit, a scheduling unit, an intervention unit, a meeting minutes generation unit, a Next Action transmission unit, and a report material generation unit. The reception unit receives input of the purpose and time of the meeting. The reception unit can, for example, analyze the purpose and time of the meeting entered by the user in natural language. For example, if the user enters "Hold a 1-hour meeting to check the progress of the project," the reception unit can analyze this information and recognize the purpose and time of the meeting. The scheduling unit sets the meeting schedule based on the information received by the reception unit. The scheduling unit can, for example, set the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress check meeting, the scheduling unit can set agenda items such as progress reporting, sharing of problems, and discussion of solutions. The intervention unit intervenes when the topic deviates from the subject during the meeting, based on the schedule set by the scheduling unit. The intervention unit can, for example, monitor the progress of the meeting in real time and intervene at an appropriate time when the topic deviates from the subject. For example, if a discussion goes in circles, the intervention unit can prompt participants to "move on to the next topic." The minutes generation unit records the content of discussions and decisions made during the meeting and generates minutes. The minutes generation unit can, for example, record the content of discussions and decisions made during the meeting and generate minutes after the meeting ends. The Next Action sending unit sends emails to each participant with the next action to be taken, based on the minutes generated by the minutes generation unit. The Next Action sending unit can, for example, extract each participant's Next Action and automatically send it via email. For example, it can notify participants via email with content such as "Mr. / Ms. A will prepare the materials by the next meeting." The report generation unit generates reports based on the information collected during the meeting. The report generation unit can, for example, automatically generate reports summarizing the project's progress, problems, and solutions based on the information collected during the meeting. As a result, the meeting support system according to this embodiment automates the input of the meeting's purpose and time, setting up the arrangements, intervention, minutes generation, Next Action sending, and report generation, improving the efficiency of meetings.

[0074] The reception desk accepts input for the purpose and duration of meetings. For example, the reception desk can analyze the purpose and duration of meetings entered by users in natural language. Specifically, if a user enters "Hold a one-hour meeting to check on the project's progress," the reception desk can analyze this information and recognize the purpose and duration of the meeting. The reception desk uses natural language processing technology to analyze user input and accurately extract the purpose and duration of the meeting. For example, if a user enters "I want to set up a two-hour brainstorming meeting next Monday," the reception desk will extract the information "next Monday," "two hours," and "brainstorming meeting," recognizing the purpose and duration of the meeting. Furthermore, the reception desk can supplement detailed meeting information based on the information entered by the user. For example, it can automatically suggest additional information such as the meeting location and participant list, making it easy for users to set up meetings. The reception desk can also refer to past meeting data and suggest similar meeting settings. This allows users to set up meetings efficiently.

[0075] The planning unit sets up the meeting schedule based on the information received by the reception unit. For example, the planning unit can set the agenda and order of proceedings based on the purpose of the meeting. Specifically, in the case of a project progress review meeting, the planning unit can set agenda items such as progress reports, problem sharing, and discussion of solutions. The planning unit uses AI to automatically generate the optimal agenda and order of proceedings for the purpose of the meeting. For example, the AI ​​learns from past meeting data and proposes the optimal agenda and order of proceedings by referring to effective procedures in similar meetings. The planning unit also provides flexible settings that can be customized by the user. Users can review the proposed agenda and order of proceedings and make modifications or additions as needed. Furthermore, the planning unit can assign speakers and responsible persons for each agenda item, taking into account the roles and expertise of the meeting participants. This ensures that the meeting proceeds smoothly and allows for efficient discussion.

[0076] The intervention unit intervenes during a meeting if the topic deviates from the main subject, based on the procedures set by the planning unit. For example, the intervention unit can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. Specifically, if the discussion goes in circles, the intervention unit can prompt participants to "move on to the next agenda item." The intervention unit uses AI to analyze the progress of the meeting in real time and detect deviations from the topic. For example, the AI ​​analyzes the content of the statements made during the meeting and issues a warning if the topics do not match the set agenda. The intervention unit can also make suggestions to facilitate the smooth progress of the meeting. For example, if the discussion reaches an impasse, it may suggest "let's reconsider this issue later" to support the progress of the meeting. Furthermore, the intervention unit provides a function to visualize the progress of the meeting, making it easier for participants to understand the current progress. In this way, the intervention unit can improve the efficiency of the meeting and promote effective discussion.

[0077] The minutes generation unit records the content of discussions and decisions made during meetings and generates meeting minutes. For example, the minutes generation unit can record discussions and decisions made during a meeting and generate minutes after the meeting ends. Specifically, the minutes generation unit uses speech recognition technology to transcribe discussions in real time and organizes them by speaker. Furthermore, it uses AI to analyze the content of discussions and extract important points and decisions. For example, the AI ​​extracts specific action items from the discussion content, such as "Prepare materials for the next meeting," and reflects them in the minutes. In addition, the minutes generation unit automatically updates the minutes according to the progress of the meeting and provides the completed minutes at the end of the meeting. Furthermore, the minutes generation unit has a function to share the generated minutes with participants, so participants can check the minutes immediately after the meeting ends. As a result, the minutes generation unit can streamline the meeting recording process and provide accurate minutes quickly.

[0078] The Next Action Sending Unit sends emails to each participant regarding their next action, based on the meeting minutes generated by the Meeting Minutes Generation Unit. For example, the Next Action Sending Unit can extract each participant's Next Action and automatically send it via email. Specifically, it creates individual emails for each participant based on the action items extracted by the Meeting Minutes Generation Unit, notifying them of their next action. For instance, it can send an email stating, "Person A should prepare the materials before the next meeting." The Next Action Sending Unit uses AI to consider each participant's role and responsibilities, assigning appropriate action items. Furthermore, the Next Action Sending Unit adjusts the timing of email delivery to ensure participants confirm their next actions at the appropriate time. In addition, the Next Action Sending Unit tracks the status of emails received and sends reminders for unread emails. This allows the Next Action Sending Unit to streamline post-meeting follow-up and support participants in ensuring they take their next actions.

[0079] The report generation unit generates reports based on information collected during meetings. For example, it can automatically generate reports summarizing project progress, problems, and solutions based on information collected during meetings. Specifically, the report generation unit automatically creates the structure of the report based on the statements and decisions recorded by the meeting minutes generation unit. Furthermore, it uses AI to analyze the statements, extract important points and data, and reflect them in the report. For example, it automatically generates graphs showing project progress, lists of problems, and proposed solutions. The report generation unit also provides flexible editing functions that users can customize, allowing them to modify or add to the report content as needed. In addition, the report generation unit has a function to share the generated report with participants, allowing them to review the report immediately after the meeting. This enables the report generation unit to effectively summarize and quickly share the results of meetings.

[0080] The reception desk can analyze the purpose and duration of meetings entered in natural language. For example, if a user enters "We will hold a one-hour meeting to check the project's progress," the reception desk can use natural language processing technology to analyze this information and recognize the purpose and duration of the meeting. For example, the reception desk can use natural language processing technology to extract the purpose and duration of a meeting from the entered text. The reception desk can also analyze the purpose and duration of meetings using voice input. For example, if a user says "We will hold a one-hour meeting to check the project's progress" aloud, the reception desk can use speech recognition technology to convert this information into text and then use natural language processing technology to recognize the purpose and duration of the meeting. This simplifies user input by analyzing the purpose and duration of meetings entered in natural language.

[0081] The planning unit can set the agenda and order of proceedings based on the purpose of the meeting. For example, in the case of a project progress review meeting, the planning unit can set agenda items such as progress reports, sharing of problems, and discussion of solutions. The planning unit can automatically set the types and order of agenda items based on the purpose of the meeting. The planning unit can also set the order of proceedings of the meeting. For example, the planning unit can set the order of speakers and the time allocation for each agenda item. By setting the agenda and order of proceedings based on the purpose of the meeting, the meeting proceeds more smoothly.

[0082] The intervention unit can continuously monitor the progress of the meeting and intervene if the topic deviates from the main subject. For example, the intervention unit can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. For example, if the discussion goes in circles, the intervention unit can prompt participants to "move on to the next agenda item." The intervention unit can also continuously monitor the progress of the meeting and set criteria for intervening when the topic deviates from the main subject. For example, the intervention unit can set criteria for determining when a topic has deviated from the main subject and the timing of intervention. This improves the efficiency of the meeting by monitoring the progress of the meeting in real time and intervening when the topic deviates from the main subject.

[0083] The minutes generation unit can record the content of discussions and decisions made during a meeting and generate meeting minutes. For example, the minutes generation unit can record the content of discussions and decisions made during a meeting and generate minutes after the meeting has ended. The minutes generation unit can record the content of discussions in real time during a meeting and generate minutes. For example, the minutes generation unit can use speech recognition technology to convert the content of discussions into text and generate minutes. In addition, the minutes generation unit can record the decisions made during a meeting and reflect them in the minutes. For example, the minutes generation unit can automatically record the matters decided during a meeting and reflect them in the minutes. In this way, by recording the content of discussions and decisions made during a meeting and generating minutes, the content of the meeting can be accurately recorded.

[0084] The Next Action Sending Unit can send Next Actions to each participant via email based on the meeting minutes. For example, it can extract each participant's Next Action and automatically send it via email. For instance, it can send an email notification stating, "Person A will prepare materials before the next meeting." Based on the meeting minutes, the Next Action Sending Unit can configure the type of next action and the details of the information to be sent. Furthermore, the Next Action Sending Unit can configure the email format. For example, it can set the subject and body format of the email, sending Next Actions in an appropriate format for each participant. This clarifies post-meeting actions by sending Next Actions to each participant via email based on the meeting minutes.

[0085] The report generation unit can generate reports based on information collected during meetings. For example, it can automatically generate reports summarizing project progress, problems, and solutions based on information collected during meetings. The report generation unit can analyze information collected during meetings and reflect it in the reports. For example, it can create reports based on statements and decisions made during meetings. Furthermore, the report generation unit can generate reports in a visually easy-to-understand format based on information collected during meetings. For example, it can visually represent data collected during meetings using graphs and charts. This streamlines the reporting process after meetings by generating reports based on information collected during meetings.

[0086] The reception system can estimate the user's emotions and adjust the input method for meeting purpose and time based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick input of meeting purpose and time. This reduces the user's burden by adjusting the input method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception desk can refer to past meeting data and automatically suggest similar meeting purposes and times. For example, it can automatically suggest similar meetings based on the purpose and duration of meetings the user has previously attended. Furthermore, the reception desk can learn the patterns of meetings the user frequently attends and suggest the most suitable meeting purposes and times. In addition, the reception desk can predict and suggest meetings to be held on specific days of the week and time slots based on past meeting data. This simplifies user input by allowing referencing past meeting data.

[0088] The reception desk can provide an appropriate input format based on the user's job title or position when entering the purpose and time of a meeting. For example, if the user is in a management position, the reception desk can provide a detailed input format for the purpose and time of the meeting. If the user is in a general position, the reception desk can provide a simpler input format for the purpose and time of the meeting. Furthermore, the reception desk can provide a customized input format according to the user's job duties. This streamlines the input process by providing an input format tailored to the user's job title or position.

[0089] The reception desk can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. In this way, the burden on the user is reduced by adjusting the design of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reception desk can provide input assistance when users enter the purpose and time of a meeting, based on their past meeting attendance history. For example, the reception desk can provide optimal input assistance based on the purpose and time of meetings the user has previously attended. Furthermore, the reception desk can suggest the purpose and time of frequently held meetings based on the user's past meeting attendance history. In addition, the reception desk can analyze the user's past meeting attendance history and provide the most suitable input format. This streamlines the input process by providing input assistance based on the user's past meeting attendance history.

[0091] The reception desk can suggest the optimal meeting time by referring to the user's calendar information when the purpose and time of the meeting are entered. For example, the reception desk can refer to appointments registered in the user's calendar and suggest the optimal meeting time. Furthermore, the reception desk can suggest meeting times related to specific events based on the user's calendar information. In addition, the reception desk can suggest the optimal meeting time tailored to the user's schedule based on their calendar information. Thus, by referring to the user's calendar information, the reception desk can suggest the optimal meeting time.

[0092] The planning unit can estimate the user's emotions and adjust the agenda and order of proceedings based on the estimated emotions. For example, if the user is nervous, the planning unit can adjust the order of agenda items to help them relax. If the user is relaxed, the planning unit can adjust the order of agenda items to ensure efficient proceedings. Furthermore, if the user is in a hurry, the planning unit can prioritize important agenda items. By adjusting the agenda and order of proceedings according to the user's emotions, the meeting proceeds more smoothly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The planning unit can automatically generate the optimal agenda and sequence of proceedings by referring to past meeting data. For example, it can automatically generate the optimal agenda based on past meeting data. Furthermore, it can automatically generate an efficient sequence of proceedings from past meeting data. In addition, it can analyze past meeting data and propose the optimal agenda and sequence of proceedings. Thus, by referring to past meeting data, the optimal agenda and sequence of proceedings can be automatically generated.

[0094] The planning section can apply different meeting templates based on the purpose of the meeting. For example, in a project progress review meeting, the planning section can apply templates for progress reporting, problem sharing, and solution discussion. In a new product planning meeting, the planning section can apply templates for idea generation, feedback, and next step decision-making. Furthermore, in a regular meeting, the planning section can apply templates for reviewing the previous meeting minutes, progress reporting, and confirming the next meeting schedule. By applying different meeting templates based on the purpose of the meeting, the meeting proceeds more smoothly.

[0095] The planning unit can estimate the user's emotions and adjust the level of detail in the planning based on the estimated emotions. For example, if the user is nervous, the planning unit can provide a simple and easy-to-understand plan. If the user is relaxed, the planning unit can provide a detailed plan. Furthermore, if the user is in a hurry, the planning unit can provide a plan that gets straight to the point. By adjusting the level of detail in the plan according to the user's emotions, the meeting proceeds more smoothly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The planning unit can automatically collect relevant materials and information based on the purpose of the meeting and incorporate them into the plan. For example, in the case of a project progress review meeting, the planning unit can automatically collect progress reports and problem lists and incorporate them into the plan. In the case of a new product planning meeting, the planning unit can automatically collect materials and feedback for idea generation and incorporate them into the plan. Furthermore, in the case of a regular meeting, the planning unit can automatically collect minutes from the previous meeting and progress reports and incorporate them into the plan. As a result, by automatically collecting relevant materials and information based on the purpose of the meeting and incorporating them into the plan, the meeting proceeds more smoothly.

[0097] The planning unit can automatically assign roles to participants based on the purpose of the meeting. For example, in a project progress review meeting, the planning unit can automatically assign roles such as progress reporter, problem sharer, and solution proposer. Similarly, in a new product planning meeting, the planning unit can automatically assign roles such as idea generation leader, feedback provider, and next step decision-maker. Furthermore, in a regular meeting, the planning unit can automatically assign roles such as minute taker, progress reporter, and next meeting schedule facilitator. By automatically assigning roles to participants based on the purpose of the meeting, the meeting proceeds more smoothly.

[0098] The intervention unit can estimate the user's emotions and adjust the timing of its intervention based on those emotions. For example, if the user is tense, the intervention unit can intervene at a time when the user can relax. If the user is relaxed, the intervention unit can intervene at a time when the meeting can proceed efficiently. Furthermore, if the user is in a hurry, the intervention unit can intervene at a time when the meeting can return to important topics. By adjusting the timing of interventions according to the user's emotions, the meeting can proceed more smoothly. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The intervention unit can analyze the progress of the meeting in real time and select the most appropriate intervention method. For example, the intervention unit can analyze the progress of the meeting in real time and intervene if the topic deviates from the main subject. It can also analyze the progress of the meeting in real time and intervene if the discussion becomes repetitive. Furthermore, the intervention unit can analyze the progress of the meeting in real time and intervene at the appropriate time to move on to the next agenda item. This allows for smoother meeting progress by analyzing the meeting's progress in real time and selecting the most appropriate intervention method.

[0100] The intervention team can apply different intervention methods based on the progress of the meeting. For example, based on the progress of the meeting, the intervention team can apply a method to subtly bring the topic back to the main subject if it strays from the subject. Also, based on the progress of the meeting, the intervention team can apply a method to move on to the next agenda item if the discussion becomes circular. Furthermore, based on the progress of the meeting, the intervention team can apply a method to mediate when participants' opinions clash. In this way, by applying different intervention methods based on the progress of the meeting, the meeting can proceed more smoothly.

[0101] The intervention unit can estimate the user's emotions and adjust the way it expresses the intervention based on the estimated emotions. For example, if the user is tense, the intervention unit can intervene with a calm expression. If the user is relaxed, the intervention unit can intervene with a cheerful expression. Furthermore, if the user is in a hurry, the intervention unit can intervene with a quick and concise expression. By adjusting the way the intervention is expressed according to the user's emotions, the meeting can proceed more smoothly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The intervention team can intervene by presenting relevant materials and information based on the progress of the meeting. For example, the intervention team can intervene by presenting relevant materials if the topic strays from the main subject, based on the progress of the meeting. Furthermore, the intervention team can intervene by presenting relevant information if the discussion becomes repetitive, based on the progress of the meeting. In addition, the intervention team can intervene by presenting relevant materials at the appropriate time to move on to the next agenda item, based on the progress of the meeting. This allows for smoother meeting progress by intervening by presenting relevant materials and information based on the meeting's progress.

[0103] The intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback. For example, the intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the topic strays from the main subject. Furthermore, the intervention team can analyze participants' comments based on the progress of the meeting and provide appropriate feedback when it is time to move on to the next agenda item. This ensures that the meeting proceeds smoothly by analyzing participants' comments based on the progress of the meeting and providing appropriate feedback.

[0104] The meeting minutes generation unit can estimate the user's emotions and adjust the presentation of the minutes based on those emotions. For example, if the user is nervous, the unit can provide a simple and easy-to-understand presentation. If the user is relaxed, the unit can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the unit can provide a concise presentation. By adjusting the presentation of the minutes according to the user's emotions, the understanding of the minutes is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The meeting minutes generation unit can analyze the content of discussions during a meeting in real time and automatically highlight important points. For example, it can analyze the content of discussions during a meeting in real time and automatically highlight important points. Furthermore, it can analyze the content of discussions during a meeting in real time and automatically highlight decisions made. In addition, it can analyze the content of discussions during a meeting in real time and automatically highlight the next actions. This real-time analysis of discussions and automatic highlighting of important points deepens the understanding of the meeting minutes.

[0106] The minutes generation unit can apply different minutes formats based on the progress of the meeting. For example, in a project progress review meeting, it can apply formats such as progress reports, problem sharing, and solution discussion. In a new product planning meeting, it can apply formats such as idea generation, feedback, and decision-making for the next steps. Furthermore, in a regular meeting, it can apply formats such as reviewing the previous minutes, progress reports, and confirming the next meeting's schedule. By applying different minutes formats based on the progress of the meeting, the understanding of the minutes is enhanced.

[0107] The meeting minutes generation unit can estimate the user's emotions and adjust the level of detail in the minutes based on the estimated emotions. For example, if the user is nervous, the meeting minutes generation unit can provide simple and easy-to-understand minutes. If the user is relaxed, the meeting minutes generation unit can provide minutes with more detailed information. Furthermore, if the user is in a hurry, the meeting minutes generation unit can provide minutes that get straight to the point. By adjusting the level of detail in the minutes according to the user's emotions, the understanding of the minutes is enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related documents and information. For example, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related documents. Furthermore, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically link related data. This allows for a deeper understanding of the meeting minutes by analyzing the content of discussions and automatically linking related documents and information.

[0109] The meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract important decisions. For example, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract important decisions. Furthermore, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract the next actions. In addition, the meeting minutes generation unit can analyze the content of discussions during meetings and automatically extract key points. This allows for a deeper understanding of the meeting minutes by analyzing the content of discussions and automatically extracting important decisions.

[0110] The Next Action sender can estimate the user's emotions and adjust the way the Next Action is presented based on those emotions. For example, if the user is nervous, the Next Action sender can provide a simple and easy-to-understand presentation. If the user is relaxed, the Next Action sender can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the Next Action sender can provide a concise presentation. By adjusting the presentation of the Next Action according to the user's emotions, the understanding of the Next Action is enhanced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The Next Action sending unit can analyze the content of discussions during a meeting and automatically extract the Next Action for each participant. For example, the Next Action sending unit can analyze the content of discussions during a meeting and automatically extract the Next Action for each participant. Furthermore, the Next Action sending unit can analyze the content of discussions during a meeting and automatically extract important points. This allows for a deeper understanding of the Next Action by analyzing the content of discussions during a meeting and automatically extracting the Next Action for each participant.

[0112] The Next Action sender can estimate the user's emotions and prioritize Next Actions based on those emotions. For example, if the user is feeling stressed, the Next Action sender can prioritize and notify them of important Next Actions. If the user is relaxed, the Next Action sender can notify them of detailed Next Actions. Furthermore, if the user is in a hurry, the Next Action sender can notify them of concise Next Actions. This allows for a deeper understanding of Next Actions by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The Next Action transmission unit can analyze the content of discussions during meetings and link relevant documents and information to Next Action. For example, the Next Action transmission unit can analyze the content of discussions during meetings and link relevant documents to Next Action. Furthermore, the Next Action transmission unit can analyze the content of discussions during meetings and link relevant data to Next Action. This allows for a deeper understanding of Next Action by analyzing the content of discussions during meetings and linking relevant documents and information to Next Action.

[0114] The report generation unit can estimate the user's emotions and adjust the presentation of the report based on those emotions. For example, if the user is nervous, the report generation unit can provide a simple and easy-to-understand presentation. If the user is relaxed, the report generation unit can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the report generation unit can provide a presentation that gets straight to the point. By adjusting the presentation of the report according to the user's emotions, the understanding of the report is enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The report generation unit can analyze information collected during a meeting and automatically highlight key points. For example, it can analyze information collected during a meeting and automatically highlight key points. Furthermore, it can analyze information collected during a meeting and automatically highlight decisions made. In addition, it can analyze information collected during a meeting and automatically highlight the next steps to take. This analysis of information collected during a meeting and the automatic highlighting of key points deepens the understanding of the report.

[0116] The report generation unit can apply different report formats based on the progress of the meeting. For example, in a project progress review meeting, it can apply formats such as progress report, problem sharing, and solution discussion. In a new product planning meeting, it can apply formats such as idea generation, feedback, and next step decision. Furthermore, in a regular meeting, it can apply formats such as review of previous meeting minutes, progress report, and confirmation of the next meeting schedule. By applying different report formats based on the progress of the meeting, the understanding of the report is enhanced.

[0117] The report generation unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated emotions. For example, if the user is nervous, the report generation unit can provide a simple and easy-to-understand report. If the user is relaxed, the report generation unit can provide a report with detailed information. Furthermore, if the user is in a hurry, the report generation unit can provide a report that gets straight to the point. In this way, adjusting the level of detail in the report according to the user's emotions deepens the understanding of the report. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The report generation unit can analyze information collected during meetings and automatically link related documents and information. For example, the report generation unit can analyze information collected during meetings and automatically link related documents. Furthermore, the report generation unit can analyze information collected during meetings and automatically link related data. This allows for a deeper understanding of the report by analyzing information collected during meetings and automatically linking related documents and information.

[0119] The report generation unit can analyze information collected during meetings and automatically extract important decisions. For example, it can analyze information collected during meetings and automatically extract important decisions. Furthermore, it can analyze information collected during meetings and automatically extract the next actions. In addition, it can analyze information collected during meetings and automatically extract key points. This allows for a deeper understanding of the report by analyzing information collected during meetings and automatically extracting important decisions.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The reception system can estimate the user's emotions and adjust the input method for meeting purpose and time based on that estimation. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of meeting purpose and time. This reduces the user's burden by adjusting the input method according to their emotions.

[0122] The planning unit can automatically collect relevant materials and information based on the purpose of the meeting and incorporate them into the planning. For example, in the case of a project progress review meeting, it can automatically collect progress reports and problem lists and incorporate them into the planning. In the case of a new product planning meeting, it can automatically collect materials and feedback for idea generation and incorporate them into the planning. Furthermore, in the case of a regular meeting, it can automatically collect the minutes of the previous meeting and progress reports and incorporate them into the planning. As a result, by automatically collecting relevant materials and information based on the purpose of the meeting and incorporating them into the planning, the meeting proceeds more smoothly.

[0123] The intervention unit can estimate the user's emotions and adjust the timing of interventions based on those emotions. For example, if the user is tense, intervention can be timed to help them relax. If the user is relaxed, intervention can be timed to ensure efficient progress. Furthermore, if the user is in a hurry, intervention can be timed to return to the important agenda items. By adjusting the timing of interventions according to the user's emotions, the meeting can proceed more smoothly.

[0124] The meeting minutes generation unit can analyze the content of discussions during a meeting in real time and automatically highlight important points. For example, it can analyze the content of discussions during a meeting in real time and automatically highlight important points. It can also analyze the content of discussions during a meeting in real time and automatically highlight decisions made. Furthermore, it can analyze the content of discussions during a meeting in real time and automatically highlight the next actions. As a result, by analyzing the content of discussions during a meeting in real time and automatically highlighting important points, the understanding of the meeting minutes is deepened.

[0125] The Next Action transmission unit can estimate the user's emotions and adjust the way the Next Action is presented based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the Next Action according to the user's emotions, the understanding of the Next Action is enhanced.

[0126] The report generation unit can analyze information collected during a meeting and automatically highlight key points. For example, it can analyze information collected during a meeting and automatically highlight important points. It can also analyze information collected during a meeting and automatically highlight decisions made. Furthermore, it can analyze information collected during a meeting and automatically highlight the next actions to be taken. As a result, by analyzing information collected during a meeting and automatically highlighting key points, the report becomes easier to understand.

[0127] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, a calming color scheme can be provided to reduce visual stress. If the user is enjoying themselves, a bright color scheme can be provided to make the input process more enjoyable. Furthermore, if the user is tired, a simple and highly visible interface can be provided to facilitate the input process. In this way, adjusting the input interface design according to the user's emotions reduces the burden on the user.

[0128] The planning unit can automatically assign roles to participants based on the purpose of the meeting. For example, in a project progress review meeting, it can automatically assign roles such as progress reporter, problem sharer, and solution proposer. In a new product planning meeting, it can automatically assign roles such as idea generation leader, feedback provider, and next step decision-maker. Furthermore, in a regular meeting, it can automatically assign roles such as minute taker, progress reporter, and next meeting schedule facilitator. By automatically assigning roles to participants based on the purpose of the meeting, the meeting proceeds more smoothly.

[0129] The intervention unit can analyze participants' comments based on the progress of the meeting and provide appropriate feedback. For example, it can analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the topic strays from the subject. It can also analyze participants' comments based on the progress of the meeting and provide appropriate feedback if the discussion becomes repetitive. Furthermore, it can analyze participants' comments based on the progress of the meeting and provide appropriate feedback when it is time to move on to the next agenda item. In this way, by analyzing participants' comments based on the progress of the meeting and providing appropriate feedback, the meeting proceeds more smoothly.

[0130] The report generation unit can estimate the user's emotions and adjust the presentation of the report based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the report according to the user's emotions, the report's understanding is enhanced.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk accepts input regarding the purpose and duration of the meeting. For example, it can analyze and recognize the purpose and duration of a meeting entered by the user in natural language. For instance, if a user enters "Hold a 1-hour meeting to check on the project's progress," the reception desk will analyze this information and recognize the purpose and duration of the meeting. Step 2: The scheduling department sets up the meeting schedule based on the information received by the reception department. For example, they can set the agenda and order of proceedings based on the purpose of the meeting. In the case of a project progress confirmation meeting, the scheduling department sets the agenda items such as progress reports, sharing of problems, and discussion of solutions. Step 3: The intervention unit intervenes during the meeting if the topic deviates from the main subject, based on the procedures set by the scheduling unit. For example, it can monitor the progress of the meeting in real time and intervene at the appropriate time if the topic deviates from the main subject. If the discussion goes in circles, the intervention unit can prompt by saying, "Let's move on to the next topic." Step 4: The minutes generation unit records the content of discussions and decisions made during the meeting and generates the minutes. For example, it can record the content of discussions and decisions made during the meeting and generate the minutes after the meeting has ended. Step 5: The action sending unit sends emails to each participant with the next action based on the meeting minutes generated by the meeting minutes generation unit. For example, it can extract each participant's Next Action and automatically send it via email. It can notify participants via email with content such as, "Person A will prepare materials for the next meeting." Step 6: The report generation unit generates reports based on the information collected during the meeting. For example, it can automatically generate reports summarizing the project's progress, problems, and solutions based on the information collected during the meeting.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, planning unit, intervention unit, meeting minutes generation unit, Next Action transmission unit, and report material generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and analyzes the purpose and time of the meeting entered by the user in natural language. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the agenda and order of proceedings based on the purpose of the meeting. The intervention unit is implemented by, for example, the control unit 46A of the smart device 14 and monitors the progress of the meeting in real time and intervenes at an appropriate time if the topic deviates from the subject. The meeting minutes generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the content of statements and decisions made during the meeting and generates meeting minutes. The Next Action transmission unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends the next action to each participant by email based on the meeting minutes. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates report materials based on the information collected during the meeting. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, planning unit, intervention unit, meeting minutes generation unit, Next Action transmission unit, and report material generation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the purpose and time of the meeting entered by the user in natural language. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the agenda and order of proceedings based on the purpose of the meeting. The intervention unit is implemented by, for example, the control unit 46A of the smart glasses 214 and monitors the progress of the meeting in real time and intervenes at an appropriate time if the topic deviates from the subject. The meeting minutes generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the content of statements and decisions made during the meeting and generates meeting minutes. The Next Action transmission unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends the next action to each participant by email based on the meeting minutes. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates report materials based on the information collected during the meeting. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, planning unit, intervention unit, meeting minutes generation unit, Next Action transmission unit, and report material generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the purpose and time of the meeting entered by the user in natural language. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the agenda and order of proceedings based on the purpose of the meeting. The intervention unit is implemented by, for example, the control unit 46A of the headset terminal 314 and monitors the progress of the meeting in real time and intervenes at an appropriate time if the topic deviates from the subject. The meeting minutes generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the content of statements and decisions made during the meeting and generates meeting minutes. The Next Action transmission unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends the next action to each participant by email based on the meeting minutes. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates report materials based on the information collected during the meeting. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the reception unit, setup unit, intervention unit, meeting minutes generation unit, Next Action transmission unit, and report material generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and analyzes the purpose and time of the meeting entered by the user in natural language. The setup unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the agenda and order of proceedings based on the purpose of the meeting. The intervention unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the progress of the meeting in real time and intervenes at an appropriate time if the topic deviates from the subject. The meeting minutes generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the content of statements and decisions made during the meeting and generates meeting minutes. The Next Action transmission unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends the next action to each participant by email based on the meeting minutes. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates report materials based on the information collected during the meeting. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A reception desk that accepts input of the purpose and time of the meeting, A scheduling unit sets up the meeting arrangements based on the information received by the aforementioned reception unit, An intervention unit intervenes when the topic of conversation deviates from the main subject during a meeting, based on the arrangement set by the aforementioned arrangement setting unit. A minutes generation unit that records the content of discussions and decisions made during meetings and generates meeting minutes, Based on the minutes generated by the minutes generation unit, the action transmission unit sends the following actions to each participant via email, It includes a report generation unit that generates report materials based on information collected during the meeting. A system characterized by the following features. (Note 2) The aforementioned reception unit is Analyze the purpose and duration of meetings entered in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned setup setting unit is, Set the agenda and order of proceedings based on the purpose of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned intervention unit is Monitor the progress of the meeting and intervene if the topic strays from the main subject. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned minutes generation unit, Record the content of discussions and decisions made during the meeting, and generate meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Next Action transmission unit is Send Next Actions to each participant via email based on the meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned report data generation unit, Generate a report based on the information collected during the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how the meeting's purpose and duration are entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is By referencing past meeting data, the system automatically suggests similar meeting objectives and times. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering the purpose and duration of a meeting, the system provides an appropriate input format based on the user's job title or position. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering the purpose and duration of a meeting, the system provides input assistance based on the user's past meeting participation history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When you enter the purpose and duration of a meeting, the system will refer to your calendar information to suggest the most suitable meeting time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned setup setting unit is, It estimates the user's emotions and adjusts the agenda and order of proceedings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned setup setting unit is, By referring to past meeting data, the system automatically generates the optimal agenda and meeting order. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned setup setting unit is, Apply different meeting templates based on the purpose of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned setup setting unit is, The system estimates the user's emotions and adjusts the level of detail in the process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned setup setting unit is, Based on the meeting's objectives, relevant materials and information are automatically collected and incorporated into the plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned setup setting unit is, The system automatically assigns roles to participants based on the meeting's objectives. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned intervention unit is It estimates the user's emotions and adjusts the timing of interventions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned intervention unit is Analyze the progress of the meeting in real time and select the optimal intervention method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned intervention unit is Apply different intervention methods based on the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned intervention unit is The system estimates the user's emotions and adjusts the way interventions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned intervention unit is Intervene by presenting relevant materials and information based on the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned intervention unit is Based on the progress of the meeting, we analyze the participants' comments and provide appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned minutes generation unit, The system estimates the user's emotions and adjusts the way the meeting minutes are written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned minutes generation unit, It analyzes what is said during a meeting in real time and automatically highlights important points. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned minutes generation unit, Apply different meeting minutes formats based on the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned minutes generation unit, The system estimates the user's emotions and adjusts the level of detail in the meeting minutes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned minutes generation unit, The system analyzes the content of discussions during meetings and automatically links to relevant documents and information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned minutes generation unit, The system analyzes the content of discussions during meetings and automatically extracts important decisions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Next Action transmission unit is It estimates the user's emotions and adjusts how the Next Action is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Next Action transmission unit is The system analyzes the content of discussions during meetings and automatically extracts each participant's Next Action. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Next Action transmission unit is It estimates the user's emotions and determines the priority of the Next Action based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Next Action transmission unit is Analyze the content of discussions during meetings and link relevant documents and information to Next Action. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned report data generation unit, We estimate the user's emotions and adjust the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned report data generation unit, It analyzes the information collected during the meeting and automatically highlights the important points. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned report data generation unit, Apply different reporting formats based on the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned report data generation unit, The system estimates user sentiment and adjusts the level of detail in the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned report data generation unit, The system analyzes information collected during meetings and automatically links relevant documents and information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned report data generation unit, The system analyzes information collected during meetings and automatically extracts key decisions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 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 reception desk that accepts input of the purpose and time of the meeting, A scheduling unit sets up the meeting arrangements based on the information received by the aforementioned reception unit, An intervention unit intervenes when the topic of conversation deviates from the main subject during a meeting, based on the arrangement set by the aforementioned arrangement setting unit. A minutes generation unit that records the content of discussions and decisions made during meetings and generates meeting minutes, Based on the minutes generated by the minutes generation unit, the action transmission unit sends the following actions to each participant via email, It includes a report generation unit that generates report materials based on information collected during the meeting. A system characterized by the following features.

2. The aforementioned reception unit is Analyze the purpose and duration of meetings entered in natural language. The system according to feature 1.

3. The aforementioned setup setting unit is, Set the agenda and order of proceedings based on the purpose of the meeting. The system according to feature 1.

4. The aforementioned intervention unit is Monitor the progress of the meeting and intervene if the topic strays from the main subject. The system according to feature 1.

5. The aforementioned minutes generation unit, Record the content of discussions and decisions made during the meeting, and generate meeting minutes. The system according to feature 1.

6. The aforementioned Next Action transmission unit is Send Next Actions to each participant via email based on the meeting minutes. The system according to feature 1.

7. The aforementioned report data generation unit, Generate a report based on the information collected during the meeting. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how the meeting's purpose and duration are entered based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is By referencing past meeting data, the system automatically suggests similar meeting objectives and times. The system according to feature 1.

10. The aforementioned reception unit is When entering the purpose and duration of a meeting, the system provides an appropriate input format based on the user's job title or position. The system according to feature 1.

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