System

Abstract

It is an object of a system according to an embodiment to efficiently organize meeting information and visualize correspondence items.SOLUTION: A system includes an extraction unit, an organization unit, an analysis unit, and a visualization unit. The extraction unit extracts conference information. The organization unit organizes the information extracted by the extraction unit. The analysis unit analyzes the response based on the information organized by the organization unit. The visualizer visualizes the action item analyzed by the analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that organizing meeting information and visualizing response items is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently organize meeting information and visualize the matters to be dealt with. [Means for solving the problem]

[0006] The system according to the embodiment includes an extraction unit, an organization unit, an analysis unit, and a visualization unit. The extraction unit extracts meeting information. The organization unit organizes the information extracted by the extraction unit. The analysis unit analyzes response matters based on the information organized by the organization unit. The visualization unit visualizes the response matters analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize meeting information and visualize the matters to be dealt with. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The MBO input support system according to an embodiment of the present invention automatically extracts, organizes, analyzes, and visualizes meeting information. The MBO input support system extracts meeting information from a calendar application for a target period and uses AI to organize the time spent in meetings and the content of each meeting for each project. Next, the AI ​​analyzes when and how each project was handled and visualizes the information in a table. For example, the MBO input support system allows users to see at a glance the meetings and actions related to a specific project. Finally, the MBO is completed by the user simply adding their own selling points. This allows for efficient MBO creation and saves time. The MBO input support system automatically extracts, organizes, analyzes, and visualizes meeting information, helping users easily create MBOs. For example, users can identify action items without having to look back at past emails, allowing for efficient MBO creation.

[0029] The MBO input support system according to the embodiment includes an extraction unit, a compilation unit, an analysis unit, and a visualization unit. The extraction unit extracts meeting information. The meeting information includes, but is not limited to, minutes, meeting dates and times, and participant lists. The extraction unit extracts the meeting information using, for example, keyword search or natural language processing technology. The extraction unit can also extract the meeting information from a calendar application. For example, the extraction unit extracts the meeting information from a calendar application such as Google® Calendar or Outlook® Calendar. The compilation unit organizes the information extracted by the extraction unit. This organization can be performed by, for example, categorizing or chronological organization, but is not limited to, these methods. For example, the compilation unit organizes the time spent in meetings and the content of meetings for each project. The analysis unit analyzes the response items based on the information compiled by the compilation unit. The analysis can be performed by, for example, statistical analysis or trend analysis, but is not limited to these methods. For example, the analysis unit analyzes when and what responses were taken. The visualization unit visualizes the response items analyzed by the analysis unit. The visualization is performed by, for example, a graph display or a dashboard display, but is not limited to these examples. For example, the visualization unit visualizes the response items in a table format. This allows the MBO input support system according to the embodiment to efficiently extract, organize, analyze, and visualize meeting information.

[0030] The visualization unit can visualize the response items in a tabular format. The tabular format includes, for example, a row and column configuration and items to be displayed, but is not limited to such examples. The visualization unit, for example, displays the response items in a tabular format to improve visibility. For example, the visualization unit organizes the response items by rows and columns to highlight important information. The visualization unit can also customize the items to be displayed in the tabular format. For example, the visualization unit selects items to be displayed according to the user's needs to improve visibility. As a result, the response items are visualized in a tabular format, improving visibility.

[0031] The extraction unit can extract the meeting information from a calendar application. Examples of calendar applications include, but are not limited to, Google Calendar and Outlook Calendar. For example, the extraction unit extracts the meeting information from Google Calendar. The extraction unit can also extract the meeting information from Outlook Calendar. For example, the extraction unit acquires the meeting information using an API of the calendar application. The extraction unit can also analyze data from the calendar application to extract the meeting information. For example, the extraction unit extracts the meeting information from the database of the calendar application. This allows the meeting information to be efficiently extracted from the calendar application.

[0032] The organizing unit can organize the time spent in meetings and the contents of meetings for each case. For example, each case includes, but is not limited to, a project unit or a client unit. The organizing unit organizes the time spent in meetings and the contents of meetings for each project, for example. The organizing unit can also organize the time spent in meetings and the contents of meetings for each client. For example, the organizing unit organizes the date and time of meetings and the list of participants for each case. The organizing unit can also organize the minutes of meetings and response matters for each case. For example, the organizing unit categorizes the contents of meetings for each case and organizes them in chronological order. This makes it possible to organize the time spent in meetings and the contents of meetings for each case.

[0033] The analysis unit can analyze the timing and content of the response. Examples of the timing and content of the response include, but are not limited to, the start date and end date of the response and details of the response. For example, the analysis unit can analyze the start date and end date of the response to identify the duration of the response. The analysis unit can also analyze the details of the response content to evaluate the effectiveness of the response. For example, the analysis unit can calculate the duration of the response based on the start date and end date of the response. The analysis unit can also evaluate the effectiveness of the response based on the details of the response content. For example, the analysis unit can organize the start date and end date of the response in chronological order and analyze the trend of the response. The analysis unit can also categorize the details of the response content and evaluate the effectiveness of the response. This makes it possible to analyze the timing and content of response items.

[0034] The product includes a supplementation unit. The supplementation unit provides a function for the user to supplement appealing points. Appealing points include, for example, features and advantages to be emphasized, but are not limited to such examples. For example, the supplementation unit provides an interface for the user to input appealing points. The supplementation unit can also provide a function for the user to edit appealing points. For example, the supplementation unit provides a text editor for the user to input and edit appealing points. The supplementation unit can also provide a function for the user to save appealing points and refer to them later. For example, the supplementation unit saves appealing points entered by the user in a database and provides an interface for later reference. This allows the user to supplement appealing points.

[0035] The extraction unit can also extract meeting information from data sources other than a calendar application. Examples of data sources other than a calendar application include, but are not limited to, a task management app, a project management tool, and an email application. For example, the extraction unit extracts task information related to a meeting from a task management app and integrates it with meeting information. The extraction unit can also extract meeting information related to a project from a project management tool and send it to the organization unit. For example, the extraction unit extracts meeting invitation information from an email application and integrates it with calendar information. This allows extraction of meeting information from multiple data sources. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input task information obtained from the task management app to a generation AI and cause the generation AI to extract meeting information.

[0036] When extracting conference information, the extraction unit can select information to extract based on the importance and priority of the conference. Examples of conference importance and priority include, but are not limited to, the positions of participants and the purpose of the conference. For example, the extraction unit prioritizes extracting conference information with high importance and transmitting it to the organization unit. The extraction unit can also process important information first, while deferring conference information with low priority. For example, the extraction unit selects whether to extract detailed information or simplified information based on the importance of the conference. This allows information to be selected based on the importance and priority of the conference. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance and priority of the conference to the generation AI and have the generation AI select the information to extract.

[0037] When extracting conference information, the extraction unit can analyze the user's past conference participation history and select an extraction method. The user's past conference participation history includes, for example, the number of conferences attended and the content of the conferences attended, but is not limited to such examples. The extraction unit, for example, preferentially extracts information about conferences that the user frequently attends. The extraction unit can also extract highly relevant conference information based on the user's past conference participation history. For example, the extraction unit preferentially extracts information about conferences that the user considered important in the past. This allows the optimal extraction method to be selected based on the user's past conference participation history. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's past conference participation history into the generation AI and cause the generation AI to select the optimal extraction method.

[0038] When extracting the conference information, the extraction unit can extract the conference information based on the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP addresses. For example, the extraction unit preferentially extracts conference information close to the user's current location. The extraction unit can also extract conference information related to locations frequently visited by the user. For example, the extraction unit selects highly relevant conference information based on the user's geographical location information. This allows highly relevant conference information to be extracted based on the user's geographical location information. Some or all of the above-described processing in the extraction unit may be performed using, or without, AI. For example, the extraction unit may input the user's geographical location information to the generation AI and cause the generation AI to extract highly relevant conference information.

[0039] When extracting the conference information, the extraction unit can analyze the user's social media activity and extract related conference information. The user's social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The extraction unit can, for example, extract conference information related to locations where the user checked in on social media. The extraction unit can also analyze the content of the user's social media posts to extract related conference information. For example, the extraction unit can extract related conference information based on the activity of the user's friends on social media. This allows related conference information to be extracted based on the user's social media activity. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input the user's social media activity into the generation AI and cause the generation AI to extract related conference information.

[0040] When extracting conference information, the extraction unit can customize the extraction method by reflecting the user's past feedback. Examples of the user's past feedback include, but are not limited to, survey results and comments. For example, the extraction unit preferentially extracts conference information that the user considered important in the past. The extraction unit can also adjust the extraction method based on the user's past feedback. For example, the extraction unit selects an optimal extraction method by reflecting feedback provided by the user in the past. This allows the extraction method to be customized based on the user's past feedback. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's past feedback into the generation AI and cause the generation AI to customize the extraction method.

[0041] When organizing the meeting information, the organizing unit can adjust the level of detail of the organization based on the importance of the meeting. The importance of the meeting includes, but is not limited to, for example, the positions of the participants and the purpose of the meeting. For example, the organizing unit organizes high-importance meeting information in detail and emphasizes important points. The organizing unit can also simplify and organize low-importance meeting information to achieve overall balance. For example, the organizing unit adjusts the method of organizing information based on the importance of the meeting. This allows the level of detail of the organization to be adjusted based on the importance of the meeting. Some or all of the above-described processing in the organizing unit may be performed using, or without, AI. For example, the organizing unit can input the importance of the meeting to a generation AI and cause the generation AI to adjust the level of detail of the organization.

[0042] When organizing meeting information, the organizing unit can apply different organizing algorithms depending on the category of the meeting. Examples of meeting categories include, but are not limited to, project meetings and regular meetings. The organizing unit can apply different organizing algorithms to project meetings and regular meetings, for example. The organizing unit can also select an optimal organizing method for each category and organize the information. For example, the organizing unit customizes the information organizing method depending on the category of the meeting. This makes it possible to apply an optimal organizing algorithm depending on the category of the meeting. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the category of the meeting into the generation AI and cause the generation AI to apply the optimal organizing algorithm.

[0043] When organizing meeting information, the organizing unit can improve the accuracy of the organization by referring to the user's past organization results. The user's past organization results include, but are not limited to, past organization methods and evaluations of the organization results. For example, the organizing unit can propose an optimal organization method based on the user's past organization results. The organizing unit can also analyze the user's past organization results to improve the accuracy of the organization. For example, the organizing unit can adjust the organization method by reflecting feedback provided by the user in the past. This improves the accuracy of the organization based on the user's past organization results. Some or all of the above-mentioned processing in the organizing unit can be performed, for example, using AI or without AI. For example, the organizing unit can input the user's past organization results into the generation AI and cause the generation AI to improve the accuracy of the organization.

[0044] When organizing the meeting information, the organizing unit can determine the priority of organizing based on the submission time of the meeting. The submission time of the meeting includes, but is not limited to, for example, the submission date and the submission time. For example, the organizing unit prioritizes organizing meeting information that is submitted soon. The organizing unit can also organize meeting information that is submitted further back. For example, the organizing unit adjusts the organizing order of information based on the submission time of the meeting. This makes it possible to determine the priority of organizing based on the submission time of the meeting. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the submission time of the meeting to the generation AI and have the generation AI determine the priority of organizing.

[0045] When organizing the meeting information, the organizing unit can adjust the order of organizing based on the relevance of the meetings. The relevance of the meetings includes, but is not limited to, for example, commonalities in the agendas and commonalities in the participants. For example, the organizing unit prioritizes organizing highly relevant meeting information. The organizing unit can also postpone organizing less relevant meeting information. For example, the organizing unit adjusts the order of organizing the information based on the relevance of the meetings. This makes it possible to adjust the order of organizing based on the relevance of the meetings. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the relevance of the meetings to a generation AI and cause the generation AI to adjust the order of organizing.

[0046] When organizing the meeting information, the organizing unit can adjust the use of technical terms in the organization according to the user's level of expertise. The user's level of expertise includes, but is not limited to, qualifications, years of experience, etc. For example, if the user's level of expertise is high, the organizing unit organizes the information using a lot of technical terms. Furthermore, if the user's level of expertise is low, the organizing unit can organize the information while avoiding technical terms. For example, the organizing unit adjusts the method of organizing the information according to the user's level of expertise. This allows the use of technical terms in the organization to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0047] When analyzing response items, the analysis unit can predict current responses by referring to past response data. Past response data includes, for example, past response history and response results, but is not limited to these examples. The analysis unit can predict current response items, for example, based on past response data. The analysis unit can also analyze past response data and propose current response methods. For example, the analysis unit can efficiently analyze current response items by referring to past response data. This makes it possible to predict current responses based on the past response data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI. For example, the analysis unit can input past response data into a generation AI and have the generation AI predict the current response.

[0048] When analyzing response matters, the analysis unit can apply different analysis methods to different meeting categories. Meeting categories include, but are not limited to, project meetings, regular meetings, etc. The analysis unit can apply different analysis methods to project meetings and regular meetings, for example. The analysis unit can also select the optimal analysis method for each category and analyze the information. For example, the analysis unit customizes the information analysis method according to the meeting category. This makes it possible to apply the optimal analysis method according to the meeting category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal analysis method.

[0049] When analyzing response items, the analysis unit can perform the analysis taking into account attribute information of the person who submitted the meeting. Attribute information of the person who submitted the meeting includes, for example, job title, department, etc., but is not limited to these examples. The analysis unit performs the analysis taking into account, for example, the job title and level of expertise of the person who submitted the meeting. The analysis unit can also perform the analysis by referring to the past statements and actions of the person who submitted the meeting. For example, the analysis unit selects an optimal analysis method based on the attribute information of the person who submitted the meeting. This allows for optimal analysis based on the attribute information of the person who submitted the meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the person who submitted the meeting into the generation AI and have the generation AI select the optimal analysis method.

[0050] When analyzing action items, the analysis unit can analyze changes in the analysis based on the submission time of the meeting. The submission time of the meeting includes, for example, but is not limited to, the submission date and submission time. For example, the analysis unit prioritizes analysis of action items for meetings that are submitted soon. The analysis unit can also postpone analysis of action items for meetings that are submitted further back. For example, the analysis unit adjusts the analysis order of action items based on the submission time of the meeting. This makes it possible to analyze changes in the analysis based on the submission time of the meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the meeting to the generation AI and have the generation AI analyze changes in the analysis.

[0051] When analyzing the countermeasures, the analysis unit can perform the analysis by referring to market data related to the conference. The market data related to the conference includes, but is not limited to, market trends, competitive information, etc. For example, the analysis unit evaluates the importance of the countermeasures based on the relevant market data. The analysis unit can also determine the priority of the countermeasures by referring to the market data. For example, the analysis unit complements the analysis results of the countermeasures based on the market data. This allows the analysis to be performed based on the market data related to the conference. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the market data related to the conference into a generation AI and have the generation AI perform the analysis.

[0052] The analysis unit may take into consideration the technical maturity of the conference when analyzing the correspondence items. The technical maturity of the conference may include, but is not limited to, the development stage of the technology and the progress of practical application. For example, the analysis unit may prioritize the analysis of correspondence items of technically mature conferences. The analysis unit may also postpone the analysis of correspondence items of technically immature conferences. For example, the analysis unit may adjust the analysis method of the correspondence items based on the technical maturity of the conference. This allows the analysis to be performed based on the technical maturity of the conference. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the technical maturity of the conference into the generation AI and cause the generation AI to adjust the analysis method.

[0053] During visualization, the visualization unit can optimize the current visualization by referring to past visualization data. Past visualization data includes, for example, past display methods and evaluations of display results, but is not limited to these examples. The visualization unit, for example, proposes an optimal display method based on past visualization data. The visualization unit can also analyze past visualization data to improve the accuracy of the current visualization. For example, the visualization unit adjusts the current visualization method by referring to past visualization data. This allows the current visualization to be optimized based on the past visualization data. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input past visualization data into a generation AI and have the generation AI optimize the current visualization.

[0054] The visualization unit can apply different visualization methods to different meeting categories during visualization. Meeting categories include, but are not limited to, project meetings, regular meetings, and the like. The visualization unit can apply different visualization methods to project meetings and regular meetings, for example. The visualization unit can also select the optimal visualization method for each category and display information. For example, the visualization unit customizes the information visualization method according to the meeting category. This allows the optimal visualization method to be applied according to the meeting category. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, AI, or can be performed without using AI. For example, the visualization unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal visualization method.

[0055] The visualization unit can perform visualization while taking into account the attribute information of the person who submitted the meeting into account. The attribute information of the person who submitted the meeting includes, for example, their job title and department, but is not limited to these examples. The visualization unit can perform visualization while taking into account, for example, the job title and level of expertise of the person who submitted the meeting into account. The visualization unit can also perform visualization while referring to the past statements and actions of the person who submitted the meeting into account. For example, the visualization unit selects an optimal visualization method based on the attribute information of the person who submitted the meeting. This allows visualization to be performed based on the attribute information of the person who submitted the meeting. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the attribute information of the person who submitted the meeting into a generation AI and have the generation AI select an optimal visualization method.

[0056] During visualization, the visualization unit can analyze changes in the visualization based on the submission time of the meeting. The submission time of the meeting includes, for example, but is not limited to, the submission date and submission time. For example, the visualization unit prioritizes visualizing information about meetings that are submitted soon. The visualization unit can also visualize information about meetings that are submitted further back. For example, the visualization unit adjusts the visualization order of information based on the submission time of the meeting. This makes it possible to analyze changes in the visualization based on the submission time of the meeting. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission time of the meeting into the generation AI and have the generation AI analyze changes in the visualization.

[0057] During visualization, the visualization unit can perform visualization by referring to market data related to the conference. Market data related to the conference includes, but is not limited to, market trends and competitive information. For example, the visualization unit evaluates the importance of information based on the related market data and performs visualization. The visualization unit can also determine the priority of information by referring to market data and perform visualization. For example, the visualization unit complements the visualization results of the information based on the market data. This allows visualization to be performed based on the market data related to the conference. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input market data related to the conference into a generation AI and have the generation AI perform visualization.

[0058] The visualization unit can perform visualization taking into account the technical maturity of the meeting. The technical maturity of the meeting includes, but is not limited to, for example, the development stage of the technology and the progress of practical application. For example, the visualization unit can prioritize visualization of information from technically mature meetings. The visualization unit can also postpone visualization of information from technically immature meetings. For example, the visualization unit adjusts the information visualization method based on the technical maturity of the meeting. In this way, visualization is performed based on the technical maturity of the meeting. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the technical maturity of the meeting into the generation AI and have the generation AI adjust the visualization method.

[0059] When supplementing appeal points, the supplementation unit can optimize the current supplementation by referring to past supplemental data. Past supplemental data includes, for example, past supplemental methods and evaluations of supplemental results, but is not limited to these examples. For example, the supplementation unit can propose an optimal supplemental method based on the past supplemental data. The supplementation unit can also analyze the past supplemental data to improve the accuracy of the current supplementation. For example, the supplementation unit can adjust the current supplemental method by referring to the past supplemental data. This allows the current supplementation to be optimized based on the past supplemental data. Some or all of the above-mentioned processing in the supplementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementation unit can input past supplemental data into a generation AI and have the generation AI optimize the current supplementation.

[0060] When supplementing appealing points, the supplementation unit can apply different supplementation methods to different meeting categories. Meeting categories include, but are not limited to, project meetings, regular meetings, etc. The supplementation unit can apply different supplementation methods to project meetings and regular meetings, for example. The supplementation unit can also select the optimal supplementation method for each category and supplement information. For example, the supplementation unit customizes the method of supplementing information depending on the meeting category. This allows the optimal supplementation method to be applied depending on the meeting category. Some or all of the above-mentioned processing in the supplementation unit can be performed using, for example, AI, or can be performed without using AI. For example, the supplementation unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal supplementation method.

[0061] When supplementing appeal points, the supplementing unit can analyze changes in the supplement based on the submission time of the meeting. The submission time of the meeting includes, but is not limited to, for example, the submission date and submission time. For example, the supplementing unit prioritizes supplementing information about meetings that are submitted soon. The supplementing unit can also postpone supplementing information about meetings that are submitted further away. For example, the supplementing unit adjusts the order in which information is supplemented based on the submission time of the meeting. This makes it possible to analyze changes in the supplement based on the submission time of the meeting. Some or all of the above-mentioned processing in the supplementing unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementing unit can input the submission time of the meeting into the generation AI and cause the generation AI to analyze changes in the supplement.

[0062] When supplementing selling points, the supplementing unit may perform the supplementation by referring to market data related to the conference. Market data related to the conference may include, but is not limited to, market trends, competitive information, etc. For example, the supplementing unit may evaluate the importance of information based on the relevant market data and perform the supplementation. The supplementing unit may also determine the priority of information and perform the supplementation by referring to the market data. For example, the supplementing unit may supplement the supplemented information based on the market data. Thus, the supplementation is performed based on the market data related to the conference. Some or all of the above-described processing in the supplementing unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementing unit may input market data related to the conference into the generation AI and have the generation AI execute the supplementation.

[0063] The supplementation unit may supplement appealing points by taking into consideration the technical maturity of the conference. Examples of the technical maturity of the conference include, but are not limited to, the development stage of the technology and the progress of practical application. For example, the supplementation unit may prioritize supplementing information about technically mature conferences. The supplementation unit may also postpone supplementing information about technically immature conferences. For example, the supplementation unit may adjust the method of supplementing information based on the technical maturity of the conference. This allows supplementation to be performed based on the technical maturity of the conference. Some or all of the above-described processing in the supplementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementation unit may input the technical maturity of the conference into the generation AI and cause the generation AI to adjust the supplementation method.

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

[0065] The extraction unit can analyze the user's past behavioral patterns and optimize the method for extracting conference information. For example, it can prioritize extraction of information about conferences that the user frequently attended in the past. It can also extract highly relevant conference information based on information about conferences that the user considered important in the past. It can also adjust the timing for extracting conference information based on the user's past behavioral patterns. This makes it possible to select the optimal extraction method based on the user's past behavioral patterns.

[0066] The analysis unit can perform analysis taking into account the attribute information of the person who submitted the meeting. For example, the analysis can be performed taking into account the position and level of expertise of the person who submitted the meeting. The analysis can also be performed by referring to the past statements and actions of the person who submitted the meeting. Furthermore, it is also possible to select the optimal analysis method based on the attribute information of the person who submitted the meeting. This allows for optimal analysis to be performed based on the attribute information of the person who submitted the meeting.

[0067] The extraction unit can also extract meeting information from data sources other than calendar applications. For example, it can extract task information related to meetings from a task management app and integrate it with meeting information. It can also extract meeting information related to projects from a project management tool and send it to the organization unit. It can also extract meeting invitation information from an email application and integrate it with calendar information. This allows it to extract meeting information from multiple data sources.

[0068] When organizing meeting information, the organizing unit can apply different organizing algorithms depending on the category of the meeting. For example, different organizing algorithms can be applied to project meetings and regular meetings. It is also possible to select the optimal organizing method for each category and organize the information. Furthermore, it is also possible to customize the information organizing method depending on the category of the meeting. This makes it possible to apply the optimal organizing algorithm depending on the category of the meeting.

[0069] When analyzing response items, the analysis unit can predict current responses by referring to past response data. For example, current response items can be predicted based on past response data. It can also analyze past response data and propose current response methods. Furthermore, it is also possible to efficiently analyze current response items by referring to past response data. This makes it possible to predict current responses based on past response data.

[0070] During visualization, the visualization unit can optimize the current visualization by referring to past visualization data. For example, it can propose an optimal display method based on past visualization data. It can also analyze past visualization data to improve the accuracy of the current visualization. Furthermore, it can also adjust the current visualization method by referring to past visualization data. This allows the current visualization to be optimized based on past visualization data.

[0071] The supplementation unit can apply different supplementation methods to different meeting categories when supplementing selling points. For example, different supplementation methods can be applied to project meetings and regular meetings. It can also select the optimal supplementation method for each category and supplement information. Furthermore, it is possible to customize the method of supplementing information depending on the meeting category. This allows the optimal supplementation method to be applied depending on the meeting category.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The extraction unit extracts meeting information, including meeting minutes, meeting dates and times, and participant lists. The extraction unit extracts meeting information using keyword search and natural language processing techniques. It can also extract meeting information from calendar applications (e.g., Google Calendar and Outlook Calendar). Step 2: The organizer organizes the information extracted by the extractor. Organization is done by categorizing, chronologically organizing, etc. For example, the time spent in meetings and the content of meetings for each project can be organized. Step 3: The analysis department analyzes the response items based on the information organized by the organization department. The analysis is carried out using methods such as statistical analysis and trend analysis. For example, they analyze what kind of response was taken and when. Step 4: The visualization department visualizes the response items analyzed by the analysis department. Visualization is performed using methods such as graph display and dashboard display. For example, the response items are visualized in table format.

[0074] (Example 2) The MBO input support system according to an embodiment of the present invention automatically extracts, organizes, analyzes, and visualizes meeting information. The MBO input support system extracts meeting information from a calendar application for a target period and uses AI to organize the time spent in meetings and the content of each meeting for each project. Next, the AI ​​analyzes when and how each project was handled and visualizes the information in a table. For example, the MBO input support system allows users to see at a glance the meetings and actions related to a specific project. Finally, the MBO is completed by the user simply adding their own selling points. This allows for efficient MBO creation and saves time. The MBO input support system automatically extracts, organizes, analyzes, and visualizes meeting information, helping users easily create MBOs. For example, users can identify action items without having to look back at past emails, allowing for efficient MBO creation.

[0075] The MBO input support system according to the embodiment includes an extraction unit, a sorting unit, an analysis unit, and a visualization unit. The extraction unit extracts meeting information. The meeting information includes, but is not limited to, minutes, meeting dates and times, and participant lists. The extraction unit extracts the meeting information using, for example, keyword search or natural language processing technology. The extraction unit can also extract the meeting information from a calendar application. For example, the extraction unit extracts the meeting information from a calendar application such as Google Calendar or Outlook Calendar. The sorting unit organizes the information extracted by the extraction unit. The sorting may be performed by, for example, categorizing or chronologically organizing, but is not limited to, these methods. For example, the sorting unit organizes the time spent in meetings and the content of meetings for each project. The analysis unit analyzes the response items based on the information organized by the sorting unit. The analysis may be performed by, for example, statistical analysis or trend analysis, but is not limited to these methods. For example, the analysis unit analyzes when and what responses were taken. The visualization unit visualizes the response items analyzed by the analysis unit. The visualization is performed by, for example, a graph display or a dashboard display, but is not limited to these examples. For example, the visualization unit visualizes the response items in a table format. This allows the MBO input support system according to the embodiment to efficiently extract, organize, analyze, and visualize meeting information.

[0076] The visualization unit can visualize the response items in a tabular format. The tabular format includes, for example, a row and column configuration and items to be displayed, but is not limited to such examples. The visualization unit, for example, displays the response items in a tabular format to improve visibility. For example, the visualization unit organizes the response items by rows and columns to highlight important information. The visualization unit can also customize the items to be displayed in the tabular format. For example, the visualization unit selects items to be displayed according to the user's needs to improve visibility. As a result, the response items are visualized in a tabular format, improving visibility.

[0077] The extraction unit can extract the meeting information from a calendar application. Examples of calendar applications include, but are not limited to, Google Calendar and Outlook Calendar. For example, the extraction unit extracts the meeting information from Google Calendar. The extraction unit can also extract the meeting information from Outlook Calendar. For example, the extraction unit acquires the meeting information using an API of the calendar application. The extraction unit can also analyze data from the calendar application to extract the meeting information. For example, the extraction unit extracts the meeting information from the database of the calendar application. This allows the meeting information to be efficiently extracted from the calendar application.

[0078] The organizing unit can organize the time spent in meetings and the contents of meetings for each case. For example, each case includes, but is not limited to, a project unit or a client unit. The organizing unit organizes the time spent in meetings and the contents of meetings for each project, for example. The organizing unit can also organize the time spent in meetings and the contents of meetings for each client. For example, the organizing unit organizes the date and time of meetings and the list of participants for each case. The organizing unit can also organize the minutes of meetings and response matters for each case. For example, the organizing unit categorizes the contents of meetings for each case and organizes them in chronological order. This makes it possible to organize the time spent in meetings and the contents of meetings for each case.

[0079] The analysis unit can analyze the timing and content of the response. Examples of the timing and content of the response include, but are not limited to, the start date and end date of the response and details of the response. For example, the analysis unit can analyze the start date and end date of the response to identify the duration of the response. The analysis unit can also analyze the details of the response content to evaluate the effectiveness of the response. For example, the analysis unit can calculate the duration of the response based on the start date and end date of the response. The analysis unit can also evaluate the effectiveness of the response based on the details of the response content. For example, the analysis unit can organize the start date and end date of the response in chronological order and analyze the trend of the response. The analysis unit can also categorize the details of the response content and evaluate the effectiveness of the response. This makes it possible to analyze the timing and content of response items.

[0080] The product includes a supplementation unit. The supplementation unit provides a function for the user to supplement appealing points. Appealing points include, for example, features and advantages to be emphasized, but are not limited to such examples. For example, the supplementation unit provides an interface for the user to input appealing points. The supplementation unit can also provide a function for the user to edit appealing points. For example, the supplementation unit provides a text editor for the user to input and edit appealing points. The supplementation unit can also provide a function for the user to save appealing points and refer to them later. For example, the supplementation unit saves appealing points entered by the user in a database and provides an interface for later reference. This allows the user to supplement appealing points.

[0081] The extraction unit can adjust the timing of extracting the meeting information based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the extraction unit automatically extracts the meeting information, thereby reducing the user's burden. The extraction unit can also provide the user with an option to manually extract the meeting information when the user is relaxed. For example, when the user is in a hurry, the extraction unit quickly extracts the meeting information and immediately sends it to the organization unit. This allows the timing of extracting the meeting information to be adjusted according to the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the extraction unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The extraction unit can also extract meeting information from data sources other than a calendar application. Examples of data sources other than a calendar application include, but are not limited to, a task management app, a project management tool, and an email application. For example, the extraction unit extracts task information related to a meeting from a task management app and integrates it with meeting information. The extraction unit can also extract meeting information related to a project from a project management tool and send it to the organization unit. For example, the extraction unit extracts meeting invitation information from an email application and integrates it with calendar information. This allows extraction of meeting information from multiple data sources. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit may input task information obtained from the task management app to a generation AI and cause the generation AI to extract meeting information.

[0083] When extracting conference information, the extraction unit can select information to extract based on the importance and priority of the conference. Examples of conference importance and priority include, but are not limited to, the positions of participants and the purpose of the conference. For example, the extraction unit prioritizes extracting conference information with high importance and transmitting it to the organization unit. The extraction unit can also process important information first, while deferring conference information with low priority. For example, the extraction unit selects whether to extract detailed information or simplified information based on the importance of the conference. This allows information to be selected based on the importance and priority of the conference. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the importance and priority of the conference to the generation AI and have the generation AI select the information to extract.

[0084] When extracting conference information, the extraction unit can analyze the user's past conference participation history and select an extraction method. The user's past conference participation history includes, for example, the number of conferences attended and the content of the conferences attended, but is not limited to such examples. The extraction unit, for example, preferentially extracts information about conferences that the user frequently attends. The extraction unit can also extract highly relevant conference information based on the user's past conference participation history. For example, the extraction unit preferentially extracts information about conferences that the user considered important in the past. This allows the optimal extraction method to be selected based on the user's past conference participation history. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's past conference participation history into the generation AI and cause the generation AI to select the optimal extraction method.

[0085] The extraction unit can estimate the user's emotions and determine the priority of the meeting information to be extracted based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the extraction unit prioritizes the extraction of important meeting information. Furthermore, when the user is relaxed, the extraction unit can equally extract all meeting information. For example, when the user is in a hurry, the extraction unit quickly extracts the most important meeting information. This allows the priority of meeting information to be determined based on the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the extraction unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0086] When extracting the conference information, the extraction unit can extract the conference information based on the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP addresses. For example, the extraction unit preferentially extracts conference information close to the user's current location. The extraction unit can also extract conference information related to locations frequently visited by the user. For example, the extraction unit selects highly relevant conference information based on the user's geographical location information. This allows highly relevant conference information to be extracted based on the user's geographical location information. Some or all of the above-described processing in the extraction unit may be performed using, or without, AI. For example, the extraction unit may input the user's geographical location information to the generation AI and cause the generation AI to extract highly relevant conference information.

[0087] When extracting the conference information, the extraction unit can analyze the user's social media activity and extract related conference information. The user's social media activity includes, but is not limited to, for example, the content of posts and the number of likes. The extraction unit can, for example, extract conference information related to locations where the user checked in on social media. The extraction unit can also analyze the content of the user's social media posts to extract related conference information. For example, the extraction unit can extract related conference information based on the activity of the user's friends on social media. This allows related conference information to be extracted based on the user's social media activity. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input the user's social media activity into the generation AI and cause the generation AI to extract related conference information.

[0088] When extracting conference information, the extraction unit can customize the extraction method by reflecting the user's past feedback. Examples of the user's past feedback include, but are not limited to, survey results and comments. For example, the extraction unit preferentially extracts conference information that the user considered important in the past. The extraction unit can also adjust the extraction method based on the user's past feedback. For example, the extraction unit selects an optimal extraction method by reflecting feedback provided by the user in the past. This allows the extraction method to be customized based on the user's past feedback. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's past feedback into the generation AI and cause the generation AI to customize the extraction method.

[0089] The organizing unit can determine how to organize the meeting information based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the organizing unit can provide a simple organizing method to avoid excessive details in the information. Alternatively, if the user is feeling relaxed, the organizing unit can provide a detailed organizing method to finely categorize the information. For example, if the user is in a hurry, the organizing unit can quickly organize the information and prioritize displaying important information. This allows the organization method of the meeting information to be adjusted based on the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the organizing unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0090] When organizing the meeting information, the organizing unit can adjust the level of detail of the organization based on the importance of the meeting. The importance of the meeting includes, but is not limited to, for example, the positions of the participants and the purpose of the meeting. For example, the organizing unit organizes high-importance meeting information in detail and emphasizes important points. The organizing unit can also simplify and organize low-importance meeting information to achieve overall balance. For example, the organizing unit adjusts the method of organizing information based on the importance of the meeting. This allows the level of detail of the organization to be adjusted based on the importance of the meeting. Some or all of the above-described processing in the organizing unit may be performed using, or without, AI. For example, the organizing unit can input the importance of the meeting to a generation AI and cause the generation AI to adjust the level of detail of the organization.

[0091] When organizing meeting information, the organizing unit can apply different organizing algorithms depending on the category of the meeting. Examples of meeting categories include, but are not limited to, project meetings and regular meetings. The organizing unit can apply different organizing algorithms to project meetings and regular meetings, for example. The organizing unit can also select an optimal organizing method for each category and organize the information. For example, the organizing unit customizes the information organizing method depending on the category of the meeting. This makes it possible to apply an optimal organizing algorithm depending on the category of the meeting. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the category of the meeting into the generation AI and cause the generation AI to apply the optimal organizing algorithm.

[0092] When organizing meeting information, the organizing unit can improve the accuracy of the organization by referring to the user's past organization results. The user's past organization results include, but are not limited to, past organization methods and evaluations of the organization results. For example, the organizing unit can propose an optimal organization method based on the user's past organization results. The organizing unit can also analyze the user's past organization results to improve the accuracy of the organization. For example, the organizing unit can adjust the organization method by reflecting feedback provided by the user in the past. This improves the accuracy of the organization based on the user's past organization results. Some or all of the above-mentioned processing in the organizing unit can be performed, for example, using AI or without AI. For example, the organizing unit can input the user's past organization results into the generation AI and cause the generation AI to improve the accuracy of the organization.

[0093] The organizing unit can determine the order of organizing based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the organizing unit prioritizes organizing information of high importance. Furthermore, when the user is relaxed, the organizing unit can equally organize all information. For example, when the user is in a hurry, the organizing unit quickly organizes the most important information. This allows the prioritization of organizing to be determined based on the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the organizing unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0094] When organizing the meeting information, the organizing unit can determine the priority of organizing based on the submission time of the meeting. The submission time of the meeting includes, but is not limited to, for example, the submission date and the submission time. For example, the organizing unit prioritizes organizing meeting information that is submitted soon. The organizing unit can also organize meeting information that is submitted further back. For example, the organizing unit adjusts the organizing order of information based on the submission time of the meeting. This makes it possible to determine the priority of organizing based on the submission time of the meeting. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the submission time of the meeting to the generation AI and have the generation AI determine the priority of organizing.

[0095] When organizing the meeting information, the organizing unit can adjust the order of organizing based on the relevance of the meetings. The relevance of the meetings includes, but is not limited to, for example, commonalities in the agendas and commonalities in the participants. For example, the organizing unit prioritizes organizing highly relevant meeting information. The organizing unit can also postpone organizing less relevant meeting information. For example, the organizing unit adjusts the order of organizing the information based on the relevance of the meetings. This makes it possible to adjust the order of organizing based on the relevance of the meetings. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the relevance of the meetings to a generation AI and cause the generation AI to adjust the order of organizing.

[0096] When organizing the meeting information, the organizing unit can adjust the use of technical terms in the organization according to the user's level of expertise. The user's level of expertise includes, but is not limited to, qualifications, years of experience, etc. For example, if the user's level of expertise is high, the organizing unit organizes the information using a lot of technical terms. Furthermore, if the user's level of expertise is low, the organizing unit can organize the information while avoiding technical terms. For example, the organizing unit adjusts the method of organizing the information according to the user's level of expertise. This allows the use of technical terms in the organization to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0097] The analysis unit can determine the analysis method for the corresponding items based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the analysis unit can provide a simple analysis method and avoid excessive details. Alternatively, if the user is feeling relaxed, the analysis unit can provide a detailed analysis method and finely categorize the information. For example, if the user is in a hurry, the analysis unit can quickly analyze the information and prioritize displaying important information. This allows the analysis method for the corresponding items to be adjusted based on the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the user's facial expression data captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0098] When analyzing response items, the analysis unit can predict current responses by referring to past response data. Past response data includes, for example, past response history and response results, but is not limited to these examples. The analysis unit can predict current response items, for example, based on past response data. The analysis unit can also analyze past response data and propose current response methods. For example, the analysis unit can efficiently analyze current response items by referring to past response data. This makes it possible to predict current responses based on the past response data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI. For example, the analysis unit can input past response data into a generation AI and have the generation AI predict the current response.

[0099] When analyzing response matters, the analysis unit can apply different analysis methods to different meeting categories. Meeting categories include, but are not limited to, project meetings, regular meetings, etc. The analysis unit can apply different analysis methods to project meetings and regular meetings, for example. The analysis unit can also select the optimal analysis method for each category and analyze the information. For example, the analysis unit customizes the information analysis method according to the meeting category. This makes it possible to apply the optimal analysis method according to the meeting category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal analysis method.

[0100] When analyzing response items, the analysis unit can perform the analysis taking into account attribute information of the person who submitted the meeting. Attribute information of the person who submitted the meeting includes, for example, job title, department, etc., but is not limited to these examples. The analysis unit performs the analysis taking into account, for example, the job title and level of expertise of the person who submitted the meeting. The analysis unit can also perform the analysis by referring to the past statements and actions of the person who submitted the meeting. For example, the analysis unit selects an optimal analysis method based on the attribute information of the person who submitted the meeting. This allows for optimal analysis based on the attribute information of the person who submitted the meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the person who submitted the meeting into the generation AI and have the generation AI select the optimal analysis method.

[0101] The analysis unit can determine the order of analysis based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the analysis unit prioritizes analysis of the most important response items. The analysis unit can also analyze all response items equally if the user is relaxed. For example, if the user is in a hurry, the analysis unit quickly analyzes the most important response items. This allows the analysis priority to be determined based on the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the user's facial expression data captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0102] When analyzing action items, the analysis unit can analyze changes in the analysis based on the submission time of the meeting. The submission time of the meeting includes, for example, but is not limited to, the submission date and submission time. For example, the analysis unit prioritizes analysis of action items for meetings that are submitted soon. The analysis unit can also postpone analysis of action items for meetings that are submitted further back. For example, the analysis unit adjusts the analysis order of action items based on the submission time of the meeting. This makes it possible to analyze changes in the analysis based on the submission time of the meeting. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the meeting to the generation AI and have the generation AI analyze changes in the analysis.

[0103] When analyzing the countermeasures, the analysis unit can perform the analysis by referring to market data related to the conference. The market data related to the conference includes, but is not limited to, market trends, competitive information, etc. For example, the analysis unit evaluates the importance of the countermeasures based on the relevant market data. The analysis unit can also determine the priority of the countermeasures by referring to the market data. For example, the analysis unit complements the analysis results of the countermeasures based on the market data. This allows the analysis to be performed based on the market data related to the conference. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the market data related to the conference into a generation AI and have the generation AI perform the analysis.

[0104] The analysis unit may take into consideration the technical maturity of the conference when analyzing the correspondence items. The technical maturity of the conference may include, but is not limited to, the development stage of the technology and the progress of practical application. For example, the analysis unit may prioritize the analysis of correspondence items of technically mature conferences. The analysis unit may also postpone the analysis of correspondence items of technically immature conferences. For example, the analysis unit may adjust the analysis method of the correspondence items based on the technical maturity of the conference. This allows the analysis to be performed based on the technical maturity of the conference. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the technical maturity of the conference into the generation AI and cause the generation AI to adjust the analysis method.

[0105] The visualization unit can determine the visualization display method based on the user's emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the visualization unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the visualization unit can also provide a display method that includes detailed information. For example, when the user is in a hurry, the visualization unit provides a display method that focuses on the main points. This allows the visualization display method to be adjusted based on the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the visualization unit can input the user's facial expression data captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0106] During visualization, the visualization unit can optimize the current visualization by referring to past visualization data. Past visualization data includes, for example, past display methods and evaluations of display results, but is not limited to these examples. The visualization unit, for example, proposes an optimal display method based on past visualization data. The visualization unit can also analyze past visualization data to improve the accuracy of the current visualization. For example, the visualization unit adjusts the current visualization method by referring to past visualization data. This allows the current visualization to be optimized based on the past visualization data. Some or all of the above-mentioned processing in the visualization unit may be performed, for example, using AI, or may be performed without using AI. For example, the visualization unit can input past visualization data into a generation AI and have the generation AI optimize the current visualization.

[0107] The visualization unit can apply different visualization methods to different meeting categories during visualization. Meeting categories include, but are not limited to, project meetings, regular meetings, and the like. The visualization unit can apply different visualization methods to project meetings and regular meetings, for example. The visualization unit can also select the optimal visualization method for each category and display information. For example, the visualization unit customizes the information visualization method according to the meeting category. This allows the optimal visualization method to be applied according to the meeting category. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, AI, or can be performed without using AI. For example, the visualization unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal visualization method.

[0108] The visualization unit can perform visualization while taking into account the attribute information of the person who submitted the meeting into account. The attribute information of the person who submitted the meeting includes, for example, their job title and department, but is not limited to these examples. The visualization unit can perform visualization while taking into account, for example, the job title and level of expertise of the person who submitted the meeting into account. The visualization unit can also perform visualization while referring to the past statements and actions of the person who submitted the meeting into account. For example, the visualization unit selects an optimal visualization method based on the attribute information of the person who submitted the meeting. This allows visualization to be performed based on the attribute information of the person who submitted the meeting. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the attribute information of the person who submitted the meeting into a generation AI and have the generation AI select an optimal visualization method.

[0109] The visualization unit can determine the visualization order based on the user's emotions. User emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the visualization unit prioritizes visualization of information of high importance. Furthermore, when the user is relaxed, the visualization unit can visualize all information equally. For example, when the user is in a hurry, the visualization unit quickly visualizes the most important information. This allows the visualization priority to be determined based on the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the visualization unit can input the user's facial expression data captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0110] During visualization, the visualization unit can analyze changes in the visualization based on the submission time of the meeting. The submission time of the meeting includes, for example, but is not limited to, the submission date and submission time. For example, the visualization unit prioritizes visualizing information about meetings that are submitted soon. The visualization unit can also visualize information about meetings that are submitted further back. For example, the visualization unit adjusts the visualization order of information based on the submission time of the meeting. This makes it possible to analyze changes in the visualization based on the submission time of the meeting. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission time of the meeting into the generation AI and have the generation AI analyze changes in the visualization.

[0111] During visualization, the visualization unit can perform visualization by referring to market data related to the conference. Market data related to the conference includes, but is not limited to, market trends and competitive information. For example, the visualization unit evaluates the importance of information based on the related market data and performs visualization. The visualization unit can also determine the priority of information by referring to market data and perform visualization. For example, the visualization unit complements the visualization results of the information based on the market data. This allows visualization to be performed based on the market data related to the conference. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input market data related to the conference into a generation AI and have the generation AI perform visualization.

[0112] The visualization unit can perform visualization taking into account the technical maturity of the meeting. The technical maturity of the meeting includes, but is not limited to, for example, the development stage of the technology and the progress of practical application. For example, the visualization unit can prioritize visualization of information from technically mature meetings. The visualization unit can also postpone visualization of information from technically immature meetings. For example, the visualization unit adjusts the information visualization method based on the technical maturity of the meeting. In this way, visualization is performed based on the technical maturity of the meeting. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the technical maturity of the meeting into the generation AI and have the generation AI adjust the visualization method.

[0113] The supplementation unit can determine how to supplement appealing points based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the supplementation unit can provide a simple supplementation method and avoid excessive details. Alternatively, if the user is feeling relaxed, the supplementation unit can provide a detailed supplementation method and finely categorize the information. For example, if the user is in a hurry, the supplementation unit can quickly supplement information and prioritize important information. This allows the method of supplementing appealing points to be adjusted based on the user's emotions. The user's emotions can be estimated using technologies such as facial expression recognition and voice analysis. For example, the supplementation unit can input the user's facial expression data captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0114] When supplementing appeal points, the supplementation unit can optimize the current supplementation by referring to past supplemental data. Past supplemental data includes, for example, past supplemental methods and evaluations of supplemental results, but is not limited to these examples. For example, the supplementation unit can propose an optimal supplemental method based on the past supplemental data. The supplementation unit can also analyze the past supplemental data to improve the accuracy of the current supplementation. For example, the supplementation unit can adjust the current supplemental method by referring to the past supplemental data. This allows the current supplementation to be optimized based on the past supplemental data. Some or all of the above-mentioned processing in the supplementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementation unit can input past supplemental data into a generation AI and have the generation AI optimize the current supplementation.

[0115] When supplementing appealing points, the supplementation unit can apply different supplementation methods to different meeting categories. Meeting categories include, but are not limited to, project meetings, regular meetings, etc. The supplementation unit can apply different supplementation methods to project meetings and regular meetings, for example. The supplementation unit can also select the optimal supplementation method for each category and supplement information. For example, the supplementation unit customizes the method of supplementing information depending on the meeting category. This allows the optimal supplementation method to be applied depending on the meeting category. Some or all of the above-mentioned processing in the supplementation unit can be performed using, for example, AI, or can be performed without using AI. For example, the supplementation unit can input the meeting category into the generation AI and cause the generation AI to apply the optimal supplementation method.

[0116] The supplementation unit can determine the order of supplementation based on the user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the supplementation unit prioritizes supplementing information of high importance. Furthermore, when the user is relaxed, the supplementation unit can equally supplement all information. For example, when the user is in a hurry, the supplementation unit quickly supplements the most important information. This allows the priority of supplementation to be determined based on the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the supplementation unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0117] When supplementing appeal points, the supplementing unit can analyze changes in the supplement based on the submission time of the meeting. The submission time of the meeting includes, but is not limited to, for example, the submission date and submission time. For example, the supplementing unit prioritizes supplementing information about meetings that are submitted soon. The supplementing unit can also postpone supplementing information about meetings that are submitted further away. For example, the supplementing unit adjusts the order in which information is supplemented based on the submission time of the meeting. This makes it possible to analyze changes in the supplement based on the submission time of the meeting. Some or all of the above-mentioned processing in the supplementing unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementing unit can input the submission time of the meeting into the generation AI and cause the generation AI to analyze changes in the supplement.

[0118] When supplementing selling points, the supplementing unit may perform the supplementation by referring to market data related to the conference. Market data related to the conference may include, but is not limited to, market trends, competitive information, etc. For example, the supplementing unit may evaluate the importance of information based on the relevant market data and perform the supplementation. The supplementing unit may also determine the priority of information and perform the supplementation by referring to the market data. For example, the supplementing unit may supplement the supplemented information based on the market data. Thus, the supplementation is performed based on the market data related to the conference. Some or all of the above-described processing in the supplementing unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementing unit may input market data related to the conference into the generation AI and have the generation AI execute the supplementation.

[0119] The supplementation unit may supplement appealing points by taking into consideration the technical maturity of the conference. Examples of the technical maturity of the conference include, but are not limited to, the development stage of the technology and the progress of practical application. For example, the supplementation unit may prioritize supplementing information about technically mature conferences. The supplementation unit may also postpone supplementing information about technically immature conferences. For example, the supplementation unit may adjust the method of supplementing information based on the technical maturity of the conference. This allows supplementation to be performed based on the technical maturity of the conference. Some or all of the above-described processing in the supplementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the supplementation unit may input the technical maturity of the conference into the generation AI and cause the generation AI to adjust the supplementation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned extraction unit, organization unit, analysis unit, visualization unit, and supplementation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the organization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the supplementation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned extraction unit, organization unit, analysis unit, visualization unit, and supplementation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the organization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the supplementation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned extraction unit, organization unit, analysis unit, visualization unit, and supplementation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the organization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the supplementation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned extraction unit, organization unit, analysis unit, visualization unit, and supplementation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the extraction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the organization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the visualization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the supplementation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

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

[0121] The extraction unit can analyze the user's past behavioral patterns and optimize the method for extracting conference information. For example, it can prioritize extraction of information about conferences that the user frequently attended in the past. It can also extract highly relevant conference information based on information about conferences that the user considered important in the past. It can also adjust the timing for extracting conference information based on the user's past behavioral patterns. This makes it possible to select the optimal extraction method based on the user's past behavioral patterns.

[0122] The organizing unit can determine how to organize the meeting information based on the user's emotions. For example, if the user is feeling stressed, a simple organization method can be provided to avoid excessive details in the information. Alternatively, if the user is feeling relaxed, a detailed organization method can be provided to finely categorize the information. Furthermore, if the user is in a hurry, it is possible to quickly organize the information and prioritize displaying important information. In this way, the organization method for the meeting information can be adjusted based on the user's emotions.

[0123] The analysis unit can perform analysis taking into account the attribute information of the person who submitted the meeting. For example, the analysis can be performed taking into account the position and level of expertise of the person who submitted the meeting. The analysis can also be performed by referring to the past statements and actions of the person who submitted the meeting. Furthermore, it is also possible to select the optimal analysis method based on the attribute information of the person who submitted the meeting. This allows for optimal analysis to be performed based on the attribute information of the person who submitted the meeting.

[0124] The visualization unit can determine the visualization display method based on the user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the visualization display method to be adjusted based on the user's emotions.

[0125] The supplementation unit can determine how to supplement appealing points based on the user's emotions. For example, if the user is stressed, a simple supplementation method can be provided to avoid excessive details. Alternatively, if the user is relaxed, a detailed supplementation method can be provided to finely categorize the information. Furthermore, if the user is in a hurry, it is possible to quickly supplement information and prioritize displaying important information. In this way, the method for supplementing appealing points can be adjusted based on the user's emotions.

[0126] The extraction unit can also extract meeting information from data sources other than calendar applications. For example, it can extract task information related to meetings from a task management app and integrate it with meeting information. It can also extract meeting information related to projects from a project management tool and send it to the organization unit. It can also extract meeting invitation information from an email application and integrate it with calendar information. This allows it to extract meeting information from multiple data sources.

[0127] When organizing meeting information, the organizing unit can apply different organizing algorithms depending on the category of the meeting. For example, different organizing algorithms can be applied to project meetings and regular meetings. It is also possible to select the optimal organizing method for each category and organize the information. Furthermore, it is also possible to customize the information organizing method depending on the category of the meeting. This makes it possible to apply the optimal organizing algorithm depending on the category of the meeting.

[0128] When analyzing response items, the analysis unit can predict current responses by referring to past response data. For example, current response items can be predicted based on past response data. It can also analyze past response data and propose current response methods. Furthermore, it is also possible to efficiently analyze current response items by referring to past response data. This makes it possible to predict current responses based on past response data.

[0129] During visualization, the visualization unit can optimize the current visualization by referring to past visualization data. For example, it can propose an optimal display method based on past visualization data. It can also analyze past visualization data to improve the accuracy of the current visualization. Furthermore, it can also adjust the current visualization method by referring to past visualization data. This allows the current visualization to be optimized based on past visualization data.

[0130] The supplementation unit can apply different supplementation methods to different meeting categories when supplementing selling points. For example, different supplementation methods can be applied to project meetings and regular meetings. It can also select the optimal supplementation method for each category and supplement information. Furthermore, it is possible to customize the method of supplementing information depending on the meeting category. This allows the optimal supplementation method to be applied depending on the meeting category.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The extraction unit extracts meeting information, including meeting minutes, meeting dates and times, and participant lists. The extraction unit extracts meeting information using keyword search and natural language processing techniques. It can also extract meeting information from calendar applications (e.g., Google Calendar and Outlook Calendar). Step 2: The organizer organizes the information extracted by the extractor. Organization is done by categorizing, chronologically organizing, etc. For example, the time spent in meetings and the content of meetings for each project can be organized. Step 3: The analysis department analyzes the response items based on the information organized by the organization department. The analysis is carried out using methods such as statistical analysis and trend analysis. For example, they analyze what kind of response was taken and when. Step 4: The visualization department visualizes the response items analyzed by the analysis department. Visualization is performed using methods such as graph display and dashboard display. For example, the response items are visualized in table format.

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

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0170] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an extraction unit that extracts conference information; a sorting unit that sorts the information extracted by the extraction unit; an analysis unit that analyzes correspondence matters based on the information organized by the organization unit; a visualization unit that visualizes the corresponding items analyzed by the analysis unit; Equipped with A system characterized by:

2. The visualization unit is Visualize response items in a table format 2. The system of claim 1.

3. The extraction unit Extracting meeting information from a calendar application 2. The system of claim 1.

4. The organizing unit Organize the time spent in meetings and the content of meetings for each project 2. The system of claim 1.

5. The analysis unit Analyze the timing and content of responses 2. The system of claim 1.

6. It has a supplement section where users can supplement their selling points.

2. The system of claim 1.

7. The extraction unit Adjusting the timing of extracting meeting information based on user emotions 2. The system of claim 1.

8. The extraction unit Extracting meeting information from data sources other than calendar applications 2. The system of claim 1.

9. The extraction unit When extracting meeting information, select the information to extract based on the importance and priority of the meeting.

2. The system of claim 1.

Images