System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system according to an embodiment includes a participant review unit, a material review unit, a facilitation unit, and an advice unit. The participant examination unit examines the participants of the conference, the necessity of the conference, and the purpose and goal. The material review unit reviews the material before the meeting and proposes an improvement. The facilitation unit performs facilitation during the conference. The advice unit provides advice in advance of a business meeting or a presentation.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 technology has the problem of meetings being unproductive and wasting time.

[0005] The system according to the embodiment aims to improve the productivity of meetings. [Means for solving the problem]

[0006] The system according to the embodiment comprises a participant review unit, a material review unit, a facilitation unit, and an advice unit. The participant review unit reviews the participants of the meeting, the necessity of the meeting, and the purpose and goals of the meeting. The material review unit reviews materials before the meeting and proposes improvements. The facilitation unit facilitates the meeting during the meeting. The advice unit provides advice before business meetings and presentations. [Effects of the Invention]

[0007] The system according to the embodiment can improve the productivity of meetings. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A meeting productivity improvement system according to an embodiment of the present invention utilizes generative AI to examine meeting participants, the necessity of the meeting, and its purpose and goals, examine pre-meeting materials to propose improvements, facilitate meetings during meetings, and provide advice before business meetings and presentations. As a result, the meeting productivity improvement system can improve meeting productivity.

[0029] A meeting productivity improvement system according to an embodiment includes a participant review unit, a document review unit, a facilitation unit, and an advice unit. The participant review unit reviews the participants, the necessity of the meeting, and the purpose and goals of the meeting. For example, the generation AI reviews the participant list to ensure that appropriate participants are selected. The generation AI can also review the necessity of the meeting and determine whether it is truly necessary. The generation AI can also review the purpose and goals of the meeting to ensure that they are clear and achievable. The document review unit reviews materials before the meeting and suggests improvements. For example, the generation AI reviews materials provided before the meeting to ensure that the content of the materials is easy to understand, contains necessary information, and is appropriately structured. The facilitation unit facilitates the meeting. For example, the generation AI encourages participants who do not speak during the meeting to speak, or encourages participants who talk too much to summarize their presentations. The generation AI can also provide advice to maintain an appropriate speaking speed during the meeting. The advice unit provides advice before business meetings and presentations. For example, generative AI analyzes the preferences and past business negotiation history of the target person, and suggests appropriate document creation and proposal methods. In this way, the meeting productivity improvement system can improve the productivity of meetings. For example, by clarifying the purpose of the meeting and selecting participants appropriately, the meeting will proceed smoothly. Also, by improving pre-meeting materials, participants will be able to grasp the necessary information in advance. Furthermore, facilitation during the meeting will prevent the meeting from becoming unproductive and will progress efficiently. Even in business meetings and presentations, advance advice will increase the success rate.

[0030] The participant screening unit can select the most suitable participants by analyzing the content of comments and the level of contribution in past meetings. For example, the participant screening unit uses a generative AI to analyze past meeting logs and score the content of comments and the level of contribution of each participant. For example, it selects the most suitable participants based on the frequency and quality of comments and the evaluations of other participants. This allows the selection of the most suitable participants to increase the effectiveness of the meeting.

[0031] The participant review unit can evaluate the effectiveness of a meeting by referring to the results and failure cases of similar past meetings. For example, the participant review unit uses generative AI to extract the results and failure cases of similar past meetings from a database and evaluate the effectiveness of the meeting. For example, it determines the necessity of a meeting based on the decisions made in past meetings and the subsequent results. In this way, the effectiveness of meetings can be improved by referring to past examples.

[0032] The material review unit can automatically optimize the visual design and layout of materials. For example, the material review unit uses generative AI to build a system that automatically optimizes the visual design and layout of meeting materials. For example, it adjusts font size and color usage to create materials that are visually easy to read. In this way, optimizing the visual design and layout of materials makes it easier for participants to understand the information.

[0033] When reviewing the contents of documents, the document review department automatically refers to the latest related research papers and industry reports, thereby improving the accuracy of the information. For example, the document review department uses generative AI to build a system that reviews the contents of meeting documents and automatically refers to the latest related research papers and industry reports. For example, the latest technology trends and market data are reflected in the documents. This allows the accuracy of the documents to be improved by referring to the latest information.

[0034] The facilitation department can analyze what is said during a meeting in real time and make suggestions to optimize the progress of the discussion. For example, the facilitation department can use generative AI to build a system that analyzes what is said during a meeting in real time and makes suggestions to optimize the progress of the discussion. For example, it can guide participants to focus on important topics. This makes it possible to analyze what is said during a meeting in real time and optimize the progress of the discussion, thereby increasing the effectiveness of the meeting.

[0035] The facilitation department can analyze the speaking speed and tone of speakers during meetings and suggest the optimal communication style. For example, the facilitation department could use generative AI to build a system that analyzes the speaking speed and tone of speakers during meetings in real time and suggests the optimal communication style. For example, if someone is speaking too fast, the system could encourage them to speak more slowly. This allows the effectiveness of meetings to be improved by analyzing the speaking speed and tone of speakers and suggesting the optimal communication style.

[0036] The Advice Department can analyze successful examples of past business negotiations and presentations and propose the optimal approach. For example, the Advice Department could use generative AI to extract successful examples of past business negotiations and presentations from a database and build a system that proposes the optimal approach. For example, it could refer to the structure and delivery style of successful presentations. This would allow the success rate of business negotiations and presentations to be increased by analyzing past successes and proposing the optimal approach.

[0037] The advice unit can analyze the past behavioral history and preferences of the target of a business meeting or presentation, and provide customized advice based on that. For example, the advice unit uses generative AI to build a system that analyzes the past behavioral history and preferences of the target of a business meeting or presentation, and provides customized advice based on that. For example, it makes suggestions tailored to the target's preferences. In this way, by analyzing the target's past behavioral history and preferences and providing customized advice, the success rate of business meetings and presentations can be increased.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The meeting productivity improvement system can also be equipped with a health management unit that monitors the health of participants. For example, the health management unit monitors participants' heart rates and stress levels in real time and adjusts the progress of the meeting based on their health status. This makes it possible to run a meeting while taking into account the health status of participants, thereby improving meeting productivity.

[0040] The meeting productivity improvement system can also be equipped with a skills analysis unit that analyzes the skill sets of participants. For example, the skills analysis unit can analyze participants' past projects and work history to select participants with the skills best suited to the meeting topic. This brings together participants with the best skills for the meeting topic, thereby increasing the effectiveness of the meeting.

[0041] The meeting productivity improvement system can further include a progress automation unit that automates the progress of the meeting. For example, the progress automation unit automatically progresses the agenda based on the meeting agenda and manages time, which allows the meeting to proceed smoothly and reduces time waste.

[0042] The meeting productivity improvement system can also include a recording and summarization unit that automatically records and summarizes the contents of the meeting. For example, the recording and summarization unit converts what is said during the meeting into text in real time and summarizes the important points. This allows the contents of the meeting to be recorded efficiently and made easy to refer to later.

[0043] The meeting productivity improvement system can also be equipped with a translation unit that automatically translates the contents of the meeting. For example, the translation unit can translate what is said during the meeting into multiple languages ​​in real time, supporting smooth communication even in international meetings. This facilitates communication between participants who speak different languages ​​and increases the effectiveness of the meeting.

[0044] The meeting productivity improvement system can also be equipped with an evaluation unit that automatically evaluates the content of the meeting. For example, the evaluation unit analyzes the progress of the meeting and the content of participants' comments, and scores the effectiveness of the meeting. This allows the effectiveness of the meeting to be objectively evaluated and areas for improvement to be identified.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The participant review unit reviews the participants, the necessity of the meeting, and the purpose and goals of the meeting. For example, the generation AI reviews the list of meeting participants to ensure that appropriate participants have been selected. The generation AI can also review the necessity of the meeting to determine whether the meeting is truly necessary. Furthermore, the generation AI can review the purpose and goals of the meeting to ensure that they are clear and achievable. Step 2: The material review department reviews materials before the meeting and proposes improvements. For example, the generation AI reviews materials provided before the meeting to check whether the content is easy to understand, whether it contains the necessary information, and whether the material is structured appropriately. Step 3: The Facilitation Department facilitates the meeting. For example, the Generation AI encourages participants who do not speak during the meeting to speak up, or encourages participants who talk too much to summarize their talk. The Generation AI can also provide advice on maintaining an appropriate speaking speed during the meeting. Step 4: The Advice Department provides advice before business meetings and presentations. For example, the Generative AI analyzes the target person's preferences and past business negotiation history, and suggests appropriate materials and presentation methods.

[0047] (Example 2) A meeting productivity improvement system according to an embodiment of the present invention utilizes generative AI to examine meeting participants, the necessity of the meeting, and its purpose and goals, examine pre-meeting materials to propose improvements, facilitate meetings during meetings, and provide advice before business meetings and presentations. As a result, the meeting productivity improvement system can improve meeting productivity.

[0048] A meeting productivity improvement system according to an embodiment includes a participant review unit, a document review unit, a facilitation unit, and an advice unit. The participant review unit reviews the participants, the necessity of the meeting, and the purpose and goals of the meeting. For example, the generation AI reviews the participant list to ensure that appropriate participants are selected. The generation AI can also review the necessity of the meeting and determine whether it is truly necessary. The generation AI can also review the purpose and goals of the meeting to ensure that they are clear and achievable. The document review unit reviews materials before the meeting and suggests improvements. For example, the generation AI reviews materials provided before the meeting to ensure that the content of the materials is easy to understand, contains necessary information, and is appropriately structured. The facilitation unit facilitates the meeting. For example, the generation AI encourages participants who do not speak during the meeting to speak, or encourages participants who talk too much to summarize their presentations. The generation AI can also provide advice to maintain an appropriate speaking speed during the meeting. The advice unit provides advice before business meetings and presentations. For example, generative AI analyzes the preferences and past business negotiation history of the target person, and suggests appropriate document creation and proposal methods. In this way, the meeting productivity improvement system can improve the productivity of meetings. For example, by clarifying the purpose of the meeting and selecting participants appropriately, the meeting will proceed smoothly. Also, by improving pre-meeting materials, participants will be able to grasp the necessary information in advance. Furthermore, facilitation during the meeting will prevent the meeting from becoming unproductive and will progress efficiently. Even in business meetings and presentations, advance advice will increase the success rate.

[0049] The participant screening unit can select the most suitable participants by analyzing the content of comments and the level of contribution in past meetings. For example, the participant screening unit uses a generative AI to analyze past meeting logs and score the content of comments and the level of contribution of each participant. For example, it selects the most suitable participants based on the frequency and quality of comments and the evaluations of other participants. This allows the selection of the most suitable participants to increase the effectiveness of the meeting.

[0050] The participant review unit can evaluate the effectiveness of a meeting by referring to the results and failure cases of similar past meetings. For example, the participant review unit uses generative AI to extract the results and failure cases of similar past meetings from a database and evaluate the effectiveness of the meeting. For example, it determines the necessity of a meeting based on the decisions made in past meetings and the subsequent results. In this way, the effectiveness of meetings can be improved by referring to past examples.

[0051] The participant review unit can use the emotion estimation function to analyze the emotional state of the participants and schedule the meeting at the optimal timing. For example, the participant review unit can use the emotion estimation function to analyze the participants' past emotional data and propose the optimal timing for the meeting. For example, it can select a time period when stress is low. This allows the effectiveness of the meeting to be improved by scheduling the meeting taking into account the emotional state of the participants.

[0052] The material review unit can automatically optimize the visual design and layout of materials. For example, the material review unit uses generative AI to build a system that automatically optimizes the visual design and layout of meeting materials. For example, it adjusts font size and color usage to create materials that are visually easy to read. In this way, optimizing the visual design and layout of materials makes it easier for participants to understand the information.

[0053] When reviewing the contents of documents, the document review department automatically refers to the latest related research papers and industry reports, thereby improving the accuracy of the information. For example, the document review department uses generative AI to build a system that reviews the contents of meeting documents and automatically refers to the latest related research papers and industry reports. For example, the latest technology trends and market data are reflected in the documents. This allows the accuracy of the documents to be improved by referring to the latest information.

[0054] The material review unit can use the emotion estimation function to analyze the emotional impact that the content of the material has on participants and improve it to elicit a positive response. The material review unit, for example, uses the emotion estimation function to analyze the emotional impact that the content of the meeting material has on participants and improve it to elicit a positive response. For example, it incorporates positive expressions and designs. This allows the effectiveness of the meeting to be improved by analyzing the emotional impact that the content of the material has on participants and eliciting a positive response.

[0055] The facilitation department can analyze what is said during a meeting in real time and make suggestions to optimize the progress of the discussion. For example, the facilitation department can use generative AI to build a system that analyzes what is said during a meeting in real time and makes suggestions to optimize the progress of the discussion. For example, it can guide participants to focus on important topics. This makes it possible to analyze what is said during a meeting in real time and optimize the progress of the discussion, thereby increasing the effectiveness of the meeting.

[0056] The facilitation department can analyze the speaking speed and tone of speakers during meetings and suggest the optimal communication style. For example, the facilitation department could use generative AI to build a system that analyzes the speaking speed and tone of speakers during meetings in real time and suggests the optimal communication style. For example, if someone is speaking too fast, the system could encourage them to speak more slowly. This allows the effectiveness of meetings to be improved by analyzing the speaking speed and tone of speakers and suggesting the optimal communication style.

[0057] The facilitation unit can use the emotion estimation function to monitor the emotional states of participants during a meeting in real time and intervene at the appropriate time. The facilitation unit, for example, uses the emotion estimation function to build a system that monitors the emotional states of participants during a meeting in real time and intervenes at the appropriate time. For example, if a participant is feeling stressed, the system suggests that they relax. This makes it possible to monitor the emotional states of participants in real time and intervene at the appropriate time, thereby improving the effectiveness of the meeting.

[0058] The Advice Department can analyze successful examples of past business negotiations and presentations and propose the optimal approach. For example, the Advice Department could use generative AI to extract successful examples of past business negotiations and presentations from a database and build a system that proposes the optimal approach. For example, it could refer to the structure and delivery style of successful presentations. This would allow the success rate of business negotiations and presentations to be increased by analyzing past successes and proposing the optimal approach.

[0059] The advice unit can analyze the past behavioral history and preferences of the target of a business meeting or presentation, and provide customized advice based on that. For example, the advice unit uses generative AI to build a system that analyzes the past behavioral history and preferences of the target of a business meeting or presentation, and provides customized advice based on that. For example, it makes suggestions tailored to the target's preferences. In this way, by analyzing the target's past behavioral history and preferences and providing customized advice, the success rate of business meetings and presentations can be increased.

[0060] The advice unit uses the emotion estimation function to analyze the emotional state of the target person in advance and conduct business negotiations or presentations at the optimal timing. The advice unit, for example, uses the emotion estimation function to analyze the emotional state of the target person in advance for business negotiations or presentations and build a system to conduct business negotiations or presentations at the optimal timing. For example, the advice unit selects a time period when the target person is relaxed. This allows the target person's emotional state to be analyzed in advance and the business negotiations or presentations to be conducted at the optimal timing, thereby increasing the success rate.

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

[0062] The meeting productivity improvement system can also be equipped with a health management unit that monitors the health of participants. For example, the health management unit monitors participants' heart rates and stress levels in real time and adjusts the progress of the meeting based on their health status. This makes it possible to run a meeting while taking into account the health status of participants, thereby improving meeting productivity.

[0063] The meeting productivity improvement system can also be equipped with a skills analysis unit that analyzes the skill sets of participants. For example, the skills analysis unit can analyze participants' past projects and work history to select participants with the skills best suited to the meeting topic. This brings together participants with the best skills for the meeting topic, thereby increasing the effectiveness of the meeting.

[0064] The meeting productivity improvement system can also include an emotion adjustment unit that estimates the emotions of participants and adjusts the progress of the meeting based on the estimated emotions. For example, the emotion adjustment unit may suggest a break if a participant is tired, or suggest an activity to relax if a participant is tense. This makes it possible to manage a meeting while taking into account the emotional state of the participants, thereby improving the effectiveness of the meeting.

[0065] The meeting productivity improvement system can further include a progress automation unit that automates the progress of the meeting. For example, the progress automation unit automatically progresses the agenda based on the meeting agenda and manages time, which allows the meeting to proceed smoothly and reduces time waste.

[0066] The meeting productivity improvement system can also include a material emotion adjustment unit that estimates the emotions of participants and adjusts the content of materials based on the estimated emotions. For example, the material emotion adjustment unit enhances the effectiveness of materials by incorporating content and expressions that are likely to interest participants. This makes it possible to create materials that take into account the emotional state of participants, thereby improving the effectiveness of meetings.

[0067] The meeting productivity improvement system can also include a recording and summarization unit that automatically records and summarizes the contents of the meeting. For example, the recording and summarization unit converts what is said during the meeting into text in real time and summarizes the important points. This allows the contents of the meeting to be recorded efficiently and made easy to refer to later.

[0068] The meeting productivity improvement system can also include an agenda adjustment unit that estimates the emotions of participants and adjusts the meeting agenda based on the estimated emotions. For example, the agenda adjustment unit adjusts the agenda according to the emotional state of a participant, such as postponing important topics if the participant is tired. This makes it possible to manage a meeting while taking into account the emotional state of the participants, thereby improving the effectiveness of the meeting.

[0069] The meeting productivity improvement system can also be equipped with a translation unit that automatically translates the contents of the meeting. For example, the translation unit can translate what is said during the meeting into multiple languages ​​in real time, supporting smooth communication even in international meetings. This facilitates communication between participants who speak different languages ​​and increases the effectiveness of the meeting.

[0070] The meeting productivity improvement system can also include an end adjustment unit that estimates the emotions of participants and adjusts the end timing of the meeting based on the estimated emotions. For example, the end adjustment unit may suggest ending the meeting earlier if a participant is tired. This allows the meeting to be run taking into account the emotional state of the participants, thereby improving the effectiveness of the meeting.

[0071] The meeting productivity improvement system can also be equipped with an evaluation unit that automatically evaluates the content of the meeting. For example, the evaluation unit analyzes the progress of the meeting and the content of participants' comments, and scores the effectiveness of the meeting. This allows the effectiveness of the meeting to be objectively evaluated and areas for improvement to be identified.

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

[0073] Step 1: The participant review unit reviews the participants, the necessity of the meeting, and the purpose and goals of the meeting. For example, the generation AI reviews the list of meeting participants to ensure that appropriate participants have been selected. The generation AI can also review the necessity of the meeting to determine whether the meeting is truly necessary. Furthermore, the generation AI can review the purpose and goals of the meeting to ensure that they are clear and achievable. Step 2: The material review department reviews materials before the meeting and proposes improvements. For example, the generation AI reviews materials provided before the meeting to check whether the content is easy to understand, whether it contains the necessary information, and whether the material is structured appropriately. Step 3: The Facilitation Department facilitates the meeting. For example, the Generation AI encourages participants who do not speak during the meeting to speak up, or encourages participants who talk too much to summarize their talk. The Generation AI can also provide advice on maintaining an appropriate speaking speed during the meeting. Step 4: The Advice Department provides advice before business meetings and presentations. For example, the Generative AI analyzes the target person's preferences and past business negotiation history, and suggests appropriate materials and presentation methods.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. [Explanation of symbols]

[0141] 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. The Participant Review Department reviews the participants, the necessity of the meeting, and the purpose and goals of the meeting. The Materials Review Department reviews materials before meetings and proposes improvements. The facilitation department facilitates meetings, An advice department that provides advice before business meetings and presentations. A system characterized by:

2. The participant vetting unit Analyze past meetings and contributions to select the best participants 2. The system of claim 1.

3. The material review unit Automatically optimize the visual design and layout of said materials 2. The system of claim 1.

4. The facilitation department Analyzes what is said during the meeting in real time and makes suggestions to optimize the progress of the discussion 2. The system of claim 1.

5. The advice unit Analyze past business negotiations and successful presentations to propose the best approach 2. The system of claim 1.

6. The participant vetting unit Analyzing the emotional state of the participants and scheduling the meeting at the optimal time 2. The system of claim 1.

7. The material review unit Analyze the emotional impact of the content of the materials on the participants and refine them to elicit a positive response 2. The system of claim 1.

8. The facilitation department Monitoring the emotional state of the participants in the meeting in real time and intervening at the appropriate time 2. The system of claim 1.

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