System

Abstract

An object of the system according to the embodiment is to efficiently perform the progress and wrap-up of a conference and to effectively utilize past discussions.SOLUTION: A system according to an embodiment includes a conference progress support unit, a wrap-up unit, and a past wrap-up reference unit. The conference progress support unit supports progress of a conference. The wrap-up unit performs wrap-up of the conference. The past wrap-up reference unit refers to a past wrap-up.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 meetings cannot be conducted efficiently and wrap-ups cannot be carried out efficiently, making it difficult to effectively utilize past discussions.

[0005] The system according to the embodiment aims to efficiently proceed with a meeting and wrap up the meeting, and to effectively utilize past discussions. [Means for solving the problem]

[0006] The system according to the embodiment includes a conference progress support unit, a wrap-up unit, and a past wrap-up reference unit. The conference progress support unit supports the progress of the conference. The wrap-up unit performs wrap-up of the conference. The past wrap-up reference unit references past wrap-ups. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently conduct meetings and wrap-ups, and can effectively utilize past discussions. [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) In the meeting support AI system according to an embodiment of the present invention, an AI assistant called Aimy cooperates with the organizer to conduct the meeting, raises issues and proposes solutions according to the status of the discussion. This enables the meeting support AI system to improve the efficiency and progress of the meeting.

[0029] A meeting support AI system according to an embodiment includes a meeting progress support unit, a wrap-up unit, and a past wrap-up reference unit. The meeting progress support unit supports the progress of a meeting. For example, the meeting progress support unit checks the agenda at the start of a meeting and raises issues at an appropriate time depending on the progress of the discussion. Furthermore, the meeting progress support unit proposes specific countermeasures when the discussion is progressing. For example, when the discussion is stalled, the meeting progress support unit raises an issue by saying, "Let's think of a specific solution to this problem." Furthermore, when the discussion is progressing, the meeting progress support unit proposes countermeasures by saying, "Let's think of specific steps for this solution." The wrap-up unit wraps up the meeting. For example, the wrap-up unit predicts the effects of the items to be discussed and creates to-do lists for the next meeting. For example, the wrap-up unit predicts the effects by saying, "By implementing this solution, we expect sales to increase by 10%." Furthermore, the wrap-up unit creates to-do lists by saying, "Each person in charge will create a specific action plan by the next meeting," and sends them to participants. The past wrap-up reference unit references past wrap-ups. For example, the past wrap-up reference unit checks the progress of the discussion based on past wrap-ups and adjusts the agenda as necessary. For example, the discussion can proceed in a format such as, "Let's check the progress on the solutions proposed in the previous meeting." This allows the meeting support AI system according to the embodiment to achieve efficient and effective progress of the meeting. For example, the discussion can proceed smoothly without stalling, and specific solutions and action plans can be clarified. Furthermore, progress based on past discussions allows for consistent meetings. This makes it possible to solve problems in corporate meetings and support effective decision-making.

[0030] The meeting progress support unit can raise issues and propose countermeasures depending on the progress of the discussion. For example, during a meeting, the generation AI performs text analysis of participants' comments in real time to evaluate the frequency and content of comments. For example, if a particular participant is speaking too much, the generation AI will suggest giving other participants an opportunity to speak as well. The meeting progress support unit also analyzes the content of comments in real time and encourages comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, the generation AI will suggest seeking business opinions. The meeting progress support unit also adjusts the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, the generation AI will suggest seeking opinions from other perspectives. This prevents the discussion from stagnating and proposes effective solutions.

[0031] The wrap-up section can predict the effectiveness of the items under discussion and create a to-do list for the next meeting. For example, the wrap-up section uses the generation AI to refer to participants' past speech history and revisit ideas and opinions previously proposed by specific participants to deepen the discussion. For example, the generation AI analyzes participants' past speech history and revisits specific ideas and opinions. For example, an idea proposed in the previous meeting but left unresolved is brought back for discussion. The wrap-up section also uses the generation AI to revisit the opinions of specific participants based on their past speech history. For example, if a previously proposed solution is relevant to the current discussion, it suggests that the solution be reconsidered. The wrap-up section also uses the generation AI to refer to past speech history to support the progress of the discussion. For example, it revisits ideas proposed in the past but not implemented to deepen the discussion. This clarifies the results of the meeting and streamlines preparations for the next meeting.

[0032] The past wrap-up reference unit can proceed with the meeting based on past wrap-ups. For example, the past wrap-up reference unit uses an emotion estimation function to grasp the emotional state of participants in real time and make suggestions to maintain a positive emotional atmosphere. For example, the emotion estimation function is used to analyze participants' facial expressions and vocal tones to grasp their emotional state in real time. For example, if negative emotions are strong, it suggests taking a break to refresh. The past wrap-up reference unit also makes suggestions to maintain a positive atmosphere based on the emotion estimation data from the generation AI. For example, if the discussion is getting too heated, it suggests switching to a more relaxed topic. The past wrap-up reference unit also uses the emotion estimation function to monitor the emotional state of participants in real time and make suggestions to elicit positive emotions. For example, it displays compliments or encouraging messages. This enables the meeting to proceed consistently based on past discussions.

[0033] The meeting progress support unit can analyze participants' comments in real time and make suggestions to adjust the balance of the discussion based on the frequency or content of their comments. For example, the meeting progress support unit uses a generation AI to perform real-time text analysis of participants' comments during a meeting and evaluate the frequency and content of their comments. For example, if a particular participant is speaking too much, the unit can suggest giving other participants an opportunity to speak as well. The meeting progress support unit also analyzes the content of comments in real time and encourages comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, the unit can suggest seeking business opinions. The meeting progress support unit also adjusts the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, the unit can suggest seeking opinions from other perspectives. This helps maintain balance in the discussion and reflect everyone's opinions.

[0034] The meeting progress support unit can deepen the discussion by referring to the participants' past speech history and revisiting ideas or opinions that specific participants have proposed in the past. In the meeting progress support unit, for example, the generation AI analyzes the participants' past speech history and revisits specific ideas or opinions. For example, an idea that was proposed in a previous meeting but remains unresolved can be brought back for discussion. In addition, the meeting progress support unit uses the generation AI to revisit the opinions of specific participants based on the past speech history. For example, if a previously proposed solution is relevant to the current discussion, it will suggest that that solution be reconsidered. In addition, the meeting progress support unit uses the generation AI to refer to the past speech history and support the progress of the discussion. For example, it will revisit ideas that were proposed in the past but not implemented, and deepen the discussion. This makes it possible to deepen the discussion by revisiting past ideas and opinions.

[0035] The meeting progress support unit can refer to an external database and provide the latest information related to the discussion. For example, the generation AI refers to an external database (e.g., a patent database or market data) while a meeting is in progress to provide the latest information related to the discussion. For example, the generation AI refers to a patent database while a meeting is in progress to provide the latest patent information related to the discussion. For example, it displays patent information on new technologies and competitors in real time. The meeting progress support unit also refers to market data to provide the latest market trends and information related to the discussion. For example, it displays the market share and growth rate of a particular product or service. The meeting progress support unit also refers to an external database to provide the latest research papers and expert opinions related to the discussion. For example, it displays the latest research results and expert comments in real time. This allows the quality of discussions to be improved by providing the latest information.

[0036] The meeting progress support unit can learn meeting progress methods from different industries and make progress proposals that incorporate best practices. For example, the generation AI in the meeting progress support unit learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, the generation AI learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, a progress proposal that incorporates agile methods from the IT industry is made. The meeting progress support unit also learns meeting progress methods from different industries and reflects them in its progress proposals. For example, a progress proposal that incorporates quality control methods from the manufacturing industry is made. The generation AI in the meeting progress support unit also learns best practices from different industries and applies them to its progress proposals. For example, a progress proposal that incorporates team collaboration methods from the medical industry is made. This makes it possible to optimize the progress of meetings by incorporating best practices from different industries.

[0037] The wrap-up section can perform a detailed analysis of the content of meeting discussions and quantitatively evaluate the risks and benefits of each proposal. For example, the generation AI in the wrap-up section performs text analysis of the content of meeting discussions and quantitatively evaluates the risks and benefits of each proposal. For example, it calculates a risk score and a benefit score and presents them to participants. The generation AI in the wrap-up section also evaluates the risks and benefits of each proposal based on the content of meeting discussions. For example, it refers to past data and case studies to quantify the risks and benefits. The generation AI in the wrap-up section also analyzes the content of meeting discussions in detail and quantitatively evaluates the risks and benefits of each proposal. For example, it extracts and scores risk and benefit factors. This quantitatively evaluates the risks and benefits of proposals, thereby improving the quality of decision-making.

[0038] The wrap-up section can refer to similar past cases and predict effects based on success or failure cases. In the wrap-up section, for example, the generation AI refers to similar past cases and predicts effects based on success or failure cases. For example, it predicts the probability of success of a proposal based on past success cases. Furthermore, during the wrap-up of a meeting, the generation AI refers to past failure cases and identifies risk factors. For example, it evaluates the risks of a proposal based on past failure cases. Furthermore, the wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, it quantifies the effects of a proposal based on past data. In this way, predicting effects based on past cases can increase the feasibility of a proposal.

[0039] The wrap-up section can provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, the generation AI can provide the meeting wrap-up content in visual note format to make it easier to understand visually. For example, important points can be shown using diagrams and icons. The wrap-up section can also provide the meeting wrap-up content in infographic format to make it easier to understand visually. For example, data and statistical information can be displayed in graphs and charts. The wrap-up section can also provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, visual notes and infographics can be automatically generated. This allows the content of the meeting to be communicated effectively by providing it in a format that is easy to understand visually.

[0040] The wrap-up unit can automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, the wrap-up unit uses a generative AI to automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, it translates into multiple languages ​​such as English, French, and Chinese. The wrap-up unit can also post the meeting wrap-up content on a multilingual platform so that it can be shared with international teams. For example, it shares the translated wrap-up content online. The wrap-up unit can also use a generative AI to automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, it sends the translated wrap-up content by email. This allows the meeting content to be shared with international teams by translating it into different languages.

[0041] The past wrap-up reference section can analyze the contents of past wrap-ups in detail and identify unresolved issues or newly emerged issues. In the past wrap-up reference section, for example, the generation AI performs text analysis of past wrap-up contents to identify unresolved issues. For example, it lists issues that were proposed in the previous meeting but remain unresolved. In addition, the generation AI identifies newly emerged issues based on the contents of past wrap-ups. For example, it extracts problems that have arisen since the previous meeting. In addition, the generation AI analyzes the contents of past wrap-ups in detail and identifies unresolved issues or newly emerged issues. For example, it suggests that unresolved issues be discussed first. This allows for the identification of unresolved issues or newly emerged issues, making it possible to effectively advance discussions at the next meeting.

[0042] The past wrap-up reference unit automatically generates the agenda for the next meeting based on the content of past wrap-ups, supporting efficient meeting progress. In the past wrap-up reference unit, for example, the generation AI analyzes the content of past wrap-ups and automatically generates the agenda for the next meeting. For example, unresolved issues and newly emerged problems are included in the agenda. In addition, in the past wrap-up reference unit, the generation AI creates the agenda for the next meeting based on the content of past wrap-ups. For example, it includes an item to check the progress of the action plan proposed in the previous meeting. In addition, in the past wrap-up reference unit, the generation AI automatically generates the agenda for the next meeting based on the content of past wrap-ups, supporting efficient meeting progress. For example, it prioritizes important topics and includes them in the agenda. In this way, the automatic generation of the agenda for the next meeting can support efficient meeting progress.

[0043] The past wrap-up reference unit can make suggestions to promote knowledge sharing between different projects or teams based on the content of past wrap-ups. In the past wrap-up reference unit, for example, the generation AI analyzes the content of past wrap-ups and makes suggestions to promote knowledge sharing between different projects or teams. For example, it suggests information sharing between related projects. Furthermore, the generation AI in the past wrap-up reference unit makes suggestions to promote knowledge sharing between different teams based on the content of past wrap-ups. For example, it suggests discussions between teams that have common challenges. Furthermore, the generation AI in the past wrap-up reference unit makes suggestions to promote knowledge sharing between different projects or teams based on the content of past wrap-ups. For example, it provides a forum for sharing success stories and failure stories. This promotes knowledge sharing between different projects and teams, thereby improving the utilization of knowledge throughout the organization.

[0044] The past wrap-up reference unit can suggest related external resources based on the content of past wrap-ups. In this case, for example, the generation AI analyzes the content of past wrap-ups and suggests related external resources. For example, it refers to expert opinions or research papers. Also, in the past wrap-up reference unit, the generation AI suggests related external resources based on the content of past wrap-ups. For example, it provides expert opinions on specific issues. Also, in the past wrap-up reference unit, the generation AI suggests related external resources based on the content of past wrap-ups. For example, it provides the latest research papers or expert comments. This makes it possible to improve the quality of discussions by suggesting related external resources.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The meeting progress support unit analyzes participants' comments in real time and can make suggestions to adjust the balance of the discussion based on the frequency or content of their comments. For example, if a particular participant is speaking too much, it will suggest giving other participants an opportunity to speak. The meeting progress support unit also uses a generation AI to analyze the content of comments in real time and encourage comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, it will suggest seeking business opinions. The meeting progress support unit also uses a generation AI to adjust the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, it will suggest seeking opinions from other perspectives. This prevents the discussion from stagnating and proposes effective solutions.

[0047] The wrap-up section can predict the effectiveness of items to be discussed and create to-do lists for the next meeting. For example, the generation AI can refer to participants' past comment history and revisit ideas and opinions previously proposed by specific participants to deepen the discussion. For example, the generation AI can analyze participants' past comment history and revisit specific ideas and opinions. For example, an idea proposed in the previous meeting but left unresolved can be brought back for discussion. The wrap-up section also uses past comment history to revisit the opinions of specific participants. For example, if a previously proposed solution is relevant to the current discussion, it can suggest that that solution be reconsidered. The wrap-up section also supports the progress of the discussion by referring to past comment history. For example, it can revisit ideas proposed in the past but not implemented to deepen the discussion. This clarifies the results of the meeting and streamlines preparations for the next meeting.

[0048] The meeting progress support unit can refer to an external database and provide the latest information related to the discussion. For example, the generation AI may refer to an external database (e.g., a patent database or market data) while a meeting is in progress and provide the latest information related to the discussion. For example, the generation AI may refer to a patent database while a meeting is in progress and provide the latest patent information related to the discussion. For example, it may display patent information on new technologies or competitors in real time. The meeting progress support unit may also refer to market data and provide the latest market trends and information related to the discussion. For example, it may display the market share and growth rate of a particular product or service. The meeting progress support unit may also refer to an external database and provide the latest research papers and expert opinions related to the discussion. For example, it may display the latest research results and expert comments in real time. This can improve the quality of discussions by providing the latest information.

[0049] The meeting progress support unit can learn meeting progress methods from different industries and make progress proposals that incorporate best practices. For example, the generation AI learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, it makes progress proposals that incorporate agile methods from the IT industry. The meeting progress support unit also learns meeting progress methods from different industries and reflects them in its progress proposals. For example, it makes progress proposals that incorporate quality control methods from the manufacturing industry. The meeting progress support unit also learns best practices from different industries and applies them to its progress proposals. For example, it makes progress proposals that incorporate team collaboration methods from the medical industry. This makes it possible to optimize the progress of meetings by incorporating best practices from different industries.

[0050] The wrap-up section can perform a detailed analysis of the content of meeting discussions and quantitatively evaluate the risks and benefits of each proposal. For example, the generation AI analyzes the text of the meeting discussions and quantitatively evaluates the risks and benefits of each proposal. For example, it calculates a risk score and a benefit score and presents them to participants. The wrap-up section also evaluates the risks and benefits of each proposal based on the content of the meeting discussions. For example, it refers to past data and case studies to quantify the risks and benefits. The generation AI also analyzes the content of the meeting discussions in detail and quantitatively evaluates the risks and benefits of each proposal. For example, it extracts and scores risk and benefit factors. This quantitatively evaluates the risks and benefits of proposals, thereby improving the quality of decision-making.

[0051] The wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, the generation AI refers to similar past cases and predicts effects based on success or failure cases. For example, it predicts the probability of success of a proposal based on past success cases. Furthermore, when wrapping up a meeting, the generation AI refers to past failure cases and identifies risk factors. For example, it evaluates the risks of a proposal based on past failure cases. Furthermore, the wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, it quantifies the effects of a proposal based on past data. In this way, by predicting effects based on past cases, it is possible to increase the feasibility of a proposal.

[0052] The wrap-up section can provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, the generation AI can provide the meeting wrap-up content in visual note format to make it easier to understand visually. For example, important points can be shown using diagrams and icons. The wrap-up section can also provide the meeting wrap-up content in infographic format to make it easier to understand visually. For example, data and statistical information can be displayed in graphs and charts. The wrap-up section can also provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, visual notes and infographics can be automatically generated. This allows the content of the meeting to be communicated effectively by providing it in a format that is easy to understand visually.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The Meeting Progress Support Department supports the progress of the meeting. Specifically, it checks the agenda at the start of the meeting and raises issues at appropriate times depending on the progress of the discussion. For example, if the discussion is stagnating, it raises an issue by saying, "Let's think of a specific solution to this problem," and if the discussion is progressing, it proposes countermeasures by saying, "Let's think of specific steps to resolve this problem." Step 2: The wrap-up department wraps up the meeting. Specifically, they predict the effects of the items discussed and create to-do lists for the next meeting. For example, they predict the effects in the form of "By implementing this solution, we expect sales to increase by 10%," and create to-do lists in the form of "Each person in charge will create a specific action plan by the next meeting," and send these to the participants. Step 3: The Past Wrap-Up Reference section refers to past wrap-ups. Specifically, the progress of the discussion is checked based on past wrap-ups, and the agenda is adjusted as necessary. For example, the discussion can proceed in the form of, "Let's check the progress on the solutions proposed in the last meeting."

[0055] (Example 2) In the meeting support AI system according to an embodiment of the present invention, an AI assistant called Aimy cooperates with the organizer to conduct the meeting, raises issues and proposes solutions according to the status of the discussion. This enables the meeting support AI system to improve the efficiency and progress of the meeting.

[0056] A meeting support AI system according to an embodiment includes a meeting progress support unit, a wrap-up unit, and a past wrap-up reference unit. The meeting progress support unit supports the progress of a meeting. For example, the meeting progress support unit checks the agenda at the start of a meeting and raises issues at an appropriate time depending on the progress of the discussion. Furthermore, the meeting progress support unit proposes specific countermeasures when the discussion is progressing. For example, when the discussion is stalled, the meeting progress support unit raises an issue by saying, "Let's think of a specific solution to this problem." Furthermore, when the discussion is progressing, the meeting progress support unit proposes countermeasures by saying, "Let's think of specific steps for this solution." The wrap-up unit wraps up the meeting. For example, the wrap-up unit predicts the effects of the items to be discussed and creates to-do lists for the next meeting. For example, the wrap-up unit predicts the effects by saying, "By implementing this solution, we expect sales to increase by 10%." Furthermore, the wrap-up unit creates to-do lists by saying, "Each person in charge will create a specific action plan by the next meeting," and sends them to participants. The past wrap-up reference unit references past wrap-ups. For example, the past wrap-up reference unit checks the progress of the discussion based on past wrap-ups and adjusts the agenda as necessary. For example, the discussion can proceed in a format such as, "Let's check the progress on the solutions proposed in the previous meeting." This allows the meeting support AI system according to the embodiment to achieve efficient and effective progress of the meeting. For example, the discussion can proceed smoothly without stalling, and specific solutions and action plans can be clarified. Furthermore, progress based on past discussions allows for consistent meetings. This makes it possible to solve problems in corporate meetings and support effective decision-making.

[0057] The meeting progress support unit can raise issues and propose countermeasures depending on the progress of the discussion. For example, during a meeting, the generation AI performs text analysis of participants' comments in real time to evaluate the frequency and content of comments. For example, if a particular participant is speaking too much, the generation AI will suggest giving other participants an opportunity to speak as well. The meeting progress support unit also analyzes the content of comments in real time and encourages comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, the generation AI will suggest seeking business opinions. The meeting progress support unit also adjusts the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, the generation AI will suggest seeking opinions from other perspectives. This prevents the discussion from stagnating and proposes effective solutions.

[0058] The wrap-up section can predict the effectiveness of the items under discussion and create a to-do list for the next meeting. For example, the wrap-up section uses the generation AI to refer to participants' past speech history and revisit ideas and opinions previously proposed by specific participants to deepen the discussion. For example, the generation AI analyzes participants' past speech history and revisits specific ideas and opinions. For example, an idea proposed in the previous meeting but left unresolved is brought back for discussion. The wrap-up section also uses the generation AI to revisit the opinions of specific participants based on their past speech history. For example, if a previously proposed solution is relevant to the current discussion, it suggests that the solution be reconsidered. The wrap-up section also uses the generation AI to refer to past speech history to support the progress of the discussion. For example, it revisits ideas proposed in the past but not implemented to deepen the discussion. This clarifies the results of the meeting and streamlines preparations for the next meeting.

[0059] The past wrap-up reference unit can proceed with the meeting based on past wrap-ups. For example, the past wrap-up reference unit uses an emotion estimation function to grasp the emotional state of participants in real time and make suggestions to maintain a positive emotional atmosphere. For example, the emotion estimation function is used to analyze participants' facial expressions and vocal tones to grasp their emotional state in real time. For example, if negative emotions are strong, it suggests taking a break to refresh. The past wrap-up reference unit also makes suggestions to maintain a positive atmosphere based on the emotion estimation data from the generation AI. For example, if the discussion is getting too heated, it suggests switching to a more relaxed topic. The past wrap-up reference unit also uses the emotion estimation function to monitor the emotional state of participants in real time and make suggestions to elicit positive emotions. For example, it displays compliments or encouraging messages. This enables the meeting to proceed consistently based on past discussions.

[0060] The meeting progress support unit can analyze participants' comments in real time and make suggestions to adjust the balance of the discussion based on the frequency or content of their comments. For example, the meeting progress support unit uses a generation AI to perform real-time text analysis of participants' comments during a meeting and evaluate the frequency and content of their comments. For example, if a particular participant is speaking too much, the unit can suggest giving other participants an opportunity to speak as well. The meeting progress support unit also analyzes the content of comments in real time and encourages comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, the unit can suggest seeking business opinions. The meeting progress support unit also adjusts the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, the unit can suggest seeking opinions from other perspectives. This helps maintain balance in the discussion and reflect everyone's opinions.

[0061] The meeting progress support unit can deepen the discussion by referring to the participants' past speech history and revisiting ideas or opinions that specific participants have proposed in the past. In the meeting progress support unit, for example, the generation AI analyzes the participants' past speech history and revisits specific ideas or opinions. For example, an idea that was proposed in a previous meeting but remains unresolved can be brought back for discussion. In addition, the meeting progress support unit uses the generation AI to revisit the opinions of specific participants based on the past speech history. For example, if a previously proposed solution is relevant to the current discussion, it will suggest that that solution be reconsidered. In addition, the meeting progress support unit uses the generation AI to refer to the past speech history and support the progress of the discussion. For example, it will revisit ideas that were proposed in the past but not implemented, and deepen the discussion. This makes it possible to deepen the discussion by revisiting past ideas and opinions.

[0062] The meeting progress support unit can use the emotion estimation function to grasp the emotional state of participants in real time and make suggestions to maintain an emotionally positive atmosphere. For example, the meeting progress support unit uses the emotion estimation function to analyze participants' facial expressions and vocal tones to grasp their emotional state in real time. For example, if negative emotions are strong, it can suggest a break to refresh. The meeting progress support unit also makes suggestions to maintain a positive atmosphere based on the emotion estimation data from the generation AI. For example, if a discussion is getting too heated, it can suggest switching to a more relaxed topic. The meeting progress support unit also uses the emotion estimation function to monitor the emotional state of participants in real time and make suggestions to bring out positive emotions. For example, it can display compliments or encouraging messages. This helps maintain an emotionally positive atmosphere and improves meeting productivity.

[0063] The meeting progress support unit can refer to an external database and provide the latest information related to the discussion. For example, the generation AI refers to an external database (e.g., a patent database or market data) while a meeting is in progress to provide the latest information related to the discussion. For example, the generation AI refers to a patent database while a meeting is in progress to provide the latest patent information related to the discussion. For example, it displays patent information on new technologies and competitors in real time. The meeting progress support unit also refers to market data to provide the latest market trends and information related to the discussion. For example, it displays the market share and growth rate of a particular product or service. The meeting progress support unit also refers to an external database to provide the latest research papers and expert opinions related to the discussion. For example, it displays the latest research results and expert comments in real time. This allows the quality of discussions to be improved by providing the latest information.

[0064] The meeting progress support unit can learn meeting progress methods from different industries and make progress proposals that incorporate best practices. For example, the generation AI in the meeting progress support unit learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, the generation AI learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, a progress proposal that incorporates agile methods from the IT industry is made. The meeting progress support unit also learns meeting progress methods from different industries and reflects them in its progress proposals. For example, a progress proposal that incorporates quality control methods from the manufacturing industry is made. The generation AI in the meeting progress support unit also learns best practices from different industries and applies them to its progress proposals. For example, a progress proposal that incorporates team collaboration methods from the medical industry is made. This makes it possible to optimize the progress of meetings by incorporating best practices from different industries.

[0065] The meeting progress support unit can use the emotion estimation function to suggest refreshment time or breakout sessions according to the emotional state of participants. For example, the meeting progress support unit uses the emotion estimation function to analyze the emotional state of participants in real time and suggest refreshment time. For example, if a participant is tired, it will suggest a short break. The meeting progress support unit also suggests breakout sessions based on the emotion estimation data from the generation AI. For example, if the discussion is stagnating, it will suggest dividing into small groups for discussion. The meeting progress support unit also uses the emotion estimation function to suggest refreshment time or breakout sessions according to the emotional state of participants. For example, if participants are feeling strongly negative, it will suggest refreshment time. In this way, by making suggestions according to the emotional state of participants, it is possible to improve the productivity of meetings.

[0066] The wrap-up section can perform a detailed analysis of the content of meeting discussions and quantitatively evaluate the risks and benefits of each proposal. For example, the generation AI in the wrap-up section performs text analysis of the content of meeting discussions and quantitatively evaluates the risks and benefits of each proposal. For example, it calculates a risk score and a benefit score and presents them to participants. The generation AI in the wrap-up section also evaluates the risks and benefits of each proposal based on the content of meeting discussions. For example, it refers to past data and case studies to quantify the risks and benefits. The generation AI in the wrap-up section also analyzes the content of meeting discussions in detail and quantitatively evaluates the risks and benefits of each proposal. For example, it extracts and scores risk and benefit factors. This quantitatively evaluates the risks and benefits of proposals, thereby improving the quality of decision-making.

[0067] The wrap-up section can refer to similar past cases and predict effects based on success or failure cases. In the wrap-up section, for example, the generation AI refers to similar past cases and predicts effects based on success or failure cases. For example, it predicts the probability of success of a proposal based on past success cases. Furthermore, during the wrap-up of a meeting, the generation AI refers to past failure cases and identifies risk factors. For example, it evaluates the risks of a proposal based on past failure cases. Furthermore, the wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, it quantifies the effects of a proposal based on past data. In this way, predicting effects based on past cases can increase the feasibility of a proposal.

[0068] The wrap-up section can use the emotion estimation function to analyze the emotional responses of participants and propose to-do items for the next meeting to elicit positive emotions. The wrap-up section, for example, uses the emotion estimation function to analyze the emotional responses of participants in real time and propose to-do items to elicit positive emotions. For example, a topic that evokes strong positive emotions can be set as the agenda for the next meeting. The wrap-up section also proposes to-do items for the next meeting based on the emotion estimation data from the generation AI. For example, a specific action plan can be created to elicit positive emotions. The wrap-up section also uses the emotion estimation function to analyze the emotional responses of participants and propose to-do items for the next meeting to elicit positive emotions. For example, a topic that evokes strong positive emotions can be set as the agenda for the next meeting. In this way, by proposing to-do items to elicit positive emotions, the productivity of the next meeting can be improved.

[0069] The wrap-up section can provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, the generation AI can provide the meeting wrap-up content in visual note format to make it easier to understand visually. For example, important points can be shown using diagrams and icons. The wrap-up section can also provide the meeting wrap-up content in infographic format to make it easier to understand visually. For example, data and statistical information can be displayed in graphs and charts. The wrap-up section can also provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, visual notes and infographics can be automatically generated. This allows the content of the meeting to be communicated effectively by providing it in a format that is easy to understand visually.

[0070] The wrap-up unit can automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, the wrap-up unit uses a generative AI to automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, it translates into multiple languages ​​such as English, French, and Chinese. The wrap-up unit can also post the meeting wrap-up content on a multilingual platform so that it can be shared with international teams. For example, it shares the translated wrap-up content online. The wrap-up unit can also use a generative AI to automatically translate the meeting wrap-up content into different languages ​​so that it can be shared with international teams. For example, it sends the translated wrap-up content by email. This allows the meeting content to be shared with international teams by translating it into different languages.

[0071] The wrap-up section can use the emotion estimation function to collect participants' emotional reactions to the wrap-up content and reflect them in the progress of the next meeting. The wrap-up section, for example, uses the emotion estimation function to collect participants' emotional reactions to the wrap-up content in real time and reflect them in the progress of the next meeting. For example, it sets topics that evoke strong positive emotions as the agenda for the next meeting. The wrap-up section also adjusts the progress of the next meeting based on the emotion estimation data from the generation AI. For example, it suggests avoiding topics that evoke strong negative emotions. The wrap-up section also uses the emotion estimation function to collect participants' emotional reactions to the wrap-up content and reflect them in the progress of the next meeting. For example, it sets topics that evoke strong positive emotions as the agenda for the next meeting. In this way, by collecting emotional reactions and reflecting them in the progress of the next meeting, the quality of the meeting can be improved.

[0072] The past wrap-up reference section can analyze the contents of past wrap-ups in detail and identify unresolved issues or newly emerged issues. In the past wrap-up reference section, for example, the generation AI performs text analysis of past wrap-up contents to identify unresolved issues. For example, it lists issues that were proposed in the previous meeting but remain unresolved. In addition, the generation AI identifies newly emerged issues based on the contents of past wrap-ups. For example, it extracts problems that have arisen since the previous meeting. In addition, the generation AI analyzes the contents of past wrap-ups in detail and identifies unresolved issues or newly emerged issues. For example, it suggests that unresolved issues be discussed first. This allows for the identification of unresolved issues or newly emerged issues, making it possible to effectively advance discussions at the next meeting.

[0073] The past wrap-up reference unit automatically generates the agenda for the next meeting based on the content of past wrap-ups, supporting efficient meeting progress. In the past wrap-up reference unit, for example, the generation AI analyzes the content of past wrap-ups and automatically generates the agenda for the next meeting. For example, unresolved issues and newly emerged problems are included in the agenda. In addition, in the past wrap-up reference unit, the generation AI creates the agenda for the next meeting based on the content of past wrap-ups. For example, it includes an item to check the progress of the action plan proposed in the previous meeting. In addition, in the past wrap-up reference unit, the generation AI automatically generates the agenda for the next meeting based on the content of past wrap-ups, supporting efficient meeting progress. For example, it prioritizes important topics and includes them in the agenda. In this way, the automatic generation of the agenda for the next meeting can support efficient meeting progress.

[0074] The past wrap-up reference unit uses an emotion estimation function to extract emotional highlights from past meetings and set the focus of discussion for the next meeting. The past wrap-up reference unit, for example, uses the emotion estimation function to extract emotional highlights from past meetings. For example, it sets topics with strong positive emotions as the focus of the next meeting. In addition, the past wrap-up reference unit uses the generation AI to extract emotional highlights from past meetings based on emotion estimation data and set the focus of discussion for the next meeting. For example, it prioritizes discussion of emotionally important topics. In addition, the past wrap-up reference unit uses the emotion estimation function to extract emotional highlights from past meetings and set the focus of discussion for the next meeting. For example, it sets topics with strong positive emotions as the focus of discussion for the next meeting. In this way, the quality of meetings can be improved by extracting emotional highlights and setting the focus of discussion for the next meeting.

[0075] The past wrap-up reference unit can make suggestions to promote knowledge sharing between different projects or teams based on the content of past wrap-ups. In the past wrap-up reference unit, for example, the generation AI analyzes the content of past wrap-ups and makes suggestions to promote knowledge sharing between different projects or teams. For example, it suggests information sharing between related projects. Furthermore, the generation AI in the past wrap-up reference unit makes suggestions to promote knowledge sharing between different teams based on the content of past wrap-ups. For example, it suggests discussions between teams that have common challenges. Furthermore, the generation AI in the past wrap-up reference unit makes suggestions to promote knowledge sharing between different projects or teams based on the content of past wrap-ups. For example, it provides a forum for sharing success stories and failure stories. This promotes knowledge sharing between different projects and teams, thereby improving the utilization of knowledge throughout the organization.

[0076] The past wrap-up reference unit can suggest related external resources based on the content of past wrap-ups. In this case, for example, the generation AI analyzes the content of past wrap-ups and suggests related external resources. For example, it refers to expert opinions or research papers. Also, in the past wrap-up reference unit, the generation AI suggests related external resources based on the content of past wrap-ups. For example, it provides expert opinions on specific issues. Also, in the past wrap-up reference unit, the generation AI suggests related external resources based on the content of past wrap-ups. For example, it provides the latest research papers or expert comments. This makes it possible to improve the quality of discussions by suggesting related external resources.

[0077] The past wrap-up reference unit can use the emotion estimation function to analyze emotional reactions in past meetings and make suggestions to increase emotional empathy in the next meeting. For example, the past wrap-up reference unit can use the emotion estimation function to analyze emotional reactions in past meetings and make suggestions to increase emotional empathy in the next meeting. For example, it can make agenda items with strong positive emotions the focus of the next meeting. In addition, the past wrap-up reference unit uses the generation AI to analyze emotional reactions in past meetings based on the emotion estimation data and make suggestions to increase emotional empathy in the next meeting. For example, it can prioritize discussion of emotionally important agenda items. In addition, the past wrap-up reference unit can use the emotion estimation function to analyze emotional reactions in past meetings and make suggestions to increase emotional empathy in the next meeting. For example, it can make agenda items with strong positive emotions the focus of the next meeting. This makes it possible to improve the productivity of the next meeting by making suggestions to increase emotional empathy.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The meeting progress support unit analyzes participants' comments in real time and can make suggestions to adjust the balance of the discussion based on the frequency or content of their comments. For example, if a particular participant is speaking too much, it will suggest giving other participants an opportunity to speak. The meeting progress support unit also uses a generation AI to analyze the content of comments in real time and encourage comments on specific topics to maintain balance in the discussion. For example, if the discussion is biased toward technical topics, it will suggest seeking business opinions. The meeting progress support unit also uses a generation AI to adjust the progress of the discussion based on the frequency and content of comments. For example, if the discussion is becoming one-sided, it will suggest seeking opinions from other perspectives. This prevents the discussion from stagnating and proposes effective solutions.

[0080] The wrap-up section can predict the effectiveness of items to be discussed and create to-do lists for the next meeting. For example, the generation AI can refer to participants' past comment history and revisit ideas and opinions previously proposed by specific participants to deepen the discussion. For example, the generation AI can analyze participants' past comment history and revisit specific ideas and opinions. For example, an idea proposed in the previous meeting but left unresolved can be brought back for discussion. The wrap-up section also uses past comment history to revisit the opinions of specific participants. For example, if a previously proposed solution is relevant to the current discussion, it can suggest that that solution be reconsidered. The wrap-up section also supports the progress of the discussion by referring to past comment history. For example, it can revisit ideas proposed in the past but not implemented to deepen the discussion. This clarifies the results of the meeting and streamlines preparations for the next meeting.

[0081] The past wrap-up reference unit can proceed with the meeting based on past wrap-ups. For example, it uses the emotion estimation function to grasp the emotional state of participants in real time and make suggestions to maintain a positive emotional atmosphere. For example, it uses the emotion estimation function to analyze participants' facial expressions and vocal tones to grasp their emotional state in real time. For example, if negative emotions are strong, it suggests taking a break to refresh. The past wrap-up reference unit also makes suggestions to maintain a positive atmosphere based on the emotion estimation data from the generation AI. For example, if the discussion is getting too heated, it suggests switching to a more relaxed topic. The past wrap-up reference unit also uses the emotion estimation function to monitor the emotional state of participants in real time and make suggestions to elicit positive emotions. For example, it displays messages of praise or encouragement. This enables the meeting to proceed consistently based on past discussions.

[0082] The meeting progress support unit uses the emotion estimation function to grasp the emotional state of participants in real time and make suggestions to maintain an emotionally positive atmosphere. For example, the emotion estimation function can be used to analyze participants' facial expressions and vocal tones to grasp their emotional state in real time. For example, if negative emotions are strong, it can suggest a break to refresh. The meeting progress support unit also makes suggestions to maintain a positive atmosphere based on the emotion estimation data from the generation AI. For example, if a discussion is getting too heated, it can suggest switching to a more relaxed topic. The meeting progress support unit also uses the emotion estimation function to monitor the emotional state of participants in real time and make suggestions to elicit positive emotions. For example, it can display compliments or encouraging messages. This helps maintain an emotionally positive atmosphere and improves meeting productivity.

[0083] The meeting progress support unit can refer to an external database and provide the latest information related to the discussion. For example, the generation AI may refer to an external database (e.g., a patent database or market data) while a meeting is in progress and provide the latest information related to the discussion. For example, the generation AI may refer to a patent database while a meeting is in progress and provide the latest patent information related to the discussion. For example, it may display patent information on new technologies or competitors in real time. The meeting progress support unit may also refer to market data and provide the latest market trends and information related to the discussion. For example, it may display the market share and growth rate of a particular product or service. The meeting progress support unit may also refer to an external database and provide the latest research papers and expert opinions related to the discussion. For example, it may display the latest research results and expert comments in real time. This can improve the quality of discussions by providing the latest information.

[0084] The meeting progress support unit can learn meeting progress methods from different industries and make progress proposals that incorporate best practices. For example, the generation AI learns meeting progress methods from different industries and makes progress proposals that incorporate best practices. For example, it makes progress proposals that incorporate agile methods from the IT industry. The meeting progress support unit also learns meeting progress methods from different industries and reflects them in its progress proposals. For example, it makes progress proposals that incorporate quality control methods from the manufacturing industry. The meeting progress support unit also learns best practices from different industries and applies them to its progress proposals. For example, it makes progress proposals that incorporate team collaboration methods from the medical industry. This makes it possible to optimize the progress of meetings by incorporating best practices from different industries.

[0085] The wrap-up section can perform a detailed analysis of the content of meeting discussions and quantitatively evaluate the risks and benefits of each proposal. For example, the generation AI analyzes the text of the meeting discussions and quantitatively evaluates the risks and benefits of each proposal. For example, it calculates a risk score and a benefit score and presents them to participants. The wrap-up section also evaluates the risks and benefits of each proposal based on the content of the meeting discussions. For example, it refers to past data and case studies to quantify the risks and benefits. The generation AI also analyzes the content of the meeting discussions in detail and quantitatively evaluates the risks and benefits of each proposal. For example, it extracts and scores risk and benefit factors. This quantitatively evaluates the risks and benefits of proposals, thereby improving the quality of decision-making.

[0086] The wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, the generation AI refers to similar past cases and predicts effects based on success or failure cases. For example, it predicts the probability of success of a proposal based on past success cases. Furthermore, when wrapping up a meeting, the generation AI refers to past failure cases and identifies risk factors. For example, it evaluates the risks of a proposal based on past failure cases. Furthermore, the wrap-up section can refer to similar past cases and predict effects based on success or failure cases. For example, it quantifies the effects of a proposal based on past data. In this way, by predicting effects based on past cases, it is possible to increase the feasibility of a proposal.

[0087] The wrap-up department can use the emotion estimation function to analyze participants' emotional responses and suggest to-do items for the next meeting to elicit positive emotions. For example, the emotion estimation function can be used to analyze participants' emotional responses in real time and suggest to-do items for eliciting positive emotions. For example, a topic that evokes strong positive emotions could be set as the agenda for the next meeting. The wrap-up department can also use the generative AI to suggest to-do items for the next meeting based on the emotion estimation data. For example, a specific action plan can be created to elicit positive emotions. The wrap-up department can also use the emotion estimation function to analyze participants' emotional responses and suggest to-do items for the next meeting to elicit positive emotions. For example, a topic that evokes strong positive emotions could be set as the agenda for the next meeting. This makes it possible to improve the productivity of the next meeting by suggesting to-do items for eliciting positive emotions.

[0088] The wrap-up section can provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, the generation AI can provide the meeting wrap-up content in visual note format to make it easier to understand visually. For example, important points can be shown using diagrams and icons. The wrap-up section can also provide the meeting wrap-up content in infographic format to make it easier to understand visually. For example, data and statistical information can be displayed in graphs and charts. The wrap-up section can also provide the meeting wrap-up content in different formats to make it easier to understand visually. For example, visual notes and infographics can be automatically generated. This allows the content of the meeting to be communicated effectively by providing it in a format that is easy to understand visually.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The Meeting Progress Support Department supports the progress of the meeting. Specifically, it checks the agenda at the start of the meeting and raises issues at appropriate times depending on the progress of the discussion. For example, if the discussion is stagnating, it raises an issue by saying, "Let's think of a specific solution to this problem," and if the discussion is progressing, it proposes countermeasures by saying, "Let's think of specific steps to resolve this problem." Step 2: The wrap-up department wraps up the meeting. Specifically, they predict the effects of the items discussed and create to-do lists for the next meeting. For example, they predict the effects in the form of "By implementing this solution, we expect sales to increase by 10%," and create to-do lists in the form of "Each person in charge will create a specific action plan by the next meeting," and send these to the participants. Step 3: The Past Wrap-Up Reference section refers to past wrap-ups. Specifically, the progress of the discussion is checked based on past wrap-ups, and the agenda is adjusted as necessary. For example, the discussion can proceed in the form of, "Let's check the progress on the solutions proposed in the last meeting."

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0112] 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.

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

[0114] 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).

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

[0116] 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0119] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0135] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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. A meeting progress support department that supports the progress of meetings; a wrap-up section that wraps up the meeting; A past wrap-up reference section for referencing past wrap-ups. A system characterized by:

2. The conference progress support unit Raise issues and propose solutions according to the progress of the discussion 2. The system of claim 1.

3. The wrap-up unit Predict the effects of the discussion items and create the above To-Do list until the next meeting.

2. The system of claim 1.

4. The past wrap-up reference section Conduct the meeting based on the previous wrap-up 2. The system of claim 1.

5. The conference progress support unit Analyze participants' comments in real time and make suggestions to rebalance the discussion based on the frequency or content of those comments 2. The system of claim 1.

6. The conference progress support unit Referencing participants' past comment history and revisiting ideas or opinions previously proposed by specific participants to deepen the discussion 2. The system of claim 1.

7. The conference progress support unit Gain real-time insight into participants' emotional state and offer suggestions to maintain an emotionally positive atmosphere 2. The system of claim 1.

8. The conference progress support unit Referencing external databases to provide up-to-date information relevant to the discussion 2. The system of claim 1.

Images