system

Abstract

The system according to this embodiment aims to provide a consistent process from idea generation and organization to visualization, decision support, and session efficiency. [Solution] The system according to the embodiment comprises a generation unit, an organization unit, a visualization unit, a support unit, and an efficiency unit. The generation unit generates ideas. The organization unit organizes the ideas generated by the generation unit. The visualization unit visualizes the ideas organized by the organization unit. The support unit supports decision-making based on the information visualized by the visualization unit. The efficiency unit streamlines the session based on the decision-making supported by the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there were problems such as stagnation in idea generation, difficulty in organizing and prioritizing ideas, and language barriers and cultural differences in multinational teams.

[0005] The system according to the embodiment aims to consistently perform from idea generation to organization, visualization, decision-making support, and session efficiency improvement. [[ID=,40]]

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, an organization unit, a visualization unit, a support unit, and an efficiency unit. The generation unit generates ideas. The organization unit organizes the ideas generated by the generation unit. The visualization unit visualizes the ideas organized by the organization unit. The support unit supports decision-making based on the information visualized by the visualization unit. The efficiency unit optimizes the session based on the decision-making supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can consistently perform tasks from idea generation and organization to visualization, decision support, and session efficiency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An idea generation support system according to an embodiment of the present invention is a system that utilizes AI to generate, organize, and prioritize ideas, and overcome language barriers in multinational teams. This idea generation support system assists idea generation by having AI activate brainstorming in real time and provide new perspectives. For example, the AI ​​can generate and present relevant ideas to a theme presented by the user. The AI ​​can also analyze the user's past idea submission history and select the optimal idea generation method. Next, the AI ​​organizes and classifies ideas by automatically grouping them and suggesting priorities. For example, the AI ​​can group ideas based on their importance and relevance and present them to the user. Furthermore, the AI ​​visualizes insights by automatically generating mind maps and related information, providing visual understanding. For example, the AI ​​can generate a mind map that visually shows the relationships between ideas and present it to the user. The AI ​​also provides real-time translation and multilingual support to assist collaboration in multiple languages ​​and appropriately explain cultural backgrounds. For example, the AI ​​can translate text entered by the user in real time and present it to other users. Furthermore, AI supports decision-making using frameworks such as SWOT analysis. For example, AI can perform a SWOT analysis on an idea presented by a user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. To improve session efficiency, AI manages time and automatically generates meeting minutes, ensuring smooth discussions. For example, AI can monitor the session's progress in real time, generate meeting minutes at the appropriate time, and present them to the user. In addition, AI assists with whiteboard design, providing optimized visual design and colorblind compatibility. For example, AI can optimize the design of a user-created whiteboard and make it colorblind compatible. Finally, AI generates suggestions for improvement from session analysis, learning and providing feedback. For example, AI can analyze the session's progress, generate suggestions for improvement for the next session, and present them to the user.This allows the idea generation support system to promote creative thinking and facilitate efficient collaboration. Furthermore, its superior design and translation capabilities can enhance diverse teams and improve the speed and quality of decision-making. Ultimately, the idea generation support system enables idea generation, organization, visualization, decision support, and session efficiency.

[0029] The idea generation support system according to the embodiment comprises a generation unit, an organization unit, a visualization unit, a support unit, and an efficiency unit. The generation unit generates ideas. The generation unit generates ideas related to a theme presented by the user, for example, using AI. The generation unit can generate related ideas based on keywords entered by the user, for example. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that have been successful for the user in the past. The organization unit organizes the ideas generated by the generation unit. The organization unit automatically groups ideas and suggests priorities, for example, using AI. The organization unit can group ideas based on their importance or relevance and present them to the user, for example. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, if the user is relaxed, the organization unit can group ideas into a wide range of categories. The visualization unit visualizes the ideas organized by the organization unit. The visualization unit, for example, uses AI to automatically generate mind maps and related information, providing visual understanding. For instance, it can generate a mind map that visually shows the relationships between ideas and present it to the user. Furthermore, the visualization unit can estimate the user's emotions and adjust the display method of the mind map based on those emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually engaging mind map. The support unit assists decision-making based on the information visualized by the visualization unit. For example, the support unit uses AI to support decision-making using frameworks such as SWOT analysis. For example, the support unit can perform a SWOT analysis on an idea presented by the user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. Furthermore, the support unit can estimate the user's emotions and adjust the decision-making support method based on those emotions. For example, if the user is relaxed, the support unit can provide detailed information to assist decision-making.The efficiency unit streamlines the session based on the decision-making supported by the support unit. For example, the efficiency unit uses AI to perform time management and automatically generate meeting minutes, thereby facilitating smooth discussion. For example, the efficiency unit can monitor the progress of the session in real time, generate meeting minutes at the appropriate time, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust the session's efficiency based on the estimated emotions. For example, if the user is relaxed, the efficiency unit can conduct the session at a relaxed pace. As a result, the idea generation support system according to this embodiment enables idea generation, organization, visualization, decision support, and session efficiency.

[0030] The generation unit generates ideas. For example, it uses AI to generate ideas related to themes presented by the user. Specifically, the generation unit utilizes natural language processing technology to analyze themes and keywords entered by the user. For example, if a user enters the theme "environmental protection," the generation unit extracts relevant keywords and phrases and generates new ideas based on them. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, it can prioritize suggesting idea generation methods that have been successful for the user in the past. This allows the generation unit to learn the user's preferences and tendencies, enabling it to provide more personalized ideas. Furthermore, the generation unit has a feedback function to evaluate the quality of the generated ideas, and can continuously improve its generation algorithm based on user feedback. This allows the generation unit to constantly incorporate the latest information and technologies and continue to provide users with the best possible ideas.

[0031] The organization unit organizes the ideas generated by the generation unit. For example, the organization unit can automatically group ideas using AI and suggest priorities. Specifically, the organization unit uses a clustering algorithm to classify the generated ideas by theme or category. For example, if multiple ideas related to environmental protection are generated, they can be divided into categories such as "recycling," "energy efficiency," and "nature conservation." The organization unit can group ideas based on their importance and relevance and present them to the user. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated emotions. For example, if the user is relaxed, ideas can be grouped into a wide range of categories. This allows the user to consider ideas from diverse perspectives. Furthermore, the organization unit can improve its grouping algorithm based on user feedback, achieving more accurate idea organization. In this way, the organization unit can provide a user-friendly and effective idea organization service.

[0032] The visualization unit visualizes the ideas organized by the organization unit. For example, the visualization unit automatically generates mind maps and related information using AI, providing a visual understanding. Specifically, the visualization unit generates a mind map by arranging the generated ideas as nodes and connecting their relationships with edges. For example, if ideas related to environmental protection are categorized as "recycling," "energy efficiency," and "nature conservation," related ideas can be arranged around each category and displayed in a visually easy-to-understand format. Furthermore, the visualization unit can estimate the user's emotions and adjust the mind map display method based on the estimated emotions. For example, if the user is relaxed, a colorful and visually appealing mind map can be displayed. This allows the user to consider ideas without stress. In addition, the visualization unit can improve the display method based on user feedback, providing a more user-friendly visualization tool. This enables the visualization unit to achieve intuitive and easy-to-understand visualization of ideas for the user.

[0033] The support department assists decision-making based on information visualized by the visualization department. For example, the support department uses AI to support decision-making using frameworks such as SWOT analysis. Specifically, the support department can perform a SWOT analysis on ideas presented by the user, analyzing their strengths, weaknesses, opportunities, and threats, and presenting them to the user. For example, if ideas related to environmental protection are categorized as "recycling," "energy efficiency," and "nature conservation," the support department can perform a SWOT analysis on each idea to clarify the strengths, weaknesses, opportunities, and threats of each idea. The support department can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions. For example, if the user is relaxed, the support department can provide detailed information to assist in decision-making. This allows the user to calmly and objectively evaluate ideas and make the optimal decision. Furthermore, the support department can improve its support methods based on user feedback and provide more effective decision-making support. In this way, the support department can realize highly reliable decision-making support for the user.

[0034] The Efficiency Department streamlines sessions based on decision-making supported by the Support Department. For example, the Efficiency Department uses AI for time management and automatic minute-taking to ensure smooth discussions. Specifically, it can monitor the session's progress in real time, generate minutes at the appropriate time, and present them to the user. For instance, if important points are discussed during a session, the Efficiency Department can automatically record them and compile them into minutes. Furthermore, the Efficiency Department can estimate the user's emotions and adjust the session's efficiency based on that estimation. For example, if the user is relaxed, the session can proceed at a more relaxed pace, allowing the user to focus on the discussion without stress. Additionally, the Efficiency Department can improve its efficiency methods based on user feedback, resulting in more effective session management. This allows the Efficiency Department to provide a user-friendly and effective session management experience.

[0035] The generation unit can activate brainstorming in real time and provide new perspectives. For example, the generation unit can use AI to generate and present relevant ideas in real time to a theme presented by the user. For example, the generation unit can generate relevant ideas in real time based on keywords entered by the user. The generation unit can also estimate the user's emotions and adjust the timing of brainstorming based on the estimated emotions. For example, if the generation unit is feeling stressed, it can start brainstorming at a time when the user can relax. This improves the quality of ideas by activating brainstorming in real time and providing new perspectives. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input keywords entered by the user into a generation AI, and the generation AI can generate relevant ideas.

[0036] The organization unit can automatically group ideas and suggest priorities. For example, the organization unit can use AI to automatically group ideas and suggest priorities. For example, the organization unit can group ideas based on their importance or relevance and present them to the user. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated emotions. For example, if the user is relaxed, the organization unit can group ideas into broad categories. This makes idea organization more efficient by automatically grouping ideas and suggesting priorities. Some or all of the above processes in the organization unit may be performed using, for example, generative AI, or not using generative AI. For example, the organization unit can input the importance or relevance of ideas into the generative AI, which can then group the ideas.

[0037] The visualization unit can automatically generate mind maps and related information, providing visual understanding. For example, the visualization unit can use AI to automatically generate mind maps and related information, providing visual understanding. For example, the visualization unit can generate a mind map that visually shows the relationships between ideas and present it to the user. Furthermore, the visualization unit can estimate the user's emotions and adjust the display method of the mind map based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually engaging mind map. This deepens the understanding of ideas by automatically generating mind maps and related information and providing visual understanding. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generating AI, or without a generating AI. For example, the visualization unit can input the relationships between ideas into a generating AI, which can then generate a mind map.

[0038] The support unit can assist in decision-making using frameworks such as SWOT analysis. For example, the support unit can use AI to assist in decision-making using frameworks such as SWOT analysis. For example, the support unit can perform a SWOT analysis on an idea presented by the user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. The support unit can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions of the user. For example, if the support unit is relaxed, it can provide detailed information to assist in decision-making. In this way, the quality of decision-making is improved by supporting decision-making using frameworks such as SWOT analysis. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit can input an idea presented by the user into a generative AI, and the generative AI can perform a SWOT analysis.

[0039] The efficiency unit can manage time and automatically generate meeting minutes, enabling discussions to proceed smoothly. For example, the efficiency unit can use AI to manage time and automatically generate meeting minutes, thereby facilitating smooth discussions. For example, the efficiency unit can monitor the progress of a session in real time, generate meeting minutes at the appropriate time, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust the session efficiency method based on the estimated emotions. For example, if the efficiency unit is relaxed, it can proceed with the session at a relaxed pace. This allows the discussion to proceed smoothly through time management and automatic minute generation. Some or all of the above processes in the efficiency unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the efficiency unit can input the progress of the session into the generation AI, which can then generate meeting minutes at the appropriate time.

[0040] The efficiency unit can assist with whiteboard design, providing optimized visual design and colorblind compatibility. For example, the efficiency unit can use AI to assist with whiteboard design, providing optimized visual design and colorblind compatibility. For example, the efficiency unit can optimize a user-created whiteboard design and provide colorblind compatibility. Furthermore, the efficiency unit can estimate the user's emotions and adjust the whiteboard design based on those emotions. For example, if the user is relaxed, the efficiency unit can provide a colorful and visually appealing design. This enables optimized visual design and colorblind compatibility through whiteboard design assistance. Some or all of the above-described processes in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input a user-created whiteboard design into a generative AI, which can then optimize it.

[0041] The efficiency unit can generate improvement suggestions from session analysis and perform learning and feedback. For example, the efficiency unit can use AI to generate improvement suggestions from session analysis and perform learning and feedback. For example, the efficiency unit can analyze the progress of a session, generate improvement suggestions for the next session, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust improvement suggestions based on the estimated emotions. For example, if the efficiency unit is relaxed, it can generate improvement suggestions that can be approached in a relaxed state. In this way, the quality of the session is improved by generating improvement suggestions from session analysis and performing learning and feedback. Some or all of the above processes in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input the progress of a session into a generative AI, and the generative AI can generate improvement suggestions.

[0042] The generation unit can analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can use AI to analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that the user has succeeded with in the past. The generation unit can also suggest avoiding idea generation methods that the user has failed with in the past. In this way, the optimal idea generation method can be selected by analyzing the user's past idea submission history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past idea submission history into a generation AI, and the generation AI can select the optimal idea generation method.

[0043] The generation unit can filter ideas based on the user's current projects and areas of interest during idea generation. For example, the generation unit can use AI to filter ideas based on the user's current projects and areas of interest during idea generation. For example, the generation unit can prioritize generating ideas related to the user's current projects. The generation unit can also filter and generate relevant ideas based on the user's areas of interest. This allows for the generation of highly relevant ideas by filtering ideas based on the user's current projects and areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the user's current projects and areas of interest into a generation AI, which can then generate relevant ideas.

[0044] The generation unit can prioritize generating highly relevant ideas by considering the user's geographical location information during idea generation. For example, the generation unit can prioritize generating highly relevant ideas by considering the user's geographical location information when generating ideas using AI. For example, if the user is in a specific region, the generation unit can prioritize generating ideas related to that region. Also, if the user is traveling, the generation unit can prioritize generating ideas related to the travel destination. In this way, highly relevant ideas can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, and the generation AI can generate relevant ideas.

[0045] The generation unit can analyze a user's social media activity and generate relevant ideas during idea generation. For example, the generation unit can use AI to analyze a user's social media activity and generate relevant ideas during idea generation. For example, the generation unit can generate ideas related to topics the user has shown interest in on social media. The generation unit can also generate ideas based on the content of posts from accounts the user follows on social media. In this way, relevant ideas can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user social media activity data into a generation AI, and the generation AI can generate relevant ideas.

[0046] The organization unit can adjust the level of detail in grouping based on the importance of the ideas. For example, the organization unit can use AI to adjust the level of detail in grouping based on the importance of the ideas. For example, the organization unit can group highly important ideas in detail and less important ideas broadly. The organization unit can also prioritize the display of highly important ideas and postpone the display of less important ideas. This allows for effective idea organization by adjusting the level of detail in grouping based on the importance of the ideas. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea importance data into a generative AI, and the generative AI can adjust the level of detail in grouping.

[0047] The organization unit can apply different grouping algorithms depending on the category of the idea. For example, the organization unit can use AI to apply different grouping algorithms depending on the category of the idea. For example, the organization unit can apply a grouping algorithm from a technical perspective to technical ideas. Also, the organization unit can apply a grouping algorithm from a marketing perspective to marketing ideas. By applying different grouping algorithms depending on the category of the idea, effective idea organization becomes possible. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea category data into a generative AI, and the generative AI can apply different grouping algorithms.

[0048] The organization unit can determine the grouping priority based on when the ideas were submitted. For example, the organization unit can use AI to determine the grouping priority based on when the ideas were submitted. For example, the organization unit can prioritize grouping recently submitted ideas. The organization unit can also postpone older submitted ideas. This allows for effective idea organization by determining the grouping priority based on when the ideas were submitted. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the organization unit can input idea submission data into a generative AI, and the generative AI can determine the grouping priority.

[0049] The organization unit can adjust the grouping order based on the relevance of ideas. For example, the organization unit can use AI to adjust the grouping order based on the relevance of ideas. For example, the organization unit can prioritize grouping highly relevant ideas. The organization unit can also postpone less relevant ideas. This allows for effective idea organization by adjusting the grouping order based on the relevance of ideas. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea relevance data into a generative AI, which can then adjust the grouping order.

[0050] The visualization unit can optimize the current mind map by referring to past data. For example, the visualization unit can use AI to optimize the current mind map by referring to past data. For example, the visualization unit can suggest the optimal display method based on past mind map data. Furthermore, the visualization unit can learn and apply the user's preferred display method from past data. This allows the current mind map to be optimized by referring to past data. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input past data into a generative AI, which can then optimize the current mind map.

[0051] The visualization unit can apply different visualization methods to each category of idea. For example, the visualization unit can use AI to apply different visualization methods to each category of idea. For example, the visualization unit can apply a visualization method from a technical perspective to technical ideas. Furthermore, the visualization unit can apply a visualization method from a marketing perspective to marketing ideas. By applying different visualization methods to each category of idea, effective visual understanding becomes possible. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input idea category data into a generative AI, and the generative AI can apply different visualization methods.

[0052] The visualization unit can analyze changes in the mind map based on the submission date of ideas. The visualization unit can, for example, use AI to analyze changes in the mind map based on the submission date of ideas. The visualization unit can, for example, prioritize the display of recently submitted ideas. The visualization unit can also postpone older submitted ideas. This enables effective visual understanding by analyzing changes in the mind map based on the submission date of ideas. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input idea submission date data into a generative AI, which can then analyze changes in the mind map.

[0053] The visualization unit can analyze mind maps by referring to relevant market data for ideas. For example, the visualization unit can use AI to analyze mind maps by referring to relevant market data for ideas. The visualization unit can, for example, evaluate the importance of ideas based on relevant market data. Furthermore, the visualization unit can analyze the relationships between ideas by referring to relevant market data. This enables effective visual understanding by referring to relevant market data for ideas. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input relevant market data into a generative AI, which can then analyze the mind map.

[0054] The support unit can optimize current decisions by referring to past decision-making data. For example, the support unit can use AI to optimize current decisions by referring to past decision-making data. For example, the support unit can propose the optimal decision-making method based on past decision-making data. The support unit can also learn and apply decision-making methods preferred by the user from past data. This allows for the optimization of current decisions by referring to past decision-making data. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or without generative AI. For example, the support unit can input past decision-making data into a generative AI, which can then optimize the current decision.

[0055] The support department can apply different decision support methods to each category of idea. For example, the support department can use AI to apply different decision support methods to each category of idea. For example, the support department can apply a technical decision support method to a technical idea. Furthermore, the support department can apply a marketing decision support method to a marketing idea. By applying different decision support methods to each category of idea, effective decision-making becomes possible. Some or all of the above processing in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input idea category data into a generative AI, and the generative AI can apply different decision support methods.

[0056] The support department can analyze changes in decision-making based on the timing of idea submissions. For example, the support department can use AI to analyze changes in decision-making based on the timing of idea submissions. For example, the support department can prioritize the incorporation of recently submitted ideas into decision-making. The support department can also postpone older submitted ideas. This enables effective decision-making by analyzing changes in decision-making based on the timing of idea submissions. Some or all of the above processes in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input idea submission timing data into a generative AI, which can then analyze changes in decision-making.

[0057] The support department can analyze decision-making by referring to relevant market data for ideas. For example, the support department can use AI to analyze decision-making by referring to relevant market data for ideas. For example, the support department can evaluate the importance of ideas based on relevant market data. The support department can also analyze the relevance of ideas by referring to relevant market data. This enables effective decision-making by referring to relevant market data for ideas. Some or all of the above processing in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input relevant market data into generative AI, and the generative AI can analyze decision-making.

[0058] The efficiency unit can optimize the current session by referring to past session data. For example, the efficiency unit can use AI to optimize the current session by referring to past session data. For example, the efficiency unit can propose the optimal way to proceed based on past session data. The efficiency unit can also learn and apply the user's preferred way of proceeding from past data. In this way, the current session can be optimized by referring to past session data. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input past session data into a generative AI, and the generative AI can optimize the current session.

[0059] The efficiency unit can apply different efficiency methods to each category of idea. For example, the efficiency unit can use AI to apply different efficiency methods to each category of idea. For example, the efficiency unit can apply efficiency methods from a technical perspective to technical ideas. Furthermore, the efficiency unit can apply efficiency methods from a marketing perspective to marketing ideas. By applying different efficiency methods to each category of idea, effective session management becomes possible. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input idea category data into a generative AI, and the generative AI can apply different efficiency methods.

[0060] The efficiency unit can analyze session changes based on the timing of idea submissions. For example, the efficiency unit can use AI to analyze session changes based on the timing of idea submissions. For example, the efficiency unit can prioritize incorporating recently submitted ideas into the session. The efficiency unit can also postpone older submitted ideas. This allows for more effective session management by analyzing session changes based on the timing of idea submissions. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input idea submission timing data into a generative AI, which can then analyze session changes.

[0061] The efficiency unit can analyze sessions by referring to relevant market data for ideas. For example, the efficiency unit can use AI to analyze sessions by referring to relevant market data for ideas. For example, the efficiency unit can evaluate the importance of ideas based on relevant market data. The efficiency unit can also analyze the relevance of ideas by referring to relevant market data. This enables effective session management by referring to relevant market data for ideas. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input relevant market data into a generative AI, and the generative AI can analyze the session.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The organization function can adjust the level of detail in grouping based on the importance of the ideas. For example, highly important ideas can be grouped in detail, while less important ideas can be grouped more broadly. Furthermore, highly important ideas can be displayed preferentially, while less important ideas can be postponed. The organization function can also adjust the order of grouping based on the importance of the ideas. This allows for effective idea organization by adjusting the level of detail in grouping based on the importance of the ideas.

[0064] The support unit can optimize current decision-making by referring to past decision-making data. For example, it can propose the optimal decision-making method based on past decision-making data. It can also learn and apply decision-making methods preferred by users from past data. Furthermore, it can analyze past success and failure cases of decision-making and reflect them in current decisions. In this way, current decision-making can be optimized by referring to past decision-making data.

[0065] The generation unit can filter ideas based on the user's current projects and areas of interest during the idea generation process. For example, it can prioritize generating ideas related to the user's current projects. It can also filter and generate relevant ideas based on the user's areas of interest. Furthermore, it can refer to the user's past project history to generate highly relevant ideas. This allows for the generation of highly relevant ideas by filtering them based on the user's current projects and areas of interest.

[0066] The organization section can apply different grouping algorithms depending on the category of the idea. For example, technical ideas can be grouped from a technical perspective, marketing ideas from a marketing perspective, and creative ideas from a creative perspective. This allows for effective idea organization by applying different grouping algorithms depending on the category of the idea.

[0067] The visualization unit can optimize the current mind map by referencing past data. For example, it can suggest the optimal display method based on data from past mind maps. It can also learn and apply display methods preferred by users from past data. Furthermore, it can analyze successful and unsuccessful examples of past mind maps and reflect them in the current mind map. In this way, the current mind map can be optimized by referring to past data.

[0068] The efficiency optimization unit can optimize the current session by referring to past session data. For example, it can suggest the optimal way to proceed based on past session data. It can also learn and apply user-preferred methods from past data. Furthermore, it can analyze successful and unsuccessful examples from past sessions and reflect them in the current session. In this way, the current session can be optimized by referring to past session data.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The generation unit generates ideas. The generation unit generates related ideas for a theme presented by the user, for example, using AI. The generation unit can generate related ideas based on keywords entered by the user. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that have been successful for the user in the past. Step 2: The organizing unit organizes the ideas generated by the generating unit. The organizing unit can, for example, use AI to automatically group ideas and suggest priorities. The organizing unit can group ideas based on their importance and relevance and present them to the user. The organizing unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, if the user is relaxed, the organizing unit can group ideas into broad categories. Step 3: The visualization unit visualizes the ideas organized by the organization unit. The visualization unit can, for example, use AI to automatically generate mind maps and related information, providing a visual understanding. The visualization unit can generate and present mind maps that visually show the relationships between ideas. The visualization unit can also estimate the user's emotions and adjust how the mind map is displayed based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually appealing mind map. Step 4: The support unit assists decision-making based on the information visualized by the visualization unit. The support unit supports decision-making using frameworks such as SWOT analysis with AI, for example. The support unit can perform a SWOT analysis on the ideas presented by the user, analyzing strengths, weaknesses, opportunities, and threats, and presenting them to the user. The support unit can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions of the user. For example, if the user is relaxed, the support unit can provide detailed information to support decision-making. Step 5: The Efficiency Department optimizes the session based on the decision-making supported by the Support Department. For example, the Efficiency Department uses AI to manage time and automatically generate meeting minutes, ensuring the discussion flows smoothly. The Efficiency Department can monitor the session's progress in real time, generate meeting minutes at the appropriate time, and present them to the user. The Efficiency Department can also estimate the user's emotions and adjust the session optimization method based on the estimated emotions. For example, if the user is relaxed, the Efficiency Department can conduct the session at a relaxed pace.

[0071] (Example of form 2) An idea generation support system according to an embodiment of the present invention is a system that utilizes AI to generate, organize, and prioritize ideas, and overcome language barriers in multinational teams. This idea generation support system assists idea generation by having AI activate brainstorming in real time and provide new perspectives. For example, the AI ​​can generate and present relevant ideas to a theme presented by the user. The AI ​​can also analyze the user's past idea submission history and select the optimal idea generation method. Next, the AI ​​organizes and classifies ideas by automatically grouping them and suggesting priorities. For example, the AI ​​can group ideas based on their importance and relevance and present them to the user. Furthermore, the AI ​​visualizes insights by automatically generating mind maps and related information, providing visual understanding. For example, the AI ​​can generate a mind map that visually shows the relationships between ideas and present it to the user. The AI ​​also provides real-time translation and multilingual support to assist collaboration in multiple languages ​​and appropriately explain cultural backgrounds. For example, the AI ​​can translate text entered by the user in real time and present it to other users. Furthermore, AI supports decision-making using frameworks such as SWOT analysis. For example, AI can perform a SWOT analysis on an idea presented by a user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. To improve session efficiency, AI manages time and automatically generates meeting minutes, ensuring smooth discussions. For example, AI can monitor the session's progress in real time, generate meeting minutes at the appropriate time, and present them to the user. In addition, AI assists with whiteboard design, providing optimized visual design and colorblind compatibility. For example, AI can optimize the design of a user-created whiteboard and make it colorblind compatible. Finally, AI generates suggestions for improvement from session analysis, learning and providing feedback. For example, AI can analyze the session's progress, generate suggestions for improvement for the next session, and present them to the user.This allows the idea generation support system to promote creative thinking and facilitate efficient collaboration. Furthermore, its superior design and translation capabilities can enhance diverse teams and improve the speed and quality of decision-making. Ultimately, the idea generation support system enables idea generation, organization, visualization, decision support, and session efficiency.

[0072] The idea generation support system according to the embodiment comprises a generation unit, an organization unit, a visualization unit, a support unit, and an efficiency unit. The generation unit generates ideas. The generation unit generates ideas related to a theme presented by the user, for example, using AI. The generation unit can generate related ideas based on keywords entered by the user, for example. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that have been successful for the user in the past. The organization unit organizes the ideas generated by the generation unit. The organization unit automatically groups ideas and suggests priorities, for example, using AI. The organization unit can group ideas based on their importance or relevance and present them to the user, for example. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, if the user is relaxed, the organization unit can group ideas into a wide range of categories. The visualization unit visualizes the ideas organized by the organization unit. The visualization unit, for example, uses AI to automatically generate mind maps and related information, providing visual understanding. For instance, it can generate a mind map that visually shows the relationships between ideas and present it to the user. Furthermore, the visualization unit can estimate the user's emotions and adjust the display method of the mind map based on those emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually engaging mind map. The support unit assists decision-making based on the information visualized by the visualization unit. For example, the support unit uses AI to support decision-making using frameworks such as SWOT analysis. For example, the support unit can perform a SWOT analysis on an idea presented by the user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. Furthermore, the support unit can estimate the user's emotions and adjust the decision-making support method based on those emotions. For example, if the user is relaxed, the support unit can provide detailed information to assist decision-making.The efficiency unit streamlines the session based on the decision-making supported by the support unit. For example, the efficiency unit uses AI to perform time management and automatically generate meeting minutes, thereby facilitating smooth discussion. For example, the efficiency unit can monitor the progress of the session in real time, generate meeting minutes at the appropriate time, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust the session's efficiency based on the estimated emotions. For example, if the user is relaxed, the efficiency unit can conduct the session at a relaxed pace. As a result, the idea generation support system according to this embodiment enables idea generation, organization, visualization, decision support, and session efficiency.

[0073] The generation unit generates ideas. For example, it uses AI to generate ideas related to themes presented by the user. Specifically, the generation unit utilizes natural language processing technology to analyze themes and keywords entered by the user. For example, if a user enters the theme "environmental protection," the generation unit extracts relevant keywords and phrases and generates new ideas based on them. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, it can prioritize suggesting idea generation methods that have been successful for the user in the past. This allows the generation unit to learn the user's preferences and tendencies, enabling it to provide more personalized ideas. Furthermore, the generation unit has a feedback function to evaluate the quality of the generated ideas, and can continuously improve its generation algorithm based on user feedback. This allows the generation unit to constantly incorporate the latest information and technologies and continue to provide users with the best possible ideas.

[0074] The organization unit organizes the ideas generated by the generation unit. For example, the organization unit can automatically group ideas using AI and suggest priorities. Specifically, the organization unit uses a clustering algorithm to classify the generated ideas by theme or category. For example, if multiple ideas related to environmental protection are generated, they can be divided into categories such as "recycling," "energy efficiency," and "nature conservation." The organization unit can group ideas based on their importance and relevance and present them to the user. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated emotions. For example, if the user is relaxed, ideas can be grouped into a wide range of categories. This allows the user to consider ideas from diverse perspectives. Furthermore, the organization unit can improve its grouping algorithm based on user feedback, achieving more accurate idea organization. In this way, the organization unit can provide a user-friendly and effective idea organization service.

[0075] The visualization unit visualizes the ideas organized by the organization unit. For example, the visualization unit automatically generates mind maps and related information using AI, providing a visual understanding. Specifically, the visualization unit generates a mind map by arranging the generated ideas as nodes and connecting their relationships with edges. For example, if ideas related to environmental protection are categorized as "recycling," "energy efficiency," and "nature conservation," related ideas can be arranged around each category and displayed in a visually easy-to-understand format. Furthermore, the visualization unit can estimate the user's emotions and adjust the mind map display method based on the estimated emotions. For example, if the user is relaxed, a colorful and visually appealing mind map can be displayed. This allows the user to consider ideas without stress. In addition, the visualization unit can improve the display method based on user feedback, providing a more user-friendly visualization tool. This enables the visualization unit to achieve intuitive and easy-to-understand visualization of ideas for the user.

[0076] The support department assists decision-making based on information visualized by the visualization department. For example, the support department uses AI to support decision-making using frameworks such as SWOT analysis. Specifically, the support department can perform a SWOT analysis on ideas presented by the user, analyzing their strengths, weaknesses, opportunities, and threats, and presenting them to the user. For example, if ideas related to environmental protection are categorized as "recycling," "energy efficiency," and "nature conservation," the support department can perform a SWOT analysis on each idea to clarify the strengths, weaknesses, opportunities, and threats of each idea. The support department can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions. For example, if the user is relaxed, the support department can provide detailed information to assist in decision-making. This allows the user to calmly and objectively evaluate ideas and make the optimal decision. Furthermore, the support department can improve its support methods based on user feedback and provide more effective decision-making support. In this way, the support department can realize highly reliable decision-making support for the user.

[0077] The Efficiency Department streamlines sessions based on decision-making supported by the Support Department. For example, the Efficiency Department uses AI for time management and automatic minute-taking to ensure smooth discussions. Specifically, it can monitor the session's progress in real time, generate minutes at the appropriate time, and present them to the user. For instance, if important points are discussed during a session, the Efficiency Department can automatically record them and compile them into minutes. Furthermore, the Efficiency Department can estimate the user's emotions and adjust the session's efficiency based on that estimation. For example, if the user is relaxed, the session can proceed at a more relaxed pace, allowing the user to focus on the discussion without stress. Additionally, the Efficiency Department can improve its efficiency methods based on user feedback, resulting in more effective session management. This allows the Efficiency Department to provide a user-friendly and effective session management experience.

[0078] The generation unit can activate brainstorming in real time and provide new perspectives. For example, the generation unit can use AI to generate and present relevant ideas in real time to a theme presented by the user. For example, the generation unit can generate relevant ideas in real time based on keywords entered by the user. The generation unit can also estimate the user's emotions and adjust the timing of brainstorming based on the estimated emotions. For example, if the generation unit is feeling stressed, it can start brainstorming at a time when the user can relax. This improves the quality of ideas by activating brainstorming in real time and providing new perspectives. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input keywords entered by the user into a generation AI, and the generation AI can generate relevant ideas.

[0079] The organization unit can automatically group ideas and suggest priorities. For example, the organization unit can use AI to automatically group ideas and suggest priorities. For example, the organization unit can group ideas based on their importance or relevance and present them to the user. The organization unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated emotions. For example, if the user is relaxed, the organization unit can group ideas into broad categories. This makes idea organization more efficient by automatically grouping ideas and suggesting priorities. Some or all of the above processes in the organization unit may be performed using, for example, generative AI, or not using generative AI. For example, the organization unit can input the importance or relevance of ideas into the generative AI, which can then group the ideas.

[0080] The visualization unit can automatically generate mind maps and related information, providing visual understanding. For example, the visualization unit can use AI to automatically generate mind maps and related information, providing visual understanding. For example, the visualization unit can generate a mind map that visually shows the relationships between ideas and present it to the user. Furthermore, the visualization unit can estimate the user's emotions and adjust the display method of the mind map based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually engaging mind map. This deepens the understanding of ideas by automatically generating mind maps and related information and providing visual understanding. Some or all of the above-described processes in the visualization unit may be performed using, for example, a generating AI, or without a generating AI. For example, the visualization unit can input the relationships between ideas into a generating AI, which can then generate a mind map.

[0081] The support unit can assist in decision-making using frameworks such as SWOT analysis. For example, the support unit can use AI to assist in decision-making using frameworks such as SWOT analysis. For example, the support unit can perform a SWOT analysis on an idea presented by the user, analyzing its strengths, weaknesses, opportunities, and threats, and presenting them to the user. The support unit can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions of the user. For example, if the support unit is relaxed, it can provide detailed information to assist in decision-making. In this way, the quality of decision-making is improved by supporting decision-making using frameworks such as SWOT analysis. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, the support unit can input an idea presented by the user into a generative AI, and the generative AI can perform a SWOT analysis.

[0082] The efficiency unit can manage time and automatically generate meeting minutes, enabling discussions to proceed smoothly. For example, the efficiency unit can use AI to manage time and automatically generate meeting minutes, thereby facilitating smooth discussions. For example, the efficiency unit can monitor the progress of a session in real time, generate meeting minutes at the appropriate time, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust the session efficiency method based on the estimated emotions. For example, if the efficiency unit is relaxed, it can proceed with the session at a relaxed pace. This allows the discussion to proceed smoothly through time management and automatic minute generation. Some or all of the above processes in the efficiency unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the efficiency unit can input the progress of the session into the generation AI, which can then generate meeting minutes at the appropriate time.

[0083] The efficiency unit can assist with whiteboard design, providing optimized visual design and colorblind compatibility. For example, the efficiency unit can use AI to assist with whiteboard design, providing optimized visual design and colorblind compatibility. For example, the efficiency unit can optimize a user-created whiteboard design and provide colorblind compatibility. Furthermore, the efficiency unit can estimate the user's emotions and adjust the whiteboard design based on those emotions. For example, if the user is relaxed, the efficiency unit can provide a colorful and visually appealing design. This enables optimized visual design and colorblind compatibility through whiteboard design assistance. Some or all of the above-described processes in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input a user-created whiteboard design into a generative AI, which can then optimize it.

[0084] The efficiency unit can generate improvement suggestions from session analysis and perform learning and feedback. For example, the efficiency unit can use AI to generate improvement suggestions from session analysis and perform learning and feedback. For example, the efficiency unit can analyze the progress of a session, generate improvement suggestions for the next session, and present them to the user. The efficiency unit can also estimate the user's emotions and adjust improvement suggestions based on the estimated emotions. For example, if the efficiency unit is relaxed, it can generate improvement suggestions that can be approached in a relaxed state. In this way, the quality of the session is improved by generating improvement suggestions from session analysis and performing learning and feedback. Some or all of the above processes in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input the progress of a session into a generative AI, and the generative AI can generate improvement suggestions.

[0085] The generation unit can estimate the user's emotions and adjust the timing of brainstorming based on the estimated emotions. For example, the generation unit can use AI to estimate the user's emotions and adjust the timing of brainstorming based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can start brainstorming at a time when the user can relax. Also, if the user is focused, the generation unit can start brainstorming immediately to maximize that focus. By adjusting the timing of brainstorming based on the user's emotions, effective idea generation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the timing of brainstorming.

[0086] The generation unit can analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can use AI to analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that the user has succeeded with in the past. The generation unit can also suggest avoiding idea generation methods that the user has failed with in the past. In this way, the optimal idea generation method can be selected by analyzing the user's past idea submission history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past idea submission history into a generation AI, and the generation AI can select the optimal idea generation method.

[0087] The generation unit can filter ideas based on the user's current projects and areas of interest during idea generation. For example, the generation unit can use AI to filter ideas based on the user's current projects and areas of interest during idea generation. For example, the generation unit can prioritize generating ideas related to the user's current projects. The generation unit can also filter and generate relevant ideas based on the user's areas of interest. This allows for the generation of highly relevant ideas by filtering ideas based on the user's current projects and areas of interest. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the user's current projects and areas of interest into a generation AI, which can then generate relevant ideas.

[0088] The generation unit can estimate the user's emotions and determine the priority of ideas to generate based on the estimated user emotions. For example, the generation unit can use AI to estimate the user's emotions and determine the priority of ideas to generate based on the estimated user emotions. For example, if the user is excited, the generation unit can prioritize generating challenging ideas. Also, if the user is relaxed, the generation unit can prioritize generating ideas that can be tackled in a relaxed state. This enables effective idea generation by prioritizing ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and determine the priority of ideas.

[0089] The generation unit can prioritize generating highly relevant ideas by considering the user's geographical location information during idea generation. For example, the generation unit can prioritize generating highly relevant ideas by considering the user's geographical location information when generating ideas using AI. For example, if the user is in a specific region, the generation unit can prioritize generating ideas related to that region. Also, if the user is traveling, the generation unit can prioritize generating ideas related to the travel destination. In this way, highly relevant ideas can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, and the generation AI can generate relevant ideas.

[0090] The generation unit can analyze a user's social media activity and generate relevant ideas during idea generation. For example, the generation unit can use AI to analyze a user's social media activity and generate relevant ideas during idea generation. For example, the generation unit can generate ideas related to topics the user has shown interest in on social media. The generation unit can also generate ideas based on the content of posts from accounts the user follows on social media. In this way, relevant ideas can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user social media activity data into a generation AI, and the generation AI can generate relevant ideas.

[0091] The organization unit can estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, the organization unit can use AI to estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, if the user is relaxed, the organization unit can group ideas into broad categories. Conversely, if the user is focused, the organization unit can group ideas into detailed categories. This allows for effective idea organization by adjusting the idea grouping method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the organization unit may be performed using generative AI, or not using generative AI. For example, the organization unit can input user emotion data into a generative AI, which can estimate emotions and adjust the idea grouping method.

[0092] The organization unit can adjust the level of detail in grouping based on the importance of the ideas. For example, the organization unit can use AI to adjust the level of detail in grouping based on the importance of the ideas. For example, the organization unit can group highly important ideas in detail and less important ideas broadly. The organization unit can also prioritize the display of highly important ideas and postpone the display of less important ideas. This allows for effective idea organization by adjusting the level of detail in grouping based on the importance of the ideas. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea importance data into a generative AI, and the generative AI can adjust the level of detail in grouping.

[0093] The organization unit can apply different grouping algorithms depending on the category of the idea. For example, the organization unit can use AI to apply different grouping algorithms depending on the category of the idea. For example, the organization unit can apply a grouping algorithm from a technical perspective to technical ideas. Also, the organization unit can apply a grouping algorithm from a marketing perspective to marketing ideas. By applying different grouping algorithms depending on the category of the idea, effective idea organization becomes possible. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea category data into a generative AI, and the generative AI can apply different grouping algorithms.

[0094] The organization unit can estimate the user's emotions and prioritize ideas based on those emotions. For example, the organization unit can use AI to estimate the user's emotions and prioritize ideas based on those emotions. For example, if the user is excited, the organization unit can prioritize displaying challenging ideas. Conversely, if the user is relaxed, the organization unit can prioritize displaying ideas that can be tackled in a relaxed state. This enables effective idea organization by prioritizing ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the organization unit may be performed using generative AI, or not. For example, the organization unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of ideas.

[0095] The organization unit can determine the grouping priority based on when the ideas were submitted. For example, the organization unit can use AI to determine the grouping priority based on when the ideas were submitted. For example, the organization unit can prioritize grouping recently submitted ideas. The organization unit can also postpone older submitted ideas. This allows for effective idea organization by determining the grouping priority based on when the ideas were submitted. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the organization unit can input idea submission data into a generative AI, and the generative AI can determine the grouping priority.

[0096] The organization unit can adjust the grouping order based on the relevance of ideas. For example, the organization unit can use AI to adjust the grouping order based on the relevance of ideas. For example, the organization unit can prioritize grouping highly relevant ideas. The organization unit can also postpone less relevant ideas. This allows for effective idea organization by adjusting the grouping order based on the relevance of ideas. Some or all of the above processing in the organization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the organization unit can input idea relevance data into a generative AI, which can then adjust the grouping order.

[0097] The visualization unit can estimate the user's emotions and adjust the display method of the mind map based on the estimated emotions. For example, the visualization unit can use AI to estimate the user's emotions and adjust the display method of the mind map based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually appealing mind map. Conversely, if the user is focused, the visualization unit can display a simple and highly visible mind map. This allows for effective visual understanding by adjusting the display method of the mind map based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not using a generative AI. For example, the visualization unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method of the mind map.

[0098] The visualization unit can optimize the current mind map by referring to past data. For example, the visualization unit can use AI to optimize the current mind map by referring to past data. For example, the visualization unit can suggest the optimal display method based on past mind map data. Furthermore, the visualization unit can learn and apply the user's preferred display method from past data. This allows the current mind map to be optimized by referring to past data. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input past data into a generative AI, which can then optimize the current mind map.

[0099] The visualization unit can apply different visualization methods to each category of idea. For example, the visualization unit can use AI to apply different visualization methods to each category of idea. For example, the visualization unit can apply a visualization method from a technical perspective to technical ideas. Furthermore, the visualization unit can apply a visualization method from a marketing perspective to marketing ideas. By applying different visualization methods to each category of idea, effective visual understanding becomes possible. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input idea category data into a generative AI, and the generative AI can apply different visualization methods.

[0100] The visualization unit can estimate the user's emotions and adjust the importance of the mind map based on the estimated emotions. For example, the visualization unit can use AI to estimate the user's emotions and adjust the importance of the mind map based on the estimated emotions. For example, if the user is excited, the visualization unit can highlight and display important ideas. If the user is relaxed, the visualization unit can also display the mind map while considering the overall balance. This allows for effective visual understanding by adjusting the importance of the mind map based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using a generative AI, or not using a generative AI. For example, the visualization unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the importance of the mind map.

[0101] The visualization unit can analyze changes in the mind map based on the submission date of ideas. The visualization unit can, for example, use AI to analyze changes in the mind map based on the submission date of ideas. The visualization unit can, for example, prioritize the display of recently submitted ideas. The visualization unit can also postpone older submitted ideas. This enables effective visual understanding by analyzing changes in the mind map based on the submission date of ideas. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input idea submission date data into a generative AI, which can then analyze changes in the mind map.

[0102] The visualization unit can analyze mind maps by referring to relevant market data for ideas. For example, the visualization unit can use AI to analyze mind maps by referring to relevant market data for ideas. The visualization unit can, for example, evaluate the importance of ideas based on relevant market data. Furthermore, the visualization unit can analyze the relationships between ideas by referring to relevant market data. This enables effective visual understanding by referring to relevant market data for ideas. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the visualization unit can input relevant market data into a generative AI, which can then analyze the mind map.

[0103] The support unit can estimate the user's emotions and adjust the decision-making support method based on the estimated emotions. For example, the support unit can use AI to estimate the user's emotions and adjust the decision-making support method based on the estimated emotions. For example, if the user is relaxed, the support unit can provide detailed information to support decision-making. Alternatively, if the user is focused, the support unit can provide concise information to support decision-making. This allows for effective decision-making by adjusting the decision-making support method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using generative AI, or not using generative AI. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the decision-making support method.

[0104] The support unit can optimize current decisions by referring to past decision-making data. For example, the support unit can use AI to optimize current decisions by referring to past decision-making data. For example, the support unit can propose the optimal decision-making method based on past decision-making data. The support unit can also learn and apply decision-making methods preferred by the user from past data. This allows for the optimization of current decisions by referring to past decision-making data. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or without generative AI. For example, the support unit can input past decision-making data into a generative AI, which can then optimize the current decision.

[0105] The support department can apply different decision support methods to each category of idea. For example, the support department can use AI to apply different decision support methods to each category of idea. For example, the support department can apply a technical decision support method to a technical idea. Furthermore, the support department can apply a marketing decision support method to a marketing idea. By applying different decision support methods to each category of idea, effective decision-making becomes possible. Some or all of the above processing in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input idea category data into a generative AI, and the generative AI can apply different decision support methods.

[0106] The support unit can estimate the user's emotions and determine decision priorities based on those estimated emotions. For example, the support unit can use AI to estimate the user's emotions and determine decision priorities based on those estimated emotions. For example, if the user is excited, the support unit can prioritize supporting challenging decisions. Conversely, if the user is relaxed, the support unit can prioritize supporting decisions that can be undertaken in a relaxed state. This enables effective decision-making by prioritizing decisions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using generative AI, or not. For example, the support unit can input user emotion data into a generative AI, which can estimate emotions and determine decision priorities.

[0107] The support department can analyze changes in decision-making based on the timing of idea submissions. For example, the support department can use AI to analyze changes in decision-making based on the timing of idea submissions. For example, the support department can prioritize the incorporation of recently submitted ideas into decision-making. The support department can also postpone older submitted ideas. This enables effective decision-making by analyzing changes in decision-making based on the timing of idea submissions. Some or all of the above processes in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input idea submission timing data into a generative AI, which can then analyze changes in decision-making.

[0108] The support department can analyze decision-making by referring to relevant market data for ideas. For example, the support department can use AI to analyze decision-making by referring to relevant market data for ideas. For example, the support department can evaluate the importance of ideas based on relevant market data. The support department can also analyze the relevance of ideas by referring to relevant market data. This enables effective decision-making by referring to relevant market data for ideas. Some or all of the above processing in the support department may be performed using, for example, generative AI, or without generative AI. For example, the support department can input relevant market data into generative AI, and the generative AI can analyze decision-making.

[0109] The efficiency unit can estimate the user's emotions and adjust the session efficiency method based on the estimated user emotions. For example, the efficiency unit can use AI to estimate the user's emotions and adjust the session efficiency method based on the estimated user emotions. For example, if the user is relaxed, the efficiency unit can conduct the session at a relaxed pace. Conversely, if the user is focused, the efficiency unit can conduct the session quickly. This allows for effective session management by adjusting the session efficiency method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the efficiency unit may be performed using a generative AI, or not using a generative AI. For example, the efficiency unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the session efficiency method.

[0110] The efficiency unit can optimize the current session by referring to past session data. For example, the efficiency unit can use AI to optimize the current session by referring to past session data. For example, the efficiency unit can propose the optimal way to proceed based on past session data. The efficiency unit can also learn and apply the user's preferred way of proceeding from past data. In this way, the current session can be optimized by referring to past session data. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input past session data into a generative AI, and the generative AI can optimize the current session.

[0111] The efficiency unit can apply different efficiency methods to each category of idea. For example, the efficiency unit can use AI to apply different efficiency methods to each category of idea. For example, the efficiency unit can apply efficiency methods from a technical perspective to technical ideas. Furthermore, the efficiency unit can apply efficiency methods from a marketing perspective to marketing ideas. By applying different efficiency methods to each category of idea, effective session management becomes possible. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input idea category data into a generative AI, and the generative AI can apply different efficiency methods.

[0112] The efficiency unit can estimate the user's emotions and determine session priorities based on those emotions. For example, the efficiency unit can use AI to estimate the user's emotions and determine session priorities based on those emotions. For example, if the user is excited, the efficiency unit can prioritize challenging sessions. Conversely, if the user is relaxed, the efficiency unit can prioritize sessions that can be approached in a relaxed state. This enables effective session management by prioritizing sessions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the efficiency unit may be performed using, for example, generative AI, or not using generative AI. For example, the efficiency unit can input user emotion data into a generative AI, which can estimate the emotions and determine session priorities.

[0113] The efficiency unit can analyze session changes based on the timing of idea submissions. For example, the efficiency unit can use AI to analyze session changes based on the timing of idea submissions. For example, the efficiency unit can prioritize incorporating recently submitted ideas into the session. The efficiency unit can also postpone older submitted ideas. This allows for more effective session management by analyzing session changes based on the timing of idea submissions. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input idea submission timing data into a generative AI, which can then analyze session changes.

[0114] The efficiency unit can analyze sessions by referring to relevant market data for ideas. For example, the efficiency unit can use AI to analyze sessions by referring to relevant market data for ideas. For example, the efficiency unit can evaluate the importance of ideas based on relevant market data. The efficiency unit can also analyze the relevance of ideas by referring to relevant market data. This enables effective session management by referring to relevant market data for ideas. Some or all of the above processing in the efficiency unit may be performed using, for example, a generative AI, or without a generative AI. For example, the efficiency unit can input relevant market data into a generative AI, and the generative AI can analyze the session.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The generation unit can estimate the user's emotions and adjust the idea generation method based on those emotions. For example, if the user is excited, the generation unit can prioritize generating challenging ideas. If the user is relaxed, it can generate ideas that can be approached in a relaxed state. Furthermore, if the user is stressed, the generation unit can generate ideas that help reduce stress. By adjusting the idea generation method based on the user's emotions, more effective idea generation becomes possible.

[0117] The organization function can adjust the level of detail in grouping based on the importance of the ideas. For example, highly important ideas can be grouped in detail, while less important ideas can be grouped more broadly. Furthermore, highly important ideas can be displayed preferentially, while less important ideas can be postponed. The organization function can also adjust the order of grouping based on the importance of the ideas. This allows for effective idea organization by adjusting the level of detail in grouping based on the importance of the ideas.

[0118] The visualization unit can estimate the user's emotions and adjust the display method of the mind map based on those emotions. For example, if the user is relaxed, a colorful and visually appealing mind map can be displayed. If the user is focused, a simple and highly visible mind map can be displayed. Furthermore, if the user is stressed, a mind map with calming colors can be displayed. By adjusting the display method of the mind map based on the user's emotions, effective visual understanding becomes possible.

[0119] The support unit can optimize current decision-making by referring to past decision-making data. For example, it can propose the optimal decision-making method based on past decision-making data. It can also learn and apply decision-making methods preferred by users from past data. Furthermore, it can analyze past success and failure cases of decision-making and reflect them in current decisions. In this way, current decision-making can be optimized by referring to past decision-making data.

[0120] The optimization unit can estimate the user's emotions and adjust the session's optimization method based on those estimates. For example, if the user is relaxed, the session can proceed at a relaxed pace. Conversely, if the user is focused, the session can proceed more quickly. Furthermore, if the user is stressed, the system can adopt a method that reduces stress. By adjusting the session's optimization method based on the user's emotions, effective session management becomes possible.

[0121] The generation unit can filter ideas based on the user's current projects and areas of interest during the idea generation process. For example, it can prioritize generating ideas related to the user's current projects. It can also filter and generate relevant ideas based on the user's areas of interest. Furthermore, it can refer to the user's past project history to generate highly relevant ideas. This allows for the generation of highly relevant ideas by filtering them based on the user's current projects and areas of interest.

[0122] The organization section can apply different grouping algorithms depending on the category of the idea. For example, technical ideas can be grouped from a technical perspective, marketing ideas from a marketing perspective, and creative ideas from a creative perspective. This allows for effective idea organization by applying different grouping algorithms depending on the category of the idea.

[0123] The visualization unit can optimize the current mind map by referencing past data. For example, it can suggest the optimal display method based on data from past mind maps. It can also learn and apply display methods preferred by users from past data. Furthermore, it can analyze successful and unsuccessful examples of past mind maps and reflect them in the current mind map. In this way, the current mind map can be optimized by referring to past data.

[0124] The support unit can estimate the user's emotions and adjust the decision-making support methods based on those estimates. For example, if the user is relaxed, it can provide detailed information to support decision-making. If the user is focused, it can provide concise information to support decision-making. Furthermore, if the user is stressed, it can provide information to alleviate that stress. By adjusting the decision-making support methods based on the user's emotions, effective decision-making becomes possible.

[0125] The efficiency optimization unit can optimize the current session by referring to past session data. For example, it can suggest the optimal way to proceed based on past session data. It can also learn and apply user-preferred methods from past data. Furthermore, it can analyze successful and unsuccessful examples from past sessions and reflect them in the current session. In this way, the current session can be optimized by referring to past session data.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The generation unit generates ideas. The generation unit generates related ideas for a theme presented by the user, for example, using AI. The generation unit can generate related ideas based on keywords entered by the user. The generation unit can also analyze the user's past idea submission history and select the optimal idea generation method. For example, the generation unit can prioritize suggesting idea generation methods that have been successful for the user in the past. Step 2: The organizing unit organizes the ideas generated by the generating unit. The organizing unit can, for example, use AI to automatically group ideas and suggest priorities. The organizing unit can group ideas based on their importance and relevance and present them to the user. The organizing unit can also estimate the user's emotions and adjust the idea grouping method based on the estimated user emotions. For example, if the user is relaxed, the organizing unit can group ideas into broad categories. Step 3: The visualization unit visualizes the ideas organized by the organization unit. The visualization unit can, for example, use AI to automatically generate mind maps and related information, providing a visual understanding. The visualization unit can generate and present mind maps that visually show the relationships between ideas. The visualization unit can also estimate the user's emotions and adjust how the mind map is displayed based on the estimated emotions. For example, if the user is relaxed, the visualization unit can display a colorful and visually appealing mind map. Step 4: The support unit assists decision-making based on the information visualized by the visualization unit. The support unit supports decision-making using frameworks such as SWOT analysis with AI, for example. The support unit can perform a SWOT analysis on the ideas presented by the user, analyzing strengths, weaknesses, opportunities, and threats, and presenting them to the user. The support unit can also estimate the user's emotions and adjust the decision-making support method based on the estimated emotions of the user. For example, if the user is relaxed, the support unit can provide detailed information to support decision-making. Step 5: The Efficiency Department optimizes the session based on the decision-making supported by the Support Department. For example, the Efficiency Department uses AI to manage time and automatically generate meeting minutes, ensuring the discussion flows smoothly. The Efficiency Department can monitor the session's progress in real time, generate meeting minutes at the appropriate time, and present them to the user. The Efficiency Department can also estimate the user's emotions and adjust the session optimization method based on the estimated emotions. For example, if the user is relaxed, the Efficiency Department can conduct the session at a relaxed pace.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the generation unit, organization unit, visualization unit, support unit, and efficiency unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates relevant ideas for a theme presented by the user. The organization unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically groups the generated ideas and suggests priorities. The visualization unit is implemented by the control unit 46A of the smart device 14 and automatically generates mind maps and related information to provide visual understanding. The support unit is implemented by the specific processing unit 290 of the data processing device 12 and supports decision-making using frameworks such as SWOT analysis. The efficiency unit is implemented by the control unit 46A of the smart device 14 and performs time management and automatic generation of meeting minutes to facilitate discussions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the generation unit, organization unit, visualization unit, support unit, and efficiency unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates relevant ideas for a theme presented by the user. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically groups the generated ideas and suggests priorities. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates mind maps and related information to provide visual understanding. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making using frameworks such as SWOT analysis. The efficiency unit is implemented by the control unit 46A of the smart glasses 214 and performs time management and automatic generation of meeting minutes to facilitate discussions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the generation unit, organization unit, visualization unit, support unit, and efficiency unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates relevant ideas for a theme presented by the user. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically groups the generated ideas and suggests priorities. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates mind maps and related information to provide visual understanding. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making using frameworks such as SWOT analysis. The efficiency unit is implemented by the control unit 46A of the headset terminal 314 and performs time management and automatic generation of meeting minutes to facilitate discussions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the generation unit, organization unit, visualization unit, support unit, and efficiency unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates relevant ideas for a theme presented by the user. The organization unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically groups the generated ideas and suggests priorities. The visualization unit is implemented by the control unit 46A of the robot 414 and automatically generates mind maps and related information to provide visual understanding. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports decision-making using frameworks such as SWOT analysis. The efficiency unit is implemented by the control unit 46A of the robot 414 and performs time management and automatic generation of meeting minutes to facilitate discussions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) An idea generation unit, A sorting unit that organizes the ideas generated by the generation unit, A visualization unit visualizes the ideas organized by the aforementioned organization unit, A support unit that assists decision-making based on the information visualized by the aforementioned visualization unit, The system includes an efficiency improvement unit that optimizes the session based on the decision-making supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The generating unit is It stimulates brainstorming in real time and provides new perspectives. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned editing unit, It automatically groups ideas and suggests priorities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, It automatically generates mind maps and related information to provide visual understanding. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, Supporting decision-making using frameworks such as SWOT analysis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned efficiency improvement unit is We enable time management and automatic generation of meeting minutes to facilitate smooth discussions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned efficiency improvement unit is We provide design assistance for whiteboards, optimizing visual design and offering colorblind compatibility. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned efficiency improvement unit is We generate suggestions for improvement based on session analysis, and then use that information for learning and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is We estimate the user's emotions and adjust the timing of brainstorming sessions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is Analyze the user's past idea submission history and select the optimal idea generation method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating ideas, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and determines the priority of ideas to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating ideas, the system prioritizes generating highly relevant ideas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During idea generation, the system analyzes users' social media activity and generates relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, We estimate user emotions and adjust the idea grouping method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, Adjust the level of detail in grouping based on the importance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, Apply different grouping algorithms depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, It estimates user emotions and prioritizes ideas based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editing unit, Prioritize grouping based on when ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editing unit, Adjust the grouping order based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, It estimates the user's emotions and adjusts how the mind map is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, Optimize the current mind map by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, Apply different visualization methods to each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, It estimates the user's emotions and adjusts the importance of the mind map based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, Analyze changes in mind maps based on when ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, Analyze mind maps by referring to relevant market data for ideas. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, It estimates the user's emotions and adjusts the decision-making support methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, Optimize current decision-making by referring to past decision-making data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, Apply different decision support methods to each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, It estimates user emotions and prioritizes decision-making based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit, Analyze changes in decision-making based on the timing of idea submission. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, Analyze decision-making by referring to relevant market data for ideas. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned efficiency improvement unit is It estimates the user's emotions and adjusts how to optimize the session based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned efficiency improvement unit is Optimize the current session by referring to past session data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned efficiency improvement unit is Apply different efficiency methods to each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned efficiency improvement unit is It estimates the user's emotions and determines session priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned efficiency improvement unit is Analyze session variations based on the timing of idea submissions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned efficiency improvement unit is Analyze the session by referring to relevant market data for the idea. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An idea generation unit, A sorting unit that organizes the ideas generated by the generation unit, A visualization unit visualizes the ideas organized by the aforementioned organization unit, A support unit that assists decision-making based on the information visualized by the aforementioned visualization unit, The system includes an efficiency improvement unit that optimizes the session based on the decision-making supported by the aforementioned support unit. A system characterized by the following features.

2. The generating unit is It stimulates brainstorming in real time and provides new perspectives. The system according to feature 1.

3. The aforementioned editing unit, It automatically groups ideas and suggests priorities. The system according to feature 1.

4. The aforementioned visualization unit, It automatically generates mind maps and related information to provide visual understanding. The system according to feature 1.

5. The aforementioned support unit, Supporting decision-making using frameworks such as SWOT analysis. The system according to feature 1.

6. The aforementioned efficiency improvement unit is We enable time management and automatic generation of meeting minutes to facilitate smooth discussions. The system according to feature 1.

7. The aforementioned efficiency improvement unit is We provide design assistance for whiteboards, optimizing visual design and offering colorblind compatibility. The system according to feature 1.

8. The aforementioned efficiency improvement unit is We generate suggestions for improvement based on session analysis, and then use that information for learning and feedback. The system according to feature 1.

Images